From df42f31c92882b4b636b9da563e72fe56dc4aa62 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Mon, 23 Feb 2026 17:46:38 -0800
Subject: [PATCH 01/24] changes for azure blob

---
 Makefile                                      |    4 +-
 pyproject.toml                                |    4 +
 serve/Dockerfile                              |    2 +-
 .../inferenceserver/README_object_storage.md  |  277 +-
 serve/inferenceserver/api_server/config.py    |   38 +-
 .../inferenceserver/api_server/cpu_worker.py  |  154 +-
 .../api_server/object_storage.py              |  354 +-
 serve/inferenceserver/requirements.txt        |    5 +
 serve/inferenceserver/scripts/startup.sh      |    9 +
 uv.lock                                       | 7689 +++++++++--------
 10 files changed, 4573 insertions(+), 3963 deletions(-)

diff --git a/Makefile b/Makefile
index 13ddf2b7a..3b7df1525 100644
--- a/Makefile
+++ b/Makefile
@@ -97,9 +97,9 @@ docs-dev:
 
 .PHONY: container-service
 # Example DOCKER_REPO?=nvcr.io/dycvht5ows21
-E2S_RELEASE_TAG?=0.11.0
+E2S_RELEASE_TAG?=0.12.0
 E2S_IMAGE_NAME=$(DOCKER_REPO)/earth2studio-scicomp
-E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260220.0
+E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260223.0
 container-service:
 	@test -n "$(DOCKER_REPO)" || (echo "DOCKER_REPO is not set!" && exit 1)
 	DOCKER_BUILDKIT=1 docker build -t $(E2S_IMAGE_NAME):$(E2S_IMAGE_TAG) -f serve/Dockerfile .
diff --git a/pyproject.toml b/pyproject.toml
index f9570b087..044f8e79e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -103,6 +103,10 @@ serve = [
     "aiofiles>=23.0.0",
     "httpx>=0.25.0",
     "hydra-core>=1.3.0",
+    # Object storage related
+    "cryptography>=41.0.0",
+    "multi-storage-client[azure-storage-blob]",
+    "azure-storage-blob>=12.19.0",
 ]
 # PX Models
 ace2 = [
diff --git a/serve/Dockerfile b/serve/Dockerfile
index 97ccf17f5..d5503e53e 100644
--- a/serve/Dockerfile
+++ b/serve/Dockerfile
@@ -13,7 +13,7 @@ ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 
 ARG MAKANI_HASH=28f38e3e929ed1303476518552c64673bbd6f722
 ARG E2G_HASH=11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3
-ARG E2S_HASH=b1f856388f7665e2cd1942e90d6460dcebffb7ba
+ARG E2S_HASH=8ca8771c6be6ca051e082d41b99378fa14b58d9f
 
 WORKDIR /workspace
 
diff --git a/serve/inferenceserver/README_object_storage.md b/serve/inferenceserver/README_object_storage.md
index b455137d1..08349e0a4 100644
--- a/serve/inferenceserver/README_object_storage.md
+++ b/serve/inferenceserver/README_object_storage.md
@@ -1,18 +1,25 @@
 # Object Storage Support
 
-This document describes how to configure and use object storage (AWS S3 with CloudFront) for storing
-workflow results in the Earth2Studio Inference Server.
+This document describes how to configure and use object storage (AWS S3 with CloudFront or
+Azure Blob Storage) for storing workflow results in the Earth2Studio Inference Server.
 
 ## Overview
 
 By default, workflow results are stored locally on the inference server. When object storage is
-enabled, results are automatically uploaded to S3 and served via CloudFront signed URLs. This
-provides:
+enabled, results are automatically uploaded to your chosen cloud storage provider (AWS S3 or
+Azure Blob Storage) and served via signed URLs. This provides:
 
 - **Scalability**: Offload storage from the inference server
-- **Performance**: CloudFront CDN for fast global access
+- **Performance**: Fast global access via CDN (CloudFront for S3) or direct Azure Blob Storage access
 - **Security**: Time-limited signed URLs for secure access
-- **Seamless Client Experience**: The Python client SDK automatically handles both storage types
+- **Seamless Client Experience**: The Python client SDK automatically handles all storage types
+
+## Storage Provider Options
+
+The inference server supports two storage providers:
+
+- **AWS S3**: With optional CloudFront CDN for enhanced performance
+- **Azure Blob Storage**: Direct access with SAS (Shared Access Signature) URLs
 
 ## AWS Prerequisites
 
@@ -75,15 +82,69 @@ users](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html) fo
 detailed instructions. The credentials need `s3:PutObject`, `s3:GetObject`,
 `s3:DeleteObject`, and `s3:ListBucket` permissions on your bucket.
 
+## Azure Prerequisites
+
+Before enabling Azure Blob Storage, you need to set up the following Azure resources:
+
+### 1. Azure Storage Account
+
+Create an Azure Storage Account:
+
+1. Go to Azure Portal → Storage accounts → Create
+2. Choose a unique storage account name
+3. Select your resource group and region
+4. Choose performance tier (Standard recommended)
+5. Note the **Storage account name** (e.g., `mystorageaccount`)
+
+### 2. Storage Container
+
+Create a blob container in your storage account:
+
+1. Go to your Storage Account → Containers
+2. Click "+ Container"
+3. Choose a container name (e.g., `workflow-results`)
+4. Set Public access level to "Private" (recommended for security)
+
+### 3. Connection String or Account Key
+
+You'll need either:
+
+#### Option A: Connection String (Recommended)
+
+1. Go to Storage Account → Access keys
+2. Click "Show" next to a key
+3. Copy the **Connection string** (format:
+   `DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=core.windows.net`)
+
+#### Option B: Account Name and Key
+
+1. Go to Storage Account → Access keys
+2. Note the **Storage account name**
+3. Click "Show" next to a key and copy the **Account key**
+
+The account key is required for generating SAS (Shared Access Signature) signed URLs.
+
+### 4. Permissions
+
+Ensure your Azure credentials have the following permissions:
+
+- `Microsoft.Storage/storageAccounts/blobServices/containers/write` (to create containers if needed)
+- `Microsoft.Storage/storageAccounts/blobServices/containers/read`
+- `Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write`
+- `Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read`
+
 ## Server Configuration
 
 ### Environment Variables
 
-Configure object storage using environment variables:
+Configure object storage using environment variables. Choose either AWS S3 or Azure Blob Storage:
+
+#### AWS S3 Configuration
 
 ```bash
 # Enable object storage
 export OBJECT_STORAGE_ENABLED=true
+export OBJECT_STORAGE_TYPE=s3
 
 # S3 Configuration
 export OBJECT_STORAGE_BUCKET=your-bucket-name
@@ -111,13 +172,46 @@ export CLOUDFRONT_PRIVATE_KEY_PATH=/path/to/cloudfront-private-key.pem
 export OBJECT_STORAGE_SIGNED_URL_EXPIRES_IN=3600  # URL expiration in seconds
 ```
 
+#### Azure Blob Storage Configuration
+
+```bash
+# Enable object storage
+export OBJECT_STORAGE_ENABLED=true
+export OBJECT_STORAGE_TYPE=azure
+
+# Azure Configuration
+export OBJECT_STORAGE_BUCKET=your-container-name  # Container name (used as bucket equivalent)
+export OBJECT_STORAGE_PREFIX=outputs  # Optional: prefix for uploaded files
+
+# Azure Credentials (Connection String - Recommended)
+export AZURE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=mystorageaccount;AccountKey=...;EndpointSuffix=core.windows.net"
+
+# OR Azure Credentials (Account Name and Key - Alternative)
+export AZURE_STORAGE_ACCOUNT_NAME=mystorageaccount
+export AZURE_STORAGE_ACCOUNT_KEY=...  # Required for SAS URL generation
+
+# Optional: Container name (defaults to OBJECT_STORAGE_BUCKET if not set)
+export AZURE_CONTAINER_NAME=workflow-results
+
+# Transfer Configuration
+export OBJECT_STORAGE_MAX_CONCURRENCY=16          # Concurrent upload threads
+export OBJECT_STORAGE_MULTIPART_CHUNKSIZE=8388608 # 8MB chunk size
+export OBJECT_STORAGE_USE_RUST_CLIENT=true        # High-performance Rust client
+
+# Signed URL Configuration
+export OBJECT_STORAGE_SIGNED_URL_EXPIRES_IN=3600  # SAS URL expiration in seconds
+```
+
 ### YAML Configuration
 
 Alternatively, configure via `config.yaml`:
 
+#### AWS S3 YAML Configuration
+
 ```yaml
 object_storage:
   enabled: true
+  storage_type: s3
   bucket: your-bucket-name
   region: us-east-1
   prefix: outputs
@@ -131,26 +225,49 @@ object_storage:
   signed_url_expires_in: 3600
 ```
 
+#### Azure Blob Storage YAML Configuration
+
+```yaml
+object_storage:
+  enabled: true
+  storage_type: azure
+  bucket: your-container-name  # Container name (used as bucket equivalent)
+  prefix: outputs
+  max_concurrency: 16
+  multipart_chunksize: 8388608
+  use_rust_client: true
+  azure_connection_string: "DefaultEndpointsProtocol=https;AccountName=mystorageaccount;AccountKey=...;EndpointSuffix=core.windows.net"
+  azure_account_name: mystorageaccount  # Optional if in connection string
+  azure_account_key: ...  # Required for SAS URL generation
+  azure_container_name: workflow-results  # Optional, defaults to bucket
+  signed_url_expires_in: 3600
+```
+
 ### Configuration Parameters Reference
 
 <!-- markdownlint-disable MD013 -->
 | Parameter | Environment Variable | Default | Description |
 |-----------|---------------------|---------|-------------|
 | `enabled` | `OBJECT_STORAGE_ENABLED` | `false` | Enable object storage |
-| `bucket` | `OBJECT_STORAGE_BUCKET` | `null` | S3 bucket name |
-| `region` | `OBJECT_STORAGE_REGION` | `us-east-1` | AWS region |
+| `storage_type` | `OBJECT_STORAGE_TYPE` | `s3` | Storage provider: `s3` or `azure` |
+| `bucket` | `OBJECT_STORAGE_BUCKET` | `null` | S3 bucket name or Azure container name |
+| `region` | `OBJECT_STORAGE_REGION` | `us-east-1` | AWS region (S3 only) |
 | `prefix` | `OBJECT_STORAGE_PREFIX` | `outputs` | Remote prefix for files |
-| `access_key_id` | `OBJECT_STORAGE_ACCESS_KEY_ID` | `null` | AWS access key ID |
-| `secret_access_key` | `OBJECT_STORAGE_SECRET_ACCESS_KEY` | `null` | AWS secret access key |
-| `session_token` | `OBJECT_STORAGE_SESSION_TOKEN` | `null` | AWS session token |
+| `access_key_id` | `OBJECT_STORAGE_ACCESS_KEY_ID` | `null` | AWS access key ID (S3 only) |
+| `secret_access_key` | `OBJECT_STORAGE_SECRET_ACCESS_KEY` | `null` | AWS secret access key (S3 only) |
+| `session_token` | `OBJECT_STORAGE_SESSION_TOKEN` | `null` | AWS session token (S3 only) |
 | `endpoint_url` | `OBJECT_STORAGE_ENDPOINT_URL` | `null` | Custom endpoint (S3-compatible) |
-| `use_transfer_acceleration` | `OBJECT_STORAGE_TRANSFER_ACCELERATION` | `true` | Enable S3 Transfer Acceleration |
+| `use_transfer_acceleration` | `OBJECT_STORAGE_TRANSFER_ACCELERATION` | `true` | Enable S3 Transfer Acceleration (S3 only) |
 | `max_concurrency` | `OBJECT_STORAGE_MAX_CONCURRENCY` | `16` | Max concurrent transfers |
 | `multipart_chunksize` | `OBJECT_STORAGE_MULTIPART_CHUNKSIZE` | `8388608` | Multipart chunk size (bytes) |
 | `use_rust_client` | `OBJECT_STORAGE_USE_RUST_CLIENT` | `true` | Use high-performance Rust client |
-| `cloudfront_domain` | `CLOUDFRONT_DOMAIN` | `null` | CloudFront distribution domain |
-| `cloudfront_key_pair_id` | `CLOUDFRONT_KEY_PAIR_ID` | `null` | CloudFront key pair ID |
-| `cloudfront_private_key_path` | `CLOUDFRONT_PRIVATE_KEY_PATH` | `null` | Path to private key PEM file |
+| `cloudfront_domain` | `CLOUDFRONT_DOMAIN` | `null` | CloudFront distribution domain (S3 only) |
+| `cloudfront_key_pair_id` | `CLOUDFRONT_KEY_PAIR_ID` | `null` | CloudFront key pair ID (S3 only) |
+| `cloudfront_private_key_path` | `CLOUDFRONT_PRIVATE_KEY_PATH` | `null` | Path to private key PEM file (S3 only) |
+| `azure_connection_string` | `AZURE_CONNECTION_STRING` | `null` | Azure connection string (Azure only, recommended) |
+| `azure_account_name` | `AZURE_STORAGE_ACCOUNT_NAME` | `null` | Azure storage account name (Azure only) |
+| `azure_account_key` | `AZURE_STORAGE_ACCOUNT_KEY` | `null` | Azure account key for SAS URLs (Azure only) |
+| `azure_container_name` | `AZURE_CONTAINER_NAME` | `null` | Azure container name (Azure only, defaults to bucket) |
 | `signed_url_expires_in` | `OBJECT_STORAGE_SIGNED_URL_EXPIRES_IN` | `3600` | Signed URL expiration (seconds) |
 <!-- markdownlint-enable MD013 -->
 
@@ -158,6 +275,8 @@ object_storage:
 
 When object storage is enabled, the workflow result metadata includes additional fields:
 
+### AWS S3 Example
+
 ```json
 {
   "request_id": "exec_1769560728_10ed9d3c",
@@ -174,12 +293,30 @@ When object storage is enabled, the workflow result metadata includes additional
 }
 ```
 
+### Azure Blob Storage Example
+
+```json
+{
+  "request_id": "exec_1769560728_10ed9d3c",
+  "status": "completed",
+  "storage_type": "azure",
+  "signed_url":
+    "https://mystorageaccount.blob.core.windows.net/workflow-results/outputs/exec_1769560728_10ed9d3c?sv=2021-06-08&ss=b&srt=co&sp=rl&se=2025-01-01T00:00:00Z&st=2024-12-31T00:00:00Z&spr=https&sig=...",
+  "remote_path": "outputs/exec_1769560728_10ed9d3c",
+  "output_files": [
+    {"path": "exec_1769560728_10ed9d3c/results.zarr/.zarray", "size": 123},
+    {"path": "exec_1769560728_10ed9d3c/results.zarr/t2m/0.0.0", "size": 4567890}
+  ]
+}
+```
+
 ### Storage Type Values
 
 | Value | Description |
 |-------|-------------|
 | `server` | Results stored locally on the inference server |
 | `s3` | Results stored in S3, accessible via CloudFront signed URL |
+| `azure` | Results stored in Azure Blob Storage, accessible via SAS signed URL |
 
 ## Client Usage
 
@@ -209,7 +346,7 @@ request_result = client.run_inference_sync(
     InferenceRequest(parameters={"start_time": [datetime(2025, 8, 21, 6)]})
 )
 
-# Automatically downloads from S3 if storage_type is "s3"
+# Automatically downloads from S3 or Azure if storage_type is "s3" or "azure"
 for file in request_result.output_files[:5]:
     content = client.download_result(request_result, file.path)
     print(f"Downloaded {file.path}: {len(content.getvalue())} bytes")
@@ -217,7 +354,10 @@ for file in request_result.output_files[:5]:
 
 ### Using Signed URLs Directly
 
-For advanced use cases, you can use the signed URL directly:
+For advanced use cases, you can use the signed URL directly. The format differs between
+S3/CloudFront and Azure:
+
+#### Using CloudFront Signed URLs
 
 ```python
 import requests
@@ -237,6 +377,22 @@ file_url = f"{base_url}/{file_path}?{query_params}"
 response = requests.get(file_url)
 ```
 
+#### Using Azure SAS URLs
+
+```python
+import requests
+
+# Get the signed URL from the result
+signed_url = request_result.signed_url
+# Example:
+# https://mystorageaccount.blob.core.windows.net/workflow-results/outputs/exec_123?sv=...&sig=...
+
+# Azure SAS URLs already include the full path - append the file path
+file_path = "results.zarr/.zarray"
+file_url = f"{signed_url}/{file_path}" if not signed_url.endswith("/") else f"{signed_url}{file_path}"
+response = requests.get(file_url)
+```
+
 ### Using with Xarray and Zarr
 
 The client provides an fsspec mapper for opening Zarr stores directly:
@@ -255,6 +411,8 @@ print(ds)
 
 ## Signed URL Format
 
+### AWS S3 / CloudFront Signed URLs
+
 CloudFront signed URLs contain three query parameters:
 
 ```text
@@ -270,10 +428,35 @@ https://d30anq61ot046p.cloudfront.net/outputs/exec_123/*?Policy=eyJTdGF0ZW1lbnQi
 
 The wildcard (`*`) in the URL path allows access to all files under that prefix.
 
+### Azure Blob Storage SAS URLs
+
+Azure SAS (Shared Access Signature) URLs contain multiple query parameters:
+
+```text
+https://mystorageaccount.blob.core.windows.net/workflow-results/outputs/exec_123?\
+sv=2021-06-08&ss=b&srt=co&sp=rl&se=2025-01-01T00:00:00Z&st=2024-12-31T00:00:00Z&spr=https&sig=ABC123...
+```
+
+| Parameter | Description |
+|-----------|-------------|
+| `sv` | Service version (API version) |
+| `ss` | Services (b=blob) |
+| `srt` | Resource types (co=container and object) |
+| `sp` | Permissions (rl=read and list) |
+| `se` | Expiry time (UTC) |
+| `st` | Start time (UTC) |
+| `spr` | Protocol (https) |
+| `sig` | Signature (HMAC-SHA256 of the string-to-sign) |
+
+The SAS token provides read and list permissions for all objects in the container under the
+specified prefix.
+
 ## Testing
 
 Run object storage integration tests:
 
+### AWS S3 Testing
+
 ```bash
 # Set required environment variables
 export TEST_S3_BUCKET=my-test-bucket
@@ -290,25 +473,71 @@ export TEST_CLOUDFRONT_PRIVATE_KEY_PATH=/path/to/private.pem
 pytest test/integration/test_object_storage.py::TestCloudFrontSignedUrl -v
 ```
 
+### Azure Blob Storage Testing
+
+```bash
+# Set required environment variables
+export TEST_AZURE_CONTAINER=my-test-container
+export AZURE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=core.windows.net"
+export AZURE_STORAGE_ACCOUNT_NAME=mystorageaccount
+export AZURE_STORAGE_ACCOUNT_KEY=...
+
+# Run Azure upload tests
+pytest test/integration/test_object_storage.py -v
+
+# Run Azure SAS URL tests
+pytest test/integration/test_object_storage.py::TestAzureSASUrl -v
+```
+
 ## Troubleshooting
 
 ### Common Issues
 
+#### AWS S3 / CloudFront Issues
+
 1. **403 Forbidden from CloudFront**
    - Verify the S3 bucket policy allows CloudFront OAC access
    - Check that the CloudFront distribution is configured with the correct origin
    - Ensure the key pair is in a Key Group associated with the distribution
 
-2. **Signed URL expired**
-   - Increase `signed_url_expires_in` configuration
-   - Request fresh results from the API (URLs are regenerated)
-
-3. **Upload failures**
+2. **Upload failures**
    - Verify IAM credentials have `s3:PutObject` permission
    - Check bucket name and region are correct
    - If using Transfer Acceleration, ensure it's enabled on the bucket
 
-4. **Slow uploads**
+3. **Slow uploads**
    - Enable `use_rust_client` for better performance
    - Increase `max_concurrency` for more parallel uploads
    - Enable `use_transfer_acceleration` if uploading from distant regions
+
+#### Azure Blob Storage Issues
+
+1. **403 Forbidden from Azure**
+   - Verify the connection string is correct and not expired
+   - Check that the storage account name and container name are correct
+   - Ensure the account key is valid (if using account key authentication)
+
+2. **SAS URL generation failures**
+   - Ensure `azure_account_name` and `azure_account_key` are provided
+   - Verify the account key has not been rotated
+   - Check that the container exists and is accessible
+
+3. **Upload failures**
+   - Verify the connection string has write permissions
+   - Check that the container exists (it will be created automatically if permissions allow)
+   - Ensure the storage account is not in a restricted network configuration
+
+4. **Connection string parsing errors**
+   - Verify the connection string format is correct: `DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=...`
+   - Ensure all required components (AccountName, AccountKey) are present
+   - For custom endpoints, include `BlobEndpoint=` in the connection string
+
+#### General Issues
+
+1. **Signed URL expired**
+   - Increase `signed_url_expires_in` configuration
+   - Request fresh results from the API (URLs are regenerated)
+
+2. **Storage type not recognized**
+   - Ensure `OBJECT_STORAGE_TYPE` is set to either `s3` or `azure` (lowercase)
+   - Check that the storage type matches your configuration (S3 config for `s3`, Azure config for `azure`)
diff --git a/serve/inferenceserver/api_server/config.py b/serve/inferenceserver/api_server/config.py
index bc4c38d69..9615e3f10 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -27,7 +27,7 @@
 from dataclasses import dataclass, field
 from logging import LogRecord
 from pathlib import Path
-from typing import Any, Literal, Optional
+from typing import Any, Literal, Optional, cast
 
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
@@ -111,9 +111,10 @@ class CORSConfig:
 
 @dataclass
 class ObjectStorageConfig:
-    """Object storage configuration for S3/CloudFront"""
+    """Object storage configuration for S3/CloudFront and Azure Blob Storage"""
 
     enabled: bool = False
+    storage_type: Literal["s3", "azure"] = "s3"  # Storage provider type
     # S3 configuration
     bucket: str | None = None
     region: str = "us-east-1"
@@ -133,6 +134,15 @@ class ObjectStorageConfig:
     cloudfront_private_key: str | None = None  # PEM private key content
     # Signed URL settings
     signed_url_expires_in: int = 86400  # Default 24 hours
+    # Azure Blob Storage configuration
+    azure_connection_string: str | None = None  # Azure connection string
+    azure_account_name: str | None = None  # Azure storage account name
+    azure_account_key: str | None = (
+        None  # Azure storage account key (for SAS token generation)
+    )
+    azure_container_name: str | None = (
+        None  # Azure container name (falls back to bucket if not set)
+    )
 
 
 @dataclass
@@ -310,6 +320,13 @@ def _apply_env_overrides(self) -> None:
             self._config.object_storage.enabled = (
                 os.getenv("OBJECT_STORAGE_ENABLED", "").lower() == "true"
             )
+        if os.getenv("OBJECT_STORAGE_TYPE"):
+            storage_type = os.getenv("OBJECT_STORAGE_TYPE", "").lower()
+            if storage_type in ["s3", "azure"]:
+                self._config.object_storage.storage_type = cast(
+                    Literal["s3", "azure"], storage_type
+                )
+
         if os.getenv("OBJECT_STORAGE_BUCKET"):
             self._config.object_storage.bucket = os.getenv("OBJECT_STORAGE_BUCKET")
         if os.getenv("OBJECT_STORAGE_REGION"):
@@ -377,6 +394,23 @@ def _apply_env_overrides(self) -> None:
                     default=self._config.object_storage.signed_url_expires_in,
                 )
             )
+        # Azure Blob Storage overrides
+        if os.getenv("AZURE_CONNECTION_STRING"):
+            self._config.object_storage.azure_connection_string = os.getenv(
+                "AZURE_CONNECTION_STRING"
+            )
+        if os.getenv("AZURE_STORAGE_ACCOUNT_NAME"):
+            self._config.object_storage.azure_account_name = os.getenv(
+                "AZURE_STORAGE_ACCOUNT_NAME"
+            )
+        if os.getenv("AZURE_STORAGE_ACCOUNT_KEY"):
+            self._config.object_storage.azure_account_key = os.getenv(
+                "AZURE_STORAGE_ACCOUNT_KEY"
+            )
+        if os.getenv("AZURE_CONTAINER_NAME"):
+            self._config.object_storage.azure_container_name = os.getenv(
+                "AZURE_CONTAINER_NAME"
+            )
 
         logger.debug("Environment variable overrides applied")
 
diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index 88e745ab4..a5021d52d 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -553,8 +553,12 @@ def process_object_storage_upload(
 
         # Upload to object storage if enabled
 
-        if config.object_storage.enabled and config.object_storage.bucket:
-            from api_server.object_storage import (
+        # Check if object storage is enabled and properly configured
+        if config.object_storage.enabled and (
+            config.object_storage.bucket
+            or config.object_storage.storage_type == "azure"
+        ):
+            from earth2studio_api_server.object_storage import (
                 MSCObjectStorage,
                 ObjectStorageError,
             )
@@ -570,41 +574,70 @@ def process_object_storage_upload(
             # Create S3 storage instance
             storage_kwargs = {
                 "bucket": config.object_storage.bucket,
-                "region": config.object_storage.region,
-                "use_transfer_acceleration": config.object_storage.use_transfer_acceleration,
+                "storage_type": config.object_storage.storage_type,
                 "max_concurrency": config.object_storage.max_concurrency,
                 "multipart_chunksize": config.object_storage.multipart_chunksize,
                 "use_rust_client": config.object_storage.use_rust_client,
             }
 
-            # Add optional credentials
-            if (
-                config.object_storage.access_key_id
-                and config.object_storage.secret_access_key
-            ):
-                storage_kwargs["access_key_id"] = config.object_storage.access_key_id
-                storage_kwargs["secret_access_key"] = (
-                    config.object_storage.secret_access_key
-                )
-            if config.object_storage.session_token:
-                storage_kwargs["session_token"] = config.object_storage.session_token
-            if config.object_storage.endpoint_url:
-                storage_kwargs["endpoint_url"] = config.object_storage.endpoint_url
-
-            # Add CloudFront configuration for signed URLs
-            if config.object_storage.cloudfront_domain:
-                storage_kwargs["cloudfront_domain"] = (
-                    config.object_storage.cloudfront_domain
-                )
-            if config.object_storage.cloudfront_key_pair_id:
-                storage_kwargs["cloudfront_key_pair_id"] = (
-                    config.object_storage.cloudfront_key_pair_id
-                )
-            if config.object_storage.cloudfront_private_key:
-                storage_kwargs["cloudfront_private_key"] = (
-                    config.object_storage.cloudfront_private_key
+            # Add storage-type-specific configuration
+            if config.object_storage.storage_type == "s3":
+                # S3-specific parameters
+                storage_kwargs["region"] = config.object_storage.region
+                storage_kwargs["use_transfer_acceleration"] = (
+                    config.object_storage.use_transfer_acceleration
                 )
 
+                # Add optional S3 credentials
+                if (
+                    config.object_storage.access_key_id
+                    and config.object_storage.secret_access_key
+                ):
+                    storage_kwargs["access_key_id"] = (
+                        config.object_storage.access_key_id
+                    )
+                    storage_kwargs["secret_access_key"] = (
+                        config.object_storage.secret_access_key
+                    )
+                if config.object_storage.session_token:
+                    storage_kwargs["session_token"] = (
+                        config.object_storage.session_token
+                    )
+                if config.object_storage.endpoint_url:
+                    storage_kwargs["endpoint_url"] = config.object_storage.endpoint_url
+
+                # Add CloudFront configuration for signed URLs
+                if config.object_storage.cloudfront_domain:
+                    storage_kwargs["cloudfront_domain"] = (
+                        config.object_storage.cloudfront_domain
+                    )
+                if config.object_storage.cloudfront_key_pair_id:
+                    storage_kwargs["cloudfront_key_pair_id"] = (
+                        config.object_storage.cloudfront_key_pair_id
+                    )
+                if config.object_storage.cloudfront_private_key:
+                    storage_kwargs["cloudfront_private_key"] = (
+                        config.object_storage.cloudfront_private_key
+                    )
+            elif config.object_storage.storage_type == "azure":
+                # Azure-specific parameters
+                if config.object_storage.azure_connection_string:
+                    storage_kwargs["azure_connection_string"] = (
+                        config.object_storage.azure_connection_string
+                    )
+                if config.object_storage.azure_account_name:
+                    storage_kwargs["azure_account_name"] = (
+                        config.object_storage.azure_account_name
+                    )
+                if config.object_storage.azure_account_key:
+                    storage_kwargs["azure_account_key"] = (
+                        config.object_storage.azure_account_key
+                    )
+                if config.object_storage.azure_container_name:
+                    storage_kwargs["azure_container_name"] = (
+                        config.object_storage.azure_container_name
+                    )
+
             try:
                 storage = MSCObjectStorage(**storage_kwargs)
             except Exception as e:
@@ -620,8 +653,13 @@ def process_object_storage_upload(
             )
 
             # Upload the output directory
+            storage_location = (
+                f"s3://{config.object_storage.bucket}"
+                if config.object_storage.storage_type == "s3"
+                else f"azure://{config.object_storage.azure_container_name or config.object_storage.bucket}"
+            )
             logger.info(
-                f"Uploading {output_path} to s3://{config.object_storage.bucket}/{remote_prefix}"
+                f"Uploading {output_path} to {storage_location}/{remote_prefix}"
             )
 
             try:
@@ -645,23 +683,36 @@ def process_object_storage_upload(
                     f"Failed to upload to object storage: {upload_result.errors}",
                 )
 
-            storage_type = "s3"
+            storage_type = config.object_storage.storage_type
             logger.info(
                 f"Successfully uploaded {upload_result.files_uploaded} files "
                 f"({upload_result.total_bytes} bytes) to {upload_result.destination}"
             )
 
-            # Generate signed URL if CloudFront is configured
-            cloudfront_configured = all(
-                [
-                    config.object_storage.cloudfront_domain,
-                    config.object_storage.cloudfront_key_pair_id,
-                    config.object_storage.cloudfront_private_key,
-                ]
-            )
+            # Generate signed URL if configured
+            # For S3: requires CloudFront configuration
+            # For Azure: requires account_name and account_key
+            can_generate_signed_url = False
+            if storage_type == "s3":
+                can_generate_signed_url = all(
+                    [
+                        config.object_storage.cloudfront_domain,
+                        config.object_storage.cloudfront_key_pair_id,
+                        config.object_storage.cloudfront_private_key,
+                    ]
+                )
+            elif storage_type == "azure":
+                can_generate_signed_url = all(
+                    [
+                        config.object_storage.azure_account_name,
+                        config.object_storage.azure_account_key,
+                    ]
+                )
 
-            if not cloudfront_configured:
-                logger.info("CloudFront not configured, skipping signed URL generation")
+            if not can_generate_signed_url:
+                logger.info(
+                    f"Signed URL generation not configured for {storage_type}, skipping"
+                )
             else:
                 try:
                     signed_url_path = f"{remote_prefix}/*"
@@ -691,10 +742,19 @@ def process_object_storage_upload(
         storage_info = {
             "storage_type": storage_type,
         }
-        if storage_type == "s3" and remote_prefix:
-            storage_info["remote_path"] = (
-                f"s3://{config.object_storage.bucket}/{remote_prefix}"
-            )
+        if remote_prefix:
+            if storage_type == "s3":
+                storage_info["remote_path"] = (
+                    f"s3://{config.object_storage.bucket}/{remote_prefix}"
+                )
+            elif storage_type == "azure":
+                container_name = (
+                    config.object_storage.azure_container_name
+                    or config.object_storage.bucket
+                )
+                storage_info["remote_path"] = (
+                    f"azure://{container_name}/{remote_prefix}"
+                )
         if signed_url:
             storage_info["signed_url"] = signed_url
 
@@ -732,7 +792,7 @@ def process_object_storage_upload(
             "signed_url": signed_url,
         }
 
-        if upload_result and storage_type == "s3":
+        if upload_result:
             result["files_uploaded"] = upload_result.files_uploaded
             result["total_bytes"] = upload_result.total_bytes
             result["destination"] = upload_result.destination
diff --git a/serve/inferenceserver/api_server/object_storage.py b/serve/inferenceserver/api_server/object_storage.py
index 6f5a80f04..1f6f104c0 100644
--- a/serve/inferenceserver/api_server/object_storage.py
+++ b/serve/inferenceserver/api_server/object_storage.py
@@ -160,25 +160,30 @@ class ObjectStorageError(Exception):
 
 class MSCObjectStorage(ObjectStorage):
     """
-    Object storage implementation using NVIDIA Multi-Storage Client with Rust backend.
+    Object storage implementation using NVIDIA Multi-Storage Client with Rust backend for AWS S3 and Azure Blob Storage.
 
     MSC provides optimized parallel transfers and the Rust client bypasses Python's
     GIL for significantly improved I/O performance (up to 12x faster).
 
     Uses sync_from for efficient directory uploads with parallel transfers.
 
-    Credentials are picked up from environment variables:
+    Supports both AWS S3 and Azure Blob Storage.
+
+    For S3, credentials are picked up from environment variables:
     - AWS_ACCESS_KEY_ID
     - AWS_SECRET_ACCESS_KEY
     - AWS_SESSION_TOKEN (optional)
     - AWS_DEFAULT_REGION
 
+    For Azure, credentials can be provided via connection string or account name/key.
+
     See: https://nvidia.github.io/multi-storage-client/user_guide/rust.html
     """
 
     def __init__(
         self,
         bucket: str,
+        storage_type: Literal["s3", "azure"] = "s3",
         region: str | None = None,
         access_key_id: str | None = None,
         secret_access_key: str | None = None,
@@ -188,16 +193,22 @@ def __init__(
         max_concurrency: int = 16,
         multipart_chunksize: int = 8 * 1024 * 1024,  # 8 MB
         use_rust_client: bool = False,
-        profile_name: str = "e2studio-s3",
+        profile_name: str | None = None,
         cloudfront_domain: str | None = None,
         cloudfront_key_pair_id: str | None = None,
         cloudfront_private_key: str | None = None,
+        # Azure-specific parameters
+        azure_connection_string: str | None = None,
+        azure_account_name: str | None = None,
+        azure_account_key: str | None = None,
+        azure_container_name: str | None = None,
     ):
         """
         Initialize MSCObjectStorage with AWS credentials and configuration.
 
         Args:
             bucket: The S3 bucket name.
+            storage_type: Storage provider type, either "s3" or "azure".
             region: AWS region (e.g., 'us-east-1').
             access_key_id: AWS access key ID (sets AWS_ACCESS_KEY_ID env var).
             secret_access_key: AWS secret access key (sets AWS_SECRET_ACCESS_KEY env var).
@@ -211,21 +222,89 @@ def __init__(
             cloudfront_domain: CloudFront distribution domain for signed URLs.
             cloudfront_key_pair_id: CloudFront key pair ID for signed URLs.
             cloudfront_private_key: PEM private key content as string.
+            azure_connection_string: Azure connection string.
+            azure_account_name: Azure storage account name.
+            azure_account_key: Azure storage account key (optional for SAS token generation).
+            azure_container_name: Azure container name.
         """
+        self.storage_type = storage_type
         self.bucket = bucket
-        self.region = region or os.environ.get("AWS_DEFAULT_REGION", "us-east-1")
         self.max_concurrency = max_concurrency
         self.multipart_chunksize = multipart_chunksize
         self.use_rust_client = use_rust_client
-        self.profile_name = profile_name
-        self.use_transfer_acceleration = use_transfer_acceleration
+        self.profile_name = profile_name or (
+            "e2studio-s3" if storage_type == "s3" else "e2studio-azure"
+        )
+        # Initialize endpoint_url as None to allow str | None type
+        self.endpoint_url: str | None = None
+
+        # S3-specific configuration
+        if storage_type == "s3":
+            self.region = region or os.environ.get("AWS_DEFAULT_REGION", "us-east-1")
+            self.use_transfer_acceleration = use_transfer_acceleration
+
+            # Use S3 Transfer Acceleration endpoint if enabled (and no custom endpoint provided)
+            if use_transfer_acceleration and not endpoint_url:
+                self.endpoint_url = f"https://{bucket}.s3-accelerate.amazonaws.com"
+                logger.info(f"S3 Transfer Acceleration enabled: {self.endpoint_url}")
+            else:
+                self.endpoint_url = endpoint_url
+
+            # CloudFront configuration for signed URLs
+            self.cloudfront_domain = cloudfront_domain
+            self.cloudfront_key_pair_id = cloudfront_key_pair_id
+            self.cloudfront_private_key = cloudfront_private_key
+
+            # Set credentials as environment variables - MSC picks these up automatically
+            if access_key_id:
+                os.environ["AWS_ACCESS_KEY_ID"] = access_key_id
+            if secret_access_key:
+                os.environ["AWS_SECRET_ACCESS_KEY"] = secret_access_key
+            if session_token:
+                os.environ["AWS_SESSION_TOKEN"] = session_token
+            if region:
+                os.environ["AWS_DEFAULT_REGION"] = region
+
+        # Azure-specific configuration
+        elif storage_type == "azure":
+            # Connection string is mandatory for Azure
+            if not azure_connection_string:
+                raise ObjectStorageError(
+                    "Azure connection string is required. "
+                    "Please provide azure_connection_string."
+                )
+
+            self.azure_container_name = azure_container_name or bucket
+            self.azure_account_key = azure_account_key
+
+            # Set Azure connection string
+            os.environ["AZURE_CONNECTION_STRING"] = azure_connection_string
+
+            # Extract account name from connection string if not provided directly
+            if not azure_account_name:
+                # Connection string format: DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...
+                for part in azure_connection_string.split(";"):
+                    if part.startswith("AccountName="):
+                        self.azure_account_name = part.split("=", 1)[1]
+                        break
+                if not self.azure_account_name:
+                    raise ObjectStorageError(
+                        "Could not extract account name from connection string. "
+                        "Please provide azure_account_name directly or ensure connection string contains AccountName."
+                    )
+            else:
+                self.azure_account_name = azure_account_name
+        else:
+            raise ValueError(
+                f"Unsupported storage_type: {storage_type}. Must be 's3' or 'azure'."
+            )
 
         # Use S3 Transfer Acceleration endpoint if enabled (and no custom endpoint provided)
         if use_transfer_acceleration and not endpoint_url:
             self.endpoint_url = f"https://{bucket}.s3-accelerate.amazonaws.com"
             logger.info(f"S3 Transfer Acceleration enabled: {self.endpoint_url}")
         else:
-            self.endpoint_url = endpoint_url or ""
+            self.endpoint_url = endpoint_url
 
         # CloudFront configuration for signed URLs
         self.cloudfront_domain = cloudfront_domain
@@ -253,44 +332,109 @@ def __init__(
 
         self._msc = msc
 
-        # Build the S3 storage provider options
-        s3_storage_provider_options: dict[str, Any] = {
-            "base_path": bucket,
-            "region_name": self.region,
-            "multipart_threshold": multipart_chunksize,
-            "multipart_chunksize": multipart_chunksize,
-            "max_concurrency": max_concurrency,
-        }
-
-        # Add endpoint URL if provided (for S3-compatible services)
-        if endpoint_url:
-            s3_storage_provider_options["endpoint_url"] = endpoint_url
-
-        # Enable Rust client for high-performance I/O
-        if use_rust_client:
-            s3_storage_provider_options["rust_client"] = {
+        # Build storage provider profile config based on storage_type
+        if storage_type == "s3":
+            # Build the S3 storage provider options
+            s3_storage_provider_options: dict[str, Any] = {
+                "base_path": bucket,
+                "region_name": self.region,
+                "multipart_threshold": multipart_chunksize,
                 "multipart_chunksize": multipart_chunksize,
                 "max_concurrency": max_concurrency,
             }
 
-        # Build the S3 profile config
-        s3_profile_config = {
-            "profiles": {
-                profile_name: {
-                    "storage_provider": {
-                        "type": "s3",
-                        "options": s3_storage_provider_options,
+            # Add endpoint URL if provided (for S3-compatible services)
+            if self.endpoint_url:
+                s3_storage_provider_options["endpoint_url"] = self.endpoint_url
+
+            # Enable Rust client for high-performance I/O
+            if use_rust_client:
+                s3_storage_provider_options["rust_client"] = {
+                    "multipart_chunksize": multipart_chunksize,
+                    "max_concurrency": max_concurrency,
+                }
+
+            # Build the S3 profile config
+            profile_config = {
+                "profiles": {
+                    self.profile_name: {
+                        "storage_provider": {
+                            "type": "s3",
+                            "options": s3_storage_provider_options,
+                        }
                     }
                 }
             }
-        }
+        elif storage_type == "azure":
+            # Build the Azure storage provider options
+            # Derive endpoint URL from connection string or account name
+            azure_endpoint_url = None
+
+            # First, try to extract BlobEndpoint directly from connection string
+            if azure_connection_string:
+                for part in azure_connection_string.split(";"):
+                    if part.startswith("BlobEndpoint="):
+                        azure_endpoint_url = part.split("=", 1)[1].rstrip("/")
+                        break
+
+            # If not found, construct from AccountName and EndpointSuffix
+            if not azure_endpoint_url:
+                account_name = None
+                endpoint_suffix = "core.windows.net"  # Default suffix
+
+                # Extract from connection string
+                if azure_connection_string:
+                    for part in azure_connection_string.split(";"):
+                        if part.startswith("AccountName="):
+                            account_name = part.split("=", 1)[1]
+                        elif part.startswith("EndpointSuffix="):
+                            endpoint_suffix = part.split("=", 1)[1]
+
+                # Fall back to provided account_name if not in connection string
+                if not account_name:
+                    account_name = self.azure_account_name
+
+                if not account_name:
+                    raise ObjectStorageError(
+                        "Azure endpoint_url cannot be determined. "
+                        "Please provide azure_connection_string (with AccountName or BlobEndpoint) "
+                        "or azure_account_name."
+                    )
+
+                azure_endpoint_url = f"https://{account_name}.blob.{endpoint_suffix}"
+
+            azure_storage_provider_options = {
+                "base_path": self.azure_container_name,
+                "endpoint_url": azure_endpoint_url,
+            }
+
+            # Build the Azure profile config with credentials provider
+            profile_config = {
+                "profiles": {
+                    self.profile_name: {
+                        "storage_provider": {
+                            "type": "azure",
+                            "options": azure_storage_provider_options,
+                        }
+                    }
+                }
+            }
+
+            # Add Azure credentials provider if connection string is provided
+            if azure_connection_string:
+                profile_config["profiles"][self.profile_name][
+                    "credentials_provider"
+                ] = {
+                    "type": "AzureCredentials",
+                    "options": {"connection": "${AZURE_CONNECTION_STRING}"},
+                }
 
-        # Initialize the S3 StorageClient (target for uploads)
-        s3_client_config = msc.StorageClientConfig.from_dict(
-            config_dict=s3_profile_config,
-            profile=profile_name,
+        # Initialize the StorageClient (target for uploads)
+        storage_client_config = msc.StorageClientConfig.from_dict(
+            config_dict=profile_config,
+            profile=self.profile_name,
         )
-        self._s3_client = msc.StorageClient(config=s3_client_config)
+        self._storage_client = msc.StorageClient(config=storage_client_config)
 
         # Initialize the local filesystem StorageClient (source for uploads)
         local_profile_config = {
@@ -312,12 +456,18 @@ def __init__(
         self._local_client = msc.StorageClient(config=local_client_config)
 
         rust_status = "enabled" if use_rust_client else "disabled"
-        accel_status = "enabled" if use_transfer_acceleration else "disabled"
-        logger.info(
-            f"MSCObjectStorage initialized: bucket={bucket}, region={self.region}, "
-            f"max_concurrency={max_concurrency}, rust_client={rust_status}, "
-            f"transfer_acceleration={accel_status}"
-        )
+        if storage_type == "s3":
+            accel_status = "enabled" if use_transfer_acceleration else "disabled"
+            logger.info(
+                f"MSCObjectStorage initialized (S3): bucket={bucket}, region={self.region}, "
+                f"max_concurrency={max_concurrency}, rust_client={rust_status}, "
+                f"transfer_acceleration={accel_status}"
+            )
+        else:
+            logger.info(
+                f"MSCObjectStorage initialized (Azure): container={self.azure_container_name}, "
+                f"max_concurrency={max_concurrency}, rust_client={rust_status}"
+            )
 
     def upload_directory(
         self,
@@ -327,13 +477,14 @@ def upload_directory(
         overwrite: bool = True,
     ) -> UploadResult:
         """
-        Upload a local directory to S3 using Multi-Storage Client sync_from.
+        Upload a local directory to object storage using Multi-Storage Client sync_from.
 
         Uses MSC's sync_from for efficient parallel directory uploads.
+        Works with both S3 and Azure Blob Storage.
 
         Args:
             local_directory: Path to the local directory to upload.
-            remote_prefix: The S3 prefix where files will be uploaded.
+            remote_prefix: The storage prefix where files will be uploaded.
             recursive: If True, recursively upload all subdirectories.
             overwrite: If True, overwrite existing files.
 
@@ -359,9 +510,14 @@ def upload_directory(
 
         total_bytes = sum(f.stat().st_size for f in files)
 
+        storage_prefix = (
+            f"s3://{self.bucket}"
+            if self.storage_type == "s3"
+            else f"azure://{self.azure_container_name if self.storage_type == 'azure' else self.bucket}"
+        )
         logger.info(
             f"[MSC] Syncing {len(files)} files ({total_bytes / (1024 * 1024):.2f} MB) "
-            f"from {local_directory} to s3://{self.bucket}/{remote_prefix}"
+            f"from {local_directory} to {storage_prefix}/{remote_prefix}"
         )
 
         errors: list[str] = []
@@ -369,7 +525,7 @@ def upload_directory(
 
         try:
             # Use sync_from for efficient parallel directory upload
-            result = self._s3_client.sync_from(
+            result = self._storage_client.sync_from(
                 source_client=self._local_client,
                 source_path=source_path,
                 target_path=f"/{remote_prefix}" if remote_prefix else "/",
@@ -386,7 +542,15 @@ def upload_directory(
 
         elapsed_time = time.time() - start_time
         success = len(errors) == 0
-        destination = f"s3://{self.bucket}/{remote_prefix}"
+        if self.storage_type == "s3":
+            destination = f"s3://{self.bucket}/{remote_prefix}"
+        else:
+            container = (
+                self.azure_container_name
+                if self.storage_type == "azure"
+                else self.bucket
+            )
+            destination = f"azure://{container}/{remote_prefix}"
 
         result = UploadResult(
             success=success,
@@ -416,11 +580,11 @@ def upload_file(
         overwrite: bool = True,
     ) -> bool:
         """
-        Upload a single file to S3 using Multi-Storage Client with Rust backend.
+        Upload a single file to object storage using Multi-Storage Client.
 
         Args:
             local_file: Path to the local file.
-            remote_key: The S3 key for the file.
+            remote_key: The storage key for the file.
             overwrite: If True, overwrite if exists.
 
         Returns:
@@ -437,7 +601,7 @@ def upload_file(
 
         try:
             remote_key = f"/{remote_key.lstrip('/')}"
-            self._s3_client.upload_file(remote_key, str(local_path))
+            self._storage_client.upload_file(remote_key, str(local_path))
             return True
 
         except Exception as e:
@@ -446,34 +610,37 @@ def upload_file(
 
     def file_exists(self, remote_key: str) -> bool:
         """
-        Check if a file exists in S3 using MSC info() method.
+        Check if a file exists in object storage using MSC info() method.
+        Works with both S3 and Azure Blob Storage.
 
         Args:
-            remote_key: The S3 key to check.
+            remote_key: The storage key to check.
 
         Returns:
             True if the file exists, False otherwise.
         """
         try:
             remote_path = f"/{remote_key.lstrip('/')}"
-            self._s3_client.info(remote_path)
+            self._storage_client.info(remote_path)
             return True
         except FileNotFoundError:
             return False
 
     def delete_file(self, remote_key: str) -> bool:
         """
-        Delete a file from S3 using MSC delete() method.
+        Delete a file from object storage using MSC delete() method.
+
+        Works with both S3 and Azure Blob Storage.
 
         Args:
-            remote_key: The S3 key to delete.
+            remote_key: The storage key to delete.
 
         Returns:
             True if successful, False otherwise.
         """
         try:
             remote_path = f"/{remote_key.lstrip('/')}"
-            self._s3_client.delete(remote_path)
+            self._storage_client.delete(remote_path)
             return True
         except FileNotFoundError:
             logger.warning(f"File not found for deletion: {remote_key}")
@@ -515,18 +682,30 @@ def _url_safe_b64(data: bytes) -> str:
 
     def generate_signed_url(self, remote_key: str, expires_in: int = 86400) -> str:
         """
-        Generate a CloudFront signed URL for accessing a file.
+        Generate a signed URL for accessing a file.
+
+        For S3, generates a CloudFront signed URL.
+        For Azure, generates a SAS (Shared Access Signature) token URL.
 
         Args:
-            remote_key: The S3 key/path to the file. Can include wildcards.
+            remote_key: The storage key/path to the file. Can include wildcards for S3.
             expires_in: Number of seconds until the URL expires (default: 86400).
 
         Returns:
-            A signed CloudFront URL string.
+            A signed URL string.
 
         Raises:
-            ObjectStorageError: If CloudFront configuration is missing.
+            ObjectStorageError: If required configuration is missing.
         """
+        if self.storage_type == "s3":
+            return self._generate_cloudfront_signed_url(remote_key, expires_in)
+        elif self.storage_type == "azure":
+            return self._generate_azure_sas_url(remote_key, expires_in)
+        else:
+            raise ObjectStorageError(f"Unsupported storage_type: {self.storage_type}")
+
+    def _generate_cloudfront_signed_url(self, remote_key: str, expires_in: int) -> str:
+        """Generate a CloudFront signed URL for S3."""
         if not all(
             [
                 self.cloudfront_domain,
@@ -576,7 +755,60 @@ def generate_signed_url(self, remote_key: str, expires_in: int = 86400) -> str:
             f"&Key-Pair-Id={self.cloudfront_key_pair_id}"
         )
 
-        logger.debug(f"Generated signed URL for {remote_key}, expires in {expires_in}s")
+        logger.debug(
+            f"Generated CloudFront signed URL for {remote_key}, expires in {expires_in}s"
+        )
+        return signed_url
+
+    def _generate_azure_sas_url(self, remote_key: str, expires_in: int) -> str:
+        """Generate an Azure SAS (Shared Access Signature) URL."""
+        if not self.azure_account_name or not self.azure_account_key:
+            raise ObjectStorageError(
+                "Azure account name and account key are required for signed URLs. "
+                "Please provide azure_account_name and azure_account_key."
+            )
+
+        try:
+            from azure.storage.blob import (
+                ContainerSasPermissions,
+                generate_container_sas,
+            )
+        except ImportError as e:
+            raise ImportError(
+                "azure-storage-blob is required for Azure signed URLs. "
+                "Install with: pip install azure-storage-blob"
+            ) from e
+
+        container_name = (
+            self.azure_container_name if self.storage_type == "azure" else self.bucket
+        )
+
+        # Define permissions (Read + List)
+        permissions = ContainerSasPermissions(read=True, list=True)
+
+        # Set the duration
+        start_time = datetime.datetime.now(datetime.timezone.utc)
+        expiry_time = start_time + datetime.timedelta(seconds=expires_in)
+
+        # Generate the SAS token
+        sas_token = generate_container_sas(
+            account_name=self.azure_account_name,
+            account_key=self.azure_account_key,
+            container_name=container_name,
+            permission=permissions,
+            expiry=expiry_time,
+            start=start_time,
+        )
+
+        # Construct the full URL with the prefix
+        # Remove leading slash from remote_key if present
+        prefix = remote_key.lstrip("/")
+        base_url = f"https://{self.azure_account_name}.blob.core.windows.net/{container_name}/{prefix}"
+        signed_url = f"{base_url}?{sas_token}"
+
+        logger.debug(
+            f"Generated Azure SAS URL for {remote_key}, expires in {expires_in}s"
+        )
         return signed_url
 
 
diff --git a/serve/inferenceserver/requirements.txt b/serve/inferenceserver/requirements.txt
index 117c0b6d0..e53074912 100644
--- a/serve/inferenceserver/requirements.txt
+++ b/serve/inferenceserver/requirements.txt
@@ -28,6 +28,11 @@ hydra-core>=1.3.0
 # Cryptography for CloudFront signed URLs
 cryptography>=41.0.0
 
+# Multi-Storage Client with Azure support
+multi-storage-client[azure-storage-blob]
+# Azure Blob Storage for SAS token generation
+azure-storage-blob>=12.19.0
+
 # Development dependencies
 pytest>=7.0.0
 pytest-asyncio>=0.21.0
diff --git a/serve/inferenceserver/scripts/startup.sh b/serve/inferenceserver/scripts/startup.sh
index 3a466b366..0d0e795d4 100755
--- a/serve/inferenceserver/scripts/startup.sh
+++ b/serve/inferenceserver/scripts/startup.sh
@@ -17,6 +17,15 @@
 
 set -euo pipefail
 
+# Set EARTH2STUDIO_MODEL_CACHE to use AZUREML_MODEL_DIR if available
+if [ -n "${AZUREML_MODEL_DIR:-}" ]; then
+    echo "AZUREML_MODEL_DIR: $AZUREML_MODEL_DIR"
+    export EARTH2STUDIO_MODEL_CACHE="$AZUREML_MODEL_DIR/${EARTH2STUDIO_MODEL_SUBPATH:-e2s_fcn3_stormscope}"
+    echo "--------------------------------"
+    echo "EARTH2STUDIO_MODEL_CACHE: $EARTH2STUDIO_MODEL_CACHE"
+    ls -la $EARTH2STUDIO_MODEL_CACHE && echo "--------------------------------"
+fi
+
 cd /workspace/earth2studio-project/service/inferenceserver
 make start-redis
 make start-api-server
diff --git a/uv.lock b/uv.lock
index b6bf74a8e..4a777b726 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
@@ -16,9 +16,9 @@ resolution-markers = [
 name = "absl-py"
 version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750 },
 ]
 
 [[package]]
@@ -28,18 +28,18 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903 },
 ]
 
 [[package]]
 name = "affine"
 version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662 },
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 [[package]]
@@ -55,27 +55,27 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/18/94/332629387f4a9fc691cac9c0cb078af877bfaba415b1a16411377f6ea310/aiobotocore-3.1.3.tar.gz", hash = "sha256:b1b6a95aa4c17410090f4adf16fd45e45a898140c83d4e9d554602f9310408c0", size = 122675, upload-time = "2026-02-14T12:11:01.745Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/94/332629387f4a9fc691cac9c0cb078af877bfaba415b1a16411377f6ea310/aiobotocore-3.1.3.tar.gz", hash = "sha256:b1b6a95aa4c17410090f4adf16fd45e45a898140c83d4e9d554602f9310408c0", size = 122675 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/14/29/a3e75834009121ebb695dc24f9fe804566b1bcc9b7d46f6fbe56fe972c6a/aiobotocore-3.1.3-py3-none-any.whl", hash = "sha256:3afc93bf14de304dbd4a2c90f36fb3ce6348b06a5a1ec7f87261be628d7876d9", size = 87717 },
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 [[package]]
 name = "aiofiles"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
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+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668 },
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 [[package]]
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 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
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 [[package]]
@@ -91,102 +91,102 @@ dependencies = [
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From 84ded7e34a1f8758784f19c23d7dc3c049ee7d61 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Mon, 23 Feb 2026 18:29:34 -0800
Subject: [PATCH 02/24] fixes

---
 .../inferenceserver/api_server/cpu_worker.py  |  2 +-
 .../api_server/object_storage.py              | 22 -------------------
 2 files changed, 1 insertion(+), 23 deletions(-)

diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index a5021d52d..19fbd39a1 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -558,7 +558,7 @@ def process_object_storage_upload(
             config.object_storage.bucket
             or config.object_storage.storage_type == "azure"
         ):
-            from earth2studio_api_server.object_storage import (
+            from api_server.object_storage import (
                 MSCObjectStorage,
                 ObjectStorageError,
             )
diff --git a/serve/inferenceserver/api_server/object_storage.py b/serve/inferenceserver/api_server/object_storage.py
index 1f6f104c0..db8730ee4 100644
--- a/serve/inferenceserver/api_server/object_storage.py
+++ b/serve/inferenceserver/api_server/object_storage.py
@@ -299,28 +299,6 @@ def __init__(
                 f"Unsupported storage_type: {storage_type}. Must be 's3' or 'azure'."
             )
 
-        # Use S3 Transfer Acceleration endpoint if enabled (and no custom endpoint provided)
-        if use_transfer_acceleration and not endpoint_url:
-            self.endpoint_url = f"https://{bucket}.s3-accelerate.amazonaws.com"
-            logger.info(f"S3 Transfer Acceleration enabled: {self.endpoint_url}")
-        else:
-            self.endpoint_url = endpoint_url
-
-        # CloudFront configuration for signed URLs
-        self.cloudfront_domain = cloudfront_domain
-        self.cloudfront_key_pair_id = cloudfront_key_pair_id
-        self.cloudfront_private_key = cloudfront_private_key
-
-        # Set credentials as environment variables - MSC picks these up automatically
-        if access_key_id:
-            os.environ["AWS_ACCESS_KEY_ID"] = access_key_id
-        if secret_access_key:
-            os.environ["AWS_SECRET_ACCESS_KEY"] = secret_access_key
-        if session_token:
-            os.environ["AWS_SESSION_TOKEN"] = session_token
-        if region:
-            os.environ["AWS_DEFAULT_REGION"] = region
-
         # Import multi-storage-client
         try:
             import multistorageclient as msc

From b91b7fd13e37e8407e463463bf5728b1a2bae481 Mon Sep 17 00:00:00 2001
From: Stefan Weissenberger <sweissenberg@nvidia.com>
Date: Tue, 24 Feb 2026 17:07:26 +0000
Subject: [PATCH 03/24] add Foundry workflows

---
 .../example_workflows/foundry_fcn3.py         | 182 ++++++++
 .../foundry_fcn3_stormscope_goes.py           | 428 ++++++++++++++++++
 2 files changed, 610 insertions(+)
 create mode 100644 serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
 create mode 100644 serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py

diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
new file mode 100644
index 000000000..0ba1f7548
--- /dev/null
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -0,0 +1,182 @@
+import logging
+from collections.abc import Sequence
+from datetime import datetime
+
+import numpy as np
+import torch
+import zarr
+
+from api_server.workflow import Earth2Workflow, workflow_registry
+from earth2studio.data import PlanetaryComputerECMWFOpenDataIFS, fetch_data
+from earth2studio.io import IOBackend, ZarrBackend
+from earth2studio.models.px import FCN3
+from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
+from earth2studio.utils.time import to_time_array
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("foundry_fcn3_workflow")
+
+
+@workflow_registry.register
+class FoundryFCN3Workflow(Earth2Workflow):
+    name = "foundry_fcn3_workflow"
+    description = "FCN3 ensemble workflow for Foundry"
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_seed: int | None = None,
+    ):
+        super().__init__()
+
+        self.device = torch.device(device)
+
+        self.fcn3 = self.load_fcn3()
+        self.rng = np.random.default_rng(init_seed)
+
+        # TODO: Do not use data cache
+        self.data = PlanetaryComputerECMWFOpenDataIFS(verbose=False)
+
+    def load_fcn3(self) -> FCN3:
+        logger.info("Loading FCN3")
+        # Assuming AZUREML_MODEL_DIR == EARTH2STUDIO_CACHE
+        package = FCN3.load_default_package()
+        fcn3 = FCN3.load_model(package)
+        fcn3.to(self.device)
+        fcn3.eval()
+        return fcn3
+
+    def get_seeds(self, n_seeds: int) -> list[int]:
+        seeds = self.rng.choice(2**32, size=n_seeds, replace=False)
+        return [int(s) for s in seeds]
+
+    def validate_start_time(self, start_time: datetime) -> None:
+        if (start_time - datetime(1900, 1, 1)).total_seconds() % 21600 != 0:
+            raise ValueError(f"Start time needs to be 6-hour interval: {start_time}")
+
+    def validate_samples(
+        self, n_samples: int, seeds: Sequence[int] | None
+    ) -> list[int]:
+        if seeds is None:
+            seeds = self.get_seeds(n_samples)
+        elif len(seeds) != n_samples:
+            logger.warning(
+                "Ignoring requested number of samples because it does not match number of seeds"
+            )
+        return list(seeds)
+
+    def validate_variables(self, variables: Sequence[str] | None) -> np.ndarray:
+        if variables is None:
+            variables = self.fcn3.variables
+        else:
+            unknown_variables = set(variables) - set(self.fcn3.variables)
+            if len(unknown_variables):
+                raise ValueError(f"Unknown variable(s) {', '.join(unknown_variables)}")
+            variables = np.array(variables)
+        return variables
+
+    def setup_io(
+        self, io: IOBackend, output_coords: CoordSystem, seeds: Sequence[int]
+    ) -> None:
+        io.add_array(
+            {k: v for k, v in output_coords.items() if k != "variable"},
+            output_coords["variable"],
+        )
+
+        # Storing seeds separately makes it easier to filer with Titiler
+        sample_coords = {"sample": output_coords["sample"]}
+        io.add_array(sample_coords, "seed", torch.tensor(seeds))
+
+        # Set attributes for automatic parsing of dimensions
+        io.root["lat"].standard_name = "latitude"
+        io.root["lat"].axis = "Y"
+        io.root["lon"].standard_name = "longitude"
+        io.root["lon"].axis = "X"
+        # io.root["time"].standard_name = "time"
+        # io.root["time"].axis = "T"
+
+        # Planetary Computer does not like grid_mapping, so we skip adding it
+
+        if isinstance(io, ZarrBackend):
+            zarr.consolidate_metadata(io.store)
+
+        return io
+
+    def get_fcn3_input(self, time: datetime) -> tuple[torch.Tensor, CoordSystem]:
+        x, coords = fetch_data(
+            self.data,
+            time=to_time_array([time]),
+            variable=self.fcn3.input_coords()["variable"],
+            device=self.device,
+        )
+        return x, coords
+
+    def __call__(
+        self,
+        io: IOBackend,
+        start_time: datetime = datetime(2025, 1, 1),
+        n_steps: int = 20,
+        n_samples: int = 16,
+        seeds: Sequence[int] | None = None,
+        variables: Sequence[str] | None = None,
+    ) -> None:
+        if isinstance(io, ZarrBackend):
+            io.chunks = {
+                "sample": 1,
+                "time": 1,
+                "variable": 1,
+            }
+
+        self.validate_start_time(start_time)
+        lead_times = np.array([np.timedelta64(i * 6, "h") for i in range(n_steps + 1)])
+        seeds = self.validate_samples(n_samples, seeds)
+        variables = self.validate_variables(variables)
+
+        x_ori, coords_ori = self.get_fcn3_input(start_time)
+
+        output_coords = CoordSystem(
+            {
+                "sample": np.arange(len(seeds)),
+                # Planetary Computer does not like separate 'lead_time'
+                "time": to_time_array([start_time]) + lead_times,
+                "variable": variables,
+                "lat": np.linspace(90.0, -90.0, 721),
+                "lon": np.linspace(0, 360, 1440, endpoint=False),
+            }
+        )
+        self.setup_io(io, output_coords, seeds)
+
+        logger.info("Starting inference")
+        for si, seed in enumerate(seeds):
+            self.fcn3.set_rng(seed=seed)
+            iterator = self.fcn3.create_iterator(x_ori.clone(), coords_ori.copy())
+            for ii, (x, coords) in enumerate(iterator):
+                logger.info(
+                    "Processing FCN3 (seed=%d) step %d/%d",
+                    seed,
+                    ii + 1,
+                    len(lead_times),
+                )
+
+                x_out, coords_out = map_coords(
+                    x, coords, CoordSystem({"variable": output_coords["variable"]})
+                )
+                # Add sample dimension
+                x_out = x_out.unsqueeze(0)
+                coords_out["sample"] = np.array([si])
+                coords_out.move_to_end("sample", last=False)
+                # Combine time and lead_time
+                lead_time_dim = list(coords_out).index("lead_time")
+                x_out = x_out.squeeze(lead_time_dim)
+                coords_out["time"] = coords_out["time"] + coords_out["lead_time"]
+                del coords_out["lead_time"]
+                # Write to disk
+                io.write(*split_coords(x_out, coords_out))
+
+                if ii == n_steps:
+                    break
+
+        # Planetary Computer does not like the original time format
+        ref_time = start_time.isoformat().replace("T", " ")
+        io["time"].units = f"hours since {ref_time}"
+        io["time"][:] = np.arange(len(io["time"])) * 6
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
new file mode 100644
index 000000000..a0e5d5fb8
--- /dev/null
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -0,0 +1,428 @@
+import logging
+from collections import OrderedDict
+from collections.abc import Sequence
+from datetime import datetime, timedelta
+
+import numpy as np
+import torch
+import xarray as xr
+import zarr
+
+from api_server.workflow import Earth2Workflow, workflow_registry
+from earth2studio.data import (
+    GOES,
+    InferenceOutputSource,
+    PlanetaryComputerECMWFOpenDataIFS,
+    PlanetaryComputerGOES,
+    fetch_data,
+)
+from earth2studio.io import IOBackend, XarrayBackend, ZarrBackend
+from earth2studio.models.dx import DerivedSurfacePressure
+from earth2studio.models.px import FCN3, DiagnosticWrapper, InterpModAFNO
+from earth2studio.models.px.stormscope import (
+    StormScopeBase,
+    StormScopeGOES,
+)
+from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
+from earth2studio.utils.time import to_time_array
+
+logger = logging.getLogger("foundry_fcn3_stormscope_goes_workflow")
+logger.setLevel(logging.INFO)
+
+GOES_MODEL_NAME = "6km_60min_natten_cos_zenith_input_eoe_v2"
+
+
+@workflow_registry.register
+class FoundryFCN3StormScopeGOESWorkflow(Earth2Workflow):
+    name = "foundry_fcn3_stormscope_goes_workflow"
+    description = "FCN3+StormScopeGOES ensemble workflow for Foundry"
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_seed: int = 1234,
+    ):
+        super().__init__()
+
+        self.device = torch.device(device)
+
+        self.fcn3_interp = self.load_fcn3_interp()
+        self.stormscope = self.load_stormscope()
+        self.rng = np.random.default_rng(init_seed)
+
+        # TODO: Do not use data cache
+        self.data_fcn3 = PlanetaryComputerECMWFOpenDataIFS(verbose=False)
+
+        scan_mode = "C"
+        # TODO: Do not use data cache
+        self.data_stormscope = {
+            satellite: PlanetaryComputerGOES(
+                satellite=satellite, scan_mode=scan_mode, verbose=False
+            )
+            for satellite in ["goes16", "goes19"]
+        }
+
+        # GOES-16 and GOES19 have the same grid
+        goes_lat, goes_lon = GOES.grid(satellite="goes16", scan_mode=scan_mode)
+        coords_out = self.fcn3_interp.output_coords(self.fcn3_interp.input_coords())
+        self.stormscope.build_input_interpolator(goes_lat, goes_lon)
+        self.stormscope.build_conditioning_interpolator(
+            coords_out["lat"], coords_out["lon"]
+        )
+
+    def load_fcn3_interp(self) -> InterpModAFNO:
+        logger.info("Loading FCN3")
+        # Assuming AZUREML_MODEL_DIR == EARTH2STUDIO_CACHE
+        package = FCN3.load_default_package()
+        fcn3 = FCN3.load_model(package)
+
+        # Surface pressure interpolation
+        orography_fn = package.resolve("orography.nc")
+        with xr.open_dataset(orography_fn) as ds:
+            z_surface = torch.as_tensor(ds["Z"][0].values)
+        z_surf_coords = OrderedDict({d: fcn3.input_coords()[d] for d in ["lat", "lon"]})
+        sp_model = DerivedSurfacePressure(
+            p_levels=[50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000],
+            surface_geopotential=z_surface,
+            surface_geopotential_coords=z_surf_coords,
+        )
+
+        # Bundle surface pressure with FCN3
+        fcn3_sp = DiagnosticWrapper(px_model=fcn3, dx_model=sp_model)
+
+        # Add temporal interpolation to 1 hour
+        fcn3_interp = InterpModAFNO.from_pretrained()
+        fcn3_interp.px_model = fcn3_sp
+        fcn3_interp.to(device=self.device)
+        fcn3_interp.eval()
+        return fcn3_interp
+
+    def load_stormscope(self) -> StormScopeGOES:
+        logger.info("Loading StormScope")
+        package = StormScopeBase.load_default_package()
+        stormscope = StormScopeGOES.load_model(
+            package=package,
+            conditioning_data_source=None,  # set later
+            model_name=GOES_MODEL_NAME,
+        )
+        stormscope.to(self.device)
+        stormscope.eval()
+        return stormscope
+
+    def get_seeds(self, n_seeds: int) -> list[int]:
+        seeds = self.rng.choice(2**32, size=n_seeds, replace=False)
+        return [int(s) for s in seeds]
+
+    def validate_start_times(
+        self, time_stormscope: datetime, time_fcn3: datetime
+    ) -> None:
+        ref = datetime(1900, 1, 1)
+        if (time_stormscope - ref).total_seconds() % (1 * 60 * 60) != 0:
+            raise ValueError(
+                f"Start time for StormScope must be 6-hour interval: {time_stormscope}"
+            )
+        if (time_fcn3 - ref).total_seconds() % (6 * 60 * 60) != 0:
+            raise ValueError(
+                f"Start time for FCN3 must be 6-hour interval: {time_fcn3}"
+            )
+        if time_stormscope < time_fcn3:
+            raise ValueError(
+                "Start time for StormScope cannot preceed start time for FCN3"
+            )
+        if time_stormscope - time_fcn3 > timedelta(hours=12):
+            logger.warning(
+                "Start times for StormScope and FCN3 should not be more than 12 hours apart but got '%s' and '%s'",
+                time_stormscope,
+                time_fcn3,
+            )
+
+    def validate_samples(
+        self, n_samples: int, seeds: Sequence[int] | None
+    ) -> list[int]:
+        if not seeds:
+            return self.get_seeds(n_samples)
+        if len(seeds) != n_samples:
+            logger.warning(
+                "Ignoring requested number of samples because it does not match number of seeds"
+            )
+        return list(seeds)
+
+    def validate_variables(self, variables: Sequence[str] | None) -> np.ndarray:
+        if variables is None:
+            variables = self.stormscope.variables
+        else:
+            unknown_variables = set(variables) - set(self.stormscope.variables)
+            if len(unknown_variables):
+                raise ValueError(f"Unknown variable(s) {', '.join(unknown_variables)}")
+            variables = np.array(variables)
+        return variables
+
+    def setup_io(
+        self,
+        io: IOBackend,
+        output_coords: CoordSystem,
+        seeds_fcn3: Sequence[int],
+        seeds_stormscope: Sequence[int],
+    ) -> None:
+        io.add_array(
+            {k: v for k, v in output_coords.items() if k != "variable"},
+            output_coords["variable"],
+        )
+
+        # Storing seeds separately makes it easier to filer with Titiler
+        sample_coords = {"sample": output_coords["sample"]}
+        n_stormscope_per_fcn3 = len(seeds_stormscope) // len(seeds_fcn3)
+        tiled_seeds_fcn3 = np.repeat(seeds_fcn3, n_stormscope_per_fcn3)
+        io.add_array(sample_coords, "seed_fcn3", torch.tensor(tiled_seeds_fcn3))
+        io.add_array(sample_coords, "seed_stormscope", torch.tensor(seeds_stormscope))
+
+        # Set attributes for automatic parsing of dimensions
+        io.root["lat"].standard_name = "latitude"
+        io.root["lat"].axis = "Y"
+        io.root["lon"].standard_name = "longitude"
+        io.root["lon"].axis = "X"
+        # io.root["time"].standard_name = "time"
+        # io.root["time"].axis = "T"
+
+        # Planetary Computer does not like grid_mapping, so we skip adding it
+
+        if isinstance(io, ZarrBackend):
+            zarr.consolidate_metadata(io.store)
+
+        return io
+
+    def get_fcn3_input(self, time: datetime) -> tuple[torch.Tensor, CoordSystem]:
+        x, coords = fetch_data(
+            self.data_fcn3,
+            time=to_time_array([time]),
+            variable=self.fcn3_interp.input_coords()["variable"],
+            device=self.device,
+        )
+        return x, coords
+
+    def get_stormscope_input(self, time: datetime) -> tuple[torch.Tensor, CoordSystem]:
+        coords_in = self.stormscope.input_coords()
+        if time < datetime(2025, 4, 7):
+            data = self.data_stormscope["goes16"]
+        else:
+            data = self.data_stormscope["goes19"]
+        x, coords = fetch_data(
+            data,
+            time=to_time_array([time]),
+            variable=coords_in["variable"],
+            lead_time=coords_in["lead_time"],
+            device=self.device,
+        )
+
+        batch_size = 1
+        if x.dim() == 5:
+            x = x.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1, 1)
+            coords["batch"] = np.arange(batch_size)
+            coords.move_to_end("batch", last=False)
+
+        x, coords = self.stormscope.prep_input(x, coords)
+        x = torch.where(self.stormscope.valid_mask, x, torch.nan)
+
+        return x, coords
+
+    def run_fcn3(
+        self,
+        io: IOBackend,
+        x: torch.Tensor,
+        coords_x: CoordSystem,
+        seed_fcn3: int,
+        start_time_stormscope: datetime,
+        lead_times: np.ndarray,
+    ) -> None:
+        # Create z500 conditioning with FCN3
+        coords_in = self.stormscope.input_coords()
+        start_time_stormscope = to_time_array([start_time_stormscope])
+        variables = self.stormscope.conditioning_variables
+        # Start time and lead times are shifted to StormScope start time
+        output_coords = {
+            "time": start_time_stormscope,
+            "lead_time": lead_times,
+            "variable": variables,
+            "y": coords_in["y"],
+            "x": coords_in["x"],
+        }
+        io.add_array(
+            {k: v for k, v in output_coords.items() if k != "variable"}, variables
+        )
+
+        model_gap = int(
+            (start_time_stormscope - coords_x["time"]) / np.timedelta64(1, "h")
+        )
+
+        self.fcn3_interp.px_model.px_model.set_rng(seed=seed_fcn3)
+        iterator = self.fcn3_interp.create_iterator(x.clone(), coords_x.copy())
+
+        total_steps = model_gap + len(lead_times)
+        for i, (x, coords_x) in enumerate(iterator):
+            logger.info(
+                "Processing FCN3 (seed_fcn3=%d) step %d/%d",
+                seed_fcn3,
+                i + 1,
+                total_steps,
+            )
+
+            if i < model_gap:
+                # Skip initial steps leading up to StormScope start time
+                continue
+
+            x, coords_x = map_coords(x, coords_x, OrderedDict({"variable": variables}))
+            x, coords_x = self.stormscope.prep_input(x, coords_x, conditioning=True)
+            coords_x["time"] = start_time_stormscope
+            coords_x["lead_time"] = coords_x["lead_time"] - np.timedelta64(
+                model_gap, "h"
+            )
+            io.write(*split_coords(x, coords_x))
+
+            if (i + 1) == total_steps:
+                break
+
+    def run_stormscope(
+        self,
+        io: IOBackend,
+        y: torch.Tensor,
+        coords_y: CoordSystem,
+        sample: int,
+        seed_fcn3: int,
+        seed_stormscope: int,
+        lead_times: np.ndarray,
+        variables: np.ndarray,
+    ) -> None:
+        def prep_output(
+            y_pred: torch.Tensor, coords_pred: CoordSystem
+        ) -> tuple[torch.Tensor, CoordSystem]:
+            y_out, coords_out = map_coords(
+                y_pred, coords_pred, CoordSystem({"variable": variables})
+            )
+            del coords_out["batch"]
+            # Reuse batch dimension as sample dimension (squeeze/unsqueeze)
+            coords_out["sample"] = np.array([sample])
+            coords_out.move_to_end("sample", last=False)
+            # Combine time and lead_time
+            lead_time_dim = list(coords_out).index("lead_time")
+            y_out = y_out.squeeze(lead_time_dim)
+            coords_out["time"] = coords_out["time"] + coords_out["lead_time"]
+            del coords_out["lead_time"]
+            # Rename x, y to lon, lat
+            mp = {"x": "lon", "y": "lat"}
+            coords_out = CoordSystem({mp.get(k, k): v for k, v in coords_out.items()})
+            return y_out, coords_out
+
+        logger.info(
+            "Processing StormScope (seed_fcn3=%d, seed_stormscope=%d) step 1/%d",
+            seed_fcn3,
+            seed_stormscope,
+            len(lead_times),
+        )
+
+        # Store initial GOES data (identical across seeds)
+        y_out, coords_out = prep_output(y, coords_y)
+        io.write(*split_coords(y_out, coords_out))
+
+        # Cannot use seeded Generator before torch==2.10
+        # Use self.stormscope.sampler_args["randn_like"] once updated
+        torch.manual_seed(seed_stormscope)
+
+        for ii in range(1, len(lead_times)):
+            y_pred, coords_pred = self.stormscope(y, coords_y)
+            logger.info(
+                "Processing StormScope (seed_fcn3=%d, seed_stormscope=%d) step %d/%d",
+                seed_fcn3,
+                seed_stormscope,
+                ii + 1,
+                len(lead_times),
+            )
+
+            y_out, coords_out = prep_output(y_pred, coords_pred)
+            io.write(*split_coords(y_out, coords_out))
+
+            if ii == (len(lead_times) - 1):
+                break
+
+            y, coords_y = self.stormscope.next_input(y_pred, coords_pred, y, coords_y)
+
+    def __call__(
+        self,
+        io: IOBackend,
+        start_time_fcn3: datetime = datetime(2025, 1, 1, 18),
+        start_time_stormscope: datetime = datetime(2025, 1, 1, 18),
+        n_steps: int = 12,
+        n_samples_fcn3: int = 4,
+        n_samples_stormscope: int = 16,
+        seeds_fcn3: Sequence[int] | None = None,
+        seeds_stormscope: Sequence[int] | None = None,
+        variables: Sequence[str] | None = None,
+    ) -> None:
+        if isinstance(io, ZarrBackend):
+            io.chunks = {
+                "sample": 1,
+                "time": 1,
+                "variable": 1,
+            }
+
+        self.validate_start_times(start_time_stormscope, start_time_fcn3)
+        lead_times = np.array([np.timedelta64(i, "h") for i in range(n_steps + 1)])
+        # Different StormScope seed for every trajectory
+        if n_samples_stormscope % n_samples_fcn3 != 0:
+            raise ValueError(
+                "'n_samples_stormscope' must be divisible by 'n_samples_fcn3'"
+            )
+        seeds_fcn3 = self.validate_samples(n_samples_fcn3, seeds_fcn3)
+        seeds_stormscope = self.validate_samples(n_samples_stormscope, seeds_stormscope)
+        n_stormscope_per_fcn3 = len(seeds_stormscope) // len(seeds_fcn3)
+        variables = self.validate_variables(variables)
+
+        x_ori, coords_x_ori = self.get_fcn3_input(start_time_fcn3)
+        y_ori, coords_y_ori = self.get_stormscope_input(start_time_stormscope)
+
+        coords_out = self.stormscope.output_coords(self.stormscope.input_coords())
+        output_coords = {
+            "sample": np.arange(len(seeds_stormscope)),
+            # Planetary Computer does not like separate 'lead_time'
+            "time": to_time_array([start_time_stormscope]) + lead_times,
+            "variable": variables,
+            "lat": coords_out["y"],
+            "lon": coords_out["x"],
+        }
+        self.setup_io(io, output_coords, seeds_fcn3, seeds_stormscope)
+
+        sample = 0
+        for seed_fcn3 in seeds_fcn3:
+            # Generate FCN3 conditioning (z500)
+            logger.info("Starting FCN3 inference")
+            io_fcn3 = XarrayBackend()
+            self.run_fcn3(
+                io=io_fcn3,
+                x=x_ori.clone(),
+                coords_x=coords_x_ori.copy(),
+                seed_fcn3=seed_fcn3,
+                start_time_stormscope=start_time_stormscope,
+                lead_times=lead_times,
+            )
+            self.stormscope.conditioning_data_source = InferenceOutputSource(
+                io_fcn3.root
+            )
+
+            # Run StormScope forecast conditioned on FCN3
+            logger.info("Starting StormScope inference")
+            for _ in range(n_stormscope_per_fcn3):
+                self.run_stormscope(
+                    io=io,
+                    y=y_ori.clone(),
+                    coords_y=coords_y_ori.copy(),
+                    sample=sample,
+                    seed_fcn3=seed_fcn3,
+                    seed_stormscope=seeds_stormscope[sample],
+                    lead_times=lead_times,
+                    variables=variables,
+                )
+                sample += 1
+
+        # Planetary Computer does not like the original time format
+        ref_time = start_time_stormscope.isoformat().replace("T", " ")
+        io["time"].units = f"hours since {ref_time}"
+        io["time"][:] = np.arange(len(io["time"]))

From a36b50bd941599898a4c5258fafa70d7c205a659 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Tue, 24 Feb 2026 13:17:24 -0800
Subject: [PATCH 04/24] fixes to get the foundry workflows to run

---
 .pre-commit-config.yaml                                   | 2 +-
 serve/Dockerfile                                          | 1 -
 .../api_server/example_workflows/foundry_fcn3.py          | 4 ++--
 .../example_workflows/foundry_fcn3_stormscope_goes.py     | 8 +++++---
 serve/inferenceserver/api_server/workflow/e2workflow.py   | 4 ++++
 serve/inferenceserver/scripts/startup.sh                  | 2 +-
 6 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0af7291f5..5624a254f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -58,7 +58,7 @@ repos:
     rev: 1.7.0
     hooks:
     - id: interrogate
-      exclude: ^(setup.py|test/|earth2studio/models/nn/)
+      exclude: ^(setup.py|test/|earth2studio/models/nn/|serve/inferenceserver/api_server/example_workflows/)
       args: [--config=pyproject.toml]
 
 -   repo: https://github.com/igorshubovych/markdownlint-cli
diff --git a/serve/Dockerfile b/serve/Dockerfile
index d5503e53e..e8fbaf74a 100644
--- a/serve/Dockerfile
+++ b/serve/Dockerfile
@@ -35,7 +35,6 @@ RUN pip uninstall -y torch-harmonics && \
 COPY serve /workspace/earth2studio-project/serve
 RUN cd / && mkdir /workspace/earth2studio-project/examples && ln -s /outputs /workspace/earth2studio-project/examples/outputs
 RUN apt-get update && apt-get install -y redis-server && \
-    curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env && \
     cd /workspace/earth2studio-project && \
     uv pip install --system --break-system-packages -r serve/inferenceserver/requirements.txt
 
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
index 0ba1f7548..0e7cb8c17 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -85,7 +85,7 @@ def setup_io(
 
         # Storing seeds separately makes it easier to filer with Titiler
         sample_coords = {"sample": output_coords["sample"]}
-        io.add_array(sample_coords, "seed", torch.tensor(seeds))
+        io.add_array(sample_coords, "seed", data=torch.tensor(seeds))
 
         # Set attributes for automatic parsing of dimensions
         io.root["lat"].standard_name = "latitude"
@@ -179,4 +179,4 @@ def __call__(
         # Planetary Computer does not like the original time format
         ref_time = start_time.isoformat().replace("T", " ")
         io["time"].units = f"hours since {ref_time}"
-        io["time"][:] = np.arange(len(io["time"])) * 6
+        io["time"][:] = np.arange(io["time"].shape[0]) * 6
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
index a0e5d5fb8..3ad47b2fe 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -173,8 +173,10 @@ def setup_io(
         sample_coords = {"sample": output_coords["sample"]}
         n_stormscope_per_fcn3 = len(seeds_stormscope) // len(seeds_fcn3)
         tiled_seeds_fcn3 = np.repeat(seeds_fcn3, n_stormscope_per_fcn3)
-        io.add_array(sample_coords, "seed_fcn3", torch.tensor(tiled_seeds_fcn3))
-        io.add_array(sample_coords, "seed_stormscope", torch.tensor(seeds_stormscope))
+        io.add_array(sample_coords, "seed_fcn3", data=torch.tensor(tiled_seeds_fcn3))
+        io.add_array(
+            sample_coords, "seed_stormscope", data=torch.tensor(seeds_stormscope)
+        )
 
         # Set attributes for automatic parsing of dimensions
         io.root["lat"].standard_name = "latitude"
@@ -425,4 +427,4 @@ def __call__(
         # Planetary Computer does not like the original time format
         ref_time = start_time_stormscope.isoformat().replace("T", " ")
         io["time"].units = f"hours since {ref_time}"
-        io["time"][:] = np.arange(len(io["time"]))
+        io["time"][:] = np.arange(io["time"].shape[0])
diff --git a/serve/inferenceserver/api_server/workflow/e2workflow.py b/serve/inferenceserver/api_server/workflow/e2workflow.py
index 56973ed63..55af4fbb6 100644
--- a/serve/inferenceserver/api_server/workflow/e2workflow.py
+++ b/serve/inferenceserver/api_server/workflow/e2workflow.py
@@ -247,3 +247,7 @@ def write(
     def __getattr__(self, name: str) -> Any:
         """Allow passthrough of unwrapped attributes."""
         return getattr(self.io, name)
+
+    def __getitem__(self, key: str) -> Any:
+        """Allow subscripting to access underlying io object."""
+        return self.io[key]
diff --git a/serve/inferenceserver/scripts/startup.sh b/serve/inferenceserver/scripts/startup.sh
index 0d0e795d4..5ba90f71f 100755
--- a/serve/inferenceserver/scripts/startup.sh
+++ b/serve/inferenceserver/scripts/startup.sh
@@ -26,7 +26,7 @@ if [ -n "${AZUREML_MODEL_DIR:-}" ]; then
     ls -la $EARTH2STUDIO_MODEL_CACHE && echo "--------------------------------"
 fi
 
-cd /workspace/earth2studio-project/service/inferenceserver
+cd /workspace/earth2studio-project/serve/inferenceserver
 make start-redis
 make start-api-server
 sleep infinity

From fe8217e4585de0e64d5fa9ac78a7143795118b01 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Tue, 24 Feb 2026 14:55:50 -0800
Subject: [PATCH 05/24] fix config not being read

---
 serve/inferenceserver/api_server/config.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/serve/inferenceserver/api_server/config.py b/serve/inferenceserver/api_server/config.py
index 9615e3f10..ef970e568 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -171,7 +171,7 @@ class ConfigManager:
     """
 
     _instance: Optional["ConfigManager"] = None
-    _config: AppConfig = AppConfig()
+    _config: AppConfig | None = None
     _workflow_config: dict[str, Any] = {}
 
     def __new__(cls) -> "ConfigManager":
@@ -432,6 +432,9 @@ def config(self) -> AppConfig:
         """Get the current configuration"""
         if self._config is None:
             self._initialize_config()
+        # After initialization, _config should never be None
+        if self._config is None:
+            raise RuntimeError("Configuration was not initialized")
         return self._config
 
     def get_redis_url(self) -> str:

From 852665dcec3fc1baedbdd907335f7df7680acf64 Mon Sep 17 00:00:00 2001
From: Stefan Weissenberger <sweissenberg@nvidia.com>
Date: Wed, 25 Feb 2026 16:08:22 +0000
Subject: [PATCH 06/24] only reformat time when NetCDF4

---
 .../example_workflows/foundry_fcn3.py         | 29 ++++++++--------
 .../foundry_fcn3_stormscope_goes.py           | 33 ++++++++++---------
 2 files changed, 33 insertions(+), 29 deletions(-)

diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
index 0ba1f7548..293bae096 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -8,7 +8,7 @@
 
 from api_server.workflow import Earth2Workflow, workflow_registry
 from earth2studio.data import PlanetaryComputerECMWFOpenDataIFS, fetch_data
-from earth2studio.io import IOBackend, ZarrBackend
+from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
 from earth2studio.models.px import FCN3
 from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
 from earth2studio.utils.time import to_time_array
@@ -78,6 +78,13 @@ def validate_variables(self, variables: Sequence[str] | None) -> np.ndarray:
     def setup_io(
         self, io: IOBackend, output_coords: CoordSystem, seeds: Sequence[int]
     ) -> None:
+        if isinstance(io, ZarrBackend):
+            io.chunks = {
+                "sample": 1,
+                "time": 1,
+                "variable": 1,
+            }
+
         io.add_array(
             {k: v for k, v in output_coords.items() if k != "variable"},
             output_coords["variable"],
@@ -85,7 +92,7 @@ def setup_io(
 
         # Storing seeds separately makes it easier to filer with Titiler
         sample_coords = {"sample": output_coords["sample"]}
-        io.add_array(sample_coords, "seed", torch.tensor(seeds))
+        io.add_array(sample_coords, "seed", data=torch.tensor(seeds))
 
         # Set attributes for automatic parsing of dimensions
         io.root["lat"].standard_name = "latitude"
@@ -100,6 +107,12 @@ def setup_io(
         if isinstance(io, ZarrBackend):
             zarr.consolidate_metadata(io.store)
 
+        if isinstance(io, NetCDF4Backend):
+            # Planetary Computer does not like the original time format
+            ref_time = np.datetime_as_string(output_coords["time"][0], unit="s")
+            io["time"].units = f"hours since {ref_time.replace('T', ' ')}"
+            io["time"][:] = np.arange(len(io["time"])) * 6
+
         return io
 
     def get_fcn3_input(self, time: datetime) -> tuple[torch.Tensor, CoordSystem]:
@@ -120,13 +133,6 @@ def __call__(
         seeds: Sequence[int] | None = None,
         variables: Sequence[str] | None = None,
     ) -> None:
-        if isinstance(io, ZarrBackend):
-            io.chunks = {
-                "sample": 1,
-                "time": 1,
-                "variable": 1,
-            }
-
         self.validate_start_time(start_time)
         lead_times = np.array([np.timedelta64(i * 6, "h") for i in range(n_steps + 1)])
         seeds = self.validate_samples(n_samples, seeds)
@@ -175,8 +181,3 @@ def __call__(
 
                 if ii == n_steps:
                     break
-
-        # Planetary Computer does not like the original time format
-        ref_time = start_time.isoformat().replace("T", " ")
-        io["time"].units = f"hours since {ref_time}"
-        io["time"][:] = np.arange(len(io["time"])) * 6
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
index a0e5d5fb8..da08694b2 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -16,7 +16,7 @@
     PlanetaryComputerGOES,
     fetch_data,
 )
-from earth2studio.io import IOBackend, XarrayBackend, ZarrBackend
+from earth2studio.io import IOBackend, NetCDF4Backend, XarrayBackend, ZarrBackend
 from earth2studio.models.dx import DerivedSurfacePressure
 from earth2studio.models.px import FCN3, DiagnosticWrapper, InterpModAFNO
 from earth2studio.models.px.stormscope import (
@@ -164,6 +164,13 @@ def setup_io(
         seeds_fcn3: Sequence[int],
         seeds_stormscope: Sequence[int],
     ) -> None:
+        if isinstance(io, ZarrBackend):
+            io.chunks = {
+                "sample": 1,
+                "time": 1,
+                "variable": 1,
+            }
+
         io.add_array(
             {k: v for k, v in output_coords.items() if k != "variable"},
             output_coords["variable"],
@@ -173,8 +180,10 @@ def setup_io(
         sample_coords = {"sample": output_coords["sample"]}
         n_stormscope_per_fcn3 = len(seeds_stormscope) // len(seeds_fcn3)
         tiled_seeds_fcn3 = np.repeat(seeds_fcn3, n_stormscope_per_fcn3)
-        io.add_array(sample_coords, "seed_fcn3", torch.tensor(tiled_seeds_fcn3))
-        io.add_array(sample_coords, "seed_stormscope", torch.tensor(seeds_stormscope))
+        io.add_array(sample_coords, "seed_fcn3", data=torch.tensor(tiled_seeds_fcn3))
+        io.add_array(
+            sample_coords, "seed_stormscope", data=torch.tensor(seeds_stormscope)
+        )
 
         # Set attributes for automatic parsing of dimensions
         io.root["lat"].standard_name = "latitude"
@@ -189,6 +198,12 @@ def setup_io(
         if isinstance(io, ZarrBackend):
             zarr.consolidate_metadata(io.store)
 
+        if isinstance(io, NetCDF4Backend):
+            # Planetary Computer does not like the original time format
+            ref_time = np.datetime_as_string(output_coords["time"][0], unit="s")
+            io["time"].units = f"hours since {ref_time.replace('T', ' ')}"
+            io["time"][:] = np.arange(len(io["time"]))
+
         return io
 
     def get_fcn3_input(self, time: datetime) -> tuple[torch.Tensor, CoordSystem]:
@@ -357,13 +372,6 @@ def __call__(
         seeds_stormscope: Sequence[int] | None = None,
         variables: Sequence[str] | None = None,
     ) -> None:
-        if isinstance(io, ZarrBackend):
-            io.chunks = {
-                "sample": 1,
-                "time": 1,
-                "variable": 1,
-            }
-
         self.validate_start_times(start_time_stormscope, start_time_fcn3)
         lead_times = np.array([np.timedelta64(i, "h") for i in range(n_steps + 1)])
         # Different StormScope seed for every trajectory
@@ -421,8 +429,3 @@ def __call__(
                     variables=variables,
                 )
                 sample += 1
-
-        # Planetary Computer does not like the original time format
-        ref_time = start_time_stormscope.isoformat().replace("T", " ")
-        io["time"].units = f"hours since {ref_time}"
-        io["time"][:] = np.arange(len(io["time"]))

From 426cec92b47435c033a5495a5849cbe47bd1f4e4 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Wed, 25 Feb 2026 11:15:29 -0800
Subject: [PATCH 07/24] cleanup readme for object storage

---
 .../inferenceserver/README_object_storage.md  | 221 +-----------------
 1 file changed, 9 insertions(+), 212 deletions(-)

diff --git a/serve/inferenceserver/README_object_storage.md b/serve/inferenceserver/README_object_storage.md
index 08349e0a4..acf41b088 100644
--- a/serve/inferenceserver/README_object_storage.md
+++ b/serve/inferenceserver/README_object_storage.md
@@ -27,60 +27,20 @@ Before enabling object storage, you need to set up the following AWS resources:
 
 ### 1. S3 Bucket
 
-Create an S3 bucket to store workflow results:
-
-```bash
-aws s3 mb s3://your-bucket-name --region us-east-1
-```
-
+Create an S3 bucket to store workflow results.
 **Must for performance**: Enable S3 Transfer Acceleration for faster uploads:
 
-```bash
-aws s3api put-bucket-accelerate-configuration \
-    --bucket your-bucket-name \
-    --accelerate-configuration Status=Enabled
-```
-
 ### 2. CloudFront Distribution
 
-Create a CloudFront distribution to serve content from your S3 bucket:
-
-1. Go to AWS CloudFront Console → Create Distribution
-2. Set Origin Domain to your S3 bucket (`your-bucket-name.s3.amazonaws.com`)
-3. Set Origin Access to "Origin access control settings (recommended)"
-4. Create a new Origin Access Control (OAC)
-5. Update the S3 bucket policy to allow CloudFront access (AWS will provide the policy)
+Create a CloudFront distribution to serve content from your S3 bucket.
 
 ### 3. CloudFront Key Pair for Signed URLs
 
-To generate signed URLs, you need a CloudFront key pair:
-
-1. Go to AWS CloudFront Console → Key Management → Public Keys
-2. Create a new public key by uploading a public key you generated:
-
-```bash
-# Generate a private key
-openssl genrsa -out cloudfront-private-key.pem 2048
-
-# Extract the public key
-openssl rsa -in cloudfront-private-key.pem -pubout -out cloudfront-public-key.pem
-```
-
-Then:
-
-1. Upload `cloudfront-public-key.pem` to CloudFront
-2. Create a Key Group containing your public key
-3. Associate the Key Group with your CloudFront distribution's behavior settings (Restrict Viewer
-Access → Yes, Trusted Key Groups)
-4. Note the **Key Pair ID** (e.g., `KUCQGLNFR6UH1`)
-5. Keep `cloudfront-private-key.pem` secure - this is used by the server to sign URLs
+To generate signed URLs, you need a CloudFront key pair.
 
 ### 4. IAM Credentials
 
-Create IAM credentials with permissions to upload to S3. See [Creating IAM
-users](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html) for
-detailed instructions. The credentials need `s3:PutObject`, `s3:GetObject`,
-`s3:DeleteObject`, and `s3:ListBucket` permissions on your bucket.
+Create IAM credentials with permissions to upload to S3.
 
 ## Azure Prerequisites
 
@@ -88,50 +48,20 @@ Before enabling Azure Blob Storage, you need to set up the following Azure resou
 
 ### 1. Azure Storage Account
 
-Create an Azure Storage Account:
-
-1. Go to Azure Portal → Storage accounts → Create
-2. Choose a unique storage account name
-3. Select your resource group and region
-4. Choose performance tier (Standard recommended)
-5. Note the **Storage account name** (e.g., `mystorageaccount`)
+Create an Azure Storage Account.
 
 ### 2. Storage Container
 
-Create a blob container in your storage account:
-
-1. Go to your Storage Account → Containers
-2. Click "+ Container"
-3. Choose a container name (e.g., `workflow-results`)
-4. Set Public access level to "Private" (recommended for security)
+Create a blob container in your storage account.
 
-### 3. Connection String or Account Key
-
-You'll need either:
-
-#### Option A: Connection String (Recommended)
-
-1. Go to Storage Account → Access keys
-2. Click "Show" next to a key
-3. Copy the **Connection string** (format:
-   `DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=core.windows.net`)
-
-#### Option B: Account Name and Key
-
-1. Go to Storage Account → Access keys
-2. Note the **Storage account name**
-3. Click "Show" next to a key and copy the **Account key**
+### 3. Connection String, Account Key
 
+You will need a connection string to write into the container.
 The account key is required for generating SAS (Shared Access Signature) signed URLs.
 
 ### 4. Permissions
 
-Ensure your Azure credentials have the following permissions:
-
-- `Microsoft.Storage/storageAccounts/blobServices/containers/write` (to create containers if needed)
-- `Microsoft.Storage/storageAccounts/blobServices/containers/read`
-- `Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write`
-- `Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read`
+Ensure your Azure credentials have the permissions to write/read into the container.
 
 ## Server Configuration
 
@@ -408,136 +338,3 @@ mapper = create_cloudfront_mapper(request_result.signed_url, zarr_path="results.
 ds = xr.open_zarr(mapper, consolidated=True)
 print(ds)
 ```
-
-## Signed URL Format
-
-### AWS S3 / CloudFront Signed URLs
-
-CloudFront signed URLs contain three query parameters:
-
-```text
-https://d30anq61ot046p.cloudfront.net/outputs/exec_123/*?Policy=eyJTdGF0ZW1lbnQiOl...\
-&Signature=ABC123...&Key-Pair-Id=KUCQGLNFR6UH1
-```
-
-| Parameter | Description |
-|-----------|-------------|
-| `Policy` | Base64-encoded JSON policy specifying resource and expiration |
-| `Signature` | RSA signature of the policy using the private key |
-| `Key-Pair-Id` | CloudFront key pair ID used to verify the signature |
-
-The wildcard (`*`) in the URL path allows access to all files under that prefix.
-
-### Azure Blob Storage SAS URLs
-
-Azure SAS (Shared Access Signature) URLs contain multiple query parameters:
-
-```text
-https://mystorageaccount.blob.core.windows.net/workflow-results/outputs/exec_123?\
-sv=2021-06-08&ss=b&srt=co&sp=rl&se=2025-01-01T00:00:00Z&st=2024-12-31T00:00:00Z&spr=https&sig=ABC123...
-```
-
-| Parameter | Description |
-|-----------|-------------|
-| `sv` | Service version (API version) |
-| `ss` | Services (b=blob) |
-| `srt` | Resource types (co=container and object) |
-| `sp` | Permissions (rl=read and list) |
-| `se` | Expiry time (UTC) |
-| `st` | Start time (UTC) |
-| `spr` | Protocol (https) |
-| `sig` | Signature (HMAC-SHA256 of the string-to-sign) |
-
-The SAS token provides read and list permissions for all objects in the container under the
-specified prefix.
-
-## Testing
-
-Run object storage integration tests:
-
-### AWS S3 Testing
-
-```bash
-# Set required environment variables
-export TEST_S3_BUCKET=my-test-bucket
-export AWS_ACCESS_KEY_ID=AKIA...
-export AWS_SECRET_ACCESS_KEY=...
-
-# Run S3 upload tests
-pytest test/integration/test_object_storage.py -v
-
-# Run CloudFront signed URL tests (requires additional config)
-export TEST_CLOUDFRONT_DOMAIN=https://d30anq61ot046p.cloudfront.net
-export TEST_CLOUDFRONT_KEY_PAIR_ID=KUCQGLNFR6UH1
-export TEST_CLOUDFRONT_PRIVATE_KEY_PATH=/path/to/private.pem
-pytest test/integration/test_object_storage.py::TestCloudFrontSignedUrl -v
-```
-
-### Azure Blob Storage Testing
-
-```bash
-# Set required environment variables
-export TEST_AZURE_CONTAINER=my-test-container
-export AZURE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=core.windows.net"
-export AZURE_STORAGE_ACCOUNT_NAME=mystorageaccount
-export AZURE_STORAGE_ACCOUNT_KEY=...
-
-# Run Azure upload tests
-pytest test/integration/test_object_storage.py -v
-
-# Run Azure SAS URL tests
-pytest test/integration/test_object_storage.py::TestAzureSASUrl -v
-```
-
-## Troubleshooting
-
-### Common Issues
-
-#### AWS S3 / CloudFront Issues
-
-1. **403 Forbidden from CloudFront**
-   - Verify the S3 bucket policy allows CloudFront OAC access
-   - Check that the CloudFront distribution is configured with the correct origin
-   - Ensure the key pair is in a Key Group associated with the distribution
-
-2. **Upload failures**
-   - Verify IAM credentials have `s3:PutObject` permission
-   - Check bucket name and region are correct
-   - If using Transfer Acceleration, ensure it's enabled on the bucket
-
-3. **Slow uploads**
-   - Enable `use_rust_client` for better performance
-   - Increase `max_concurrency` for more parallel uploads
-   - Enable `use_transfer_acceleration` if uploading from distant regions
-
-#### Azure Blob Storage Issues
-
-1. **403 Forbidden from Azure**
-   - Verify the connection string is correct and not expired
-   - Check that the storage account name and container name are correct
-   - Ensure the account key is valid (if using account key authentication)
-
-2. **SAS URL generation failures**
-   - Ensure `azure_account_name` and `azure_account_key` are provided
-   - Verify the account key has not been rotated
-   - Check that the container exists and is accessible
-
-3. **Upload failures**
-   - Verify the connection string has write permissions
-   - Check that the container exists (it will be created automatically if permissions allow)
-   - Ensure the storage account is not in a restricted network configuration
-
-4. **Connection string parsing errors**
-   - Verify the connection string format is correct: `DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=...`
-   - Ensure all required components (AccountName, AccountKey) are present
-   - For custom endpoints, include `BlobEndpoint=` in the connection string
-
-#### General Issues
-
-1. **Signed URL expired**
-   - Increase `signed_url_expires_in` configuration
-   - Request fresh results from the API (URLs are regenerated)
-
-2. **Storage type not recognized**
-   - Ensure `OBJECT_STORAGE_TYPE` is set to either `s3` or `azure` (lowercase)
-   - Check that the storage type matches your configuration (S3 config for `s3`, Azure config for `azure`)

From 3db602fc8abbdb5332a8d93d6b19077a13214f58 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Wed, 25 Feb 2026 15:15:31 -0800
Subject: [PATCH 08/24] update the startup

---
 .../api_server/conf/config.yaml               | 10 +++
 serve/inferenceserver/api_server/config.py    | 33 +++++++++
 serve/inferenceserver/api_server/main.py      | 34 +++++++--
 .../api_server/workflow/workflow.py           | 71 +++++++++++++++++--
 .../scripts/start_api_server.sh               |  2 +-
 5 files changed, 136 insertions(+), 14 deletions(-)

diff --git a/serve/inferenceserver/api_server/conf/config.yaml b/serve/inferenceserver/api_server/conf/config.yaml
index f654305da..4a5581a81 100644
--- a/serve/inferenceserver/api_server/conf/config.yaml
+++ b/serve/inferenceserver/api_server/conf/config.yaml
@@ -41,6 +41,7 @@ worker:
 paths:
   default_output_dir: /outputs
   results_zip_dir: /workspace/earth2studio-project/examples/outputs
+  output_format: zarr  # Output format: "zarr" or "netcdf4"
   result_zip_enabled: false
 
 logging:
@@ -88,3 +89,12 @@ object_storage:
   cloudfront_private_key: null  # PEM private key content
   # Signed URL settings
   signed_url_expires_in: 86400  # 24 hours
+
+workflow_exposure:
+  # List of workflow names to expose via API endpoints
+  # Empty list means all workflows are exposed
+  exposed_workflows: []
+  # Workflows accessible for warmup even if not in exposed_workflows
+  # These workflows can be called via API for warmup purposes
+  warmup_workflows:
+    - example_user_workflow
diff --git a/serve/inferenceserver/api_server/config.py b/serve/inferenceserver/api_server/config.py
index ef970e568..a6b836bf0 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -145,6 +145,18 @@ class ObjectStorageConfig:
     )
 
 
+@dataclass
+class WorkflowExposureConfig:
+    """Configuration for controlling which workflows are exposed via API endpoints"""
+
+    exposed_workflows: list[str] = field(
+        default_factory=lambda: []
+    )  # Empty list means all workflows are exposed
+    warmup_workflows: list[str] = field(
+        default_factory=lambda: ["example_user_workflow"]
+    )  # Workflows accessible for warmup even if not exposed
+
+
 @dataclass
 class AppConfig:
     """Root configuration for the application"""
@@ -156,6 +168,9 @@ class AppConfig:
     server: ServerConfig = field(default_factory=ServerConfig)
     cors: CORSConfig = field(default_factory=CORSConfig)
     object_storage: ObjectStorageConfig = field(default_factory=ObjectStorageConfig)
+    workflow_exposure: WorkflowExposureConfig = field(
+        default_factory=WorkflowExposureConfig
+    )
 
 
 class ConfigManager:
@@ -233,6 +248,9 @@ def _dict_to_config(self, cfg_dict: dict) -> AppConfig:
             server=ServerConfig(**cfg_dict.get("server", {})),
             cors=CORSConfig(**cfg_dict.get("cors", {})),
             object_storage=ObjectStorageConfig(**cfg_dict.get("object_storage", {})),
+            workflow_exposure=WorkflowExposureConfig(
+                **cfg_dict.get("workflow_exposure", {})
+            ),
         )
 
     def _create_default_config_object(self) -> AppConfig:
@@ -245,6 +263,7 @@ def _create_default_config_object(self) -> AppConfig:
             server=ServerConfig(),
             cors=CORSConfig(),
             object_storage=ObjectStorageConfig(),
+            workflow_exposure=WorkflowExposureConfig(),
         )
 
     def _apply_env_overrides(self) -> None:
@@ -289,6 +308,12 @@ def _apply_env_overrides(self) -> None:
             self._config.paths.results_zip_dir = os.getenv(
                 "RESULTS_ZIP_DIR", default=self._config.paths.results_zip_dir
             )
+        if os.getenv("OUTPUT_FORMAT"):
+            output_format = os.getenv("OUTPUT_FORMAT", "").lower()
+            if output_format in ["zarr", "netcdf4"]:
+                self._config.paths.output_format = cast(
+                    Literal["zarr", "netcdf4"], output_format
+                )
 
         # Logging overrides
         if os.getenv("LOG_LEVEL"):
@@ -412,6 +437,14 @@ def _apply_env_overrides(self) -> None:
                 "AZURE_CONTAINER_NAME"
             )
 
+        # Workflow exposure overrides
+        if os.getenv("EXPOSED_WORKFLOWS"):
+            # Parse comma-separated list of workflow names
+            exposed_workflows_str = os.getenv("EXPOSED_WORKFLOWS", "")
+            self._config.workflow_exposure.exposed_workflows = [
+                w.strip() for w in exposed_workflows_str.split(",") if w.strip()
+            ]
+
         logger.debug("Environment variable overrides applied")
 
     def _ensure_paths_exist(self) -> None:
diff --git a/serve/inferenceserver/api_server/main.py b/serve/inferenceserver/api_server/main.py
index b51e7f214..e1f6393ee 100644
--- a/serve/inferenceserver/api_server/main.py
+++ b/serve/inferenceserver/api_server/main.py
@@ -324,8 +324,8 @@ async def get_metrics() -> Response:
 @app.get("/v1/workflows")
 @app.get("/v1/infer/workflows")
 async def list_workflows() -> dict[str, dict[str, str]]:
-    """List all available workflows"""
-    workflows = workflow_registry.list_workflows()
+    """List all available workflows (only exposed workflows)"""
+    workflows = workflow_registry.list_workflows(exposed_only=True)
     return {"workflows": workflows}
 
 
@@ -345,12 +345,16 @@ async def get_workflow_schema(workflow_name: str) -> dict[str, Any]:
     Returns:
         JSON schema for the workflow parameters
     """
-    # Check if workflow exists
+    # Check if workflow exists and is exposed
     workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not workflow_class:
         raise HTTPException(
             status_code=404, detail=f"Workflow '{workflow_name}' not found"
         )
+    if not workflow_registry.is_workflow_exposed(workflow_name):
+        raise HTTPException(
+            status_code=404, detail=f"Workflow '{workflow_name}' is not exposed"
+        )
 
     try:
         # Get the Parameters class from the workflow
@@ -406,12 +410,16 @@ async def execute_workflow(
     workflow_name: str, request: WorkflowExecutionRequest
 ) -> WorkflowExecutionResponse:
     """Execute a custom workflow"""
-    # Check if workflow exists and get the workflow class for validation
+    # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
             status_code=404, detail=f"Workflow '{workflow_name}' not found"
         )
+    if not workflow_registry.is_workflow_exposed(workflow_name):
+        raise HTTPException(
+            status_code=404, detail=f"Workflow '{workflow_name}' is not exposed"
+        )
 
     # Validate parameters early to provide immediate feedback using classmethod
     try:
@@ -515,12 +523,16 @@ async def get_workflow_status(workflow_name: str, execution_id: str) -> Workflow
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
-    # Check if workflow exists
+    # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
             status_code=404, detail=f"Custom workflow '{workflow_name}' not found"
         )
+    if not workflow_registry.is_workflow_exposed(workflow_name):
+        raise HTTPException(
+            status_code=404, detail=f"Workflow '{workflow_name}' is not exposed"
+        )
 
     try:
         result = custom_workflow_class._get_execution_data(
@@ -563,12 +575,16 @@ async def get_workflow_results(
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
-    # Check if workflow exists
+    # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
             status_code=404, detail=f"Custom workflow '{workflow_name}' not found"
         )
+    if not workflow_registry.is_workflow_exposed(workflow_name):
+        raise HTTPException(
+            status_code=404, detail=f"Workflow '{workflow_name}' is not exposed"
+        )
 
     # Check workflow status first
     try:
@@ -673,12 +689,16 @@ async def get_workflow_result_file(
     Returns:
         Streaming response with the file contents
     """
-    # Check if workflow exists
+    # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
             status_code=404, detail=f"Custom workflow '{workflow_name}' not found"
         )
+    if not workflow_registry.is_workflow_exposed(workflow_name):
+        raise HTTPException(
+            status_code=404, detail=f"Workflow '{workflow_name}' is not exposed"
+        )
 
     # Check workflow status first
     try:
diff --git a/serve/inferenceserver/api_server/workflow/workflow.py b/serve/inferenceserver/api_server/workflow/workflow.py
index 01c93992e..8e786c34b 100644
--- a/serve/inferenceserver/api_server/workflow/workflow.py
+++ b/serve/inferenceserver/api_server/workflow/workflow.py
@@ -662,12 +662,71 @@ def get(
 
         return instance
 
-    def list_workflows(self) -> dict[str, str]:
-        """List all registered workflows"""
-        return {
-            name: workflow_class.description
-            for name, workflow_class in self._workflows.items()
-        }
+    def is_workflow_exposed(self, workflow_name: str) -> bool:
+        """
+        Check if a workflow is exposed via API.
+
+        A workflow is exposed if:
+        - The exposed_workflows list is empty (all workflows exposed by default), OR
+        - The workflow name is in the exposed_workflows list, OR
+        - The workflow name is in the warmup_workflows list (accessible for warmup)
+
+        Args:
+            workflow_name: Name of the workflow to check
+
+        Returns:
+            True if workflow should be exposed, False otherwise
+        """
+        from api_server.config import get_config
+
+        config = get_config()
+        exposed_workflows = config.workflow_exposure.exposed_workflows
+        warmup_workflows = config.workflow_exposure.warmup_workflows
+
+        # Empty list means all workflows are exposed
+        if not exposed_workflows:
+            return True
+
+        # Check if in exposed list or warmup list
+        return workflow_name in exposed_workflows or workflow_name in warmup_workflows
+
+    def list_workflows(self, exposed_only: bool = True) -> dict[str, str]:
+        """
+        List registered workflows.
+
+        Args:
+            exposed_only: If True, only return workflows that are in exposed_workflows
+                         (warmup-only workflows are excluded from public listing).
+                         If False, return all registered workflows.
+
+        Returns:
+            Dictionary mapping workflow names to descriptions
+        """
+        if exposed_only:
+            from api_server.config import get_config
+
+            config = get_config()
+            exposed_workflows = config.workflow_exposure.exposed_workflows
+
+            # Empty list means all workflows are exposed (including warmup)
+            if not exposed_workflows:
+                return {
+                    name: workflow_class.description
+                    for name, workflow_class in self._workflows.items()
+                }
+
+            # Only return workflows in the exposed_workflows list
+            # (warmup-only workflows are excluded from public listing)
+            return {
+                name: workflow_class.description
+                for name, workflow_class in self._workflows.items()
+                if name in exposed_workflows
+            }
+        else:
+            return {
+                name: workflow_class.description
+                for name, workflow_class in self._workflows.items()
+            }
 
     def discover_and_register_from_directories(
         self, workflow_dirs: list, include_builtin: bool = True
diff --git a/serve/inferenceserver/scripts/start_api_server.sh b/serve/inferenceserver/scripts/start_api_server.sh
index 5e22fa04f..edde13f2b 100755
--- a/serve/inferenceserver/scripts/start_api_server.sh
+++ b/serve/inferenceserver/scripts/start_api_server.sh
@@ -235,7 +235,7 @@ echo "API docs at http://localhost:$API_PORT/docs"
 # Wait for health check to pass before invoking warmup workflow
 echo ""
 echo "Waiting for health check to pass..."
-MAX_HEALTH_RETRIES=30
+MAX_HEALTH_RETRIES=60
 HEALTH_RETRY_INTERVAL=2
 for i in $(seq 1 $MAX_HEALTH_RETRIES); do
     if curl -s "http://localhost:$API_PORT/health" | grep -q '"status":"healthy"'; then

From 0d5c60ced6cf1e007451a167c845eb41a0bde9a4 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 26 Feb 2026 11:43:20 -0800
Subject: [PATCH 09/24] update uv.lock

---
 uv.lock | 865 +++++++++++++++++++++++++++++++-------------------------
 1 file changed, 475 insertions(+), 390 deletions(-)

diff --git a/uv.lock b/uv.lock
index 4a777b726..ce74ef1a0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,14 +2,30 @@ version = 1
 revision = 1
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and sys_platform != 'linux'",
-    "python_full_version == '3.13.*' and sys_platform != 'linux'",
-    "python_full_version == '3.12.*' and sys_platform == 'linux'",
-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
-    "python_full_version < '3.12' and sys_platform == 'linux'",
-    "python_full_version < '3.12' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
 ]
 
 [[package]]
@@ -315,15 +331,15 @@ wheels = [
 
 [[package]]
 name = "azure-core"
-version = "1.38.1"
+version = "1.38.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "requests" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/53/9b/23893febea484ad8183112c9419b5eb904773adb871492b5fa8ff7b21e09/azure_core-1.38.1.tar.gz", hash = "sha256:9317db1d838e39877eb94a2240ce92fa607db68adf821817b723f0d679facbf6", size = 363323 }
+sdist = { url = "https://files.pythonhosted.org/packages/00/fe/5c7710bc611a4070d06ba801de9a935cc87c3d4b689c644958047bdf2cba/azure_core-1.38.2.tar.gz", hash = "sha256:67562857cb979217e48dc60980243b61ea115b77326fa93d83b729e7ff0482e7", size = 363734 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/db/88/aaea2ad269ce70b446660371286272c1f6ba66541a7f6f635baf8b0db726/azure_core-1.38.1-py3-none-any.whl", hash = "sha256:69f08ee3d55136071b7100de5b198994fc1c5f89d2b91f2f43156d20fcf200a4", size = 217930 },
+    { url = "https://files.pythonhosted.org/packages/42/23/6371a551800d3812d6019cd813acd985f9fac0fedc1290129211a73da4ae/azure_core-1.38.2-py3-none-any.whl", hash = "sha256:074806c75cf239ea284a33a66827695ef7aeddac0b4e19dda266a93e4665ead9", size = 217957 },
 ]
 
 [[package]]
@@ -575,15 +591,15 @@ wheels = [
 
 [[package]]
 name = "cattrs"
-version = "25.3.0"
+version = "26.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "attrs" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6e/00/2432bb2d445b39b5407f0a90e01b9a271475eea7caf913d7a86bcb956385/cattrs-25.3.0.tar.gz", hash = "sha256:1ac88d9e5eda10436c4517e390a4142d88638fe682c436c93db7ce4a277b884a", size = 509321 }
+sdist = { url = "https://files.pythonhosted.org/packages/a0/ec/ba18945e7d6e55a58364d9fb2e46049c1c2998b3d805f19b703f14e81057/cattrs-26.1.0.tar.gz", hash = "sha256:fa239e0f0ec0715ba34852ce813986dfed1e12117e209b816ab87401271cdd40", size = 495672 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d8/2b/a40e1488fdfa02d3f9a653a61a5935ea08b3c2225ee818db6a76c7ba9695/cattrs-25.3.0-py3-none-any.whl", hash = "sha256:9896e84e0a5bf723bc7b4b68f4481785367ce07a8a02e7e9ee6eb2819bc306ff", size = 70738 },
+    { url = "https://files.pythonhosted.org/packages/80/56/60547f7801b97c67e97491dc3d9ade9fbccbd0325058fd3dfcb2f5d98d90/cattrs-26.1.0-py3-none-any.whl", hash = "sha256:d1e0804c42639494d469d08d4f26d6b9de9b8ab26b446db7b5f8c2e97f7c3096", size = 73054 },
 ]
 
 [[package]]
@@ -773,11 +789,11 @@ wheels = [
 
 [[package]]
 name = "chardet"
-version = "5.2.0"
+version = "6.0.0.post1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/f7b6ab21ec75897ed80c17d79b15951a719226b9fababf1e40ea74d69079/chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7", size = 2069618 }
+sdist = { url = "https://files.pythonhosted.org/packages/7f/42/fb9436c103a881a377e34b9f58d77b5f503461c702ff654ebe86151bcfe9/chardet-6.0.0.post1.tar.gz", hash = "sha256:6b78048c3c97c7b2ed1fbad7a18f76f5a6547f7d34dbab536cc13887c9a92fa4", size = 12521798 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/38/6f/f5fbc992a329ee4e0f288c1fe0e2ad9485ed064cac731ed2fe47dcc38cbf/chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970", size = 199385 },
+    { url = "https://files.pythonhosted.org/packages/66/42/5de54f632c2de53cd3415b3703383d5fff43a94cbc0567ef362515261a21/chardet-6.0.0.post1-py3-none-any.whl", hash = "sha256:c894a36800549adf7bb5f2af47033281b75fdfcd2aa0f0243be0ad22a52e2dcb", size = 627245 },
 ]
 
 [[package]]
@@ -1211,7 +1227,7 @@ name = "cuda-bindings"
 version = "12.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
+    { name = "cuda-pathfinder", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593 },
@@ -1766,7 +1782,7 @@ docs = [
     { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
     { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
-    { name = "sphinx-autodoc-typehints", version = "3.6.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "sphinx-autodoc-typehints", version = "3.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "sphinx-design" },
     { name = "sphinx-favicon" },
     { name = "sphinx-gallery" },
@@ -2126,7 +2142,7 @@ epy = [
 
 [[package]]
 name = "fastapi"
-version = "0.129.0"
+version = "0.132.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-doc" },
@@ -2135,9 +2151,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/48/47/75f6bea02e797abff1bca968d5997793898032d9923c1935ae2efdece642/fastapi-0.129.0.tar.gz", hash = "sha256:61315cebd2e65df5f97ec298c888f9de30430dd0612d59d6480beafbc10655af", size = 375450 }
+sdist = { url = "https://files.pythonhosted.org/packages/a0/55/f1b4d4e478a0a1b4b1113d0f610a1b08e539b69900f97fdc97155d62fdee/fastapi-0.132.0.tar.gz", hash = "sha256:ef687847936d8a57ea6ea04cf9a85fe5f2c6ba64e22bfa721467094b69d48d92", size = 372422 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/9e/dd/d0ee25348ac58245ee9f90b6f3cbb666bf01f69be7e0911f9851bddbda16/fastapi-0.129.0-py3-none-any.whl", hash = "sha256:b4946880e48f462692b31c083be0432275cbfb6e2274566b1be91479cc1a84ec", size = 102950 },
+    { url = "https://files.pythonhosted.org/packages/a8/de/6171c3363bbc5e01686e200e0880647c9270daa476d91030435cf14d32f5/fastapi-0.132.0-py3-none-any.whl", hash = "sha256:3c487d5afce196fa8ea509ae1531e96ccd5cdd2fd6eae78b73e2c20fba706689", size = 104652 },
 ]
 
 [[package]]
@@ -2179,11 +2195,11 @@ wheels = [
 
 [[package]]
 name = "filelock"
-version = "3.24.2"
+version = "3.24.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/02/a8/dae62680be63cbb3ff87cfa2f51cf766269514ea5488479d42fec5aa6f3a/filelock-3.24.2.tar.gz", hash = "sha256:c22803117490f156e59fafce621f0550a7a853e2bbf4f87f112b11d469b6c81b", size = 37601 }
+sdist = { url = "https://files.pythonhosted.org/packages/73/92/a8e2479937ff39185d20dd6a851c1a63e55849e447a55e798cc2e1f49c65/filelock-3.24.3.tar.gz", hash = "sha256:011a5644dc937c22699943ebbfc46e969cdde3e171470a6e40b9533e5a72affa", size = 37935 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/9c/0f/5d0c71a1aefeb08efff26272149e07ab922b64f46c63363756224bd6872e/filelock-3.24.3-py3-none-any.whl", hash = "sha256:426e9a4660391f7f8a810d71b0555bce9008b0a1cc342ab1f6947d37639e002d", size = 24331 },
 ]
 
 [[package]]
@@ -2510,7 +2526,7 @@ wheels = [
 
 [[package]]
 name = "google-api-core"
-version = "2.29.0"
+version = "2.30.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "google-auth" },
@@ -2519,9 +2535,9 @@ dependencies = [
     { name = "protobuf" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0d/10/05572d33273292bac49c2d1785925f7bc3ff2fe50e3044cf1062c1dde32e/google_api_core-2.29.0.tar.gz", hash = "sha256:84181be0f8e6b04006df75ddfe728f24489f0af57c96a529ff7cf45bc28797f7", size = 177828 }
+sdist = { url = "https://files.pythonhosted.org/packages/22/98/586ec94553b569080caef635f98a3723db36a38eac0e3d7eb3ea9d2e4b9a/google_api_core-2.30.0.tar.gz", hash = "sha256:02edfa9fab31e17fc0befb5f161b3bf93c9096d99aed584625f38065c511ad9b", size = 176959 }
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 resolution-markers = [
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 ]
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@@ -7087,12 +7144,24 @@ name = "sphinx"
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-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
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+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform != 'linux'",
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+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'linux'",
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@@ -7123,8 +7192,12 @@ name = "sphinx-autodoc-typehints"
 version = "3.6.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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-    "python_full_version < '3.12' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'linux'",
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@@ -7136,22 +7209,34 @@ wheels = [
 
 [[package]]
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-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
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+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
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+    { url = "https://files.pythonhosted.org/packages/7b/e8/c6ba7ceee3ec58d21156b4968449e6a12af15eea8d26308b3b3ffeef2baf/uv-0.10.4-py3-none-win_arm64.whl", hash = "sha256:28c59a02d7a648b75a9c2ea735773d9d357a1eee773b78593c275b0bef1a4b73", size = 22180241 },
 ]
 
 [[package]]
@@ -8238,14 +8323,14 @@ wheels = [
 
 [[package]]
 name = "werkzeug"
-version = "3.1.5"
+version = "3.1.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "markupsafe", marker = "python_full_version >= '3.12'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5a/70/1469ef1d3542ae7c2c7b72bd5e3a4e6ee69d7978fa8a3af05a38eca5becf/werkzeug-3.1.5.tar.gz", hash = "sha256:6a548b0e88955dd07ccb25539d7d0cc97417ee9e179677d22c7041c8f078ce67", size = 864754 }
+sdist = { url = "https://files.pythonhosted.org/packages/61/f1/ee81806690a87dab5f5653c1f146c92bc066d7f4cebc603ef88eb9e13957/werkzeug-3.1.6.tar.gz", hash = "sha256:210c6bede5a420a913956b4791a7f4d6843a43b6fcee4dfa08a65e93007d0d25", size = 864736 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ad/e4/8d97cca767bcc1be76d16fb76951608305561c6e056811587f36cb1316a8/werkzeug-3.1.5-py3-none-any.whl", hash = "sha256:5111e36e91086ece91f93268bb39b4a35c1e6f1feac762c9c822ded0a4e322dc", size = 225025 },
+    { url = "https://files.pythonhosted.org/packages/4d/ec/d58832f89ede95652fd01f4f24236af7d32b70cab2196dfcc2d2fd13c5c2/werkzeug-3.1.6-py3-none-any.whl", hash = "sha256:7ddf3357bb9564e407607f988f683d72038551200c704012bb9a4c523d42f131", size = 225166 },
 ]
 
 [[package]]

From d30153c7023263e2b1e173763d5f5a19dbfdfc1e Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 26 Feb 2026 13:47:23 -0800
Subject: [PATCH 10/24] fix greptile

---
 serve/inferenceserver/api_server/cpu_worker.py     | 4 +++-
 serve/inferenceserver/api_server/object_storage.py | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index 19fbd39a1..268730010 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -573,7 +573,9 @@ def process_object_storage_upload(
 
             # Create S3 storage instance
             storage_kwargs = {
-                "bucket": config.object_storage.bucket,
+                "bucket": config.object_storage.bucket
+                or config.object_storage.azure_container_name
+                or "",
                 "storage_type": config.object_storage.storage_type,
                 "max_concurrency": config.object_storage.max_concurrency,
                 "multipart_chunksize": config.object_storage.multipart_chunksize,
diff --git a/serve/inferenceserver/api_server/object_storage.py b/serve/inferenceserver/api_server/object_storage.py
index db8730ee4..9d18205cc 100644
--- a/serve/inferenceserver/api_server/object_storage.py
+++ b/serve/inferenceserver/api_server/object_storage.py
@@ -282,6 +282,7 @@ def __init__(
 
             # Extract account name from connection string if not provided directly
             if not azure_account_name:
+                self.azure_account_name = None
                 # Connection string format: DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...
                 for part in azure_connection_string.split(";"):
                     if part.startswith("AccountName="):

From f2001f0047f025b0a506a38bbd162b64fcab5173 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 26 Feb 2026 14:17:55 -0800
Subject: [PATCH 11/24] update tests

---
 .../api_server/conf/config.yaml               |   5 +
 test/serve/test_workflow.py                   | 168 ++++++++++++++++++
 2 files changed, 173 insertions(+)

diff --git a/serve/inferenceserver/api_server/conf/config.yaml b/serve/inferenceserver/api_server/conf/config.yaml
index 4a5581a81..2f123bbde 100644
--- a/serve/inferenceserver/api_server/conf/config.yaml
+++ b/serve/inferenceserver/api_server/conf/config.yaml
@@ -89,6 +89,11 @@ object_storage:
   cloudfront_private_key: null  # PEM private key content
   # Signed URL settings
   signed_url_expires_in: 86400  # 24 hours
+  # Azure Blob Storage configuration
+  azure_connection_string: null
+  azure_account_name: null
+  azure_account_key: null
+  azure_container_name: null
 
 workflow_exposure:
   # List of workflow names to expose via API endpoints
diff --git a/test/serve/test_workflow.py b/test/serve/test_workflow.py
index f357f81d6..eaf28877e 100644
--- a/test/serve/test_workflow.py
+++ b/test/serve/test_workflow.py
@@ -1411,6 +1411,174 @@ def test_list_workflows_empty(self):
         assert len(workflows) == 0
         assert isinstance(workflows, dict)
 
+    def test_list_workflows_exposed_only_true_with_empty_exposed_list(self):
+        """Test list_workflows with exposed_only=True when exposed_workflows is empty"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+        self.registry.register(Workflow3)
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=get_test_config(),
+        ):
+            workflows = self.registry.list_workflows(exposed_only=True)
+            # Empty list means all workflows are exposed
+            assert len(workflows) == 3
+            assert "workflow1" in workflows
+            assert "workflow2" in workflows
+            assert "workflow3" in workflows
+
+    def test_list_workflows_exposed_only_true_with_specific_exposed(self):
+        """Test list_workflows with exposed_only=True when specific workflows are exposed"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+        self.registry.register(Workflow3)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1", "workflow2"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            workflows = self.registry.list_workflows(exposed_only=True)
+            # Only exposed workflows should be returned
+            assert len(workflows) == 2
+            assert "workflow1" in workflows
+            assert "workflow2" in workflows
+            assert "workflow3" not in workflows
+
+    def test_list_workflows_exposed_only_true_excludes_warmup_only(self):
+        """Test list_workflows with exposed_only=True excludes warmup-only workflows"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+        test_config.workflow_exposure.warmup_workflows = ["workflow2"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            workflows = self.registry.list_workflows(exposed_only=True)
+            # Only exposed workflows, not warmup-only
+            assert len(workflows) == 1
+            assert "workflow1" in workflows
+            assert "workflow2" not in workflows
+
+    def test_list_workflows_exposed_only_false(self):
+        """Test list_workflows with exposed_only=False returns all workflows"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+        self.registry.register(Workflow3)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            workflows = self.registry.list_workflows(exposed_only=False)
+            # Should return all workflows regardless of exposure
+            assert len(workflows) == 3
+            assert "workflow1" in workflows
+            assert "workflow2" in workflows
+            assert "workflow3" in workflows
+
+    def test_is_workflow_exposed_with_empty_exposed_list(self):
+        """Test is_workflow_exposed when exposed_workflows is empty (all exposed)"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=get_test_config(),
+        ):
+            # Empty list means all workflows are exposed
+            assert self.registry.is_workflow_exposed("workflow1") is True
+            assert self.registry.is_workflow_exposed("workflow2") is True
+            assert self.registry.is_workflow_exposed("nonexistent") is True
+
+    def test_is_workflow_exposed_with_specific_exposed(self):
+        """Test is_workflow_exposed when specific workflows are exposed"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+        self.registry.register(Workflow3)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1", "workflow2"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            assert self.registry.is_workflow_exposed("workflow1") is True
+            assert self.registry.is_workflow_exposed("workflow2") is True
+            assert self.registry.is_workflow_exposed("workflow3") is False
+
+    def test_is_workflow_exposed_with_warmup_workflows(self):
+        """Test is_workflow_exposed includes warmup workflows"""
+        self.registry.register(Workflow1)
+        self.registry.register(Workflow2)
+        self.registry.register(Workflow3)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+        test_config.workflow_exposure.warmup_workflows = ["workflow2"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            # Exposed workflow should be accessible
+            assert self.registry.is_workflow_exposed("workflow1") is True
+            # Warmup workflow should also be accessible
+            assert self.registry.is_workflow_exposed("workflow2") is True
+            # Neither exposed nor warmup should not be accessible
+            assert self.registry.is_workflow_exposed("workflow3") is False
+
+    def test_is_workflow_exposed_with_overlapping_exposed_and_warmup(self):
+        """Test is_workflow_exposed when workflow is both exposed and warmup"""
+        self.registry.register(Workflow1)
+
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+        test_config.workflow_exposure.warmup_workflows = ["workflow1"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            # Should be accessible if in either list
+            assert self.registry.is_workflow_exposed("workflow1") is True
+
+    def test_is_workflow_exposed_with_nonexistent_workflow(self):
+        """Test is_workflow_exposed with nonexistent workflow name"""
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            # Nonexistent workflow not in exposed list should return False
+            assert self.registry.is_workflow_exposed("nonexistent") is False
+
+    def test_is_workflow_exposed_with_nonexistent_workflow_in_warmup(self):
+        """Test is_workflow_exposed with nonexistent workflow in warmup list"""
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = ["workflow1"]
+        test_config.workflow_exposure.warmup_workflows = ["nonexistent"]
+
+        with patch(
+            "api_server.workflow.workflow.get_config",
+            return_value=test_config,
+        ):
+            # Should return True if in warmup list even if not registered
+            assert self.registry.is_workflow_exposed("nonexistent") is True
+
     def test_set_redis_client(self):
         """Test setting Redis client for workflow instances"""
         self.registry.register(Workflow1)

From d6e899c6037d1cc03c40de07512486b862aafd39 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 26 Feb 2026 14:28:45 -0800
Subject: [PATCH 12/24] profiled ensemble

---
 test/serve/test_workflow.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/test/serve/test_workflow.py b/test/serve/test_workflow.py
index eaf28877e..36fc7af4f 100644
--- a/test/serve/test_workflow.py
+++ b/test/serve/test_workflow.py
@@ -49,6 +49,8 @@ def get_test_config(base_path: str | None = None):  # type: ignore[no-untyped-de
         base_path = tempfile.mkdtemp(prefix="e2s_testing_")
     config.paths.default_output_dir = base_path
     config.paths.results_zip_dir = base_path
+    # Ensure exposed_workflows is empty list for tests (empty means all exposed)
+    config.workflow_exposure.exposed_workflows = []
     return config
 
 
@@ -59,7 +61,7 @@ def setup_test_paths():  # type: ignore[no-untyped-def]
         test_config = get_test_config(tmpdir)
         with (
             patch(
-                "api_server.workflow.workflow.get_config",
+                "api_server.config.get_config",
                 return_value=test_config,
             ),
             patch("api_server.config.get_config", return_value=test_config),
@@ -1418,7 +1420,7 @@ def test_list_workflows_exposed_only_true_with_empty_exposed_list(self):
         self.registry.register(Workflow3)
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=get_test_config(),
         ):
             workflows = self.registry.list_workflows(exposed_only=True)
@@ -1438,7 +1440,7 @@ def test_list_workflows_exposed_only_true_with_specific_exposed(self):
         test_config.workflow_exposure.exposed_workflows = ["workflow1", "workflow2"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             workflows = self.registry.list_workflows(exposed_only=True)
@@ -1458,7 +1460,7 @@ def test_list_workflows_exposed_only_true_excludes_warmup_only(self):
         test_config.workflow_exposure.warmup_workflows = ["workflow2"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             workflows = self.registry.list_workflows(exposed_only=True)
@@ -1477,7 +1479,7 @@ def test_list_workflows_exposed_only_false(self):
         test_config.workflow_exposure.exposed_workflows = ["workflow1"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             workflows = self.registry.list_workflows(exposed_only=False)
@@ -1492,9 +1494,11 @@ def test_is_workflow_exposed_with_empty_exposed_list(self):
         self.registry.register(Workflow1)
         self.registry.register(Workflow2)
 
+        test_config = get_test_config()
+        test_config.workflow_exposure.exposed_workflows = []
         with patch(
-            "api_server.workflow.workflow.get_config",
-            return_value=get_test_config(),
+            "api_server.config.get_config",
+            return_value=test_config,
         ):
             # Empty list means all workflows are exposed
             assert self.registry.is_workflow_exposed("workflow1") is True
@@ -1511,7 +1515,7 @@ def test_is_workflow_exposed_with_specific_exposed(self):
         test_config.workflow_exposure.exposed_workflows = ["workflow1", "workflow2"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             assert self.registry.is_workflow_exposed("workflow1") is True
@@ -1529,7 +1533,7 @@ def test_is_workflow_exposed_with_warmup_workflows(self):
         test_config.workflow_exposure.warmup_workflows = ["workflow2"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             # Exposed workflow should be accessible
@@ -1548,7 +1552,7 @@ def test_is_workflow_exposed_with_overlapping_exposed_and_warmup(self):
         test_config.workflow_exposure.warmup_workflows = ["workflow1"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             # Should be accessible if in either list
@@ -1560,7 +1564,7 @@ def test_is_workflow_exposed_with_nonexistent_workflow(self):
         test_config.workflow_exposure.exposed_workflows = ["workflow1"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             # Nonexistent workflow not in exposed list should return False
@@ -1573,7 +1577,7 @@ def test_is_workflow_exposed_with_nonexistent_workflow_in_warmup(self):
         test_config.workflow_exposure.warmup_workflows = ["nonexistent"]
 
         with patch(
-            "api_server.workflow.workflow.get_config",
+            "api_server.config.get_config",
             return_value=test_config,
         ):
             # Should return True if in warmup list even if not registered

From ddcf46e48a826e557bb89e73aab3a241fe39722f Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Mon, 2 Mar 2026 09:02:17 -0800
Subject: [PATCH 13/24] cleanup dockerfile

---
 serve/Dockerfile | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/serve/Dockerfile b/serve/Dockerfile
index e8fbaf74a..6c9cde587 100644
--- a/serve/Dockerfile
+++ b/serve/Dockerfile
@@ -27,10 +27,6 @@ RUN uv pip install --system --break-system-packages "earth2studio[cbottle,corrdi
 RUN uv pip install --system --break-system-packages "earth2studio[data,perturbation,statistics] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
 RUN uv pip install --system --break-system-packages cartopy xskillscore matplotlib
 
-# Install the requirements for stormcast.
-RUN pip uninstall -y torch-harmonics && \
-    FORCE_CUDA_EXTENSION=1 uv pip install --system --break-system-packages --no-build-isolation torch-harmonics==0.8.0
-
 # Install the requirements for the inference server.
 COPY serve /workspace/earth2studio-project/serve
 RUN cd / && mkdir /workspace/earth2studio-project/examples && ln -s /outputs /workspace/earth2studio-project/examples/outputs

From a6472faa6c59cd96c774c35b311a734baea8d2f7 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Mon, 2 Mar 2026 17:12:58 -0800
Subject: [PATCH 14/24] fixes, and add ranged streaming

---
 serve/inferenceserver/api_server/main.py      |  85 +++++++-----
 serve/inferenceserver/api_server/utils.py     | 129 ++++++++++++++++++
 .../scripts/start_api_server.sh               |  10 +-
 3 files changed, 183 insertions(+), 41 deletions(-)

diff --git a/serve/inferenceserver/api_server/main.py b/serve/inferenceserver/api_server/main.py
index e1f6393ee..96f7146b6 100644
--- a/serve/inferenceserver/api_server/main.py
+++ b/serve/inferenceserver/api_server/main.py
@@ -35,7 +35,7 @@
 import aiofiles  # type: ignore[import-untyped]
 import redis as redis_sync  # type: ignore[import-untyped]  # For RQ (synchronous)
 import redis.asyncio as redis  # type: ignore[import-untyped]
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import Response, StreamingResponse
 from prometheus_client import CONTENT_TYPE_LATEST, generate_latest
@@ -45,8 +45,10 @@
 # Import configuration
 from api_server.config import get_config, get_config_manager
 from api_server.utils import (
+    create_file_stream,
     get_inference_request_output_path_key,
     get_inference_request_zip_key,
+    parse_range_header,
 )
 
 # Import workflow registry
@@ -676,7 +678,10 @@ async def get_workflow_results(
 
 @app.get("/v1/infer/{workflow_name}/{execution_id}/results/{filepath:path}")
 async def get_workflow_result_file(
-    workflow_name: str, execution_id: str, filepath: str
+    workflow_name: str,
+    execution_id: str,
+    filepath: str,
+    request: Request,
 ) -> StreamingResponse:
     """
     Stream a specific file from the workflow execution results.
@@ -751,29 +756,32 @@ async def get_workflow_result_file(
                     },
                 )
 
-            # Stream the zip file
-            async def stream_zip_file() -> AsyncGenerator[bytes, None]:
-                """Stream the zip file contents"""
-                try:
-                    chunk_size = 8192
-                    async with aiofiles.open(zip_file_path, "rb") as f:
-                        while True:
-                            chunk = await f.read(chunk_size)
-                            if not chunk:
-                                break
-                            yield chunk
-                            await asyncio.sleep(0)
-                except Exception:
-                    logger.exception(f"Error streaming zip file {zip_file_path}")
-                    raise
+            # Get file size and parse range header
+            zip_file_size = zip_file_path.stat().st_size
+            range_header = request.headers.get("Range")
+            start, end, content_length, status_code = parse_range_header(
+                range_header, zip_file_size
+            )
+
+            # Create streaming response
+            stream_generator = create_file_stream(
+                zip_file_path, start, content_length, "zip file"
+            )
 
             headers = {
                 "Content-Disposition": f'attachment; filename="{zip_filename}"',
-                "Content-Length": str(zip_file_path.stat().st_size),
+                "Content-Length": str(content_length),
+                "Accept-Ranges": "bytes",
             }
 
+            if range_header:
+                headers["Content-Range"] = f"bytes {start}-{end}/{zip_file_size}"
+
             return StreamingResponse(
-                stream_zip_file(), media_type="application/zip", headers=headers
+                stream_generator,
+                media_type="application/zip",
+                headers=headers,
+                status_code=status_code,
             )
 
         # Regular case: get file from output directory
@@ -854,29 +862,34 @@ async def stream_zip_file() -> AsyncGenerator[bytes, None]:
         if media_type is None:
             media_type = "application/octet-stream"
 
-        # Stream the file
-        async def stream_file() -> AsyncGenerator[bytes, None]:
-            """Stream the file contents"""
-            try:
-                chunk_size = 8192
-                async with aiofiles.open(requested_path, "rb") as f:
-                    while True:
-                        chunk = await f.read(chunk_size)
-                        if not chunk:
-                            break
-                        yield chunk
-                        await asyncio.sleep(0)
-            except Exception:
-                logger.exception(f"Error streaming file {requested_path}")
-                raise
+        # Get file size and parse range header
+        file_size = requested_path.stat().st_size
+        range_header = request.headers.get("Range")
+        start, end, content_length, status_code = parse_range_header(
+            range_header, file_size
+        )
+
+        # Create streaming response
+        stream_generator = create_file_stream(
+            requested_path, start, content_length, "file"
+        )
 
         # Set appropriate headers
         headers = {
             "Content-Disposition": f'attachment; filename="{requested_path.name}"',
-            "Content-Length": str(requested_path.stat().st_size),
+            "Content-Length": str(content_length),
+            "Accept-Ranges": "bytes",
         }
 
-        return StreamingResponse(stream_file(), media_type=media_type, headers=headers)
+        if range_header:
+            headers["Content-Range"] = f"bytes {start}-{end}/{file_size}"
+
+        return StreamingResponse(
+            stream_generator,
+            media_type=media_type,
+            headers=headers,
+            status_code=status_code,
+        )
 
     except HTTPException:
         raise
diff --git a/serve/inferenceserver/api_server/utils.py b/serve/inferenceserver/api_server/utils.py
index 0beffb3b5..1901ca0ad 100644
--- a/serve/inferenceserver/api_server/utils.py
+++ b/serve/inferenceserver/api_server/utils.py
@@ -20,12 +20,18 @@
 This module contains:
 - Redis key generation functions
 - Pipeline stage queuing utilities
+- File streaming utilities with range support
 """
 
+import asyncio
 import logging
+from collections.abc import AsyncGenerator
+from pathlib import Path
 from typing import Any, Literal
 
+import aiofiles  # type: ignore[import-untyped]
 import redis  # type: ignore[import-untyped]
+from fastapi import HTTPException
 from rq import Queue
 
 from api_server.config import get_config
@@ -63,6 +69,129 @@ def get_signed_url_key(request_id: str) -> str:
     return f"inference_request:{request_id}:signed_url"
 
 
+# =============================================================================
+# File Streaming Utilities
+# =============================================================================
+
+
+def parse_range_header(
+    range_header: str | None, file_size: int
+) -> tuple[int, int, int, int]:
+    """
+    Parse Range header and return start, end, content_length, and status_code.
+
+    Args:
+        range_header: The Range header value from the request, or None
+        file_size: Total size of the file in bytes
+
+    Returns:
+        Tuple of (start, end, content_length, status_code)
+
+    Raises:
+        HTTPException: If the range is invalid (416 status)
+    """
+    start = 0
+    end = file_size - 1
+    status_code = 200
+
+    if not range_header:
+        return (start, end, file_size, status_code)
+
+    # Parse Range header: "bytes=start-end" or "bytes=start-" or "bytes=-suffix"
+    if not range_header.startswith("bytes="):
+        raise HTTPException(
+            status_code=416,
+            detail={
+                "error": "Range Not Satisfiable",
+                "details": "Only byte ranges are supported",
+            },
+        )
+
+    range_spec = range_header[6:]  # Remove "bytes=" prefix
+    ranges = range_spec.split(",")
+
+    # For now, only handle single range (first range)
+    if len(ranges) > 1:
+        logger.warning(f"Multiple ranges requested, using first: {range_header}")
+
+    range_part = ranges[0].strip()
+    if "-" not in range_part:
+        raise HTTPException(
+            status_code=416,
+            detail={
+                "error": "Range Not Satisfiable",
+                "details": "Invalid range format",
+            },
+        )
+
+    start_str, end_str = range_part.split("-", 1)
+
+    if start_str:
+        start = int(start_str)
+        if end_str:
+            end = int(end_str)
+        else:
+            end = file_size - 1
+    else:
+        # Suffix range: "-suffix" means last N bytes
+        suffix = int(end_str)
+        start = max(0, file_size - suffix)
+        end = file_size - 1
+
+    # Validate range
+    if start < 0 or start >= file_size or end < start or end >= file_size:
+        raise HTTPException(
+            status_code=416,
+            headers={
+                "Content-Range": f"bytes */{file_size}",
+            },
+            detail={
+                "error": "Range Not Satisfiable",
+                "details": f"Requested range {start}-{end} is invalid for file size {file_size}",
+            },
+        )
+
+    status_code = 206  # Partial Content
+    content_length = end - start + 1
+    return (start, end, content_length, status_code)
+
+
+async def create_file_stream(
+    file_path: Path, start: int, content_length: int, file_description: str = "file"
+) -> AsyncGenerator[bytes, None]:
+    """
+    Create an async generator that streams a file with optional range support.
+
+    Args:
+        file_path: Path to the file to stream
+        start: Starting byte position (0 for full file, or range start)
+        content_length: Number of bytes to stream
+        file_description: Description for error logging
+
+    Yields:
+        Bytes chunks from the file
+    """
+    try:
+        chunk_size = 1048576  # 1MB chunks for better performance
+        async with aiofiles.open(file_path, "rb") as f:
+            # Seek to start position if range request
+            if start > 0:
+                await f.seek(start)
+
+            remaining = content_length
+            while remaining > 0:
+                read_size = min(chunk_size, remaining)
+                chunk = await f.read(read_size)
+                if not chunk:
+                    break
+                yield chunk
+                remaining -= len(chunk)
+                await asyncio.sleep(0)
+    except Exception:
+        logger.exception(f"Error streaming {file_description} {file_path}")
+        raise
+
+
 # =============================================================================
 # Pipeline Stage Utilities
 # =============================================================================
diff --git a/serve/inferenceserver/scripts/start_api_server.sh b/serve/inferenceserver/scripts/start_api_server.sh
index edde13f2b..a53bf7cd1 100755
--- a/serve/inferenceserver/scripts/start_api_server.sh
+++ b/serve/inferenceserver/scripts/start_api_server.sh
@@ -119,7 +119,7 @@ trap cleanup SIGINT SIGTERM
 export EARTH2STUDIO_API_ACTIVE=1
 
 # Start multiple workers using uvicorn with extended timeouts for large file downloads
-uvicorn api_server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
+CUDA_VISIBLE_DEVICES="" uvicorn api_server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
 UVICORN_PID=$!
 
 # Start RQ workers
@@ -143,7 +143,7 @@ done
 echo "Starting $NUM_ZIP_WORKERS zip workers for result_zip queue..."
 ZIP_WORKER_PIDS=()
 for i in $(seq 1 $NUM_ZIP_WORKERS); do
-    rq worker -w rq.worker.SimpleWorker result_zip &
+    CUDA_VISIBLE_DEVICES="" rq worker -w rq.worker.SimpleWorker result_zip &
     ZIP_WORKER_PIDS+=($!)
     echo "Started zip worker $i for result_zip queue (PID: $!)"
 done
@@ -152,7 +152,7 @@ done
 echo "Starting $NUM_OBJSTORE_WORKERS object storage workers..."
 OBJSTORE_WORKER_PIDS=()
 for i in $(seq 1 $NUM_OBJSTORE_WORKERS); do
-    rq worker -w rq.worker.SimpleWorker object_storage &
+    CUDA_VISIBLE_DEVICES="" rq worker -w rq.worker.SimpleWorker object_storage &
     OBJSTORE_WORKER_PIDS+=($!)
     echo "Started object storage worker $i (PID: $!)"
 done
@@ -161,13 +161,13 @@ done
 echo "Starting $NUM_FINALIZE_WORKERS finalize metadata workers..."
 FINALIZE_WORKER_PIDS=()
 for i in $(seq 1 $NUM_FINALIZE_WORKERS); do
-    rq worker -w rq.worker.SimpleWorker finalize_metadata &
+    CUDA_VISIBLE_DEVICES="" rq worker -w rq.worker.SimpleWorker finalize_metadata &
     FINALIZE_WORKER_PIDS+=($!)
     echo "Started finalize metadata worker $i (PID: $!)"
 done
 
 # Start cleanup daemon
-python -m api_server.cleanup_daemon &
+CUDA_VISIBLE_DEVICES="" python -m api_server.cleanup_daemon &
 CLEANUP_DAEMON_PID=$!
 echo "Started cleanup daemon (PID: $CLEANUP_DAEMON_PID)"
 

From 5b1125ec2c2794ad4418987348628a431f52e159 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Tue, 3 Mar 2026 14:56:00 -0800
Subject: [PATCH 15/24] add progress update

---
 .../example_workflows/foundry_fcn3.py         | 12 +++++-
 .../foundry_fcn3_stormscope_goes.py           | 27 ++++++++++++-
 .../api_server/workflow/e2workflow.py         | 38 ++++++++++++++++++-
 3 files changed, 73 insertions(+), 4 deletions(-)

diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
index 293bae096..050416459 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -6,7 +6,7 @@
 import torch
 import zarr
 
-from api_server.workflow import Earth2Workflow, workflow_registry
+from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 from earth2studio.data import PlanetaryComputerECMWFOpenDataIFS, fetch_data
 from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
 from earth2studio.models.px import FCN3
@@ -153,7 +153,9 @@ def __call__(
         self.setup_io(io, output_coords, seeds)
 
         logger.info("Starting inference")
+        total_samples = len(seeds)
         for si, seed in enumerate(seeds):
+
             self.fcn3.set_rng(seed=seed)
             iterator = self.fcn3.create_iterator(x_ori.clone(), coords_ori.copy())
             for ii, (x, coords) in enumerate(iterator):
@@ -179,5 +181,13 @@ def __call__(
                 # Write to disk
                 io.write(*split_coords(x_out, coords_out))
 
+                # Update progress for step within sample
+                progress = WorkflowProgress(
+                    progress=f"Processing sample {si + 1}/{total_samples} (seed={seed}), step {ii + 1}/{n_steps + 1}",
+                    current_step=ii + 1,
+                    total_steps=n_steps + 1,
+                )
+                self.update_progress(progress)
+
                 if ii == n_steps:
                     break
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
index da08694b2..4e111c365 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -8,7 +8,7 @@
 import xarray as xr
 import zarr
 
-from api_server.workflow import Earth2Workflow, workflow_registry
+from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 from earth2studio.data import (
     GOES,
     InferenceOutputSource,
@@ -248,6 +248,8 @@ def run_fcn3(
         seed_fcn3: int,
         start_time_stormscope: datetime,
         lead_times: np.ndarray,
+        current_sample: int | None = None,
+        total_samples: int | None = None,
     ) -> None:
         # Create z500 conditioning with FCN3
         coords_in = self.stormscope.input_coords()
@@ -293,6 +295,15 @@ def run_fcn3(
             )
             io.write(*split_coords(x, coords_x))
 
+            # Update progress for FCN3 step
+            if current_sample is not None and total_samples is not None:
+                progress = WorkflowProgress(
+                    progress=f"Processing FCN3 conditioning (seed_fcn3={seed_fcn3}) for sample {current_sample}/{total_samples}, step {i + 1}/{total_steps}",
+                    current_step=i + 1,
+                    total_steps=total_steps,
+                )
+                self.update_progress(progress)
+
             if (i + 1) == total_steps:
                 break
 
@@ -306,6 +317,7 @@ def run_stormscope(
         seed_stormscope: int,
         lead_times: np.ndarray,
         variables: np.ndarray,
+        total_samples: int | None = None,
     ) -> None:
         def prep_output(
             y_pred: torch.Tensor, coords_pred: CoordSystem
@@ -355,6 +367,15 @@ def prep_output(
             y_out, coords_out = prep_output(y_pred, coords_pred)
             io.write(*split_coords(y_out, coords_out))
 
+            # Update progress for step within sample
+            if total_samples is not None:
+                progress = WorkflowProgress(
+                    progress=f"Processing sample {sample + 1}/{total_samples} (seed_fcn3={seed_fcn3}, seed_stormscope={seed_stormscope}), step {ii + 1}/{len(lead_times)}",
+                    current_step=ii + 1,
+                    total_steps=len(lead_times),
+                )
+                self.update_progress(progress)
+
             if ii == (len(lead_times) - 1):
                 break
 
@@ -398,6 +419,7 @@ def __call__(
         }
         self.setup_io(io, output_coords, seeds_fcn3, seeds_stormscope)
 
+        total_samples = len(seeds_stormscope)
         sample = 0
         for seed_fcn3 in seeds_fcn3:
             # Generate FCN3 conditioning (z500)
@@ -410,6 +432,8 @@ def __call__(
                 seed_fcn3=seed_fcn3,
                 start_time_stormscope=start_time_stormscope,
                 lead_times=lead_times,
+                current_sample=sample + 1,
+                total_samples=total_samples,
             )
             self.stormscope.conditioning_data_source = InferenceOutputSource(
                 io_fcn3.root
@@ -427,5 +451,6 @@ def __call__(
                     seed_stormscope=seeds_stormscope[sample],
                     lead_times=lead_times,
                     variables=variables,
+                    total_samples=total_samples,
                 )
                 sample += 1
diff --git a/serve/inferenceserver/api_server/workflow/e2workflow.py b/serve/inferenceserver/api_server/workflow/e2workflow.py
index 55af4fbb6..3e52df372 100644
--- a/serve/inferenceserver/api_server/workflow/e2workflow.py
+++ b/serve/inferenceserver/api_server/workflow/e2workflow.py
@@ -98,6 +98,7 @@ class Earth2Workflow(Workflow, metaclass=AutoParameters):
 
     def __init__(self) -> None:
         super().__init__()
+        self.execution_id: str | None = None
 
     @abstractmethod
     def __call__(self, io: IOBackend) -> None:
@@ -122,6 +123,9 @@ def run(
     ) -> dict[str, Any]:
         """Run custom workflow"""
 
+        # Store execution_id for use in update_progress
+        self.execution_id = execution_id
+
         # Validate and convert parameters
         parameters = self.validate_parameters(parameters)
 
@@ -184,6 +188,36 @@ def run(
             self.update_execution_data(execution_id, progress)
             raise
 
+    def update_progress(self, progress: WorkflowProgress) -> None:
+        """
+        Update workflow execution progress.
+
+        This method is intended for child workflows to update progress
+        information during execution. It uses the execution_id stored
+        during the run() method.
+
+        If execution_id is not set (e.g., when running outside the API server),
+        this method is a no-op and silently returns without updating progress.
+
+        Parameters
+        ----------
+        progress : WorkflowProgress
+            WorkflowProgress object containing progress information to update.
+
+        Examples
+        --------
+        >>> progress = WorkflowProgress(
+        ...     progress="Processing data...",
+        ...     current_step=5,
+        ...     total_steps=10
+        ... )
+        >>> self.update_progress(progress)
+        """
+        if self.execution_id is None:
+            # No-op when running outside API server context
+            return
+        self.update_execution_data(self.execution_id, progress)
+
 
 logger = logging.getLogger(__name__)
 
@@ -225,7 +259,7 @@ def add_array(
                 progress = WorkflowProgress(
                     current_step=0, total_steps=len(self.progress_coords)
                 )
-                self.workflow.update_execution_data(self.execution_id, progress)
+                self.workflow.update_progress(progress)
 
     def write(
         self,
@@ -242,7 +276,7 @@ def write(
             step_index = self.progress_coords.index(current_coord)
             # Update progress using WorkflowProgress
             progress = WorkflowProgress(current_step=step_index + 1)
-            self.workflow.update_execution_data(self.execution_id, progress)
+            self.workflow.update_progress(progress)
 
     def __getattr__(self, name: str) -> Any:
         """Allow passthrough of unwrapped attributes."""

From c081df5b13f6d9e5114983d11168267be7256bbe Mon Sep 17 00:00:00 2001
From: Stefan Weissenberger <sweissenberg@nvidia.com>
Date: Wed, 4 Mar 2026 13:24:21 +0000
Subject: [PATCH 16/24] fix CRS information

---
 .../example_workflows/foundry_fcn3.py         |  83 ++++-----
 .../foundry_fcn3_stormscope_goes.py           | 161 +++++++++---------
 2 files changed, 122 insertions(+), 122 deletions(-)

diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
index 050416459..192f0e190 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -34,12 +34,10 @@ def __init__(
         self.fcn3 = self.load_fcn3()
         self.rng = np.random.default_rng(init_seed)
 
-        # TODO: Do not use data cache
-        self.data = PlanetaryComputerECMWFOpenDataIFS(verbose=False)
+        self.data = PlanetaryComputerECMWFOpenDataIFS(verbose=False, cache=False)
 
     def load_fcn3(self) -> FCN3:
         logger.info("Loading FCN3")
-        # Assuming AZUREML_MODEL_DIR == EARTH2STUDIO_CACHE
         package = FCN3.load_default_package()
         fcn3 = FCN3.load_model(package)
         fcn3.to(self.device)
@@ -78,31 +76,35 @@ def validate_variables(self, variables: Sequence[str] | None) -> np.ndarray:
     def setup_io(
         self, io: IOBackend, output_coords: CoordSystem, seeds: Sequence[int]
     ) -> None:
-        if isinstance(io, ZarrBackend):
-            io.chunks = {
-                "sample": 1,
-                "time": 1,
-                "variable": 1,
-            }
-
         io.add_array(
             {k: v for k, v in output_coords.items() if k != "variable"},
             output_coords["variable"],
         )
 
-        # Storing seeds separately makes it easier to filer with Titiler
-        sample_coords = {"sample": output_coords["sample"]}
-        io.add_array(sample_coords, "seed", data=torch.tensor(seeds))
+        # Storing seeds separately makes it easier to filter with Titiler
+        e_coords = {"ensemble": output_coords["ensemble"]}
+        io.add_array(e_coords, "seed", data=torch.tensor(seeds))
+
+        # Add CRS definition
+        io.add_array({}, "crs")
+        io.root["crs"].grid_mapping_name = "latitude_longitude"
+        io.root["crs"].longitude_of_prime_meridian = 0.0
+        io.root["crs"].semi_major_axis = 6378137.0
+        io.root["crs"].inverse_flattening = 298.257223563
+
+        for var in output_coords["variable"]:
+            io.root[var].grid_mapping = "crs"
 
         # Set attributes for automatic parsing of dimensions
+        io.root["ensemble"].standard_name = "realization"
+        io.root["time"].standard_name = "time"
+        io.root["time"].axis = "T"
         io.root["lat"].standard_name = "latitude"
+        io.root["lat"].units = "degrees_north"
         io.root["lat"].axis = "Y"
         io.root["lon"].standard_name = "longitude"
+        io.root["lon"].units = "degrees_east"
         io.root["lon"].axis = "X"
-        # io.root["time"].standard_name = "time"
-        # io.root["time"].axis = "T"
-
-        # Planetary Computer does not like grid_mapping, so we skip adding it
 
         if isinstance(io, ZarrBackend):
             zarr.consolidate_metadata(io.store)
@@ -142,37 +144,48 @@ def __call__(
 
         output_coords = CoordSystem(
             {
-                "sample": np.arange(len(seeds)),
-                # Planetary Computer does not like separate 'lead_time'
+                "ensemble": np.arange(len(seeds)),
+                # Combine 'time' and 'lead_time' into single dimension
                 "time": to_time_array([start_time]) + lead_times,
                 "variable": variables,
                 "lat": np.linspace(90.0, -90.0, 721),
-                "lon": np.linspace(0, 360, 1440, endpoint=False),
+                "lon": np.linspace(-180, 180, 1440, endpoint=False),
             }
         )
         self.setup_io(io, output_coords, seeds)
 
         logger.info("Starting inference")
         total_samples = len(seeds)
-        for si, seed in enumerate(seeds):
+        n_steps += 1  # add 1 for step 0 (initial conditions)
+        for sample, seed in enumerate(seeds):
 
             self.fcn3.set_rng(seed=seed)
             iterator = self.fcn3.create_iterator(x_ori.clone(), coords_ori.copy())
-            for ii, (x, coords) in enumerate(iterator):
-                logger.info(
-                    "Processing FCN3 (seed=%d) step %d/%d",
-                    seed,
-                    ii + 1,
-                    len(lead_times),
+            for step, (x, coords) in enumerate(iterator):
+                # Update progress for step within sample
+                msg = (
+                    f"Processing sample {sample + 1}/{total_samples} "
+                    f"(seed={seed}), step {step + 1}/{len(lead_times)}"
                 )
+                progress = WorkflowProgress(
+                    progress=msg,
+                    current_step=step + 1,
+                    total_steps=n_steps,
+                )
+                self.update_progress(progress)
+                logger.info(msg)
 
+                # Select variables
                 x_out, coords_out = map_coords(
                     x, coords, CoordSystem({"variable": output_coords["variable"]})
                 )
-                # Add sample dimension
+                # Roll longitudes (for raster visualization)
+                x_out = torch.roll(x_out, 720, dims=-1)
+                coords_out["lon"] = np.linspace(-180, 180, 1440, endpoint=False)
+                # Add ensemble dimension
                 x_out = x_out.unsqueeze(0)
-                coords_out["sample"] = np.array([si])
-                coords_out.move_to_end("sample", last=False)
+                coords_out["ensemble"] = np.array([sample])
+                coords_out.move_to_end("ensemble", last=False)
                 # Combine time and lead_time
                 lead_time_dim = list(coords_out).index("lead_time")
                 x_out = x_out.squeeze(lead_time_dim)
@@ -181,13 +194,5 @@ def __call__(
                 # Write to disk
                 io.write(*split_coords(x_out, coords_out))
 
-                # Update progress for step within sample
-                progress = WorkflowProgress(
-                    progress=f"Processing sample {si + 1}/{total_samples} (seed={seed}), step {ii + 1}/{n_steps + 1}",
-                    current_step=ii + 1,
-                    total_steps=n_steps + 1,
-                )
-                self.update_progress(progress)
-
-                if ii == n_steps:
+                if step == (n_steps - 1):
                     break
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
index 4e111c365..642b0ea89 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -50,14 +50,12 @@ def __init__(
         self.stormscope = self.load_stormscope()
         self.rng = np.random.default_rng(init_seed)
 
-        # TODO: Do not use data cache
-        self.data_fcn3 = PlanetaryComputerECMWFOpenDataIFS(verbose=False)
+        self.data_fcn3 = PlanetaryComputerECMWFOpenDataIFS(verbose=False, cache=False)
 
         scan_mode = "C"
-        # TODO: Do not use data cache
         self.data_stormscope = {
             satellite: PlanetaryComputerGOES(
-                satellite=satellite, scan_mode=scan_mode, verbose=False
+                satellite=satellite, scan_mode=scan_mode, verbose=False, cache=False
             )
             for satellite in ["goes16", "goes19"]
         }
@@ -72,7 +70,6 @@ def __init__(
 
     def load_fcn3_interp(self) -> InterpModAFNO:
         logger.info("Loading FCN3")
-        # Assuming AZUREML_MODEL_DIR == EARTH2STUDIO_CACHE
         package = FCN3.load_default_package()
         fcn3 = FCN3.load_model(package)
 
@@ -164,36 +161,40 @@ def setup_io(
         seeds_fcn3: Sequence[int],
         seeds_stormscope: Sequence[int],
     ) -> None:
-        if isinstance(io, ZarrBackend):
-            io.chunks = {
-                "sample": 1,
-                "time": 1,
-                "variable": 1,
-            }
-
         io.add_array(
             {k: v for k, v in output_coords.items() if k != "variable"},
             output_coords["variable"],
         )
 
-        # Storing seeds separately makes it easier to filer with Titiler
-        sample_coords = {"sample": output_coords["sample"]}
+        # Storing seeds separately makes it easier to filter with Titiler
+        e_coords = {"ensemble": output_coords["ensemble"]}
         n_stormscope_per_fcn3 = len(seeds_stormscope) // len(seeds_fcn3)
         tiled_seeds_fcn3 = np.repeat(seeds_fcn3, n_stormscope_per_fcn3)
-        io.add_array(sample_coords, "seed_fcn3", data=torch.tensor(tiled_seeds_fcn3))
-        io.add_array(
-            sample_coords, "seed_stormscope", data=torch.tensor(seeds_stormscope)
-        )
+        io.add_array(e_coords, "seed_fcn3", data=torch.tensor(tiled_seeds_fcn3))
+        io.add_array(e_coords, "seed_stormscope", data=torch.tensor(seeds_stormscope))
 
-        # Set attributes for automatic parsing of dimensions
-        io.root["lat"].standard_name = "latitude"
-        io.root["lat"].axis = "Y"
-        io.root["lon"].standard_name = "longitude"
-        io.root["lon"].axis = "X"
-        # io.root["time"].standard_name = "time"
-        # io.root["time"].axis = "T"
+        # Add CRS definition
+        io.add_array({}, "crs")
+        io.root["crs"].grid_mapping_name = "lambert_conformal_conic"
+        io.root["crs"].standard_parallel = 38.5
+        io.root["crs"].longitude_of_central_meridian = 262.5
+        io.root["crs"].latitude_of_projection_origin = 38.5
+        io.root["crs"].semi_major_axis = 6371229
+        io.root["crs"].semi_minor_axis = 6371229
+
+        for var in output_coords["variable"]:
+            io.root[var].grid_mapping = "crs"
 
-        # Planetary Computer does not like grid_mapping, so we skip adding it
+        # Set attributes for automatic parsing of dimensions
+        io.root["ensemble"].standard_name = "realization"
+        io.root["time"].standard_name = "time"
+        io.root["time"].axis = "T"
+        io.root["y"].standard_name = "projection_y_coordinate"
+        io.root["y"].units = "m"
+        io.root["y"].axis = "Y"
+        io.root["x"].standard_name = "projection_x_coordinate"
+        io.root["x"].units = "m"
+        io.root["x"].axis = "X"
 
         if isinstance(io, ZarrBackend):
             zarr.consolidate_metadata(io.store)
@@ -248,8 +249,8 @@ def run_fcn3(
         seed_fcn3: int,
         start_time_stormscope: datetime,
         lead_times: np.ndarray,
-        current_sample: int | None = None,
-        total_samples: int | None = None,
+        sample: int,
+        total_samples: int,
     ) -> None:
         # Create z500 conditioning with FCN3
         coords_in = self.stormscope.input_coords()
@@ -274,16 +275,23 @@ def run_fcn3(
         self.fcn3_interp.px_model.px_model.set_rng(seed=seed_fcn3)
         iterator = self.fcn3_interp.create_iterator(x.clone(), coords_x.copy())
 
-        total_steps = model_gap + len(lead_times)
-        for i, (x, coords_x) in enumerate(iterator):
-            logger.info(
-                "Processing FCN3 (seed_fcn3=%d) step %d/%d",
-                seed_fcn3,
-                i + 1,
-                total_steps,
+        n_steps = model_gap + len(lead_times)
+        for step, (x, coords_x) in enumerate(iterator):
+            # Update progress for FCN3 step
+            msg = (
+                f"Processing FCN3 for sample {sample + 1}/{total_samples} "
+                f"(seed_fcn3={seed_fcn3}) "
+                f"step {step + 1}/{n_steps}"
             )
+            progress = WorkflowProgress(
+                progress=msg,
+                current_step=step + 1,
+                total_steps=n_steps,
+            )
+            self.update_progress(progress)
+            logger.info(msg)
 
-            if i < model_gap:
+            if step < model_gap:
                 # Skip initial steps leading up to StormScope start time
                 continue
 
@@ -295,16 +303,7 @@ def run_fcn3(
             )
             io.write(*split_coords(x, coords_x))
 
-            # Update progress for FCN3 step
-            if current_sample is not None and total_samples is not None:
-                progress = WorkflowProgress(
-                    progress=f"Processing FCN3 conditioning (seed_fcn3={seed_fcn3}) for sample {current_sample}/{total_samples}, step {i + 1}/{total_steps}",
-                    current_step=i + 1,
-                    total_steps=total_steps,
-                )
-                self.update_progress(progress)
-
-            if (i + 1) == total_steps:
+            if step == (n_steps - 1):
                 break
 
     def run_stormscope(
@@ -312,13 +311,29 @@ def run_stormscope(
         io: IOBackend,
         y: torch.Tensor,
         coords_y: CoordSystem,
-        sample: int,
         seed_fcn3: int,
         seed_stormscope: int,
         lead_times: np.ndarray,
         variables: np.ndarray,
-        total_samples: int | None = None,
+        sample: int,
+        total_samples: int,
     ) -> None:
+        n_steps = len(lead_times)
+
+        def log_progress(step: int) -> None:
+            msg = (
+                f"Processing sample {sample + 1}/{total_samples} "
+                f"(seed_fcn3={seed_fcn3}, seed_stormscope={seed_stormscope}), "
+                f"step {step + 1}/{n_steps}"
+            )
+            progress = WorkflowProgress(
+                progress=msg,
+                current_step=step + 1,
+                total_steps=n_steps,
+            )
+            self.update_progress(progress)
+            logger.info(msg)
+
         def prep_output(
             y_pred: torch.Tensor, coords_pred: CoordSystem
         ) -> tuple[torch.Tensor, CoordSystem]:
@@ -326,25 +341,18 @@ def prep_output(
                 y_pred, coords_pred, CoordSystem({"variable": variables})
             )
             del coords_out["batch"]
-            # Reuse batch dimension as sample dimension (squeeze/unsqueeze)
-            coords_out["sample"] = np.array([sample])
-            coords_out.move_to_end("sample", last=False)
+            # Reuse batch dimension as ensemble dimension (squeeze/unsqueeze)
+            coords_out["ensemble"] = np.array([sample])
+            coords_out.move_to_end("ensemble", last=False)
             # Combine time and lead_time
             lead_time_dim = list(coords_out).index("lead_time")
             y_out = y_out.squeeze(lead_time_dim)
             coords_out["time"] = coords_out["time"] + coords_out["lead_time"]
             del coords_out["lead_time"]
-            # Rename x, y to lon, lat
-            mp = {"x": "lon", "y": "lat"}
-            coords_out = CoordSystem({mp.get(k, k): v for k, v in coords_out.items()})
             return y_out, coords_out
 
-        logger.info(
-            "Processing StormScope (seed_fcn3=%d, seed_stormscope=%d) step 1/%d",
-            seed_fcn3,
-            seed_stormscope,
-            len(lead_times),
-        )
+        # Update progress for step within sample
+        log_progress(0)
 
         # Store initial GOES data (identical across seeds)
         y_out, coords_out = prep_output(y, coords_y)
@@ -354,29 +362,16 @@ def prep_output(
         # Use self.stormscope.sampler_args["randn_like"] once updated
         torch.manual_seed(seed_stormscope)
 
-        for ii in range(1, len(lead_times)):
+        for step in range(1, n_steps):
             y_pred, coords_pred = self.stormscope(y, coords_y)
-            logger.info(
-                "Processing StormScope (seed_fcn3=%d, seed_stormscope=%d) step %d/%d",
-                seed_fcn3,
-                seed_stormscope,
-                ii + 1,
-                len(lead_times),
-            )
+
+            # Update progress for step within sample
+            log_progress(step)
 
             y_out, coords_out = prep_output(y_pred, coords_pred)
             io.write(*split_coords(y_out, coords_out))
 
-            # Update progress for step within sample
-            if total_samples is not None:
-                progress = WorkflowProgress(
-                    progress=f"Processing sample {sample + 1}/{total_samples} (seed_fcn3={seed_fcn3}, seed_stormscope={seed_stormscope}), step {ii + 1}/{len(lead_times)}",
-                    current_step=ii + 1,
-                    total_steps=len(lead_times),
-                )
-                self.update_progress(progress)
-
-            if ii == (len(lead_times) - 1):
+            if step == (n_steps - 1):
                 break
 
             y, coords_y = self.stormscope.next_input(y_pred, coords_pred, y, coords_y)
@@ -410,12 +405,12 @@ def __call__(
 
         coords_out = self.stormscope.output_coords(self.stormscope.input_coords())
         output_coords = {
-            "sample": np.arange(len(seeds_stormscope)),
+            "ensemble": np.arange(len(seeds_stormscope)),
             # Planetary Computer does not like separate 'lead_time'
             "time": to_time_array([start_time_stormscope]) + lead_times,
             "variable": variables,
-            "lat": coords_out["y"],
-            "lon": coords_out["x"],
+            "y": coords_out["y"],
+            "x": coords_out["x"],
         }
         self.setup_io(io, output_coords, seeds_fcn3, seeds_stormscope)
 
@@ -432,7 +427,7 @@ def __call__(
                 seed_fcn3=seed_fcn3,
                 start_time_stormscope=start_time_stormscope,
                 lead_times=lead_times,
-                current_sample=sample + 1,
+                sample=sample,
                 total_samples=total_samples,
             )
             self.stormscope.conditioning_data_source = InferenceOutputSource(
@@ -446,11 +441,11 @@ def __call__(
                     io=io,
                     y=y_ori.clone(),
                     coords_y=coords_y_ori.copy(),
-                    sample=sample,
                     seed_fcn3=seed_fcn3,
                     seed_stormscope=seeds_stormscope[sample],
                     lead_times=lead_times,
                     variables=variables,
+                    sample=sample,
                     total_samples=total_samples,
                 )
                 sample += 1

From 52447c100d0a16566fc2da92239372d10e9f542c Mon Sep 17 00:00:00 2001
From: Stefan Weissenberger <sweissenberg@nvidia.com>
Date: Wed, 4 Mar 2026 17:58:04 +0000
Subject: [PATCH 17/24] add Planetary Computer utils

---
 .../planetary_computer/pc_client.py           | 234 ++++++++++++++++++
 ...plate-collection-fcn3-stormscope-goes.json |  42 ++++
 .../template-collection-fcn3.json             |  42 ++++
 ...template-feature-fcn3-stormscope-goes.json |  54 ++++
 .../template-feature-fcn3.json                |  54 ++++
 serve/inferenceserver/requirements.txt        |   1 +
 6 files changed, 427 insertions(+)
 create mode 100644 serve/inferenceserver/api_server/planetary_computer/pc_client.py
 create mode 100644 serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
 create mode 100644 serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
 create mode 100644 serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
 create mode 100644 serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json

diff --git a/serve/inferenceserver/api_server/planetary_computer/pc_client.py b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
new file mode 100644
index 000000000..1c64b7bc0
--- /dev/null
+++ b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
@@ -0,0 +1,234 @@
+import json
+import logging
+import os
+import time
+from datetime import datetime, timedelta
+from typing import Literal
+from uuid import uuid4
+
+import requests
+from api_server.workflow import WorkflowParameters
+from azure.identity import DefaultAzureCredential
+
+logger = logging.getLogger("planetary_computer")
+logger.setLevel(logging.INFO)
+
+APPLICATION_URL = "https://geocatalog.spatio.azure.com/"
+REQUESTS_TIMEOUT = 30
+
+
+def get_headers() -> dict:
+    """Get the Azure authorization headers."""
+    credential = DefaultAzureCredential()
+    token = credential.get_token(APPLICATION_URL)
+    return {"Authorization": f"Bearer {token.token}"}
+
+
+def _create_element(url: str, stac_config: dict, headers: dict) -> None:
+    """Create a STAC collection or feature and wait for it to finish."""
+    response = requests.post(
+        url,
+        json=stac_config,
+        headers=headers,
+        params={"api-version": "2025-04-30-preview"},
+        timeout=REQUESTS_TIMEOUT,
+    )
+    location = response.headers["location"]
+
+    logger.info("Creating '%s'...", stac_config["id"])
+    while True:
+        response = requests.get(location, headers=headers, timeout=REQUESTS_TIMEOUT)
+        status = response.json()["status"]
+        logger.info(status)
+        if status not in {"Pending", "Running"}:
+            break
+        time.sleep(5)
+
+    if status == "Finished":
+        logger.info("Successfully created '%s'", stac_config["id"])
+    else:
+        logger.error("Failed to create '%s': %s", stac_config["id"], response.text)
+
+
+def get_collection_json(
+    workflow_name: Literal[
+        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+    ],
+    collection_id: str | None,
+) -> dict:
+    """
+    Load the STAC collection template and set the collection ID.
+
+    Args:
+        workflow_name: Name of the workflow
+        collection_id: STAC collection ID
+
+    Returns:
+        STAC config as dict
+    """
+    template_fns = {
+        "foundry_fcn3_workflow": "template-collection-fcn3.json",
+        "foundry_fcn3_stormscope_goes_workflow": "template-collection-fcn3-stormscope-goes.json",
+    }
+    with open(
+        os.path.join(os.path.dirname(__file__), template_fns[workflow_name])
+    ) as f:
+        stac_config = json.load(f)
+    if collection_id is None:
+        stac_config["id"] = stac_config["id"].format(uuid=uuid4())
+    else:
+        stac_config["id"] = collection_id
+    return stac_config
+
+
+def get_feature_json(
+    workflow_name: Literal[
+        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+    ],
+    start_time: datetime,
+    end_time: datetime,
+    blob_url: str,
+) -> dict:
+    """
+    Load the STAC feature template and set the workflow parameters.
+
+    Args:
+        workflow_name: Name of the workflow
+        start_time: Forecast start time
+        end_time: Forecast end time
+        blob_url: Blob location on Azure Blob Storage
+
+    Returns:
+        STAC config as dict
+    """
+    template_fns = {
+        "foundry_fcn3_workflow": "template-feature-fcn3.json",
+        "foundry_fcn3_stormscope_goes_workflow": "template-feature-fcn3-stormscope-goes.json",
+    }
+    with open(
+        os.path.join(os.path.dirname(__file__), template_fns[workflow_name])
+    ) as f:
+        stac_config = json.load(f)
+
+    iso_start = start_time.isoformat()
+    iso_end = end_time.isoformat()
+
+    stac_config["id"] = stac_config["id"].format(
+        start_time=iso_start[:13], uuid=uuid4()
+    )
+    stac_config["properties"]["datetime"] = iso_start
+    stac_config["properties"]["start_datetime"] = iso_end
+    stac_config["properties"]["end_datetime"] = end_time
+    stac_config["assets"]["data"]["href"] = blob_url
+    return stac_config
+
+
+def create_collection(
+    workflow_name: Literal[
+        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+    ],
+    geocatalog_url: str,
+    collection_id: str | None,
+    headers: dict,
+) -> str:
+    """
+    Create a new STAC collection.
+
+    Args:
+        workflow_name: Name of the workflow
+        geocatalog_url: URL to the Planetary Computer Pro catalog
+        collection_id: STAC collection ID
+        headers: Azure authorization headers
+
+    Returns:
+        ID of the created collection
+    """
+    stac_config = get_collection_json(workflow_name, collection_id)
+    _create_element(
+        url=f"{geocatalog_url}/stac/collections",
+        stac_config=stac_config,
+        headers=headers,
+    )
+    return stac_config["id"]
+
+
+def ensure_collection_exists(
+    workflow_name: Literal[
+        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+    ],
+    geocatalog_url: str,
+    collection_id: str,
+    headers: dict,
+) -> str:
+    """
+    Check whether a collection already exists and if not create it.
+
+    Args:
+        workflow_name: Name of the workflow
+        geocatalog_url: URL to the Planetary Computer Pro catalog
+        collection_id: STAC collection ID
+        headers: Azure authorization headers
+
+    Returns:
+        ID of the collection
+    """
+    # Check whether collection already exists
+    response = requests.get(
+        f"{geocatalog_url}/stac/collections/{collection_id}",
+        headers=headers,
+        params={"api-version": "2025-04-30-preview"},
+        timeout=REQUESTS_TIMEOUT,
+    )
+    status = response.status_code
+    if status == 200:
+        return collection_id
+    if status != 404:
+        raise RuntimeError(
+            f"Failed to retrieve collection: Error {status} - {response.text}"
+        )
+
+    # Create new collection
+    return create_collection(workflow_name, geocatalog_url, collection_id, headers)
+
+
+def create_feature(
+    workflow_name: Literal[
+        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+    ],
+    geocatalog_url: str,
+    collection_id: str | None,
+    parameters: dict | WorkflowParameters,
+    blob_url: str,
+) -> None:
+    """
+    Ingest a new STAC feature into the collection.
+
+    Args:
+        workflow_name: Name of the workflow
+        geocatalog_url: URL to the Planetary Computer Pro catalog
+        collection_id: STAC collection ID
+        parameters: Workflow parameters with start time and number of steps
+        blob_url: Blob location on Azure Blob Storage
+    """
+    headers = get_headers()
+
+    # Make sure the target collection exists
+    if collection_id is None:
+        collection_id = create_collection(workflow_name, geocatalog_url, None, headers)
+    else:
+        ensure_collection_exists(workflow_name, geocatalog_url, collection_id, headers)
+
+    start_time = parameters["start_time"]
+    step_sizes = {
+        # Forecast step size in hours
+        "foundry_fcn3_workflow": 6,
+        "foundry_fcn3_stormscope_goes_workflow": 1,
+    }
+    end_time = start_time + timedelta(hours=step_sizes[workflow_name])
+    stac_config = get_feature_json(workflow_name, start_time, end_time, blob_url)
+
+    _create_element(
+        url=f"{geocatalog_url}/stac/collections/{collection_id}/items",
+        stac_config=stac_config,
+        headers=headers,
+    )
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
new file mode 100644
index 000000000..62d5205b3
--- /dev/null
+++ b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
@@ -0,0 +1,42 @@
+{
+  "type": "Collection",
+  "stac_version": "1.0.0",
+  "id": "earth-2-fcn3-stormscope-goes-{uuid}",
+  "title": "Earth-2 StormScope-GOES conditioned on FourCastNet3",
+  "description": "Forecasts generated with the FourCastNet3-conditioned StormScope-GOES workflow on Microsoft Foundry.",
+  "links": [],
+  "stac_extensions": [],
+  "item_assets": {
+    "data": {
+      "type": "application/x-netcdf",
+      "roles": []
+    }
+  },
+  "extent": {
+    "spatial": {
+      "bbox": [
+        [
+          -135,
+          20,
+          -60,
+          53
+        ]
+      ]
+    },
+    "temporal": {
+      "interval": [
+        [
+          null,
+          null
+        ]
+      ]
+    }
+  },
+  "license": "other",
+  "keywords": [
+    "CONUS",
+    "Forecast",
+    "Earth-2"
+  ],
+  "providers": []
+}
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
new file mode 100644
index 000000000..21fc63b56
--- /dev/null
+++ b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
@@ -0,0 +1,42 @@
+{
+  "type": "Collection",
+  "stac_version": "1.0.0",
+  "id": "earth-2-fcn3-{uuid}",
+  "title": "Earth-2 FourCastNet3",
+  "description": "Forecasts generated with the FourCastNet3 workflow on Microsoft Foundry.",
+  "links": [],
+  "stac_extensions": [],
+  "item_assets": {
+    "data": {
+      "type": "application/x-netcdf",
+      "roles": []
+    }
+  },
+  "extent": {
+    "spatial": {
+      "bbox": [
+        [
+          -180,
+          -90,
+          180,
+          90
+        ]
+      ]
+    },
+    "temporal": {
+      "interval": [
+        [
+          null,
+          null
+        ]
+      ]
+    }
+  },
+  "license": "other",
+  "keywords": [
+    "Global",
+    "Forecast",
+    "Earth-2"
+  ],
+  "providers": []
+}
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
new file mode 100644
index 000000000..3a8d389f9
--- /dev/null
+++ b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
@@ -0,0 +1,54 @@
+{
+  "type": "Feature",
+  "stac_version": "1.1.0",
+  "id": "fcn3-stormscope-goes-{start_time}-{uuid}",
+  "stac_extensions": [],
+  "geometry": {
+    "type": "Polygon",
+    "coordinates": [
+      [
+        [
+          -60,
+          20
+        ],
+        [
+          -60,
+          53
+        ],
+        [
+          -135,
+          53
+        ],
+        [
+          -135,
+          20
+        ],
+        [
+          -60,
+          20
+        ]
+      ]
+    ]
+  },
+  "bbox": [
+    -135,
+    20,
+    -60,
+    53
+  ],
+  "properties": {
+    "datetime": "{start_time}",
+    "start_datetime": "{start_time}",
+    "end_datetime": "{end_time}"
+  },
+  "links": [],
+  "assets": {
+    "data": {
+      "href": "{blob_url}",
+      "type": "application/x-netcdf",
+      "title": "StormScope-GOES forecast conditioned on FourCastNet3",
+      "description": "FourCastNet3-conditioned StormScope-GOES forecast from {start_time} to {end_time}.",
+      "roles": []
+    }
+  }
+}
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json
new file mode 100644
index 000000000..38fb9a801
--- /dev/null
+++ b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json
@@ -0,0 +1,54 @@
+{
+  "type": "Feature",
+  "stac_version": "1.1.0",
+  "id": "fcn3-{start_time}-{uuid}",
+  "stac_extensions": [],
+  "geometry": {
+    "type": "Polygon",
+    "coordinates": [
+      [
+        [
+          180,
+          -90
+        ],
+        [
+          180,
+          90
+        ],
+        [
+          -180,
+          90
+        ],
+        [
+          -180,
+          -90
+        ],
+        [
+          180,
+          -90
+        ]
+      ]
+    ]
+  },
+  "bbox": [
+    -180,
+    -90,
+    180,
+    90
+  ],
+  "properties": {
+    "datetime": "{start_time}",
+    "start_datetime": "{start_time}",
+    "end_datetime": "{end_time}"
+  },
+  "links": [],
+  "assets": {
+    "data": {
+      "href": "{blob_url}",
+      "type": "application/x-netcdf",
+      "title": "FourCastNet3 forecast",
+      "description": "FourCastNet3 forecast from {start_time} to {end_time}.",
+      "roles": []
+    }
+  }
+}
diff --git a/serve/inferenceserver/requirements.txt b/serve/inferenceserver/requirements.txt
index e53074912..65646deea 100644
--- a/serve/inferenceserver/requirements.txt
+++ b/serve/inferenceserver/requirements.txt
@@ -32,6 +32,7 @@ cryptography>=41.0.0
 multi-storage-client[azure-storage-blob]
 # Azure Blob Storage for SAS token generation
 azure-storage-blob>=12.19.0
+azure-identity
 
 # Development dependencies
 pytest>=7.0.0

From 31049b55e063fdc9eeae182b8d8eb94b3484172e Mon Sep 17 00:00:00 2001
From: Stefan Weissenberger <sweissenberg@nvidia.com>
Date: Thu, 5 Mar 2026 13:56:31 +0000
Subject: [PATCH 18/24] add default render options

---
 .../planetary_computer/pc_client.py           | 536 +++++++++++-------
 1 file changed, 330 insertions(+), 206 deletions(-)

diff --git a/serve/inferenceserver/api_server/planetary_computer/pc_client.py b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
index 1c64b7bc0..773a03b16 100644
--- a/serve/inferenceserver/api_server/planetary_computer/pc_client.py
+++ b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
@@ -3,6 +3,7 @@
 import os
 import time
 from datetime import datetime, timedelta
+from time import perf_counter
 from typing import Literal
 from uuid import uuid4
 
@@ -13,222 +14,345 @@
 logger = logging.getLogger("planetary_computer")
 logger.setLevel(logging.INFO)
 
-APPLICATION_URL = "https://geocatalog.spatio.azure.com/"
-REQUESTS_TIMEOUT = 30
-
-
-def get_headers() -> dict:
-    """Get the Azure authorization headers."""
-    credential = DefaultAzureCredential()
-    token = credential.get_token(APPLICATION_URL)
-    return {"Authorization": f"Bearer {token.token}"}
-
-
-def _create_element(url: str, stac_config: dict, headers: dict) -> None:
-    """Create a STAC collection or feature and wait for it to finish."""
-    response = requests.post(
-        url,
-        json=stac_config,
-        headers=headers,
-        params={"api-version": "2025-04-30-preview"},
-        timeout=REQUESTS_TIMEOUT,
-    )
-    location = response.headers["location"]
-
-    logger.info("Creating '%s'...", stac_config["id"])
-    while True:
-        response = requests.get(location, headers=headers, timeout=REQUESTS_TIMEOUT)
-        status = response.json()["status"]
-        logger.info(status)
-        if status not in {"Pending", "Running"}:
-            break
-        time.sleep(5)
-
-    if status == "Finished":
-        logger.info("Successfully created '%s'", stac_config["id"])
-    else:
-        logger.error("Failed to create '%s': %s", stac_config["id"], response.text)
-
-
-def get_collection_json(
-    workflow_name: Literal[
-        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
-    ],
-    collection_id: str | None,
-) -> dict:
-    """
-    Load the STAC collection template and set the collection ID.
-
-    Args:
-        workflow_name: Name of the workflow
-        collection_id: STAC collection ID
 
-    Returns:
-        STAC config as dict
-    """
-    template_fns = {
-        "foundry_fcn3_workflow": "template-collection-fcn3.json",
-        "foundry_fcn3_stormscope_goes_workflow": "template-collection-fcn3-stormscope-goes.json",
-    }
-    with open(
-        os.path.join(os.path.dirname(__file__), template_fns[workflow_name])
-    ) as f:
-        stac_config = json.load(f)
-    if collection_id is None:
-        stac_config["id"] = stac_config["id"].format(uuid=uuid4())
-    else:
-        stac_config["id"] = collection_id
-    return stac_config
-
-
-def get_feature_json(
-    workflow_name: Literal[
-        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
-    ],
-    start_time: datetime,
-    end_time: datetime,
-    blob_url: str,
-) -> dict:
+class PlanetaryComputerClient:
     """
-    Load the STAC feature template and set the workflow parameters.
+    Simple client for the interaction with Planetary Computer.
 
-    Args:
-        workflow_name: Name of the workflow
-        start_time: Forecast start time
-        end_time: Forecast end time
-        blob_url: Blob location on Azure Blob Storage
-
-    Returns:
-        STAC config as dict
-    """
-    template_fns = {
-        "foundry_fcn3_workflow": "template-feature-fcn3.json",
-        "foundry_fcn3_stormscope_goes_workflow": "template-feature-fcn3-stormscope-goes.json",
-    }
-    with open(
-        os.path.join(os.path.dirname(__file__), template_fns[workflow_name])
-    ) as f:
-        stac_config = json.load(f)
-
-    iso_start = start_time.isoformat()
-    iso_end = end_time.isoformat()
-
-    stac_config["id"] = stac_config["id"].format(
-        start_time=iso_start[:13], uuid=uuid4()
-    )
-    stac_config["properties"]["datetime"] = iso_start
-    stac_config["properties"]["start_datetime"] = iso_end
-    stac_config["properties"]["end_datetime"] = end_time
-    stac_config["assets"]["data"]["href"] = blob_url
-    return stac_config
-
-
-def create_collection(
-    workflow_name: Literal[
-        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
-    ],
-    geocatalog_url: str,
-    collection_id: str | None,
-    headers: dict,
-) -> str:
-    """
-    Create a new STAC collection.
+    The official SDK does not work as documented, hence this implementation.
 
     Args:
-        workflow_name: Name of the workflow
-        geocatalog_url: URL to the Planetary Computer Pro catalog
-        collection_id: STAC collection ID
-        headers: Azure authorization headers
-
-    Returns:
-        ID of the created collection
-    """
-    stac_config = get_collection_json(workflow_name, collection_id)
-    _create_element(
-        url=f"{geocatalog_url}/stac/collections",
-        stac_config=stac_config,
-        headers=headers,
-    )
-    return stac_config["id"]
-
-
-def ensure_collection_exists(
-    workflow_name: Literal[
-        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
-    ],
-    geocatalog_url: str,
-    collection_id: str,
-    headers: dict,
-) -> str:
+        workflow_name: Name of the Earth2Studio workflow
     """
-    Check whether a collection already exists and if not create it.
 
-    Args:
-        workflow_name: Name of the workflow
-        geocatalog_url: URL to the Planetary Computer Pro catalog
-        collection_id: STAC collection ID
-        headers: Azure authorization headers
+    APPLICATION_URL = "https://geocatalog.spatio.azure.com/"
+    REQUESTS_TIMEOUT = 30
+    CREATION_TIMEOUT = 120
+
+    def __init__(
+        self,
+        workflow_name: Literal[
+            "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
+        ],
+    ):
+        self.workflow_name = workflow_name
+        self.headers: dict | None = None
+
+    def update_headers(self) -> None:
+        """
+        Get the Azure authorization headers.
+        """
+        credential = DefaultAzureCredential()
+        token = credential.get_token(self.APPLICATION_URL)
+        self.headers = {"Authorization": f"Bearer {token.token}"}
+
+    def _get(self, url: str) -> requests.Response:
+        return requests.get(
+            url,
+            headers=self.headers,
+            params={"api-version": "2025-04-30-preview"},
+            timeout=self.REQUESTS_TIMEOUT,
+        )
 
-    Returns:
-        ID of the collection
-    """
-    # Check whether collection already exists
-    response = requests.get(
-        f"{geocatalog_url}/stac/collections/{collection_id}",
-        headers=headers,
-        params={"api-version": "2025-04-30-preview"},
-        timeout=REQUESTS_TIMEOUT,
-    )
-    status = response.status_code
-    if status == 200:
-        return collection_id
-    if status != 404:
-        raise RuntimeError(
-            f"Failed to retrieve collection: Error {status} - {response.text}"
+    def _post(self, url: str, body: dict | None = None) -> requests.Response:
+        return requests.post(
+            url,
+            json=body,
+            headers=self.headers,
+            params={"api-version": "2025-04-30-preview"},
+            timeout=self.REQUESTS_TIMEOUT,
         )
 
-    # Create new collection
-    return create_collection(workflow_name, geocatalog_url, collection_id, headers)
+    def _put(self, url: str, body: dict | None = None) -> requests.Response:
+        return requests.put(
+            url,
+            json=body,
+            headers=self.headers,
+            params={"api-version": "2025-04-30-preview"},
+            timeout=self.REQUESTS_TIMEOUT,
+        )
 
+    def _create_element(self, url: str, stac_config: dict) -> None:
+        """
+        Create a STAC collection or feature and wait for it to finish.
+        """
+        response = self._post(
+            url,
+            body=stac_config,
+        )
+        location = response.headers["location"]
+
+        logger.info("Creating '%s'...", stac_config["id"])
+        start = perf_counter()
+        while True:
+            if (perf_counter() - start) > self.CREATION_TIMEOUT:
+                logger.error("Creation of '%s' timed out", stac_config["id"])
+                return
+
+            response = self._get(location)
+            status = response.json()["status"]
+            logger.info(status)
+            if status not in {"Pending", "Running"}:
+                break
+            time.sleep(5)
+
+        if status == "Succeeded":
+            logger.info("Successfully created '%s'", stac_config["id"])
+        else:
+            logger.error("Failed to create '%s': %s", stac_config["id"], response.text)
+
+    def _get_collection_json(self, collection_id: str | None) -> dict:
+        """
+        Load the STAC collection template and set the collection ID.
+
+        Args:
+            collection_id: STAC collection ID
+
+        Returns:
+            STAC config as dict
+        """
+        template_fns = {
+            "foundry_fcn3_workflow": "template-collection-fcn3.json",
+            "foundry_fcn3_stormscope_goes_workflow": "template-collection-fcn3-stormscope-goes.json",
+        }
+        template_fn = os.path.join(
+            os.path.dirname(__file__), template_fns[self.workflow_name]
+        )
+        with open(template_fn) as f:
+            stac_config = json.load(f)
+
+        if collection_id is None:
+            stac_config["id"] = stac_config["id"].format(uuid=uuid4())
+        else:
+            stac_config["id"] = collection_id
+        return stac_config
+
+    def _get_feature_json(
+        self,
+        start_time: datetime,
+        end_time: datetime,
+        blob_url: str,
+    ) -> dict:
+        """
+        Load the STAC feature template and set the workflow parameters.
+
+        Args:
+            start_time: Forecast start time
+            end_time: Forecast end time
+            blob_url: Blob location on Azure Blob Storage
+
+        Returns:
+            STAC config as dict
+        """
+        template_fns = {
+            "foundry_fcn3_workflow": "template-feature-fcn3.json",
+            "foundry_fcn3_stormscope_goes_workflow": "template-feature-fcn3-stormscope-goes.json",
+        }
+        template_fn = os.path.join(
+            os.path.dirname(__file__), template_fns[self.workflow_name]
+        )
+        with open(template_fn) as f:
+            stac_config = json.load(f)
 
-def create_feature(
-    workflow_name: Literal[
-        "foundry_fcn3_workflow", "foundry_fcn3_stormscope_goes_workflow"
-    ],
-    geocatalog_url: str,
-    collection_id: str | None,
-    parameters: dict | WorkflowParameters,
-    blob_url: str,
-) -> None:
-    """
-    Ingest a new STAC feature into the collection.
+        iso_start = start_time.isoformat()
+        iso_end = end_time.isoformat()
 
-    Args:
-        workflow_name: Name of the workflow
-        geocatalog_url: URL to the Planetary Computer Pro catalog
-        collection_id: STAC collection ID
-        parameters: Workflow parameters with start time and number of steps
-        blob_url: Blob location on Azure Blob Storage
-    """
-    headers = get_headers()
-
-    # Make sure the target collection exists
-    if collection_id is None:
-        collection_id = create_collection(workflow_name, geocatalog_url, None, headers)
-    else:
-        ensure_collection_exists(workflow_name, geocatalog_url, collection_id, headers)
-
-    start_time = parameters["start_time"]
-    step_sizes = {
-        # Forecast step size in hours
-        "foundry_fcn3_workflow": 6,
-        "foundry_fcn3_stormscope_goes_workflow": 1,
-    }
-    end_time = start_time + timedelta(hours=step_sizes[workflow_name])
-    stac_config = get_feature_json(workflow_name, start_time, end_time, blob_url)
-
-    _create_element(
-        url=f"{geocatalog_url}/stac/collections/{collection_id}/items",
-        stac_config=stac_config,
-        headers=headers,
-    )
+        stac_config["id"] = stac_config["id"].format(
+            start_time=iso_start[:13], uuid=uuid4()
+        )
+        stac_config["properties"]["datetime"] = iso_start
+        stac_config["properties"]["start_datetime"] = iso_end
+        stac_config["properties"]["end_datetime"] = iso_end
+        stac_config["assets"]["data"]["href"] = blob_url
+        return stac_config
+
+    def _update_tile_settings(self, geocatalog_url: str, collection_id: str) -> None:
+        """
+        Update 'minZoom' of the tile settings so user can zoom out.
+
+        Args:
+            geocatalog_url: URL to the Planetary Computer Pro catalog
+            collection_id: STAC collection ID
+        """
+        tile_settings = {
+            "minZoom": 0,
+            "maxItemsPerTile": 35,
+        }
+        response = self._put(
+            f"{geocatalog_url}/stac/collections/{collection_id}/configurations/tile-settings",
+            body=tile_settings,
+        )
+        status = response.status_code
+        if status not in {200, 201}:
+            logger.error(
+                f"Could not update tile settings: Error {status} - {response.text}"
+            )
+
+    def _update_render_options(
+        self,
+        geocatalog_url: str,
+        collection_id: str,
+    ) -> None:
+        """
+        Add some example render options for a new collection.
+
+        Args:
+            geocatalog_url: URL to the Planetary Computer Pro catalog
+            collection_id: STAC collection ID
+        """
+        if self.workflow_name == "foundry_fcn3_workflow":
+            render_params = [
+                {
+                    "id": "t2m",
+                    "scale": [263, 313],
+                    "cmap": "balance",
+                },
+                {
+                    "id": "t850",
+                    "scale": [263, 313],
+                    "cmap": "balance",
+                },
+                {
+                    "id": "u10m",
+                    "scale": [-20, 20],
+                    "cmap": "prgn",
+                },
+                {
+                    "id": "v10m",
+                    "scale": [-20, 20],
+                    "cmap": "prgn",
+                },
+                {
+                    "id": "z500",
+                    "scale": [45000, 60000],
+                    "cmap": "viridis",
+                },
+            ]
+        elif self.workflow_name == "foundry_fcn3_stormscope_goes_workflow":
+            render_params = [
+                {
+                    "id": f"abi{aid:02}c",
+                    "scale": [0, 1],
+                    "cmap": "plasma",
+                }
+                for aid in [1, 2, 3, 7, 8, 9, 10, 13]
+            ]
+
+        for params in render_params:
+            render_option = {
+                "id": f"auto-{params['id']}",
+                "name": params["id"],
+                "type": "raster-tile",
+                "options": (
+                    f"assets=data&subdataset_name={params['id']}"
+                    "&sel=time=2100-01-01&sel=ensemble=0&sel_method=nearest"
+                    f"&rescale={params['scale'][0]},{params['scale'][1]}"
+                    f"&colormap_name={params['cmap']}"
+                ),
+                "minZoom": 0,
+            }
+            response = self._post(
+                f"{geocatalog_url}/stac/collections/{collection_id}/configurations/render-options",
+                body=render_option,
+            )
+            status = response.status_code
+            if status not in {200, 201}:
+                logger.error(
+                    f"Could not update render options: Error {status} - {response.text}"
+                )
+
+    def _create_collection(
+        self,
+        geocatalog_url: str,
+        collection_id: str | None,
+    ) -> str:
+        """
+        Create a new STAC collection.
+
+        Args:
+            geocatalog_url: URL to the Planetary Computer Pro catalog
+            collection_id: STAC collection ID
+
+        Returns:
+            ID of the created collection
+        """
+        stac_config = self._get_collection_json(collection_id)
+        self._create_element(
+            url=f"{geocatalog_url}/stac/collections",
+            stac_config=stac_config,
+        )
+        self._update_tile_settings(geocatalog_url, stac_config["id"])
+        self._update_render_options(geocatalog_url, stac_config["id"])
+        return stac_config["id"]
+
+    def _ensure_collection_exists(
+        self,
+        geocatalog_url: str,
+        collection_id: str,
+    ) -> str:
+        """
+        Check whether a collection already exists and if not create it.
+
+        Args:
+            geocatalog_url: URL to the Planetary Computer Pro catalog
+            collection_id: STAC collection ID
+
+        Returns:
+            ID of the collection
+        """
+        # Check whether collection already exists
+        response = self._get(f"{geocatalog_url}/stac/collections/{collection_id}")
+        status = response.status_code
+        if status == 200:
+            return collection_id
+        if status != 404:
+            raise RuntimeError(
+                f"Failed to retrieve collection: Error {status} - {response.text}"
+            )
+
+        # Create new collection
+        return self._create_collection(geocatalog_url, collection_id)
+
+    def create_feature(
+        self,
+        geocatalog_url: str,
+        collection_id: str | None,
+        parameters: dict | WorkflowParameters,
+        blob_url: str,
+    ) -> tuple[str, str]:
+        """
+        Ingest a new STAC feature into the collection.
+
+        Args:
+            workflow_name: Name of the workflow
+            geocatalog_url: URL to the Planetary Computer Pro catalog
+            collection_id: STAC collection ID
+            parameters: Workflow parameters with start time and number of steps
+            blob_url: Blob location on Azure Blob Storage
+
+        Returns:
+            Tuple of collection ID and feature ID
+        """
+        self.update_headers()
+
+        # Make sure the target collection exists
+        if collection_id is None:
+            collection_id = self._create_collection(geocatalog_url, None)
+        else:
+            self._ensure_collection_exists(geocatalog_url, collection_id)
+
+        start_time = parameters["start_time"]
+        step_sizes = {
+            # Forecast step size in hours
+            "foundry_fcn3_workflow": 6,
+            "foundry_fcn3_stormscope_goes_workflow": 1,
+        }
+        end_time = start_time + timedelta(hours=step_sizes[self.workflow_name])
+        stac_config = self._get_feature_json(start_time, end_time, blob_url)
+
+        self._create_element(
+            url=f"{geocatalog_url}/stac/collections/{collection_id}/items",
+            stac_config=stac_config,
+        )
+
+        return collection_id, stac_config["id"]

From 3cfd3059061952f258b0fa28536423fefc80bf8a Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 5 Mar 2026 16:28:22 -0800
Subject: [PATCH 19/24] add support for azure managed identity

---
 pyproject.toml                                |   1 +
 serve/inferenceserver/api_server/config.py    |   3 +
 .../inferenceserver/api_server/cpu_worker.py  |   3 +
 .../api_server/object_storage.py              | 101 ++++++++++++------
 serve/inferenceserver/requirements.txt        |   3 +-
 uv.lock                                       |  44 ++++++++
 6 files changed, 122 insertions(+), 33 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ad67f08e4..bac01edbb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -107,6 +107,7 @@ serve = [
     "cryptography>=41.0.0",
     "multi-storage-client[azure-storage-blob]",
     "azure-storage-blob>=12.19.0",
+    "azure-identity>=1.15.0",
 ]
 # PX Models
 ace2 = [
diff --git a/serve/inferenceserver/api_server/config.py b/serve/inferenceserver/api_server/config.py
index a6b836bf0..e1d8c4bc8 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -436,6 +436,9 @@ def _apply_env_overrides(self) -> None:
             self._config.object_storage.azure_container_name = os.getenv(
                 "AZURE_CONTAINER_NAME"
             )
+        # Support AZURE_ENDPOINT_URL for managed identity scenarios
+        if os.getenv("AZURE_ENDPOINT_URL"):
+            self._config.object_storage.endpoint_url = os.getenv("AZURE_ENDPOINT_URL")
 
         # Workflow exposure overrides
         if os.getenv("EXPOSED_WORKFLOWS"):
diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index 268730010..007aa0434 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -639,6 +639,9 @@ def process_object_storage_upload(
                     storage_kwargs["azure_container_name"] = (
                         config.object_storage.azure_container_name
                     )
+                # Support endpoint_url for Azure (useful for managed identity)
+                if config.object_storage.endpoint_url:
+                    storage_kwargs["endpoint_url"] = config.object_storage.endpoint_url
 
             try:
                 storage = MSCObjectStorage(**storage_kwargs)
diff --git a/serve/inferenceserver/api_server/object_storage.py b/serve/inferenceserver/api_server/object_storage.py
index 9d18205cc..7961df4ff 100644
--- a/serve/inferenceserver/api_server/object_storage.py
+++ b/serve/inferenceserver/api_server/object_storage.py
@@ -175,7 +175,9 @@ class MSCObjectStorage(ObjectStorage):
     - AWS_SESSION_TOKEN (optional)
     - AWS_DEFAULT_REGION
 
-    For Azure, credentials can be provided via connection string or account name/key.
+    For Azure, credentials can be provided via:
+    - Connection string (AzureCredentials): Provide azure_connection_string
+    - Managed Identity (DefaultAzureCredentials): Omit azure_connection_string and provide endpoint_url or azure_account_name
 
     See: https://nvidia.github.io/multi-storage-client/user_guide/rust.html
     """
@@ -213,7 +215,7 @@ def __init__(
             access_key_id: AWS access key ID (sets AWS_ACCESS_KEY_ID env var).
             secret_access_key: AWS secret access key (sets AWS_SECRET_ACCESS_KEY env var).
             session_token: AWS session token for temporary credentials.
-            endpoint_url: Custom endpoint URL (for S3-compatible services).
+            endpoint_url: Custom endpoint URL (for S3-compatible services or Azure Blob Storage).
             use_transfer_acceleration: Enable S3 Transfer Acceleration (bucket must have it enabled).
             max_concurrency: Maximum number of concurrent transfers (default: 16).
             multipart_chunksize: Chunk size for multipart uploads in bytes (default: 8MB).
@@ -222,10 +224,16 @@ def __init__(
             cloudfront_domain: CloudFront distribution domain for signed URLs.
             cloudfront_key_pair_id: CloudFront key pair ID for signed URLs.
             cloudfront_private_key: PEM private key content as string.
-            azure_connection_string: Azure connection string.
-            azure_account_name: Azure storage account name.
+            azure_connection_string: Azure connection string (optional if using managed identity).
+            azure_account_name: Azure storage account name (required if using managed identity without endpoint_url).
             azure_account_key: Azure storage account key (optional for SAS token generation).
             azure_container_name: Azure container name.
+
+        Note:
+            For Azure with managed identity (DefaultAzureCredentials):
+            - Omit azure_connection_string to use managed identity
+            - Provide endpoint_url or azure_account_name to determine the storage endpoint
+            - Example: endpoint_url="https://account.blob.core.windows.net"
         """
         self.storage_type = storage_type
         self.bucket = bucket
@@ -267,34 +275,48 @@ def __init__(
 
         # Azure-specific configuration
         elif storage_type == "azure":
-            # Connection string is mandatory for Azure
-            if not azure_connection_string:
-                raise ObjectStorageError(
-                    "Azure connection string is required. "
-                    "Please provide azure_connection_string."
-                )
-
             self.azure_container_name = azure_container_name or bucket
             self.azure_account_key = azure_account_key
 
-            # Set Azure connection string
-            os.environ["AZURE_CONNECTION_STRING"] = azure_connection_string
+            # Determine if using managed identity (DefaultAzureCredentials)
+            # Managed identity is used when connection string is not provided
+            self.use_managed_identity = azure_connection_string is None
 
-            # Extract account name from connection string if not provided directly
-            if not azure_account_name:
-                self.azure_account_name = None
-                # Connection string format: DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...
-                for part in azure_connection_string.split(";"):
-                    if part.startswith("AccountName="):
-                        self.azure_account_name = part.split("=", 1)[1]
-                        break
-                if not self.azure_account_name:
+            if self.use_managed_identity:
+                # Using managed identity - connection string not needed
+                logger.info(
+                    "Using Azure Managed Identity (DefaultAzureCredentials). "
+                    f"Account: {azure_account_name or 'will be determined from endpoint'}, "
+                    f"Container: {self.azure_container_name}"
+                )
+                self.azure_account_name = azure_account_name
+            else:
+                # Using connection string authentication
+                # At this point, azure_connection_string is guaranteed to be not None
+                # (since use_managed_identity is False only when azure_connection_string is not None)
+                if azure_connection_string is None:
                     raise ObjectStorageError(
-                        "Could not extract account name from connection string. "
-                        "Please provide azure_account_name directly or ensure connection string contains AccountName."
+                        "Connection string required for non-managed identity mode"
                     )
-            else:
-                self.azure_account_name = azure_account_name
+
+                # Set Azure connection string
+                os.environ["AZURE_CONNECTION_STRING"] = azure_connection_string
+
+                # Extract account name from connection string if not provided directly
+                if not azure_account_name:
+                    self.azure_account_name = None
+                    # Connection string format: DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...
+                    for part in azure_connection_string.split(";"):
+                        if part.startswith("AccountName="):
+                            self.azure_account_name = part.split("=", 1)[1]
+                            break
+                    if not self.azure_account_name:
+                        raise ObjectStorageError(
+                            "Could not extract account name from connection string. "
+                            "Please provide azure_account_name directly or ensure connection string contains AccountName."
+                        )
+                else:
+                    self.azure_account_name = azure_account_name
         else:
             raise ValueError(
                 f"Unsupported storage_type: {storage_type}. Must be 's3' or 'azure'."
@@ -346,11 +368,14 @@ def __init__(
             }
         elif storage_type == "azure":
             # Build the Azure storage provider options
-            # Derive endpoint URL from connection string or account name
+            # Derive endpoint URL from endpoint_url parameter, connection string, or account name
             azure_endpoint_url = None
 
-            # First, try to extract BlobEndpoint directly from connection string
-            if azure_connection_string:
+            # First, check if endpoint_url was provided directly (for managed identity or custom endpoints)
+            if endpoint_url:
+                azure_endpoint_url = endpoint_url.rstrip("/")
+            # Then, try to extract BlobEndpoint directly from connection string
+            elif azure_connection_string:
                 for part in azure_connection_string.split(";"):
                     if part.startswith("BlobEndpoint="):
                         azure_endpoint_url = part.split("=", 1)[1].rstrip("/")
@@ -376,11 +401,14 @@ def __init__(
                 if not account_name:
                     raise ObjectStorageError(
                         "Azure endpoint_url cannot be determined. "
-                        "Please provide azure_connection_string (with AccountName or BlobEndpoint) "
+                        "Please provide endpoint_url, azure_connection_string (with AccountName or BlobEndpoint), "
                         "or azure_account_name."
                     )
 
                 azure_endpoint_url = f"https://{account_name}.blob.{endpoint_suffix}"
+                logger.info(
+                    f"Constructed Azure endpoint URL from account name: {azure_endpoint_url}"
+                )
 
             azure_storage_provider_options = {
                 "base_path": self.azure_container_name,
@@ -399,8 +427,17 @@ def __init__(
                 }
             }
 
-            # Add Azure credentials provider if connection string is provided
-            if azure_connection_string:
+            # Add Azure credentials provider
+            if self.use_managed_identity:
+                # Use DefaultAzureCredentials for managed identity
+                profile_config["profiles"][self.profile_name][
+                    "credentials_provider"
+                ] = {
+                    "type": "DefaultAzureCredentials",
+                    "options": {},
+                }
+            elif azure_connection_string:
+                # Use AzureCredentials with connection string
                 profile_config["profiles"][self.profile_name][
                     "credentials_provider"
                 ] = {
diff --git a/serve/inferenceserver/requirements.txt b/serve/inferenceserver/requirements.txt
index 65646deea..6429289e5 100644
--- a/serve/inferenceserver/requirements.txt
+++ b/serve/inferenceserver/requirements.txt
@@ -32,7 +32,8 @@ cryptography>=41.0.0
 multi-storage-client[azure-storage-blob]
 # Azure Blob Storage for SAS token generation
 azure-storage-blob>=12.19.0
-azure-identity
+# Azure Identity for managed identity authentication
+azure-identity>=1.15.0
 
 # Development dependencies
 pytest>=7.0.0
diff --git a/uv.lock b/uv.lock
index e7764e4fd..a3c21b447 100644
--- a/uv.lock
+++ b/uv.lock
@@ -342,6 +342,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/42/23/6371a551800d3812d6019cd813acd985f9fac0fedc1290129211a73da4ae/azure_core-1.38.2-py3-none-any.whl", hash = "sha256:074806c75cf239ea284a33a66827695ef7aeddac0b4e19dda266a93e4665ead9", size = 217957 },
 ]
 
+[[package]]
+name = "azure-identity"
+version = "1.25.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "cryptography" },
+    { name = "msal" },
+    { name = "msal-extensions" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c2/3a/439a32a5e23e45f6a91f0405949dc66cfe6834aba15a430aebfc063a81e7/azure_identity-1.25.2.tar.gz", hash = "sha256:030dbaa720266c796221c6cdbd1999b408c079032c919fef725fcc348a540fe9", size = 284709 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9b/77/f658c76f9e9a52c784bd836aaca6fd5b9aae176f1f53273e758a2bcda695/azure_identity-1.25.2-py3-none-any.whl", hash = "sha256:1b40060553d01a72ba0d708b9a46d0f61f56312e215d8896d836653ffdc6753d", size = 191423 },
+]
+
 [[package]]
 name = "azure-storage-blob"
 version = "12.28.0"
@@ -1874,6 +1890,7 @@ precip-afno-v2 = [
 ]
 serve = [
     { name = "aiofiles" },
+    { name = "azure-identity" },
     { name = "azure-storage-blob" },
     { name = "cryptography" },
     { name = "fastapi" },
@@ -1979,6 +1996,7 @@ requires-dist = [
     { name = "anemoi-models", marker = "extra == 'aifs'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'aifsens'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'all'", specifier = "==0.5.1" },
+    { name = "azure-identity", marker = "extra == 'serve'", specifier = ">=1.15.0" },
     { name = "azure-storage-blob", marker = "extra == 'serve'", specifier = ">=12.19.0" },
     { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'all'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
     { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'cbottle'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
@@ -4398,6 +4416,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 },
 ]
 
+[[package]]
+name = "msal"
+version = "1.35.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "pyjwt", extra = ["crypto"] },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3c/aa/5a646093ac218e4a329391d5a31e5092a89db7d2ef1637a90b82cd0b6f94/msal-1.35.1.tar.gz", hash = "sha256:70cac18ab80a053bff86219ba64cfe3da1f307c74b009e2da57ef040eb1b5656", size = 165658 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/96/86/16815fddf056ca998853c6dc525397edf0b43559bb4073a80d2bc7fe8009/msal-1.35.1-py3-none-any.whl", hash = "sha256:8f4e82f34b10c19e326ec69f44dc6b30171f2f7098f3720ea8a9f0c11832caa3", size = 119909 },
+]
+
+[[package]]
+name = "msal-extensions"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "msal" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/01/99/5d239b6156eddf761a636bded1118414d161bd6b7b37a9335549ed159396/msal_extensions-1.3.1.tar.gz", hash = "sha256:c5b0fd10f65ef62b5f1d62f4251d51cbcaf003fcedae8c91b040a488614be1a4", size = 23315 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5e/75/bd9b7bb966668920f06b200e84454c8f3566b102183bc55c5473d96cb2b9/msal_extensions-1.3.1-py3-none-any.whl", hash = "sha256:96d3de4d034504e969ac5e85bae8106c8373b5c6568e4c8fa7af2eca9dbe6bca", size = 20583 },
+]
+
 [[package]]
 name = "msgpack"
 version = "1.1.2"

From 6aafb442b6b66d0ffc20f19cd5532621ff0d08de Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Mon, 9 Mar 2026 11:18:00 -0700
Subject: [PATCH 20/24] initial integration with azure pc

---
 .../api_server/conf/config.yaml               |   2 +
 serve/inferenceserver/api_server/config.py    |   9 +
 .../inferenceserver/api_server/cpu_worker.py  | 199 ++++++++++++++++++
 serve/inferenceserver/api_server/main.py      |   1 +
 serve/inferenceserver/api_server/utils.py     |  18 +-
 .../scripts/start_api_server.sh               |  28 ++-
 serve/inferenceserver/scripts/status.sh       |   2 +-
 .../scripts/stop_api_server.sh                |   4 +
 8 files changed, 257 insertions(+), 6 deletions(-)

diff --git a/serve/inferenceserver/api_server/conf/config.yaml b/serve/inferenceserver/api_server/conf/config.yaml
index 2f123bbde..bab0788dc 100644
--- a/serve/inferenceserver/api_server/conf/config.yaml
+++ b/serve/inferenceserver/api_server/conf/config.yaml
@@ -36,6 +36,7 @@ worker:
   num_workers: 1    # The number of RQ inference workers to create by default
   zip_num_workers: 1  # The number of RQ workers for result_zip queue
   objstore_num_workers: 1  # The number of RQ workers for object_storage queue
+  geocatalog_num_workers: 1  # The number of RQ workers for geocatalog_ingestion queue (used when AZURE_GEOCATALOG_URL is set)
   finalize_num_workers: 1  # The number of RQ workers for finalize_metadata queue
 
 paths:
@@ -94,6 +95,7 @@ object_storage:
   azure_account_name: null
   azure_account_key: null
   azure_container_name: null
+  azure_geocatalog_url: null  # When set, triggers Planetary Computer ingestion after Azure upload
 
 workflow_exposure:
   # List of workflow names to expose via API endpoints
diff --git a/serve/inferenceserver/api_server/config.py b/serve/inferenceserver/api_server/config.py
index e1d8c4bc8..fc1f86fee 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -57,6 +57,7 @@ class QueueConfig:
     name: str = "inference"
     result_zip_queue_name: str = "result_zip"
     object_storage_queue_name: str = "object_storage"
+    geocatalog_ingestion_queue_name: str = "geocatalog_ingestion"
     finalize_metadata_queue_name: str = "finalize_metadata"
     max_size: int = 10
     default_timeout: str = "1h"
@@ -143,6 +144,10 @@ class ObjectStorageConfig:
     azure_container_name: str | None = (
         None  # Azure container name (falls back to bucket if not set)
     )
+    # Azure Planetary Computer / GeoCatalog ingestion (optional)
+    azure_geocatalog_url: str | None = (
+        None  # When set, triggers PC ingestion after upload
+    )
 
 
 @dataclass
@@ -439,6 +444,10 @@ def _apply_env_overrides(self) -> None:
         # Support AZURE_ENDPOINT_URL for managed identity scenarios
         if os.getenv("AZURE_ENDPOINT_URL"):
             self._config.object_storage.endpoint_url = os.getenv("AZURE_ENDPOINT_URL")
+        if os.getenv("AZURE_GEOCATALOG_URL"):
+            self._config.object_storage.azure_geocatalog_url = os.getenv(
+                "AZURE_GEOCATALOG_URL"
+            )
 
         # Workflow exposure overrides
         if os.getenv("EXPOSED_WORKFLOWS"):
diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index 007aa0434..3f2517d94 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -25,6 +25,7 @@
     "create_results_zip",
     "process_result_zip",
     "process_object_storage_upload",
+    "process_geocatalog_ingestion",
     "process_finalize_metadata",
 ]
 
@@ -760,6 +761,23 @@ def process_object_storage_upload(
                 storage_info["remote_path"] = (
                     f"azure://{container_name}/{remote_prefix}"
                 )
+                # Build HTTPS blob URL for primary netcdf file (for GeoCatalog ingestion)
+                if (
+                    config.object_storage.azure_account_name
+                    and config.object_storage.azure_geocatalog_url
+                ):
+                    primary_nc = None
+                    if output_path.is_file() and output_path.suffix.lower() == ".nc":
+                        primary_nc = output_path.name
+                    elif output_path.is_dir():
+                        nc_files = sorted(output_path.rglob("*.nc"))
+                        if nc_files:
+                            primary_nc = nc_files[0].relative_to(output_path).as_posix()
+                    if primary_nc:
+                        storage_info["blob_url"] = (
+                            f"https://{config.object_storage.azure_account_name}.blob.core.windows.net/"
+                            f"{container_name}/{remote_prefix}/{primary_nc}"
+                        )
         if signed_url:
             storage_info["signed_url"] = signed_url
 
@@ -815,6 +833,187 @@ def process_object_storage_upload(
         )
 
 
+def process_geocatalog_ingestion(
+    workflow_name: str,
+    execution_id: str,
+) -> dict[str, Any] | None:
+    """
+    RQ Worker function to trigger ingestion of uploaded inference results into
+    Azure Planetary Computer / GeoCatalog when AZURE_GEOCATALOG_URL is configured.
+
+    This function is intended to be executed by the CPU worker from the
+    geocatalog_ingestion_queue, after process_object_storage_upload. It reads
+    storage info and parameters from Redis and calls the Planetary Computer
+    client to create a STAC feature for the uploaded netcdf blob.
+
+    Args:
+        workflow_name: Name of the workflow
+        execution_id: Execution ID of the workflow
+
+    Returns:
+        Dict containing result info, None on critical failure
+    """
+    request_id = f"{workflow_name}:{execution_id}"
+    logger.info(f"Processing geocatalog ingestion for {request_id}")
+
+    try:
+        geocatalog_url = config.object_storage.azure_geocatalog_url
+        if not geocatalog_url:
+            logger.warning(
+                f"AZURE_GEOCATALOG_URL not set, skipping geocatalog ingestion for {request_id}"
+            )
+            job_id = queue_next_stage(
+                redis_client=redis_client,
+                current_stage="geocatalog_ingestion",
+                workflow_name=workflow_name,
+                execution_id=execution_id,
+                output_path_str="",
+            )
+            if not job_id:
+                return fail_workflow(
+                    workflow_name,
+                    execution_id,
+                    f"Failed to queue finalize_metadata for {request_id}",
+                )
+            return {
+                "success": True,
+                "skipped": True,
+                "reason": "AZURE_GEOCATALOG_URL not set",
+            }
+
+        storage_info_key = f"inference_request:{request_id}:storage_info"
+        metadata_key = get_inference_request_metadata_key(request_id)
+        storage_info_json = redis_client.get(storage_info_key)
+        pending_metadata_json = redis_client.get(metadata_key)
+
+        if not storage_info_json or not pending_metadata_json:
+            logger.warning(
+                f"Storage info or pending metadata missing for {request_id}, skipping geocatalog ingestion"
+            )
+            job_id = queue_next_stage(
+                redis_client=redis_client,
+                current_stage="geocatalog_ingestion",
+                workflow_name=workflow_name,
+                execution_id=execution_id,
+                output_path_str="",
+            )
+            if not job_id:
+                return fail_workflow(
+                    workflow_name,
+                    execution_id,
+                    f"Failed to queue finalize_metadata for {request_id}",
+                )
+            return {
+                "success": True,
+                "skipped": True,
+                "reason": "missing storage/metadata",
+            }
+
+        storage_info = json.loads(storage_info_json)
+        metadata_dict = json.loads(pending_metadata_json)
+        blob_url = storage_info.get("blob_url")
+        parameters = metadata_dict.get("parameters") or {}
+
+        if not blob_url:
+            logger.warning(
+                f"No blob_url in storage info for {request_id} (e.g. not Azure or no .nc file), skipping geocatalog ingestion"
+            )
+            job_id = queue_next_stage(
+                redis_client=redis_client,
+                current_stage="geocatalog_ingestion",
+                workflow_name=workflow_name,
+                execution_id=execution_id,
+                output_path_str="",
+            )
+            if not job_id:
+                return fail_workflow(
+                    workflow_name,
+                    execution_id,
+                    f"Failed to queue finalize_metadata for {request_id}",
+                )
+            return {"success": True, "skipped": True, "reason": "no blob_url"}
+
+        # Only trigger PC ingestion for workflows supported by PlanetaryComputerClient
+        _PC_SUPPORTED_WORKFLOWS = (
+            "foundry_fcn3_workflow",
+            "foundry_fcn3_stormscope_goes_workflow",
+        )
+        if workflow_name not in _PC_SUPPORTED_WORKFLOWS:
+            logger.info(
+                f"Workflow {workflow_name} not supported by Planetary Computer client, skipping ingestion for {request_id}"
+            )
+            job_id = queue_next_stage(
+                redis_client=redis_client,
+                current_stage="geocatalog_ingestion",
+                workflow_name=workflow_name,
+                execution_id=execution_id,
+                output_path_str="",
+            )
+            if not job_id:
+                return fail_workflow(
+                    workflow_name,
+                    execution_id,
+                    f"Failed to queue finalize_metadata for {request_id}",
+                )
+            return {
+                "success": True,
+                "skipped": True,
+                "reason": "workflow not supported",
+            }
+
+        # Normalize parameters for pc_client: start_time must be datetime
+        if "start_time" in parameters:
+            st = parameters["start_time"]
+            if isinstance(st, str):
+                # ISO format: allow 'Z' suffix
+                normalized = st.replace("Z", "+00:00")
+                parameters = dict(parameters)
+                parameters["start_time"] = datetime.fromisoformat(normalized)
+
+        try:
+            from api_server.planetary_computer.pc_client import PlanetaryComputerClient
+
+            pc_client = PlanetaryComputerClient(workflow_name)
+            pc_client.create_feature(
+                geocatalog_url=geocatalog_url,
+                collection_id=None,
+                parameters=parameters,
+                blob_url=blob_url,
+            )
+            logger.info(f"GeoCatalog ingestion completed for {request_id}")
+        except Exception as e:
+            # Log but do not fail the pipeline; finalize_metadata should still run
+            logger.exception(
+                f"GeoCatalog ingestion failed for {request_id}: {e}. Queuing finalize_metadata anyway."
+            )
+
+        job_id = queue_next_stage(
+            redis_client=redis_client,
+            current_stage="geocatalog_ingestion",
+            workflow_name=workflow_name,
+            execution_id=execution_id,
+            output_path_str="",
+        )
+        if not job_id:
+            return fail_workflow(
+                workflow_name,
+                execution_id,
+                f"Failed to queue next pipeline stage for {request_id}",
+            )
+        logger.info(
+            f"Queued finalize_metadata for {workflow_name}:{execution_id} with RQ job ID: {job_id}"
+        )
+        return {"success": True}
+
+    except Exception as e:
+        logger.exception(f"Failed in geocatalog ingestion for {request_id}")
+        return fail_workflow(
+            workflow_name,
+            execution_id,
+            f"Geocatalog ingestion failed for {request_id}: {str(e)}",
+        )
+
+
 def process_finalize_metadata(
     workflow_name: str,
     execution_id: str,
diff --git a/serve/inferenceserver/api_server/main.py b/serve/inferenceserver/api_server/main.py
index 96f7146b6..96905a618 100644
--- a/serve/inferenceserver/api_server/main.py
+++ b/serve/inferenceserver/api_server/main.py
@@ -92,6 +92,7 @@ def check_admission_control() -> None:
         config.queue.name,
         config.queue.result_zip_queue_name,
         config.queue.object_storage_queue_name,
+        config.queue.geocatalog_ingestion_queue_name,
         config.queue.finalize_metadata_queue_name,
     ]
     for queue_name in queue_names:
diff --git a/serve/inferenceserver/api_server/utils.py b/serve/inferenceserver/api_server/utils.py
index 1901ca0ad..2c6d725df 100644
--- a/serve/inferenceserver/api_server/utils.py
+++ b/serve/inferenceserver/api_server/utils.py
@@ -196,7 +196,7 @@ async def create_file_stream(
 # Pipeline Stage Utilities
 # =============================================================================
 
-Stage = Literal["inference", "result_zip", "object_storage"]
+Stage = Literal["inference", "result_zip", "object_storage", "geocatalog_ingestion"]
 
 
 def queue_next_stage(
@@ -211,12 +211,12 @@ def queue_next_stage(
     Queue the next pipeline stage based on configuration.
 
     Pipeline flow:
-    - If result_zip_enabled: inference -> result_zip -> object_storage (if enabled) -> finalize
-    - If not result_zip_enabled: inference -> object_storage (if enabled) -> finalize
+    - If result_zip_enabled: inference -> result_zip -> object_storage (if enabled) -> [geocatalog_ingestion (if AZURE_GEOCATALOG_URL)] -> finalize
+    - If not result_zip_enabled: inference -> object_storage (if enabled) -> [geocatalog_ingestion (if AZURE_GEOCATALOG_URL)] -> finalize
 
     Args:
         redis_client: Redis client for queue connection
-        current_stage: The stage that just completed ("inference", "result_zip", "object_storage")
+        current_stage: The stage that just completed ("inference", "result_zip", "object_storage", "geocatalog_ingestion")
         workflow_name: Name of the workflow
         execution_id: Execution ID of the workflow
         output_path_str: Path to the output files
@@ -257,6 +257,16 @@ def queue_next_stage(
             args = (workflow_name, execution_id)
 
     elif current_stage == "object_storage":
+        if config.object_storage.azure_geocatalog_url:
+            next_queue = "geocatalog_ingestion"
+            next_func = "api_server.cpu_worker.process_geocatalog_ingestion"
+            args = (workflow_name, execution_id)
+        else:
+            next_queue = "finalize_metadata"
+            next_func = "api_server.cpu_worker.process_finalize_metadata"
+            args = (workflow_name, execution_id)
+
+    elif current_stage == "geocatalog_ingestion":
         next_queue = "finalize_metadata"
         next_func = "api_server.cpu_worker.process_finalize_metadata"
         args = (workflow_name, execution_id)
diff --git a/serve/inferenceserver/scripts/start_api_server.sh b/serve/inferenceserver/scripts/start_api_server.sh
index a53bf7cd1..ca92db083 100755
--- a/serve/inferenceserver/scripts/start_api_server.sh
+++ b/serve/inferenceserver/scripts/start_api_server.sh
@@ -54,6 +54,7 @@ if [ -f "$CONFIG_FILE" ]; then
     CONFIG_ZIP_NUM_WORKERS=$(read_config "worker.zip_num_workers")
     CONFIG_OBJSTORE_NUM_WORKERS=$(read_config "worker.objstore_num_workers")
     CONFIG_FINALIZE_NUM_WORKERS=$(read_config "worker.finalize_num_workers")
+    CONFIG_GEOCATALOG_NUM_WORKERS=$(read_config "worker.geocatalog_num_workers")
     CONFIG_PERSISTENT_WORKER=$(read_config "worker.persistent")
 fi
 
@@ -63,10 +64,11 @@ REDIS_HOST=${3:-${CONFIG_REDIS_HOST:-localhost}}  # Default Redis host
 NUM_RQ_WORKERS=${4:-${CONFIG_RQ_NUM_WORKERS:-1}}  # Default to 1 RQ workers
 NUM_ZIP_WORKERS=${5:-${CONFIG_ZIP_NUM_WORKERS:-1}}  # Default to 1 workers for result_zip queue
 NUM_OBJSTORE_WORKERS=${CONFIG_OBJSTORE_NUM_WORKERS:-1}  # Default to 1 object storage worker
+NUM_GEOCATALOG_WORKERS=${CONFIG_GEOCATALOG_NUM_WORKERS:-1}  # Default to 1 geocatalog ingestion worker
 NUM_FINALIZE_WORKERS=${CONFIG_FINALIZE_NUM_WORKERS:-1}  # Default to 1 finalize metadata worker
 PERSISTENT_WORKER=${CONFIG_PERSISTENT_WORKER:-false}
 
-echo "Starting Earth2Studio with $NUM_WORKERS API workers, $NUM_RQ_WORKERS RQ workers, $NUM_ZIP_WORKERS zip workers, $NUM_OBJSTORE_WORKERS object storage workers, and $NUM_FINALIZE_WORKERS finalize workers on port $API_PORT..."
+echo "Starting Earth2Studio with $NUM_WORKERS API workers, $NUM_RQ_WORKERS RQ workers, $NUM_ZIP_WORKERS zip workers, $NUM_OBJSTORE_WORKERS object storage workers, $NUM_GEOCATALOG_WORKERS geocatalog workers, and $NUM_FINALIZE_WORKERS finalize workers on port $API_PORT..."
 echo "Configuration: Redis=$REDIS_HOST, Persistent Worker=$PERSISTENT_WORKER"
 
 # Function to cleanup on exit
@@ -103,6 +105,12 @@ cleanup() {
         pkill -f "rq.*worker.*object_storage"
     fi
 
+    # Stop all geocatalog ingestion workers
+    if pgrep -f "rq.*worker.*geocatalog_ingestion" > /dev/null; then
+        echo "Stopping geocatalog ingestion workers..."
+        pkill -f "rq.*worker.*geocatalog_ingestion"
+    fi
+
     # Stop all finalize metadata workers
     if pgrep -f "rq.*worker.*finalize_metadata" > /dev/null; then
         echo "Stopping finalize metadata workers..."
@@ -157,6 +165,15 @@ for i in $(seq 1 $NUM_OBJSTORE_WORKERS); do
     echo "Started object storage worker $i (PID: $!)"
 done
 
+# Start geocatalog ingestion workers (used when AZURE_GEOCATALOG_URL is set)
+echo "Starting $NUM_GEOCATALOG_WORKERS geocatalog ingestion workers..."
+GEOCATALOG_WORKER_PIDS=()
+for i in $(seq 1 $NUM_GEOCATALOG_WORKERS); do
+    CUDA_VISIBLE_DEVICES="" rq worker -w rq.worker.SimpleWorker geocatalog_ingestion &
+    GEOCATALOG_WORKER_PIDS+=($!)
+    echo "Started geocatalog ingestion worker $i (PID: $!)"
+done
+
 # Start finalize metadata workers
 echo "Starting $NUM_FINALIZE_WORKERS finalize metadata workers..."
 FINALIZE_WORKER_PIDS=()
@@ -202,6 +219,13 @@ if [ "$OBJSTORE_WORKER_COUNT" -eq 0 ]; then
     exit 1
 fi
 
+# Check if geocatalog ingestion workers are running
+GEOCATALOG_WORKER_COUNT=$(pgrep -f "rq.*worker.*geocatalog_ingestion" | wc -l)
+if [ "$GEOCATALOG_WORKER_COUNT" -eq 0 ]; then
+    echo "Failed to start geocatalog ingestion workers..."
+    exit 1
+fi
+
 # Check if finalize metadata workers are running
 FINALIZE_WORKER_COUNT=$(pgrep -f "rq.*worker.*finalize_metadata" | wc -l)
 if [ "$FINALIZE_WORKER_COUNT" -eq 0 ]; then
@@ -221,12 +245,14 @@ echo "Uvicorn PID: $UVICORN_PID"
 echo "RQ Worker PIDs: ${RQ_WORKER_PIDS[*]}"
 echo "Zip Worker PIDs: ${ZIP_WORKER_PIDS[*]}"
 echo "Object Storage Worker PIDs: ${OBJSTORE_WORKER_PIDS[*]}"
+echo "Geocatalog Ingestion Worker PIDs: ${GEOCATALOG_WORKER_PIDS[*]}"
 echo "Finalize Metadata Worker PIDs: ${FINALIZE_WORKER_PIDS[*]}"
 echo "Cleanup Daemon PID: $CLEANUP_DAEMON_PID"
 echo "Active API workers: $API_WORKER_COUNT"
 echo "Active RQ inference workers: $RQ_WORKER_COUNT"
 echo "Active zip workers: $ZIP_WORKER_COUNT"
 echo "Active object storage workers: $OBJSTORE_WORKER_COUNT"
+echo "Active geocatalog ingestion workers: $GEOCATALOG_WORKER_COUNT"
 echo "Active finalize metadata workers: $FINALIZE_WORKER_COUNT"
 echo "API available at http://localhost:$API_PORT"
 echo "API docs at http://localhost:$API_PORT/docs"
diff --git a/serve/inferenceserver/scripts/status.sh b/serve/inferenceserver/scripts/status.sh
index d99a58185..5fc56b48c 100755
--- a/serve/inferenceserver/scripts/status.sh
+++ b/serve/inferenceserver/scripts/status.sh
@@ -65,7 +65,7 @@ fi
 echo ""
 
 # Check RQ workers status per queue
-RQ_QUEUES=("inference" "result_zip" "object_storage" "finalize_metadata")
+RQ_QUEUES=("inference" "result_zip" "object_storage" "geocatalog_ingestion" "finalize_metadata")
 RQ_ALL_OK=1
 
 echo "RQ Workers:"
diff --git a/serve/inferenceserver/scripts/stop_api_server.sh b/serve/inferenceserver/scripts/stop_api_server.sh
index b160c922a..fc612450f 100755
--- a/serve/inferenceserver/scripts/stop_api_server.sh
+++ b/serve/inferenceserver/scripts/stop_api_server.sh
@@ -33,6 +33,10 @@ pkill -f "rq.*worker.*result_zip"
 echo "Stopping object storage workers..."
 pkill -f "rq.*worker.*object_storage"
 
+# Stop geocatalog ingestion workers
+echo "Stopping geocatalog ingestion workers..."
+pkill -f "rq.*worker.*geocatalog_ingestion"
+
 # Stop finalize metadata workers
 echo "Stopping finalize metadata workers..."
 pkill -f "rq.*worker.*finalize_metadata"

From dc6e63aacd75d88371a0fb23c367ae8bfac514a9 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Wed, 11 Mar 2026 16:13:12 -0700
Subject: [PATCH 21/24] e2e works on azure

---
 pyproject.toml                                |  2 +-
 .../inferenceserver/api_server/cpu_worker.py  | 10 +------
 .../planetary_computer/pc_client.py           | 30 +++++++++++++++++--
 serve/inferenceserver/requirements.txt        |  2 +-
 uv.lock                                       | 27 ++++++++---------
 5 files changed, 43 insertions(+), 28 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index bac01edbb..e9d82c3fd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -105,7 +105,7 @@ serve = [
     "hydra-core>=1.3.0",
     # Object storage related
     "cryptography>=41.0.0",
-    "multi-storage-client[azure-storage-blob]",
+    "multi-storage-client>=0.44.0",
     "azure-storage-blob>=12.19.0",
     "azure-identity>=1.15.0",
 ]
diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
index 3f2517d94..99ac82099 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -933,6 +933,7 @@ def process_geocatalog_ingestion(
                 )
             return {"success": True, "skipped": True, "reason": "no blob_url"}
 
+        logger.info(f"Blob URL: {blob_url}")
         # Only trigger PC ingestion for workflows supported by PlanetaryComputerClient
         _PC_SUPPORTED_WORKFLOWS = (
             "foundry_fcn3_workflow",
@@ -961,15 +962,6 @@ def process_geocatalog_ingestion(
                 "reason": "workflow not supported",
             }
 
-        # Normalize parameters for pc_client: start_time must be datetime
-        if "start_time" in parameters:
-            st = parameters["start_time"]
-            if isinstance(st, str):
-                # ISO format: allow 'Z' suffix
-                normalized = st.replace("Z", "+00:00")
-                parameters = dict(parameters)
-                parameters["start_time"] = datetime.fromisoformat(normalized)
-
         try:
             from api_server.planetary_computer.pc_client import PlanetaryComputerClient
 
diff --git a/serve/inferenceserver/api_server/planetary_computer/pc_client.py b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
index 773a03b16..7ff5703ae 100644
--- a/serve/inferenceserver/api_server/planetary_computer/pc_client.py
+++ b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
@@ -27,7 +27,7 @@ class PlanetaryComputerClient:
 
     APPLICATION_URL = "https://geocatalog.spatio.azure.com/"
     REQUESTS_TIMEOUT = 30
-    CREATION_TIMEOUT = 120
+    CREATION_TIMEOUT = 300
 
     def __init__(
         self,
@@ -313,6 +313,32 @@ def _ensure_collection_exists(
         # Create new collection
         return self._create_collection(geocatalog_url, collection_id)
 
+    def _resolve_start_time(self, parameters: dict) -> datetime:
+        """
+        Resolve start_time from workflow parameters.
+        foundry_fcn3_workflow uses 'start_time'; foundry_fcn3_stormscope_goes_workflow uses 'start_time_stormscope'.
+        """
+        raw: datetime | str | None = None
+        if self.workflow_name == "foundry_fcn3_workflow":
+            raw = parameters.get("start_time")
+        elif self.workflow_name == "foundry_fcn3_stormscope_goes_workflow":
+            raw = parameters.get("start_time_stormscope")
+        else:
+            raise ValueError(f"Unsupported workflow name: {self.workflow_name}")
+        if raw is None:
+            raise ValueError(
+                f"Missing start time in parameters for workflow {self.workflow_name}. "
+                "Expected 'start_time' or (for stormscope) 'start_time_stormscope'."
+            )
+        if isinstance(raw, str):
+            normalized = raw.replace("Z", "+00:00")
+            return datetime.fromisoformat(normalized)
+        if isinstance(raw, datetime):
+            return raw
+        if hasattr(raw, "isoformat"):
+            return datetime.fromisoformat(raw.isoformat())
+        raise TypeError(f"start_time must be str or datetime, got {type(raw)}")
+
     def create_feature(
         self,
         geocatalog_url: str,
@@ -341,7 +367,7 @@ def create_feature(
         else:
             self._ensure_collection_exists(geocatalog_url, collection_id)
 
-        start_time = parameters["start_time"]
+        start_time = self._resolve_start_time(parameters)
         step_sizes = {
             # Forecast step size in hours
             "foundry_fcn3_workflow": 6,
diff --git a/serve/inferenceserver/requirements.txt b/serve/inferenceserver/requirements.txt
index 6429289e5..30815a96e 100644
--- a/serve/inferenceserver/requirements.txt
+++ b/serve/inferenceserver/requirements.txt
@@ -29,7 +29,7 @@ hydra-core>=1.3.0
 cryptography>=41.0.0
 
 # Multi-Storage Client with Azure support
-multi-storage-client[azure-storage-blob]
+multi-storage-client>=0.44.0
 # Azure Blob Storage for SAS token generation
 azure-storage-blob>=12.19.0
 # Azure Identity for managed identity authentication
diff --git a/uv.lock b/uv.lock
index a3c21b447..e83b710f9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1897,7 +1897,7 @@ serve = [
     { name = "hiredis" },
     { name = "httpx" },
     { name = "hydra-core" },
-    { name = "multi-storage-client", extra = ["azure-storage-blob"] },
+    { name = "multi-storage-client" },
     { name = "prometheus-client" },
     { name = "pydantic" },
     { name = "python-dotenv" },
@@ -2076,7 +2076,7 @@ requires-dist = [
     { name = "moviepy", marker = "extra == 'all'" },
     { name = "moviepy", marker = "extra == 'fcn3'" },
     { name = "moviepy", marker = "extra == 'sfno'" },
-    { name = "multi-storage-client", extras = ["azure-storage-blob"], marker = "extra == 'serve'" },
+    { name = "multi-storage-client", marker = "extra == 'serve'", specifier = ">=0.44.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'all'", specifier = ">=0.33.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'data'", specifier = ">=0.33.0" },
     { name = "natten", marker = "extra == 'all'" },
@@ -4497,7 +4497,7 @@ wheels = [
 
 [[package]]
 name = "multi-storage-client"
-version = "0.42.0"
+version = "0.44.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
@@ -4515,21 +4515,18 @@ dependencies = [
     { name = "xattr" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e1/90/1e69cb6d71418b38a9409b0e2564efe1e7c12e18e63e478591ae0317dbcc/multi_storage_client-0.42.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3db30610d6bb15a5c211af9d7b11c8a1a13265893c1a625d5aaadacdb61a9a8e", size = 8805275 },
-    { url = "https://files.pythonhosted.org/packages/de/dd/a55dc9e60113f98af10075c3e33b97007bfbfd2e6f8bc6a1b2b1b43857c8/multi_storage_client-0.42.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a8c2d491475eec5e80ad706eca7005d9bd17d30b29166e891c18695b42336493", size = 5155309 },
-    { url = "https://files.pythonhosted.org/packages/b9/b6/648a1d6b4482634fbb0d5bc0ba156b42fafd4f364227f9203bc4ac70dbac/multi_storage_client-0.42.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:91a87e05e0e09b8fbd6804bb1ac85a28213d4371e91d06d9c35ad12b247f28ec", size = 5422770 },
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 ]
 
 [package.optional-dependencies]
-azure-storage-blob = [
-    { name = "azure-storage-blob" },
-]
 boto3 = [
     { name = "boto3" },
 ]

From 1811ceca9806750fc7afbd1855e1f39ce3cb5857 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 12 Mar 2026 11:57:35 -0700
Subject: [PATCH 22/24] fix issues with template filling

---
 .../api_server/planetary_computer/pc_client.py               | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/serve/inferenceserver/api_server/planetary_computer/pc_client.py b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
index 7ff5703ae..e27016a6b 100644
--- a/serve/inferenceserver/api_server/planetary_computer/pc_client.py
+++ b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
@@ -161,9 +161,12 @@ def _get_feature_json(
             start_time=iso_start[:13], uuid=uuid4()
         )
         stac_config["properties"]["datetime"] = iso_start
-        stac_config["properties"]["start_datetime"] = iso_end
+        stac_config["properties"]["start_datetime"] = iso_start
         stac_config["properties"]["end_datetime"] = iso_end
         stac_config["assets"]["data"]["href"] = blob_url
+        stac_config["assets"]["data"]["description"] = stac_config["assets"]["data"][
+            "description"
+        ].format(start_time=iso_start, end_time=iso_end)
         return stac_config
 
     def _update_tile_settings(self, geocatalog_url: str, collection_id: str) -> None:

From 8750f5a161138e57a9897b1810cd29f36234a924 Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 12 Mar 2026 14:15:13 -0700
Subject: [PATCH 23/24] merge upstream/main

---
 .cursor/rules/e2s-000-python-style-guide.mdc  |   12 +
 .cursor/rules/e2s-002-api-documentation.mdc   |    1 +
 .cursor/rules/e2s-009-prognostic-models.mdc   |    2 +
 .github/ISSUE_TEMPLATE/bug_report.yml         |    2 +-
 .github/ISSUE_TEMPLATE/chore.yml              |   59 +
 .../ISSUE_TEMPLATE/documentation_request.yml  |    2 +-
 .github/ISSUE_TEMPLATE/feature_request.yml    |    2 +-
 .gitlab-ci.yml                                |   64 +-
 CHANGELOG.md                                  |   15 +
 Makefile                                      |    2 +-
 docs/conf.py                                  |    3 +-
 docs/modules/models.rst                       |   25 +
 docs/templates/dataassim.rst                  |   23 +
 docs/userguide/about/install.md               |   62 +-
 earth2studio/data/__init__.py                 |    2 +-
 earth2studio/data/ufs.py                      |    7 -
 earth2studio/data/utils.py                    |  144 +-
 earth2studio/data/xr.py                       |   22 +
 earth2studio/io/zarr.py                       |    3 +-
 earth2studio/models/da/__init__.py            |   18 +
 earth2studio/models/da/base.py                |  188 +
 earth2studio/models/da/interp.py              |  589 ++
 earth2studio/models/da/sda_stormcast.py       |  914 ++
 earth2studio/models/da/utils.py               |  167 +
 earth2studio/models/dx/cbottle_sr.py          |    7 +-
 earth2studio/models/dx/corrdiff.py            |   19 +-
 earth2studio/models/dx/corrdiff_cmip6.py      |   16 +-
 earth2studio/models/nn/afno_ssrd.py           |    2 +-
 earth2studio/models/nn/atlas.py               |   17 +-
 earth2studio/models/nn/stormscope_util.py     |   17 +-
 earth2studio/models/px/aifsens.py             |    5 +-
 earth2studio/models/px/cbottle_video.py       |   12 +-
 earth2studio/models/px/dlesym.py              |    2 +-
 earth2studio/models/px/fcn.py                 |   24 +-
 .../models/px/graphcast_operational.py        |  105 +-
 earth2studio/models/px/graphcast_small.py     |  107 +-
 earth2studio/models/px/stormcast.py           |   21 +-
 earth2studio/models/px/stormscope.py          |    2 +-
 earth2studio/perturbation/gaussian.py         |   18 +-
 earth2studio/perturbation/spherical.py        |   12 +-
 earth2studio/run.py                           |   13 +-
 earth2studio/serve/client/__init__.py         |   15 +
 earth2studio/serve/client/client.py           |  550 +
 earth2studio/serve/client/e2client.py         |  479 +
 earth2studio/serve/client/exceptions.py       |  137 +
 earth2studio/serve/client/fsspec_utils.py     |  302 +
 earth2studio/serve/client/models.py           |  314 +
 .../serve/server}/__init__.py                 |   30 +-
 .../serve/server}/cleanup_daemon.py           |   84 +-
 .../serve/server}/config.py                   |   39 +-
 .../serve/server}/cpu_worker.py               |   25 +-
 .../serve/server}/e2workflow.py               |   12 +-
 .../serve/server}/main.py                     |  261 +-
 .../serve/server}/object_storage.py           |  207 +-
 .../serve/server}/utils.py                    |   16 +-
 .../serve/server}/worker.py                   |   68 +-
 .../serve/server}/workflow.py                 |  433 +-
 earth2studio/utils/coords.py                  |  409 +-
 earth2studio/utils/time.py                    |   63 +-
 earth2studio/utils/type.py                    |    9 +
 examples/16_cbottle_super_resolution.py       |    1 +
 examples/21_stormcast_sda.py                  |  488 +
 pyproject.toml                                |   95 +-
 recipes/hens/cfg/storm_bernd.yaml             |    2 +-
 recipes/hens/src/hens_ensemble.py             |   13 +-
 recipes/hens/src/hens_utilities.py            |   50 -
 recipes/s2s/src/s2s_ensemble.py               |   24 +-
 recipes/s2s/src/s2s_utilities.py              |   51 +-
 serve/Dockerfile                              |   29 +-
 .../__init__.py => client/Makefile}           |   20 +-
 serve/client/README.md                        |  442 +
 serve/client/examples/basic_forecast.py       |  116 +
 serve/client/examples/diagnostic_analysis.py  |  155 +
 serve/client/examples/downscaled_forecast.py  |  128 +
 serve/client/pyproject.toml                   |   88 +
 serve/client/requirements.txt                 |   14 +
 serve/{inferenceserver => server}/Makefile    |    2 +-
 serve/{inferenceserver => server}/README.md   |    4 +-
 .../README_earth2workflows.md                 |    2 +-
 .../README_object_storage.md                  |    0
 .../README_workflows.md                       |   10 +-
 .../api_server => server}/conf/config.yaml    |    0
 .../api_server => server}/conf/profiles.json  |    0
 .../api_server => server}/conf/workflows.yaml |    0
 serve/{inferenceserver => server}/env.example |    0
 .../example_workflows/__init__.py             |    0
 .../deterministic_earth2_workflow.py          |    2 +-
 .../example_workflows/deterministic_fcn.py    |    2 +-
 .../deterministic_workflow.py                 |    2 +-
 .../example_workflows/diagnostic_workflow.py  |    2 +-
 .../example_user_workflow.py                  |    2 +-
 .../example_workflows/foundry_fcn3.py         |    2 +-
 .../foundry_fcn3_stormscope_goes.py           |   20 +-
 .../stormcast_fcn3_workflow.py                |    2 +-
 .../{inferenceserver => server}/openapi.yaml  |    0
 .../planetary_computer/pc_client.py           |    0
 ...plate-collection-fcn3-stormscope-goes.json |    0
 .../template-collection-fcn3.json             |    0
 ...template-feature-fcn3-stormscope-goes.json |    0
 .../template-feature-fcn3.json                |    0
 .../requirements.txt                          |    4 +
 .../scripts/profile_workflow.sh               |    0
 .../scripts/redis.conf                        |    0
 .../scripts/start_api_server.sh               |   21 +-
 .../scripts/start_redis.sh                    |    0
 .../scripts/startup.sh                        |   20 +-
 .../scripts/status.sh                         |    2 +-
 .../scripts/stop_api_server.sh                |    2 +-
 .../scripts/stop_redis.sh                     |    0
 .../{ => server}/test/unit/test_e2workflow.py |   89 +-
 .../test/unit/test_e2workflow_params.py       |   65 +-
 test/Dockerfile                               |    2 +-
 test/data/test_data_utils.py                  |  181 +-
 test/data/test_goes.py                        |    1 +
 test/models/da/test_da_interp.py              |  359 +
 test/models/da/test_da_sda_stormcast.py       |  537 +
 test/models/da/test_utils.py                  |  204 +
 test/models/px/test_ace2.py                   |    2 +
 test/models/px/test_aurora.py                 |    6 +-
 test/models/px/test_dxwrapper.py              |    1 +
 test/models/px/test_fcn.py                    |    1 -
 test/models/px/test_graphcast.py              |   27 +-
 test/serve/client/conftest.py                 |  135 +
 test/serve/client/test_client.py              |  444 +
 test/serve/client/test_e2client.py            |  663 ++
 test/serve/client/test_fsspec_utils.py        |  378 +
 test/serve/conftest.py                        |  112 -
 test/serve/server/conftest.py                 |   88 +
 test/serve/server/test_cleanup_daemon.py      |  775 ++
 test/serve/{ => server}/test_config.py        |   30 +-
 test/serve/server/test_cpu_worker.py          | 1403 +++
 test/serve/server/test_object_storage.py      |  294 +
 test/serve/server/test_server_main.py         | 2132 ++++
 test/serve/server/test_utils.py               |  266 +
 test/serve/server/test_worker.py              |  241 +
 test/serve/{ => server}/test_workflow.py      |   45 +-
 test/serve/test_cleanup_daemon.py             |  502 -
 test/serve/test_cpu_worker.py                 |  743 --
 test/serve/test_main.py                       | 1213 ---
 test/utils/test_coords.py                     |  446 +-
 tox-full.ini                                  |    2 +
 tox.ini                                       |   25 +-
 uv.lock                                       | 8991 +++++++++--------
 143 files changed, 20408 insertions(+), 7992 deletions(-)
 create mode 100644 .github/ISSUE_TEMPLATE/chore.yml
 create mode 100644 docs/templates/dataassim.rst
 create mode 100644 earth2studio/models/da/__init__.py
 create mode 100644 earth2studio/models/da/base.py
 create mode 100644 earth2studio/models/da/interp.py
 create mode 100644 earth2studio/models/da/sda_stormcast.py
 create mode 100644 earth2studio/models/da/utils.py
 create mode 100644 earth2studio/serve/client/__init__.py
 create mode 100644 earth2studio/serve/client/client.py
 create mode 100644 earth2studio/serve/client/e2client.py
 create mode 100644 earth2studio/serve/client/exceptions.py
 create mode 100644 earth2studio/serve/client/fsspec_utils.py
 create mode 100644 earth2studio/serve/client/models.py
 rename {serve/inferenceserver/api_server/workflow => earth2studio/serve/server}/__init__.py (62%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/cleanup_daemon.py (82%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/config.py (95%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/cpu_worker.py (98%)
 rename {serve/inferenceserver/api_server/workflow => earth2studio/serve/server}/e2workflow.py (97%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/main.py (83%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/object_storage.py (90%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/utils.py (94%)
 rename {serve/inferenceserver/api_server => earth2studio/serve/server}/worker.py (79%)
 rename {serve/inferenceserver/api_server/workflow => earth2studio/serve/server}/workflow.py (75%)
 create mode 100644 examples/21_stormcast_sda.py
 rename serve/{inferenceserver/api_server/__init__.py => client/Makefile} (65%)
 create mode 100644 serve/client/README.md
 create mode 100644 serve/client/examples/basic_forecast.py
 create mode 100644 serve/client/examples/diagnostic_analysis.py
 create mode 100644 serve/client/examples/downscaled_forecast.py
 create mode 100644 serve/client/pyproject.toml
 create mode 100644 serve/client/requirements.txt
 rename serve/{inferenceserver => server}/Makefile (96%)
 rename serve/{inferenceserver => server}/README.md (99%)
 rename serve/{inferenceserver => server}/README_earth2workflows.md (98%)
 rename serve/{inferenceserver => server}/README_object_storage.md (100%)
 rename serve/{inferenceserver => server}/README_workflows.md (98%)
 rename serve/{inferenceserver/api_server => server}/conf/config.yaml (100%)
 rename serve/{inferenceserver/api_server => server}/conf/profiles.json (100%)
 rename serve/{inferenceserver/api_server => server}/conf/workflows.yaml (100%)
 rename serve/{inferenceserver => server}/env.example (100%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/__init__.py (100%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/deterministic_earth2_workflow.py (96%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/deterministic_fcn.py (99%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/deterministic_workflow.py (99%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/diagnostic_workflow.py (99%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/example_user_workflow.py (99%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/foundry_fcn3.py (100%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/foundry_fcn3_stormscope_goes.py (97%)
 rename serve/{inferenceserver/api_server => server}/example_workflows/stormcast_fcn3_workflow.py (98%)
 rename serve/{inferenceserver => server}/openapi.yaml (100%)
 rename serve/{inferenceserver/api_server => server}/planetary_computer/pc_client.py (100%)
 rename serve/{inferenceserver/api_server => server}/planetary_computer/template-collection-fcn3-stormscope-goes.json (100%)
 rename serve/{inferenceserver/api_server => server}/planetary_computer/template-collection-fcn3.json (100%)
 rename serve/{inferenceserver/api_server => server}/planetary_computer/template-feature-fcn3-stormscope-goes.json (100%)
 rename serve/{inferenceserver/api_server => server}/planetary_computer/template-feature-fcn3.json (100%)
 rename serve/{inferenceserver => server}/requirements.txt (80%)
 rename serve/{inferenceserver => server}/scripts/profile_workflow.sh (100%)
 rename serve/{inferenceserver => server}/scripts/redis.conf (100%)
 rename serve/{inferenceserver => server}/scripts/start_api_server.sh (92%)
 rename serve/{inferenceserver => server}/scripts/start_redis.sh (100%)
 rename serve/{inferenceserver => server}/scripts/startup.sh (59%)
 rename serve/{inferenceserver => server}/scripts/status.sh (98%)
 rename serve/{inferenceserver => server}/scripts/stop_api_server.sh (96%)
 rename serve/{inferenceserver => server}/scripts/stop_redis.sh (100%)
 rename serve/{ => server}/test/unit/test_e2workflow.py (82%)
 rename serve/{ => server}/test/unit/test_e2workflow_params.py (86%)
 create mode 100644 test/models/da/test_da_interp.py
 create mode 100644 test/models/da/test_da_sda_stormcast.py
 create mode 100644 test/models/da/test_utils.py
 create mode 100644 test/serve/client/conftest.py
 create mode 100644 test/serve/client/test_client.py
 create mode 100644 test/serve/client/test_e2client.py
 create mode 100644 test/serve/client/test_fsspec_utils.py
 delete mode 100644 test/serve/conftest.py
 create mode 100644 test/serve/server/conftest.py
 create mode 100644 test/serve/server/test_cleanup_daemon.py
 rename test/serve/{ => server}/test_config.py (96%)
 create mode 100644 test/serve/server/test_cpu_worker.py
 create mode 100644 test/serve/server/test_object_storage.py
 create mode 100644 test/serve/server/test_server_main.py
 create mode 100644 test/serve/server/test_utils.py
 create mode 100644 test/serve/server/test_worker.py
 rename test/serve/{ => server}/test_workflow.py (97%)
 delete mode 100644 test/serve/test_cleanup_daemon.py
 delete mode 100644 test/serve/test_cpu_worker.py
 delete mode 100644 test/serve/test_main.py

diff --git a/.cursor/rules/e2s-000-python-style-guide.mdc b/.cursor/rules/e2s-000-python-style-guide.mdc
index eb6f2cc04..d885e42a2 100644
--- a/.cursor/rules/e2s-000-python-style-guide.mdc
+++ b/.cursor/rules/e2s-000-python-style-guide.mdc
@@ -75,6 +75,17 @@ This rule enforces the coding standards for Earth2Studio as defined in [style.md
 - Run `make lint` to check linting
 - Pre-commit hooks will automatically format and lint code
 
+## Logging
+
+- Print statements should always be done via [loguru.logger](https://loguru.readthedocs.io/en/stable/api.html) or using [rich.console](https://rich.readthedocs.io/en/stable/reference/console.html)
+- Generally, debugging messages should be placed behind a verbose toggle
+- For tqdm progress bars be sure to use the following snippet at the top of the file to ensure progress bars are not disrupted
+
+  ```
+  logger.remove()
+  add(lambda msg: tqdm.write(msg, end=""), colorize=True)
+  ```
+
 ## Code Quality Reminders
 
 - Always include type hints for function parameters and return values
@@ -82,3 +93,4 @@ This rule enforces the coding standards for Earth2Studio as defined in [style.md
 - Prefer modern Python 3.10+ type syntax (`list[str]`, `str | None`)
 - Ensure all files have the SPDX license header at the top
 - Follow black formatting (run `make format` if unsure)
+- Do not use Python's native print() inside any files of the earth2studio folder
diff --git a/.cursor/rules/e2s-002-api-documentation.mdc b/.cursor/rules/e2s-002-api-documentation.mdc
index 707efdaf0..65882aba3 100644
--- a/.cursor/rules/e2s-002-api-documentation.mdc
+++ b/.cursor/rules/e2s-002-api-documentation.mdc
@@ -238,6 +238,7 @@ class CorrelatedSphericalGaussian:
 - Use periods for complete sentences, omit for "by default" phrases
 - Include `Raises` section when exceptions are raised
 - Include `Warning` section for classes with important warnings
+- Do not put doc strings at the top of python files, only license headers should be above imports
 
 ## Tools
 
diff --git a/.cursor/rules/e2s-009-prognostic-models.mdc b/.cursor/rules/e2s-009-prognostic-models.mdc
index e71df34dd..ee53da683 100644
--- a/.cursor/rules/e2s-009-prognostic-models.mdc
+++ b/.cursor/rules/e2s-009-prognostic-models.mdc
@@ -328,6 +328,8 @@ def to(self, device: torch.device | str) -> PrognosticModel:
 - Call `super().to(device)` for PyTorch module
 - Move any custom buffers/parameters to device
 - Return `self` for chaining
+- Torch.nn.Module parent class addresses this requirement most of the time
+- Generally its good to have `self.register_buffer("device_buffer", torch.empty(0))` in their init to help track what the current device of the model is
 
 ## Data Operations on GPU
 
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 0b2dfb9fe..d74d80c90 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.github/ISSUE_TEMPLATE/chore.yml b/.github/ISSUE_TEMPLATE/chore.yml
new file mode 100644
index 000000000..abe1d7022
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/chore.yml
@@ -0,0 +1,59 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: Chore
+description: Maintenance task, refactor, cleanup, or other housekeeping work
+title: "🧹[CHORE]: "
+labels: ["chore", "? - Needs Triage"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to help keep Earth2Studio clean and maintainable!
+        - By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/NVIDIA/earth2studio/blob/main/CONTRIBUTING.md)
+        - You also confirm that you have searched the [open issues](https://github.com/NVIDIA/earth2studio/issues) and have found no duplicates for this request
+
+  - type: dropdown
+    id: chore_type
+    attributes:
+      label: What type of chore is this?
+      options:
+        - Refactor
+        - Cleanup
+        - Dependency Update
+        - CI/CD
+        - Testing
+        - Other
+    validations:
+      required: true
+
+  - type: dropdown
+    id: criticality
+    attributes:
+      label: How would you describe the priority of this chore
+      options:
+        - Critical (blocking other work)
+        - Medium
+        - Low (nice to have)
+    validations:
+      required: true
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Please provide a clear description of the chore.
+      description: Describe what needs to be done and why. Include relevant file paths, modules, or areas of the codebase affected.
diff --git a/.github/ISSUE_TEMPLATE/documentation_request.yml b/.github/ISSUE_TEMPLATE/documentation_request.yml
index 38f540cdf..8b07bd757 100644
--- a/.github/ISSUE_TEMPLATE/documentation_request.yml
+++ b/.github/ISSUE_TEMPLATE/documentation_request.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index d6aa3f0a7..0c734d3a5 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 4baf555e6..096bcd82d 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -122,13 +122,6 @@ test-data:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-      changes:
-        paths:
-          - earth2studio/data/**/*
-          - earth2studio/lexicon/**/*
-          - test/data/**/*
-          - test/lexicon/**/*
-        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -155,11 +148,6 @@ test-perturb:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-      changes:
-        paths:
-          - earth2studio/perturbation/**/*
-          - test/perturbation/**/*
-        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -182,11 +170,6 @@ test-stats:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-      changes:
-        paths:
-          - earth2studio/statistics/**/*
-          - test/statistics/**/*
-        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -209,11 +192,29 @@ test-px-models:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+    - <<: *test_rule_non_github_with_changes
+      changes:
+        paths:
+          - earth2studio/models/px/**/*
+          - test/models/px/**/*
+        compare_to: 'refs/heads/main'
+    - <<: *test_rule_changes_only
       changes:
         paths:
           - earth2studio/models/px/**/*
           - test/models/px/**/*
         compare_to: 'refs/heads/main'
+    - <<: *test_rule_never
+
+# TODO: REMOVE, CONFLICT CURRENTLY
+test-px-ace2:
+  extends:
+    - .test:base
+  variables:
+    TOX_ENV: test-px-models-ace2
+  rules:
+    - <<: *test_rule_always_if_pytest_all
+    - <<: *test_rule_main_with_changes
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -236,22 +237,39 @@ test-dx-models:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+    - <<: *test_rule_non_github_with_changes
       changes:
         paths:
           - earth2studio/models/dx/**/*
           - test/models/dx/**/*
         compare_to: 'refs/heads/main'
-    - <<: *test_rule_non_github_with_changes
+    - <<: *test_rule_changes_only
       changes:
         paths:
           - earth2studio/models/dx/**/*
           - test/models/dx/**/*
         compare_to: 'refs/heads/main'
+    - <<: *test_rule_never
+
+test-da-models:
+  extends:
+    - .test:base
+  variables:
+    TOX_ENV: test-da-models
+  rules:
+    - <<: *test_rule_always_if_pytest_all
+    - <<: *test_rule_main_with_changes
+    - <<: *test_rule_non_github_with_changes
+      changes:
+        paths:
+          - earth2studio/models/da/**/*
+          - test/models/da/**/*
+        compare_to: 'refs/heads/main'
     - <<: *test_rule_changes_only
       changes:
         paths:
-          - earth2studio/models/dx/**/*
-          - test/models/dx/**/*
+          - earth2studio/models/da/**/*
+          - test/models/da/**/*
         compare_to: 'refs/heads/main'
     - <<: *test_rule_never
 
@@ -263,12 +281,6 @@ test-serve:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-      changes:
-        paths:
-          - earth2studio/serve/**/*
-          - test/serve/**/*
-          - serve/**/*
-        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 36273bd9f..5b5e9b0c8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,25 +15,39 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added Earth2Studio base schema for dataframe sources
 - Added Planetary Computer data source for ECMWF IFS analysis data
 - Added accumulated variable support to NCAR ERA5 data source
+- Added routine to tile tensors to higher-dimensional tensors
+- Added routine to concatenate tensors along specified coordinate dimension
 - Added Planetary Computer data source for GOES cloud and moisture imagery
 - Added ability to have seperate data and model cache locations via env variables
 - Added random dataframe source
 - Added base reflectivity to MRMS data source
+- Added `fetch_dataframe` utility function
+- Added data assimilation model class
+- Added equirectangular interpolation data assimilation model
+- Added StormCast SDA model
+- Adding Beta serve utils with inference server and client implementations
 
 ### Changed
 
 - Changed available date for ARCO data source to be dynamically updated
 - Changed ISD data frame return to master schema
+- handshake_coords is now accepting list of dimensions while remaining backwards-compatible
 - Updated CBottle infill to mixture of model checkpoints
+- Updated GraphCastOperational and GraphCastSmall latitude input / output to be [90,-90]
+- Updated GraphCast models to support multiple time inputs, multiple times will be looped not batched
 
 ### Deprecated
 
 ### Removed
 
+- Removed device from cbottle SR load_model api
+
 ### Fixed
 
+- Fixed timezone bug in `CBottleVideo` that converted UTC time to local system time
 - Bug in cbottle datasource resulting identical samples for multiple samples
 - Bug in StormCast loading out-of-date model package, introduced in `5518edecbabee371c824b34f0f2ec269a4d6094f`
+- Bug in spherical perturbations which did not use lmax from the SHT transform
 
 ### Security
 
@@ -43,6 +57,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Updated CBottle repo to NVlabs location
 - Updated Makani dependency hash to later version with pyproject toml fix
 - Updated ACE2 dependency hash with setuptools fix
+- Updated models using PhysicsNeMo to compliance with `nvidia-physicsnemo>=2.0`
 
 ## [0.12.1] - 2026-01-29
 
diff --git a/Makefile b/Makefile
index ef427b48f..8d6efa653 100644
--- a/Makefile
+++ b/Makefile
@@ -101,7 +101,7 @@ docs-dev:
 # Example DOCKER_REPO?=nvcr.io/dycvht5ows21
 E2S_RELEASE_TAG?=0.12.0
 E2S_IMAGE_NAME=$(DOCKER_REPO)/earth2studio-scicomp
-E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260223.0
+E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260311.0
 container-service:
 	@test -n "$(DOCKER_REPO)" || (echo "DOCKER_REPO is not set!" && exit 1)
 	DOCKER_BUILDKIT=1 docker build -t $(E2S_IMAGE_NAME):$(E2S_IMAGE_TAG) -f serve/Dockerfile .
diff --git a/docs/conf.py b/docs/conf.py
index 6ed0ed6a8..ee64c639e 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -50,7 +50,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 version = ".".join(release.split(".")[:2])
 project = "Earth2Studio"
-copyright = "2025, NVIDIA"
+copyright = "2026, NVIDIA"
 author = "NVIDIA"
 
 # -- General configuration ---------------------------------------------------
@@ -83,6 +83,7 @@
     "css/nvidia-sphinx-theme.css",
 ]
 html_theme_options = {
+    "public_docs_features": True,
     "logo": {
         "text": "Earth2Studio",
         "image_light": "_static/NVIDIA-Logo-V-ForScreen-ForLightBG.png",
diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 1d1f89e77..3df2f16a0 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -96,6 +96,31 @@ etc.
    WindgustAFNO
    Identity
 
+:mod:`earth2studio.models.da`: Data Assimilation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Data assimilation models are a class of models that incorporate observational data
+into model states or grids. These models process sparse, irregularly-distributed
+observations (typically from DataFrames) and map them to regular grids or model
+coordinate systems (typically as DataArrays). Data assimilation models support both
+stateless and stateful operation, allowing them to process observations independently
+or maintain internal state across time steps.
+
+.. warning ::
+
+   Data Assimilation models are a new addition to Earth2Studio and APIs might be subject
+   to change without warning while the implementation is hardened.
+
+.. currentmodule:: earth2studio.models.da
+
+.. autosummary::
+   :nosignatures:
+   :toctree: generated/models/da/
+   :template: dataassim.rst
+
+   InterpEquirectangular
+   StormCastSDA
+
 :mod:`earth2studio.models`: Utilities
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/templates/dataassim.rst b/docs/templates/dataassim.rst
new file mode 100644
index 000000000..c3e9907c5
--- /dev/null
+++ b/docs/templates/dataassim.rst
@@ -0,0 +1,23 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{module}}
+
+.. autoclass:: {{ objname }}
+
+   {% block methods %}
+    .. automethod:: __call__
+    .. automethod:: create_generator
+    .. automethod:: load_default_package
+    .. automethod:: load_model
+   {% endblock %}
+
+.. _sphx_glr_backref_{{module}}.{{objname}}:
+
+.. minigallery:: {{module}}.{{objname}}
+      :add-heading:
+      :heading-level: ^
+
+.. raw:: html
+
+    <div class="clearer"></div>
diff --git a/docs/userguide/about/install.md b/docs/userguide/about/install.md
index 443c8fe7b..4905ce138 100644
--- a/docs/userguide/about/install.md
+++ b/docs/userguide/about/install.md
@@ -163,6 +163,7 @@ can take a long time to compile.
 :::{tab-item} pip
 
 ```bash
+pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
 pip install earth2studio[atlas]
 ```
 
@@ -267,7 +268,7 @@ for known suggestions/fixes related to this install process.
 
 ```bash
 export FORCE_CUDA_EXTENSION=1
-pip install --no-build-isolation torch-harmonics==0.8.0
+pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
 pip install "makani @ git+https://github.com/NVIDIA/makani.git@fd1a62b9f27aebbc793f0cbafd838a3811de43fd"
 pip install earth2studio[fcn3]
 ```
@@ -277,7 +278,6 @@ pip install earth2studio[fcn3]
 
 ```bash
 export FORCE_CUDA_EXTENSION=1
-uv add torch-harmonics==0.8.0 --no-build-isolation
 uv add earth2studio --extra fcn3
 ```
 
@@ -375,6 +375,7 @@ installed manually.
 :::{tab-item} pip
 
 ```bash
+pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
 pip install "makani @ git+https://github.com/NVIDIA/makani.git@fd1a62b9f27aebbc793f0cbafd838a3811de43fd"
 pip install earth2studio[sfno]
 ```
@@ -641,6 +642,63 @@ uv add earth2studio --extra windgust-afno
 :::::
 ::::::
 
+#### Data Assimilation
+
+:::{admonition} Warning
+:class: warning
+
+Data assimilation model APIs are currently **in Beta** and may change in future
+releases. Expect possible breaking changes as these APIs mature.
+:::
+
+:::{admonition} Warning
+:class: warning
+
+All data assimilation models require [CuPy](https://docs.cupy.dev/en/stable/) and [cuDF](https://docs.rapids.ai/api/cudf/stable/),
+which are CUDA-dependent libraries.
+The default installation uses CUDA 12 (i.e., `cupy-cuda12x`, `cudf-cu12`).
+If your system uses a different CUDA version, you may need to adjust the dependencies.
+:::
+
+::::::{tab-set}
+:::::{tab-item} InterpEquirectangular
+::::{tab-set}
+:::{tab-item} pip
+
+```bash
+pip install earth2studio[da-interp]
+```
+
+:::
+:::{tab-item} uv
+
+```bash
+uv add earth2studio --extra da-interp
+```
+
+:::
+::::
+:::::
+:::::{tab-item} StormCast SDA
+::::{tab-set}
+:::{tab-item} pip
+
+```bash
+pip install earth2studio[da-stormcast]
+```
+
+:::
+:::{tab-item} uv
+
+```bash
+uv add earth2studio --extra da-stormcast
+```
+
+:::
+::::
+:::::
+::::::
+
 ### Submodule Dependencies
 
 A few features in various submodules require some specific dependencies that have been
diff --git a/earth2studio/data/__init__.py b/earth2studio/data/__init__.py
index f33b6038c..a27049741 100644
--- a/earth2studio/data/__init__.py
+++ b/earth2studio/data/__init__.py
@@ -41,7 +41,7 @@
 from .rx import CosineSolarZenith, LandSeaMask, SurfaceGeoPotential
 from .time_window import TimeWindow
 from .ufs import UFSObsConv, UFSObsSat
-from .utils import datasource_to_file, fetch_data, prep_data_array
+from .utils import datasource_to_file, fetch_data, fetch_dataframe, prep_data_array
 from .wb2 import WB2ERA5, WB2Climatology, WB2ERA5_32x64, WB2ERA5_121x240
 from .xr import (
     DataArrayDirectory,
diff --git a/earth2studio/data/ufs.py b/earth2studio/data/ufs.py
index b3335b23b..b84566bc8 100644
--- a/earth2studio/data/ufs.py
+++ b/earth2studio/data/ufs.py
@@ -733,10 +733,3 @@ def _transform_column(
     def _add_task_columns(self, df: pd.DataFrame, task: _GSIAsyncTask) -> None:
         """Add satellite column for satellite data."""
         df["satellite"] = task.satellite
-
-
-if __name__ == "__main__":
-
-    ds = UFSObsSat(satellites=["npp"], tolerance=timedelta(hours=6))
-    df = ds(datetime(2024, 2, 1), ["atms"], ["lon", "variable"])
-    print(df)
diff --git a/earth2studio/data/utils.py b/earth2studio/data/utils.py
index ba155969f..aa8bcb54f 100644
--- a/earth2studio/data/utils.py
+++ b/earth2studio/data/utils.py
@@ -43,18 +43,39 @@
 from fsspec.utils import isfilelike
 from loguru import logger
 
-from earth2studio.data.base import DataSource, ForecastSource
+from earth2studio.data.base import (
+    DataFrameSource,
+    DataSource,
+    ForecastFrameSource,
+    ForecastSource,
+)
 from earth2studio.utils.interp import LatLonInterpolation
 from earth2studio.utils.time import (
     leadtimearray_to_timedelta,
     timearray_to_datetime,
     to_time_array,
 )
-from earth2studio.utils.type import CoordSystem, LeadTimeArray, TimeArray, VariableArray
+from earth2studio.utils.type import (
+    CoordSystem,
+    FieldArray,
+    LeadTimeArray,
+    TimeArray,
+    VariableArray,
+)
 
 if TYPE_CHECKING:
     from fsspec.implementations.cache_mapper import AbstractCacheMapper
 
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+try:
+    import cudf
+except ImportError:
+    cudf = None
+
 
 def fetch_data(
     source: DataSource | ForecastSource,
@@ -62,16 +83,18 @@ def fetch_data(
     variable: VariableArray,
     lead_time: LeadTimeArray = np.array([np.timedelta64(0, "h")]),
     device: torch.device = "cpu",
-    interp_to: CoordSystem = None,
+    interp_to: CoordSystem | None = None,
     interp_method: str = "nearest",
-) -> tuple[torch.Tensor, CoordSystem]:
-    """Utility function to fetch data for models and load data on the target device.
-    If desired, xarray interpolation/regridding in the spatial domain can be used
-    by passing a target coordinate system via the optional `interp_to` argument.
+    legacy: bool = True,
+) -> tuple[torch.Tensor, CoordSystem] | xr.DataArray:
+    """Utility function to fetch data arrays from particular sources and load data on
+    the target device. If desired, xarray interpolation/regridding in the spatial
+    domain can be used by passing a target coordinate system via the optional
+    `interp_to` argument.
 
     Parameters
     ----------
-    source : DataSource
+    source : DataSource | ForecastSource
         The data source to fetch from
     time : TimeArray
         Timestamps to return data for (UTC).
@@ -81,20 +104,24 @@ def fetch_data(
         Lead times to fetch for each provided time, by default
         np.array(np.timedelta64(0, "h"))
     device : torch.device, optional
-        Torch devive to load data tensor to, by default "cpu"
+        Torch device to load data tensor to, by default "cpu"
     interp_to : CoordSystem, optional
         If provided, the fetched data will be interpolated to the coordinates
         specified by lat/lon arrays in this CoordSystem
     interp_method : str
         Interpolation method to use with xarray (by default 'nearest')
+    legacy : bool, optional
+        If True (default), returns tuple of (torch.Tensor, CoordSystem).
+        If False, returns xr.DataArray with numpy arrays for CPU or cupy arrays for CUDA.
 
     Returns
     -------
-    tuple[torch.Tensor, CoordSystem]
-        Tuple containing output tensor and coordinate OrderedDict
+    tuple[torch.Tensor, CoordSystem] | xr.DataArray
+        If legacy=True: Tuple containing output tensor and coordinate OrderedDict.
+        If legacy=False: xr.DataArray with numpy arrays (CPU) or cupy arrays (CUDA).
     """
-
     sig = signature(source.__call__)
+    device = torch.device(device)
 
     if "lead_time" in sig.parameters:
         # Working with a Forecast Data Source
@@ -112,12 +139,90 @@ def fetch_data(
 
         da = xr.concat(da, "lead_time")
 
-    return prep_data_array(
-        da,
-        device=device,
-        interp_to=interp_to,
-        interp_method=interp_method,
-    )
+    if legacy:
+        return prep_data_array(
+            da,
+            device=device,
+            interp_to=interp_to,
+            interp_method=interp_method,
+        )
+
+    # Non-legacy path: return xr.DataArray
+    else:
+        if interp_to is not None:
+            raise ValueError(
+                "The interp_to argument is not supported when legacy is False. Set legacy=True to use interpolation."
+            )
+        # Convert to cupy arrays if CUDA device and cupy is available
+        if device.type == "cuda":
+            if cp is not None:
+                with cp.cuda.Device(device.index or 0):
+                    da = da.copy(data=cp.asarray(da.values))
+            else:
+                raise ImportError(
+                    "cupy is required when using device='cuda' with legacy=False. "
+                    "Install cupy or use legacy=True."
+                )
+        return da
+
+
+def fetch_dataframe(
+    source: DataFrameSource | ForecastFrameSource,
+    time: TimeArray,
+    variable: VariableArray,
+    fields: FieldArray | None = None,
+    lead_time: LeadTimeArray = np.array([np.timedelta64(0, "h")]),
+    device: torch.device = "cpu",
+) -> pd.DataFrame | cudf.DataFrame:
+    """Utility function to fetch data frames from particular sources
+
+    Parameters
+    ----------
+    source : DataFrameSource | ForecastFrameSource
+        The data source to fetch from
+    time : TimeArray
+        Timestamps to return data for (UTC).
+    variable : VariableArray
+        Strings or list of strings that refer to variables to return
+    fields : FieldArray | None
+        Array of strings indicating which fields to return in data frame, if None all
+        possible fields will be returned, by default None
+    lead_time : LeadTimeArray, optional
+        Lead times to fetch for each provided time, by default
+        np.array(np.timedelta64(0, "h"))
+    device : torch.device, optional
+        Torch device to load data tensor to, by default "cpu"
+
+    Returns
+    -------
+    pd.DataFrame | cudf.DataFrame
+        Pandas dataframe if device is CPU, cudf DataFrame if device is CUDA
+    """
+    sig = signature(source.__call__)
+
+    if "lead_time" in sig.parameters:
+        # Working with a ForecastFrameSource
+        df = source(time, lead_time, variable, fields=fields)  # type: ignore
+    else:
+        # Combine all adjusted times and get unique values using broadcasting
+        all_times = (time[:, None] + lead_time).flatten()
+        unique_times = np.unique(all_times)
+        df = source(unique_times, variable, fields=fields)  # type: ignore
+    # Add request meta-data
+    df.attrs = {"request_time": time, "request_lead_time": lead_time}
+
+    # Convert to appropriate format based on device
+    device = torch.device(device)
+    if device.type == "cuda":
+        if cudf is None:
+            raise ImportError(
+                "cudf is required for CUDA device. Install with: pip install cudf"
+            )
+        with cp.cuda.Device(device.index):
+            result = cudf.from_pandas(df)
+        return result
+    else:
+        return df
 
 
 def prep_data_array(
@@ -239,6 +344,9 @@ def prep_data_inputs(
     if isinstance(variable, str):
         variable = [variable]
 
+    if isinstance(variable, np.ndarray):
+        variable = variable.astype(str).tolist()
+
     if isinstance(time, datetime):
         time = [time]
 
diff --git a/earth2studio/data/xr.py b/earth2studio/data/xr.py
index afcc4098e..d15e9df51 100644
--- a/earth2studio/data/xr.py
+++ b/earth2studio/data/xr.py
@@ -187,6 +187,28 @@ def __call__(
 
         return self.da.sel(time=time, variable=variable)
 
+    async def fetch(  # type: ignore[override]
+        self,
+        time: datetime | list[datetime] | TimeArray,
+        variable: str | list[str] | VariableArray,
+    ) -> xr.DataArray:
+        """Async function to get data
+
+        Parameters
+        ----------
+        time : datetime | list[datetime] | TimeArray
+            Timestamps to return data for (UTC).
+        variable : str | list[str] | VariableArray
+            String, list of strings or array of strings that refer to variables to
+            return. Must be in the GFS lexicon.
+
+        Returns
+        -------
+        xr.DataArray
+            Inference output data array
+        """
+        return self.__call__(time, variable)
+
 
 class DataSetFile:
     """A local xarray dataset file data source. This file should be compatible with
diff --git a/earth2studio/io/zarr.py b/earth2studio/io/zarr.py
index 6894ce81b..608ccdcfa 100644
--- a/earth2studio/io/zarr.py
+++ b/earth2studio/io/zarr.py
@@ -202,7 +202,8 @@ def add_array(
             if name in self.root and not kwargs.get("overwrite", False):
                 raise RuntimeError(
                     f"{name} is already in Zarr Store. "
-                    + "To overwrite Zarr array pass overwrite=True to this function"
+                    + "To overwrite Zarr array pass overwrite=True to this function or"
+                    + " backend_kwargs = {'overwrite': True} to the ZarrBackend constructor"
                 )
 
             di = di.cpu().numpy() if di is not None else None
diff --git a/earth2studio/models/da/__init__.py b/earth2studio/models/da/__init__.py
new file mode 100644
index 000000000..cbb768276
--- /dev/null
+++ b/earth2studio/models/da/__init__.py
@@ -0,0 +1,18 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .interp import InterpEquirectangular
+from .sda_stormcast import StormCastSDA
diff --git a/earth2studio/models/da/base.py b/earth2studio/models/da/base.py
new file mode 100644
index 000000000..e0819ed81
--- /dev/null
+++ b/earth2studio/models/da/base.py
@@ -0,0 +1,188 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from collections.abc import Generator
+from typing import Any, Protocol, runtime_checkable
+
+import pandas as pd
+import xarray as xr
+
+from earth2studio.utils.type import CoordSystem, FrameSchema
+
+try:
+    import cudf
+except ImportError:
+    cudf = None  # type: ignore[assignment, misc]
+
+
+@runtime_checkable
+class AssimilationModel(Protocol):
+    """Data assimilation model interface"""
+
+    def __call__(
+        self,
+        *args: pd.DataFrame | xr.DataArray | None,
+    ) -> tuple[pd.DataFrame | xr.DataArray, ...]:
+        """Stateless iteration for the data assimilation model.
+
+        Processes observations and returns assimilated data without maintaining
+        internal state between calls. This method is suitable for independent
+        processing of observation batches.
+
+        Parameters
+        ----------
+        *args : pd.DataFrame | xr.DataArray | None
+            Variable number of observation arguments. Each argument can be a
+            DataFrame (pandas or cudf DataFrame) or xarray DataArray
+            containing observation data. None can be passed for optional
+            arguments when no input data is available.
+
+        Returns
+        -------
+        tuple[pd.DataFrame | xr.DataArray, ...]
+            Assimilated data output. Can return a combination of DataFrames or
+            xarray DataArrays depending on the particular model. Output is expect to be
+            on the same device as the model.
+        """
+
+    def create_generator(
+        self,
+        *args: pd.DataFrame | xr.DataArray,
+    ) -> Generator[
+        tuple[pd.DataFrame | xr.DataArray, ...],
+        tuple[pd.DataFrame | xr.DataArray | None, ...],
+        None,
+    ]:
+        """Creates a generator which accepts collection of input observations and
+        outputs a collection of assimilated data. Used for both stateless and stateful
+        iterations of the data assimilation model
+
+        The generator accepts observations (DataFrame or DataArray) via the send()
+        method and yields assimilated data (DataFrame or DataArray) as output.
+        Supports any number of arguments (variadic).
+
+        Parameters
+        ----------
+        *args : pd.DataFrame | xr.DataArray
+            Variable number of initialization arguments, if any are required by
+            the model. Each argument can be a DataFrame (pandas or cudf
+            DataFrame) or xarray DataArray containing initial state data.
+
+        Yields
+        ------
+        tuple[pd.DataFrame | xr.DataArray, ...]
+            Generator yields multiple arguments of assimilated data. Each argument
+            can be a DataFrame (PyArrow Table or cudf DataFrame) or xarray DataArray.
+            Supports any number of arguments.
+
+        Receives
+        --------
+        tuple[pd.DataFrame | xr.DataArray | None, ...]
+            Observations sent via generator.send() as multiple arguments. Each
+            argument can be a DataFrame (PyArrow Table or cudf DataFrame) or xarray
+            DataArray. None is sent initially to start the generator and can also be
+            sent for iterations where no input data is available. Supports any number
+            of arguments.
+
+        Examples
+        --------
+        >>> generator = model.create_generator()
+        >>> generator.send(None)  # Prime the generator
+        >>> # Process observations over time
+        >>> for obs in observations:
+        ...     result = generator.send(obs)  # Send observations, receive assimilated data
+        ...     # result is a tuple of DataFrames or DataArrays
+        >>> generator.close()  # Clean up
+        """
+        pass
+
+    def init_coords(self) -> tuple[FrameSchema | CoordSystem, ...] | None:
+        """Initialization coordinate system required by the assimilation model.
+
+        Specifies the coordinate system(s) for initial state data that must be provided
+        before the model can process observations. The returned coordinate systems should
+        match the expected input format for the first argument(s) passed to ``__call__``
+        or sent to ``create_generator`` when initializing the model state.
+
+        Returns
+        -------
+        tuple[FrameSchema | CoordSystem, ...] | None
+            Tuple of coordinate systems or frame schemas defining the structure of
+            required initialization data. Returns ``None`` if the model does not require
+            initialization data (e.g., stateless models).
+        """
+        pass
+
+    def input_coords(self) -> tuple[FrameSchema | CoordSystem, ...]:
+        """Input coordinate system of assimilation model.
+
+        For DataFrame inputs, this should return a PyArrow schema (or a wrapper
+        containing schema and constraints). For tensor inputs, this should return
+        a CoordSystem.
+
+        Returns
+        -------
+        tuple[FrameSchema | CoordSystem, ...]
+            Tuple of coordinate systems or frame schemas, one for each input argument
+            that __call__ or create_generator accepts
+        """
+        pass
+
+    def output_coords(
+        self,
+        input_coords: tuple[FrameSchema | CoordSystem, ...],
+        *args: Any,
+        **kwargs: Any,
+    ) -> tuple[FrameSchema | CoordSystem, ...]:
+        """Output coordinate system of the assimilation model given an input coordinate
+        system.
+
+        Parameters
+        ----------
+        input_coords : tuple[FrameSchema | CoordSystem, ...]
+            Input coordinate system tuple. FrameSchema (OrderedDict mapping field names
+            to numpy arrays) for DataFrame inputs, or CoordSystem (OrderedDict mapping
+            dimension names to coordinate arrays) for tensor inputs
+        *args
+            Additional positional arguments
+        **kwargs
+            Additional keyword arguments, typically including request metadata such as
+            request_time and request_lead_time from DataFrame attrs
+
+        Returns
+        -------
+        tuple[FrameSchema | CoordSystem, ...]
+            Tuple of coordinate systems or frame schemas, one for each output argument
+            that __call__ or create_generator returns
+        """
+        pass
+
+    def to(self, device: Any) -> AssimilationModel:
+        """Moves assimilation model onto inference device, this is typically satisfied
+        via `torch.nn.Module`.
+
+        Parameters
+        ----------
+        device : Any
+            Object representing the inference device, typically `torch.device` or str
+
+        Returns
+        -------
+        AssimilationModel
+            Returns instance of prognostic
+        """
+        pass
diff --git a/earth2studio/models/da/interp.py b/earth2studio/models/da/interp.py
new file mode 100644
index 000000000..f51c2aacb
--- /dev/null
+++ b/earth2studio/models/da/interp.py
@@ -0,0 +1,589 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections.abc import Generator
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import torch
+import xarray as xr
+from loguru import logger
+
+from earth2studio.models.da.utils import (
+    dfseries_to_torch,
+    filter_time_range,
+    validate_observation_fields,
+)
+from earth2studio.utils.imports import (
+    OptionalDependencyFailure,
+    check_optional_dependencies,
+)
+from earth2studio.utils.time import normalize_time_tolerance
+from earth2studio.utils.type import CoordSystem, FrameSchema, TimeTolerance
+
+try:
+    import cupy as cp
+except ImportError:
+    OptionalDependencyFailure("gpu")
+    cp = None  # type: ignore[assignment]
+
+
+@check_optional_dependencies()
+class InterpEquirectangular(torch.nn.Module):
+    """Interpolation assimilation model that interpolates sparse observations to a
+    lat-lon grid.
+
+    This is a sample implementation of an AssimilationModel that:
+    - Accepts DataFrames with observations (time, lat, lon, observation, variable)
+    - Interpolates observations to a regular lat-lon grid using specified method
+    - Validates input observations against schema constraints
+    - Supports interpolation methods: 'nearest' or 'smolyak' (Smolyak sparse grid algorithm)
+
+    Parameters
+    ----------
+    lat : np.ndarray | None, optional
+        Latitude coordinates for output grid, by default None (uses default grid over
+        CONUS)
+    lon : np.ndarray | None, optional
+        Longitude coordinates for output grid, by default None (uses default
+        grid over CONUS)
+    interp_method : str, optional
+        Interpolation method to use: 'nearest' or 'smolyak', by default "smolyak"
+    time_tolerance : TimeTolerance, optional
+        Time tolerance for filtering observations. Observations within the tolerance
+        window around each requested time will be used for interpolation, by default
+        np.timedelta64(10, "m")
+
+    Raises
+    ------
+    ValueError
+        If interp_method is not one of the supported methods
+    """
+
+    # Acceptable variables for this model
+    VARIABLES = ["t2m", "u10m", "v10m", "sp"]
+
+    def __init__(
+        self,
+        lat: np.ndarray | None = None,
+        lon: np.ndarray | None = None,
+        interp_method: str = "smolyak",
+        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
+    ) -> None:
+        if interp_method not in ["nearest", "smolyak"]:
+            raise ValueError(
+                f"interp_method must be one of ['nearest', 'smolyak'], "
+                f"got {interp_method}"
+            )
+        super().__init__()
+        self._lat = (
+            lat if lat is not None else np.linspace(25.0, 50.0, 101, dtype=np.float32)
+        )
+        self._lon = (
+            lon if lon is not None else np.linspace(235.0, 295.0, 241, dtype=np.float32)
+        )
+        self.interp_method = interp_method
+        self._tolerance = normalize_time_tolerance(time_tolerance)
+        self.register_buffer("device_buffer", torch.empty(0), persistent=False)
+
+    def init_coords(self) -> None:
+        """Initialization coords (not required)"""
+        return None
+
+    def input_coords(self) -> tuple[FrameSchema]:
+        """Input coordinate system specifying required DataFrame fields.
+
+        Returns
+        -------
+         tuple[FrameSchema]
+            Tuple containing coordinate system dictionary with field names as keys:
+            - variable: array of acceptable variable values
+            - time, lat, lon, observation: empty arrays (dynamic dimensions)
+        """
+        return (
+            FrameSchema(
+                {
+                    "time": np.empty(0, dtype="datetime64[ns]"),
+                    "lat": np.empty(0, dtype=np.float32),
+                    "lon": np.empty(0, dtype=np.float32),
+                    "observation": np.empty(0, dtype=np.float32),
+                    "variable": np.array(self.VARIABLES, dtype=str),
+                }
+            ),
+        )
+
+    def output_coords(
+        self,
+        input_coords: tuple[CoordSystem],
+        request_time: np.ndarray,
+        **kwargs: Any,
+    ) -> tuple[CoordSystem]:
+        """Output coordinate system for assimilated data.
+
+        Parameters
+        ----------
+        input_coords : tuple[CoordSystem, ...]
+            Input coordinate system (CoordSystem for DataFrame input)
+
+        Returns
+        -------
+        tuple[CoordSystem]
+            Coordinate system dictionary with time, variable, lat, and lon dimensions
+
+        Raises
+        ------
+        ValueError
+            If input_coords are not valid
+        """
+        # Extract variables from first input coord system
+        if len(input_coords) > 0 and "variable" in input_coords[0]:
+            variables = input_coords[0]["variable"]
+        else:
+            variables = self.VARIABLES
+
+        return (
+            CoordSystem(
+                {
+                    "time": request_time,
+                    "variable": np.array(variables, dtype=str),
+                    "lat": self._lat,
+                    "lon": self._lon,
+                }
+            ),
+        )
+
+    def __call__(self, obs: pd.DataFrame) -> xr.DataArray:
+        """Stateless forward pass"""
+        input_coords = self.input_coords()
+        (output_coords,) = self.output_coords(input_coords, **obs.attrs)
+        # Validate observation types match input_coords
+        validate_observation_fields(obs, required_fields=list(input_coords[0].keys()))
+        return self._interpolate_dataframe(obs, output_coords)
+
+    def create_generator(self) -> Generator[
+        xr.DataArray,
+        pd.DataFrame,
+        None,
+    ]:
+        """Creates a generator which accepts collection of input observations and
+        outputs a collection of assimilated data.
+
+        Parameters
+        ----------
+        x : AssimilationInput
+            Input configuration for the assimilation model
+
+        Yields
+        ------
+        xr.DataArray
+            Assimilated data output the generator yields
+
+        Receives
+        --------
+        *DataFrame
+            Observations sent via generator.send()
+        """
+        # Validate input configuration
+
+        # Initialize generator - first yield is None to start
+        observations = yield None  # type: ignore[misc]
+        try:
+            while True:
+                da = self.__call__(observations)
+                observations = yield da
+        except GeneratorExit:
+            logger.debug("InterpEquirectangular clean up complete.")
+
+    def _interpolate_dataframe(
+        self, df: pd.DataFrame, output_coords: CoordSystem
+    ) -> xr.DataArray:
+        """Interpolate DataFrame observations to a regular lat-lon grid.
+
+        Parameters
+        ----------
+        df : pd.DataFrame
+            Input observations DataFrame with columns: time, lat, lon, observation, variable
+        output_coords : CoordSystem
+            Output coordinate system with time, variable, lat, lon dimensions
+
+        Returns
+        -------
+        xr.DataArray
+            Interpolated data array on the output grid
+        """
+        variables = output_coords["variable"]
+        lat_grid = output_coords["lat"]
+        lon_grid = output_coords["lon"]
+        time_coords = output_coords["time"]
+
+        # Initialize output array: (time, variable, lat, lon)
+        n_time = len(time_coords)
+        n_var = len(variables)
+        n_lat = len(lat_grid)
+        n_lon = len(lon_grid)
+
+        device = self.device_buffer.device
+        lat_grid_t = torch.tensor(lat_grid, dtype=torch.float32, device=device)
+        lon_grid_t = torch.tensor(lon_grid, dtype=torch.float32, device=device)
+
+        # Create meshgrid using torch
+        lon_mesh_t, lat_mesh_t = torch.meshgrid(lon_grid_t, lat_grid_t, indexing="ij")
+        # Stack and reshape to [n_lat * n_lon, 2] with (lat, lon) pairs
+        target_points_t = torch.stack(
+            [lat_mesh_t.T.ravel(), lon_mesh_t.T.ravel()], dim=1
+        )
+
+        # Initialize output tensor
+        interpolated_data = torch.full(
+            (n_time, n_var, n_lat, n_lon),
+            float("nan"),
+            dtype=torch.float32,
+            device=device,
+        )
+
+        # Process each time step separately
+        for t_idx, request_time in enumerate(time_coords):
+            # Filter observations within tolerance window for this time step
+            time_filtered_df = filter_time_range(
+                df, request_time, self._tolerance, time_column="time"
+            )
+
+            # Group observations by variable and interpolate each
+            for var_idx, variable in enumerate(variables):
+                var_df = time_filtered_df[
+                    time_filtered_df["variable"] == variable
+                ].copy()
+
+                if len(var_df) == 0:
+                    # No observations for this variable at this time, leave as NaN
+                    continue
+
+                # Extract observation points and values, convert to torch
+                source_lat = dfseries_to_torch(
+                    var_df["lat"], dtype=torch.float32, device=device
+                )
+                source_lon = dfseries_to_torch(
+                    var_df["lon"], dtype=torch.float32, device=device
+                )
+                source_points_t = torch.stack([source_lat, source_lon], dim=1)
+                values_t = dfseries_to_torch(
+                    var_df["observation"], dtype=torch.float32, device=device
+                )
+
+                # Interpolate to grid using PyTorch
+                grid_values = self._interpolate_scattered(
+                    source_points_t, values_t, target_points_t, n_lat, n_lon
+                )
+
+                # Assign to this time step
+                interpolated_data[t_idx, var_idx, :, :] = grid_values
+
+        # Create DataArray (convert tensor to numpy or cupy based on device)
+        if device.type == "cuda" and cp is not None:
+            data_array = cp.asarray(interpolated_data)  # Zero-copy
+        else:
+            data_array = interpolated_data.cpu().numpy()
+
+        da = xr.DataArray(
+            data=data_array,
+            dims=["time", "variable", "lat", "lon"],
+            coords={
+                "time": time_coords,
+                "variable": variables,
+                "lat": lat_grid,
+                "lon": lon_grid,
+            },
+        )
+
+        return da
+
+    def _interpolate_scattered(
+        self,
+        source_points: torch.Tensor,
+        values: torch.Tensor,
+        target_points: torch.Tensor,
+        n_lat: int,
+        n_lon: int,
+    ) -> torch.Tensor:
+        """Interpolate scattered points to target grid using PyTorch.
+
+        Accounts for periodic boundary condition in longitude.
+        Automatically converts longitude from -180,180 range to 0,360 range if needed.
+
+        Parameters
+        ----------
+        source_points : torch.Tensor
+            Source points [N, 2] with (lat, lon) coordinates. Longitude can be in
+            -180,180 or 0,360 range.
+        values : torch.Tensor
+            Values at source points [N]
+        target_points : torch.Tensor
+            Target grid points [M, 2] with (lat, lon) coordinates. Longitude can be in
+            -180,180 or 0,360 range.
+        n_lat : int
+            Number of latitude points in output grid
+        n_lon : int
+            Number of longitude points in output grid
+
+        Returns
+        -------
+        torch.Tensor
+            Interpolated values [n_lat, n_lon]
+        """
+        target_lat = target_points[:, 0:1]  # [M, 1]
+        target_lon = target_points[:, 1:2]  # [M, 1]
+        source_lat = source_points[:, 0:1].T  # [1, N]
+        source_lon = source_points[:, 1:2].T  # [1, N]
+
+        # Normalize longitude from -180,180 to 0,360 range
+        # Convert negative longitudes to positive: -180 -> 180, -90 -> 270, etc.
+        target_lon = torch.where(target_lon < 0, target_lon + 360.0, target_lon)
+        source_lon = torch.where(source_lon < 0, source_lon + 360.0, source_lon)
+
+        # Latitude distance (not periodic)
+        lat_diff = target_lat - source_lat  # [M, N]
+        lat_dist_sq = lat_diff**2
+
+        # Longitude distance with periodic boundary (0-360)
+        lon_diff = target_lon - source_lon  # [M, N]
+        # Compute periodic distance: min(|diff|, 360 - |diff|)
+        lon_diff_abs = torch.abs(lon_diff)
+        lon_dist = torch.minimum(lon_diff_abs, 360.0 - lon_diff_abs)
+        lon_dist_sq = lon_dist**2
+        distances = torch.sqrt(lat_dist_sq + lon_dist_sq)
+
+        if self.interp_method == "nearest":
+            # Nearest neighbor interpolation using distance
+            nearest_indices = torch.argmin(distances, dim=1)
+            interpolated = values[nearest_indices]
+        elif self.interp_method == "smolyak":
+            interpolated = self._smolyak_interpolate(
+                source_points, values, target_points
+            )
+        else:
+            raise ValueError(f"Unsupported interpolation type: {self.interp_method}")
+
+        # Reshape to grid
+        return interpolated.reshape(n_lat, n_lon)
+
+    def _chebyshev_polynomials(self, x: torch.Tensor, degree: int) -> torch.Tensor:
+        """Evaluate Chebyshev polynomials T_0(x) through T_degree(x) using recurrence.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input values [..., N]
+        degree : int
+            Maximum polynomial degree
+
+        Returns
+        -------
+        torch.Tensor
+            Chebyshev polynomial values [..., N, degree+1]
+        """
+        shape = x.shape
+        out = torch.zeros(*shape, degree + 1, dtype=x.dtype, device=x.device)
+
+        # T_0(x) = 1
+        out[..., 0] = 1.0
+
+        if degree >= 1:
+            # T_1(x) = x
+            out[..., 1] = x
+
+            # Recurrence: T_n(x) = 2*x*T_{n-1}(x) - T_{n-2}(x)
+            for n in range(2, degree + 1):
+                out[..., n] = 2.0 * x * out[..., n - 1] - out[..., n - 2]
+
+        return out
+
+    def _smolyak_interpolate(
+        self,
+        source_points: torch.Tensor,
+        values: torch.Tensor,
+        target_points: torch.Tensor,
+    ) -> torch.Tensor:
+        """Interpolate using Smolyak's algorithm with Chebyshev polynomial basis.
+
+        Vectorized implementation using Smolyak sparse grid structure for efficient
+        polynomial interpolation. Uses tensor products of Chebyshev polynomials.
+
+        Parameters
+        ----------
+        source_points : torch.Tensor
+            Source points [N, 2] with (lat, lon) coordinates
+        values : torch.Tensor
+            Values at source points [N]
+        target_points : torch.Tensor
+            Target points [M, 2] with (lat, lon) coordinates
+
+        Returns
+        -------
+        torch.Tensor
+            Interpolated values [M]
+        """
+        device = source_points.device
+        N = source_points.shape[0]
+        M = target_points.shape[0]
+        d = 2  # 2D: lat and lon
+
+        # Normalize coordinates to [-1, 1]^2 for Chebyshev polynomials
+        source_lat = source_points[:, 0]
+        source_lon = source_points[:, 1]
+        target_lat = target_points[:, 0]
+        target_lon = target_points[:, 1]
+
+        # Normalize longitude to 0-360 range first
+        source_lon = torch.where(source_lon < 0, source_lon + 360.0, source_lon)
+        target_lon = torch.where(target_lon < 0, target_lon + 360.0, target_lon)
+
+        # Get bounds with padding
+        lat_min, lat_max = source_lat.min().item(), source_lat.max().item()
+        lon_min, lon_max = source_lon.min().item(), source_lon.max().item()
+
+        lat_range = lat_max - lat_min
+        lon_range = lon_max - lon_min
+        lat_min -= lat_range * 0.1
+        lat_max += lat_range * 0.1
+        lon_min -= lon_range * 0.1
+        lon_max += lon_range * 0.1
+
+        # Clamp to valid ranges
+        lat_min = max(lat_min, -90.0)
+        lat_max = min(lat_max, 90.0)
+        lon_min = max(lon_min, 0.0)
+        lon_max = min(lon_max, 360.0)
+
+        # Normalize to [-1, 1]
+        source_lat_norm = 2.0 * (source_lat - lat_min) / (lat_max - lat_min) - 1.0
+        source_lon_norm = 2.0 * (source_lon - lon_min) / (lon_max - lon_min) - 1.0
+        target_lat_norm = 2.0 * (target_lat - lat_min) / (lat_max - lat_min) - 1.0
+        target_lon_norm = 2.0 * (target_lon - lon_min) / (lon_max - lon_min) - 1.0
+
+        # Determine Smolyak level mu adaptively based on number of points
+        # For 2D: mu=1->5, mu=2->13, mu=3->29, mu=4->65, mu=5->145, mu=6->321, mu=7->705 points
+        mu_thresholds = [(500, 7), (200, 6), (100, 5), (50, 4), (25, 3), (9, 2)]
+        mu = 1
+        max_degree = 1
+        for threshold, level in mu_thresholds:
+            if N >= threshold:
+                mu = level
+                max_degree = level
+                break
+
+        # Generate Smolyak indices: combinations where d < sum(i) <= d + mu
+        # Vectorized generation
+        i1_range = torch.arange(1, mu + 2, device=device)
+        i2_range = torch.arange(1, mu + 2, device=device)
+        i1_grid, i2_grid = torch.meshgrid(i1_range, i2_range, indexing="ij")
+        mask = (d < (i1_grid + i2_grid)) & ((i1_grid + i2_grid) <= (d + mu))
+        smol_i1 = i1_grid[mask]
+        smol_i2 = i2_grid[mask]
+
+        # Compute m_i(i) = 2^(i-1) + 1 for i > 1, else special cases
+        def m_i(i: torch.Tensor) -> torch.Tensor:
+            result = torch.where(
+                i == 1,
+                torch.tensor(1, device=device),
+                torch.where(i == 0, torch.tensor(0, device=device), (2 ** (i - 1) + 1)),
+            )
+            return result
+
+        # Get degrees for each dimension
+        deg1_all = torch.clamp(m_i(smol_i1) - 1, max=max_degree)
+        deg2_all = torch.clamp(m_i(smol_i2) - 1, max=max_degree)
+
+        # Build unique basis terms (deg_lat, deg_lon) pairs
+        basis_terms = set()
+        for idx in range(len(smol_i1)):
+            d1 = int(deg1_all[idx].item())
+            d2 = int(deg2_all[idx].item())
+            for d1_val in range(d1 + 1):
+                for d2_val in range(d2 + 1):
+                    if d1_val + d2_val <= max_degree:
+                        basis_terms.add((d1_val, d2_val))
+
+        # Fallback if no terms
+        if len(basis_terms) == 0:
+            for deg_lat in range(max_degree + 1):
+                for deg_lon in range(max_degree + 1):
+                    if deg_lat + deg_lon <= max_degree:
+                        basis_terms.add((deg_lat, deg_lon))
+
+        # Evaluate Chebyshev polynomials for source points
+        cheb_lat = self._chebyshev_polynomials(
+            source_lat_norm, max_degree
+        )  # [N, max_degree+1]
+        cheb_lon = self._chebyshev_polynomials(
+            source_lon_norm, max_degree
+        )  # [N, max_degree+1]
+
+        # Build basis matrix vectorized
+        basis_list = []
+        for deg_lat, deg_lon in sorted(basis_terms):
+            basis_list.append(cheb_lat[:, deg_lat] * cheb_lon[:, deg_lon])
+
+        if len(basis_list) == 0:
+            basis_matrix = torch.ones(N, 1, device=device)
+        else:
+            basis_matrix = torch.stack(basis_list, dim=1)  # [N, n_basis]
+
+        # Limit basis size to avoid overfitting
+        if basis_matrix.shape[1] > N:
+            basis_matrix = basis_matrix[:, :N]
+
+        # Solve for coefficients using regularized least squares (vectorized)
+        reg = 1e-6 * torch.eye(basis_matrix.shape[1], device=device)
+        ATA = basis_matrix.T @ basis_matrix + reg
+        ATb = basis_matrix.T @ values.unsqueeze(1)
+        try:
+            coeffs = torch.linalg.solve(ATA, ATb).squeeze(1)  # [n_basis]
+        except Exception:
+            # Fallback to SVD
+            U, S, Vt = torch.linalg.svd(basis_matrix, full_matrices=False)
+            threshold_svd = 1e-6 * S[0] if S[0] > 0 else 1e-6
+            S_inv = torch.where(S > threshold_svd, 1.0 / S, torch.zeros_like(S))
+            coeffs = (Vt.T @ (S_inv[:, None] * (U.T @ values.unsqueeze(1)))).squeeze(1)
+
+        # Evaluate polynomial at target points (vectorized)
+        cheb_lat_target = self._chebyshev_polynomials(
+            target_lat_norm, max_degree
+        )  # [M, max_degree+1]
+        cheb_lon_target = self._chebyshev_polynomials(
+            target_lon_norm, max_degree
+        )  # [M, max_degree+1]
+
+        # Build target basis matrix with same structure
+        target_basis_list = []
+        for deg_lat, deg_lon in sorted(basis_terms):
+            target_basis_list.append(
+                cheb_lat_target[:, deg_lat] * cheb_lon_target[:, deg_lon]
+            )
+
+        if len(target_basis_list) == 0:
+            target_basis = torch.ones(M, 1, device=device)
+        else:
+            target_basis = torch.stack(target_basis_list, dim=1)  # [M, n_basis]
+            # Match coefficient size
+            if target_basis.shape[1] > len(coeffs):
+                target_basis = target_basis[:, : len(coeffs)]
+            elif target_basis.shape[1] < len(coeffs):
+                coeffs = coeffs[: target_basis.shape[1]]
+
+        # Evaluate: target_basis @ coeffs (fully vectorized)
+        interpolated = target_basis @ coeffs  # [M]
+
+        return interpolated
diff --git a/earth2studio/models/da/sda_stormcast.py b/earth2studio/models/da/sda_stormcast.py
new file mode 100644
index 000000000..59980d2b0
--- /dev/null
+++ b/earth2studio/models/da/sda_stormcast.py
@@ -0,0 +1,914 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import warnings
+from collections import OrderedDict
+from collections.abc import Generator
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import torch
+import xarray as xr
+import zarr
+from loguru import logger
+
+from earth2studio.data import GFS_FX, HRRR, DataSource, ForecastSource, fetch_data
+from earth2studio.models.auto import AutoModelMixin, Package
+from earth2studio.models.da.base import AssimilationModel
+from earth2studio.models.da.utils import filter_time_range
+from earth2studio.utils import (
+    handshake_coords,
+    handshake_dim,
+    handshake_size,
+)
+from earth2studio.utils.imports import (
+    OptionalDependencyFailure,
+    check_optional_dependencies,
+)
+from earth2studio.utils.time import normalize_time_tolerance
+from earth2studio.utils.type import CoordSystem, FrameSchema, TimeTolerance
+
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+try:
+    from scipy.spatial import cKDTree
+except ImportError:
+    OptionalDependencyFailure("stormcast")
+    cKDTree = None
+
+try:
+    from omegaconf import OmegaConf
+    from physicsnemo.diffusion.guidance import (
+        DataConsistencyDPSGuidance,
+        DPSScorePredictor,
+    )
+    from physicsnemo.diffusion.noise_schedulers import EDMNoiseScheduler
+    from physicsnemo.diffusion.preconditioners import EDMPreconditioner
+    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecond
+    from physicsnemo.diffusion.samplers import sample
+    from physicsnemo.models.diffusion_unets import StormCastUNet
+except ImportError:
+    OptionalDependencyFailure("stormcast")
+    StormCastUNet = None
+    EDMPreconditioner = None
+    OmegaConf = None
+
+
+# Variables used in StormCastV1 paper
+VARIABLES = (
+    ["u10m", "v10m", "t2m", "msl"]
+    + [
+        var + str(level)
+        for var, level in product(
+            ["u", "v", "t", "q", "Z", "p"],
+            map(
+                lambda x: str(x) + "hl",
+                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 20, 25, 30],
+            ),
+        )
+        if not ((var == "p") and (int(level.replace("hl", "")) > 20))
+    ]
+    + [
+        "refc",
+    ]
+)
+
+CONDITIONING_VARIABLES = ["u10m", "v10m", "t2m", "tcwv", "sp", "msl"] + [
+    var + str(level)
+    for var, level in product(["u", "v", "z", "t", "q"], [1000, 850, 500, 250])
+]
+
+INVARIANTS = ["lsm", "orography"]
+
+
+@check_optional_dependencies()
+class StormCastSDA(torch.nn.Module, AutoModelMixin):
+    """StormCast with score-based data assimilation (SDA) using diffusion posterior
+    sampling for convection-allowing regional forecasts. Combines a regression and
+    diffusion model with DPS guidance to assimilate observations during inference.
+    Model time step size is 1 hour, taking as input:
+
+    - High-resolution (3km) HRRR state over the central United States (99 vars)
+    - High-resolution land-sea mask and orography invariants
+    - Coarse resolution (25km) global state (26 vars)
+    - Point observations for data assimilation
+
+    The high-resolution grid is the HRRR Lambert conformal projection.
+    Coarse-resolution inputs are regridded to the HRRR grid internally.
+
+    Note
+    ----
+    For more information see the following references:
+
+    - https://arxiv.org/abs/2408.10958
+    - https://huggingface.co/nvidia/stormcast-v1-era5-hrrr
+    - https://arxiv.org/abs/2306.10574
+
+    Parameters
+    ----------
+    regression_model : torch.nn.Module
+        Deterministic model used to make an initial prediction
+    diffusion_model : torch.nn.Module
+        Generative model correcting the deterministic prediciton
+    means : torch.Tensor
+        Mean value of each input high-resolution variable
+    stds : torch.Tensor
+        Standard deviation of each input high-resolution variable
+    invariants : torch.Tensor
+        Static invariant  quantities
+    hrrr_lat_lim : tuple[int, int], optional
+        HRRR grid latitude limits, defaults to be the StormCastV1 region in central
+        United States, by default (273, 785)
+    hrrr_lon_lim : tuple[int, int], optional
+        HRRR grid longitude limits, defaults to be the StormCastV1 region in central
+        United States,, by default (579, 1219)
+    variables : np.array, optional
+        High-resolution variables, by default np.array(VARIABLES)
+    conditioning_means : torch.Tensor | None, optional
+        Means to normalize conditioning data, by default None
+    conditioning_stds : torch.Tensor | None, optional
+        Standard deviations to normalize conditioning data, by default None
+    conditioning_variables : np.array, optional
+        Global variables for conditioning, by default np.array(CONDITIONING_VARIABLES)
+    conditioning_data_source : DataSource | ForecastSource | None, optional
+        Data Source to use for global conditioning. Required for running in iterator mode, by default None
+    time_tolerance : TimeTolerance, optional
+        Time tolerance for filtering observations. Observations within the tolerance
+        window around each requested time will be used for data assimilation,
+        by default np.timedelta64(10, "m")
+    sampler_steps : int, optional
+        Number of diffusion sampler steps, by default 36
+    sampler_args : dict[str, float  |  int] | None, optional
+        Arguments to pass to the diffusion sampler, by default None
+    sda_std_obs : float, optional
+        Observation noise standard deviation for DPS guidance, by default 0.1
+    sda_gamma : float, optional
+        SDA scaling factor for DPS guidance, by default 0.001
+    """
+
+    def __init__(
+        self,
+        regression_model: torch.nn.Module,
+        diffusion_model: torch.nn.Module,
+        means: torch.Tensor,
+        stds: torch.Tensor,
+        invariants: torch.Tensor,
+        hrrr_lat_lim: tuple[int, int] = (273, 785),
+        hrrr_lon_lim: tuple[int, int] = (579, 1219),
+        variables: np.array = np.array(VARIABLES),
+        conditioning_means: torch.Tensor | None = None,
+        conditioning_stds: torch.Tensor | None = None,
+        conditioning_variables: np.array = np.array(CONDITIONING_VARIABLES),
+        conditioning_data_source: DataSource | ForecastSource | None = None,
+        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
+        sampler_steps: int = 36,
+        sampler_args: dict[str, float | int] | None = None,
+        sda_std_obs: float = 0.1,
+        sda_gamma: float = 0.001,
+    ):
+        super().__init__()
+        self.regression_model = regression_model
+        self.diffusion_model = diffusion_model
+        self.register_buffer("means", means)
+        self.register_buffer("stds", stds)
+        self.register_buffer("invariants", invariants)
+        self.register_buffer("device_buffer", torch.empty(0))
+        self.sampler_steps = sampler_steps
+        self.sampler_args = {
+            "sigma_min": 0.002,
+            "sigma_max": 800,
+            "rho": 7,
+            "S_churn": 0.0,
+            "S_min": 0.0,
+            "S_max": float("inf"),
+            "S_noise": 1,
+        }
+        if sampler_args is not None:
+            self.sampler_args.update(sampler_args)
+        self._tolerance = normalize_time_tolerance(time_tolerance)
+        self.sda_std_obs = sda_std_obs
+        self.sda_dps_norm = 2
+        self.sda_gamma = sda_gamma
+
+        hrrr_lat, hrrr_lon = HRRR.grid()
+        self.lat = hrrr_lat[
+            hrrr_lat_lim[0] : hrrr_lat_lim[1], hrrr_lon_lim[0] : hrrr_lon_lim[1]
+        ]
+        self.lon = hrrr_lon[
+            hrrr_lat_lim[0] : hrrr_lat_lim[1], hrrr_lon_lim[0] : hrrr_lon_lim[1]
+        ]
+
+        self.hrrr_x = HRRR.HRRR_X[hrrr_lon_lim[0] : hrrr_lon_lim[1]]
+        self.hrrr_y = HRRR.HRRR_Y[hrrr_lat_lim[0] : hrrr_lat_lim[1]]
+
+        # Build ordered boundary polygon from 2D grid perimeter for
+        # point-in-grid testing (top row -> right col -> bottom row -> left col)
+        self._grid_boundary = np.column_stack(
+            [
+                np.concatenate(
+                    [
+                        self.lat[0, :],
+                        self.lat[1:, -1],
+                        self.lat[-1, -2::-1],
+                        self.lat[-2:0:-1, 0],
+                    ]
+                ),
+                np.concatenate(
+                    [
+                        self.lon[0, :],
+                        self.lon[1:, -1],
+                        self.lon[-1, -2::-1],
+                        self.lon[-2:0:-1, 0],
+                    ]
+                ),
+            ]
+        )  # [n_boundary, 2] ordered (lat, lon)
+
+        # Build a KD-tree over (lat, lon) for efficient nearest-grid-point queries
+        # TODO: Make cpu and gpu support
+        self._grid_tree = cKDTree(np.column_stack([self.lat.ravel(), self.lon.ravel()]))
+
+        self.variables = variables
+
+        self.conditioning_variables = conditioning_variables
+        self.conditioning_data_source = conditioning_data_source
+        if conditioning_data_source is None:
+            warnings.warn(
+                "No conditioning data source was provided to StormCast, "
+                + "set the conditioning_data_source attribute of the model "
+                + "before running inference."
+            )
+
+        if conditioning_means is not None:
+            self.register_buffer("conditioning_means", conditioning_means)
+
+        if conditioning_stds is not None:
+            self.register_buffer("conditioning_stds", conditioning_stds)
+
+    @property
+    def device(self) -> torch.device:
+        return self.device_buffer.device
+
+    def init_coords(self) -> tuple[CoordSystem]:
+        """Initialization coordinate system"""
+        return (
+            OrderedDict(
+                {
+                    "time": np.empty(0),
+                    "lead_time": np.array([np.timedelta64(0, "h")]),
+                    "variable": np.array(self.variables),
+                    "hrrr_y": self.hrrr_y,
+                    "hrrr_x": self.hrrr_x,
+                }
+            ),
+        )
+
+    def input_coords(self) -> tuple[FrameSchema]:
+        """Input coordinate system specifying required DataFrame fields."""
+        return (
+            FrameSchema(
+                {
+                    "time": np.empty(0, dtype="datetime64[ns]"),
+                    "lat": np.empty(0, dtype=np.float32),
+                    "lon": np.empty(0, dtype=np.float32),
+                    "observation": np.empty(0, dtype=np.float32),
+                    "variable": np.array(self.variables, dtype=str),
+                }
+            ),
+        )
+
+    def output_coords(self, input_coords: tuple[CoordSystem]) -> tuple[CoordSystem]:
+        """Output coordinate system of the assimilation model
+
+        Parameters
+        ----------
+        input_coords : tuple[CoordSystem]
+            Coordinates of tensor used to initialize the forecast model.
+
+        Returns
+        -------
+        CoordSystem
+            Coordinate system dictionary
+        """
+
+        output_coords = OrderedDict(
+            {
+                "time": np.empty(0),
+                "lead_time": np.array([np.timedelta64(1, "h")]),
+                "variable": np.array(self.variables),
+                "hrrr_y": self.hrrr_y,
+                "hrrr_x": self.hrrr_x,
+            }
+        )
+
+        target_input_coords = self.init_coords()[0]
+
+        handshake_dim(input_coords[0], "hrrr_x", 4)
+        handshake_dim(input_coords[0], "hrrr_y", 3)
+        handshake_dim(input_coords[0], "variable", 2)
+        # Index coords are arbitrary as long its on the HRRR grid, so just check size
+        handshake_size(input_coords[0], "hrrr_y", self.lat.shape[0])
+        handshake_size(input_coords[0], "hrrr_x", self.lat.shape[1])
+        handshake_coords(input_coords[0], target_input_coords, "variable")
+
+        output_coords["time"] = input_coords[0]["time"]
+        output_coords["lead_time"] = (
+            output_coords["lead_time"] + input_coords[0]["lead_time"]
+        )
+        return (output_coords,)
+
+    @classmethod
+    def load_default_package(cls) -> Package:
+        """Load assimilation package"""
+        package = Package(
+            "hf://nvidia/stormcast-v1-era5-hrrr@6c89a0877a0d6b231033d3b0d8b9828a6f833ed8",
+            cache_options={
+                "cache_storage": Package.default_cache("stormcast"),
+                "same_names": True,
+            },
+        )
+        return package
+
+    @classmethod
+    @check_optional_dependencies()
+    def load_model(
+        cls,
+        package: Package,
+        conditioning_data_source: DataSource | ForecastSource = GFS_FX(verbose=False),
+        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
+        sampler_steps: int = 36,
+        sda_std_obs: float = 0.1,
+        sda_gamma: float = 0.001,
+    ) -> AssimilationModel:
+        """Load assimilation from package
+
+        Parameters
+        ----------
+        package : Package
+            Package to load model from
+        conditioning_data_source : DataSource | ForecastSource, optional
+            Data source to use for global conditioning, by default GFS_FX
+        time_tolerance : TimeTolerance, optional
+            Time tolerance for filtering observations. Observations within the tolerance
+            window around each requested time will be used for data assimilation,
+            by default np.timedelta64(10, "m")
+        sampler_steps : int, optional
+            Number of diffusion sampler steps, by default 36
+        sda_std_obs : float, optional
+            Observation noise standard deviation for DPS guidance, by default 0.1
+        sda_gamma : float, optional
+            SDA scaling factor for DPS guidance, by default 0.001
+
+        Returns
+        -------
+        AssimilationModel
+            Assimilation model
+        """
+        try:
+            package.resolve("config.json")  # HF tracking download statistics
+        except FileNotFoundError:
+            pass
+
+        try:
+            OmegaConf.register_new_resolver("eval", eval)
+        except ValueError:
+            # Likely already registered so skip
+            pass
+
+        # load model registry:
+        config = OmegaConf.load(package.resolve("model.yaml"))
+
+        # TODO: remove strict=False once checkpoints/imports updated to new diffusion API
+        regression = StormCastUNet.from_checkpoint(
+            package.resolve("StormCastUNet.0.0.mdlus"),
+            strict=False,
+        )
+        diffusion = EDMPrecond.from_checkpoint(
+            package.resolve("EDMPrecond.0.0.mdlus"),
+            strict=False,
+        )
+
+        # Load metadata: means, stds, grid
+        store = zarr.storage.ZipStore(package.resolve("metadata.zarr.zip"), mode="r")
+        metadata = xr.open_zarr(store, zarr_format=2)
+
+        variables = metadata["variable"].values
+        conditioning_variables = metadata["conditioning_variable"].values
+
+        # Expand dims and tensorify normalization buffers
+        means = torch.from_numpy(metadata["means"].values[None, :, None, None])
+        stds = torch.from_numpy(metadata["stds"].values[None, :, None, None])
+        conditioning_means = torch.from_numpy(
+            metadata["conditioning_means"].values[None, :, None, None]
+        )
+        conditioning_stds = torch.from_numpy(
+            metadata["conditioning_stds"].values[None, :, None, None]
+        )
+
+        # Load invariants
+        invariants = metadata["invariants"].sel(invariant=config.data.invariants).values
+        invariants = torch.from_numpy(invariants).repeat(1, 1, 1, 1)
+
+        # EDM sampler arguments
+        if config.sampler_args is not None:
+            sampler_args = config.sampler_args
+        else:
+            sampler_args = {}
+
+        return cls(
+            regression,
+            diffusion,
+            means,
+            stds,
+            invariants,
+            variables=variables,
+            conditioning_means=conditioning_means,
+            conditioning_stds=conditioning_stds,
+            conditioning_data_source=conditioning_data_source,
+            conditioning_variables=conditioning_variables,
+            time_tolerance=time_tolerance,
+            sampler_steps=sampler_steps,
+            sampler_args=sampler_args,
+            sda_std_obs=sda_std_obs,
+            sda_gamma=sda_gamma,
+        )
+
+    @torch.no_grad()
+    def _forward(
+        self,
+        x: torch.Tensor,
+        conditioning: torch.Tensor,
+        y_obs: torch.Tensor,
+        mask: torch.Tensor,
+    ) -> torch.Tensor:
+
+        # Scale data
+        if "conditioning_means" in self._buffers:
+            conditioning = conditioning - self.conditioning_means
+        if "conditioning_stds" in self._buffers:
+            conditioning = conditioning / self.conditioning_stds
+
+        x = (x - self.means) / self.stds
+        y_obs = (y_obs - self.means) / self.stds
+
+        # Run regression model
+        invariant_tensor = self.invariants.repeat(x.shape[0], 1, 1, 1)
+        concats = torch.cat((x, conditioning, invariant_tensor), dim=1)
+
+        out = self.regression_model(concats)
+        y_obs = y_obs - out  # Convert to residual obs
+
+        # Concat for diffusion conditioning
+        condition = torch.cat((x, out, invariant_tensor), dim=1)
+        latents = torch.randn_like(x, dtype=torch.float64)
+        latents = self.sampler_args["sigma_max"] * latents  # Initial guess
+
+        def _conditional_diffusion(x: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
+            return self.diffusion_model(x, t, condition=condition)
+
+        scheduler = EDMNoiseScheduler(
+            sigma_min=self.sampler_args["sigma_min"],
+            sigma_max=self.sampler_args["sigma_max"],
+            rho=self.sampler_args["rho"],
+        )
+
+        guidance = DataConsistencyDPSGuidance(
+            mask=mask,
+            y=y_obs,
+            std_y=self.sda_std_obs,
+            norm=self.sda_dps_norm,
+            gamma=self.sda_gamma,
+            sigma_fn=scheduler.sigma,
+            alpha_fn=scheduler.alpha,
+        )
+        score_predictor = DPSScorePredictor(
+            x0_predictor=_conditional_diffusion,
+            x0_to_score_fn=scheduler.x0_to_score,
+            guidances=guidance,
+        )
+        denoiser = scheduler.get_denoiser(score_predictor=score_predictor)
+
+        edm_out = sample(
+            denoiser,
+            latents,
+            noise_scheduler=scheduler,
+            num_steps=self.sampler_steps,
+            solver="edm_stochastic_heun",
+            solver_options={
+                "S_churn": self.sampler_args["S_churn"],
+                "S_min": self.sampler_args["S_min"],
+                "S_max": self.sampler_args["S_max"],
+                "S_noise": self.sampler_args["S_noise"],
+            },
+        )
+
+        out += edm_out
+        out = out * self.stds + self.means
+        return out
+
+    @staticmethod
+    def _points_in_polygon(points: np.ndarray, polygon: np.ndarray) -> np.ndarray:
+        """Vectorized ray casting point-in-polygon test.
+        TODO: Improved this (GPU and reduce memory requirement)
+        make a general purpose util maybe...
+
+        Note
+        ----
+        For more information see the following references:
+        https://observablehq.com/@tmcw/understanding-point-in-polygon
+
+        Parameters
+        ----------
+        points : np.ndarray
+            Points to test, shape [n, 2]
+        polygon : np.ndarray
+            Ordered polygon vertices, shape [m, 2]
+
+        Returns
+        -------
+        np.ndarray
+            Boolean array of shape [n], True if point is inside polygon
+        """
+        px, py = points[:, 0], points[:, 1]  # [n]
+        vx, vy = polygon[:, 0], polygon[:, 1]  # [m]
+        vx_next = np.roll(vx, -1)
+        vy_next = np.roll(vy, -1)
+
+        # For each edge (m) and each point (n), check if horizontal ray crosses
+        # Broadcasting: [m, 1] vs [1, n] -> [m, n]
+        crosses = (vy[:, None] > py[None, :]) != (vy_next[:, None] > py[None, :])
+        dvy = vy_next[:, None] - vy[:, None]
+        safe_dvy = np.where(dvy == 0, 1.0, dvy)  # avoid division by zero; masked later
+        x_intersect = (vx_next[:, None] - vx[:, None]) * (
+            py[None, :] - vy[:, None]
+        ) / safe_dvy + vx[:, None]
+        hits = crosses & (px[None, :] < x_intersect)
+
+        # Odd number of crossings = inside
+        return (np.sum(hits, axis=0) % 2) == 1
+
+    def _build_obs_tensors(
+        self,
+        obs: pd.DataFrame | None,
+        request_time: np.datetime64,
+        device: torch.device,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        n_var = len(self.variables)
+        n_hrrr_y, n_hrrr_x = self.lat.shape
+
+        y_obs = torch.zeros(
+            1, n_var, n_hrrr_y, n_hrrr_x, device=device, dtype=torch.float32
+        )
+        mask = torch.zeros(
+            1, n_var, n_hrrr_y, n_hrrr_x, device=device, dtype=torch.float32
+        )
+
+        if obs is None or len(obs) == 0:
+            return y_obs, mask
+
+        # Filter observations within tolerance window
+        time_filtered = filter_time_range(
+            obs, request_time, self._tolerance, time_column="time"
+        )
+
+        if len(time_filtered) == 0:
+            return y_obs, mask
+
+        # TODO, make native cudf support
+        # Convert to pandas if cudf for reliable string/value access
+        if hasattr(time_filtered, "to_pandas"):
+            time_filtered = time_filtered.to_pandas()
+
+        obs_lat = time_filtered["lat"].values.astype(np.float64)
+        obs_lon = time_filtered["lon"].values.astype(np.float64)
+        obs_var = time_filtered["variable"].values
+        obs_val = time_filtered["observation"].values.astype(np.float32)
+
+        # Normalize lon to 0-360 to match HRRR grid
+        obs_lon = np.where(obs_lon < 0, obs_lon + 360.0, obs_lon)
+
+        # Filter observations to those inside the curvilinear grid boundary
+        # using ray casting point-in-polygon on the precomputed perimeter
+        obs_points = np.column_stack([obs_lat, obs_lon])
+        in_grid = self._points_in_polygon(obs_points, self._grid_boundary)
+
+        if not in_grid.any():
+            return y_obs, mask
+
+        obs_lat = obs_lat[in_grid]
+        obs_lon = obs_lon[in_grid]
+        obs_var = obs_var[in_grid]
+        obs_val = obs_val[in_grid]
+
+        # Find nearest HRRR grid point for each observation using a KD-tree
+        # to avoid allocating the full [n_obs, n_grid] distance matrix.
+        _, nearest_flat = self._grid_tree.query(np.column_stack([obs_lat, obs_lon]))
+        nearest_y = nearest_flat // n_hrrr_x
+        nearest_x = nearest_flat % n_hrrr_x
+
+        # Map variable names to indices
+        var_to_idx = {str(v): i for i, v in enumerate(self.variables)}
+        var_indices = np.array([var_to_idx.get(str(v), -1) for v in obs_var])
+        valid = var_indices >= 0
+
+        # Average multiple observations that map to the same grid cell
+        if valid.any():
+            vi = torch.tensor(var_indices[valid], device=device, dtype=torch.long)
+            yi = torch.tensor(nearest_y[valid], device=device, dtype=torch.long)
+            xi = torch.tensor(nearest_x[valid], device=device, dtype=torch.long)
+            vals = torch.tensor(obs_val[valid], device=device, dtype=torch.float32)
+
+            # Flatten (vi, yi, xi) into a single linear index for scatter ops
+            flat_idx = vi * (n_hrrr_y * n_hrrr_x) + yi * n_hrrr_x + xi
+            flat_sum = torch.zeros(
+                n_var * n_hrrr_y * n_hrrr_x, device=device, dtype=torch.float32
+            )
+            flat_cnt = torch.zeros_like(flat_sum)
+            flat_sum.scatter_add_(0, flat_idx, vals)
+            flat_cnt.scatter_add_(0, flat_idx, torch.ones_like(vals))
+
+            occupied = flat_cnt > 0
+            flat_avg = torch.where(occupied, flat_sum / flat_cnt, flat_sum)
+            y_obs[0] = flat_avg.view(n_var, n_hrrr_y, n_hrrr_x)
+            mask[0] = occupied.float().view(n_var, n_hrrr_y, n_hrrr_x)
+
+        return y_obs, mask
+
+    def _fetch_and_interp_conditioning(self, x: xr.DataArray) -> xr.DataArray:
+        """Fetch global conditioning data and interpolate to HRRR curvilinear grid.
+
+        Parameters
+        ----------
+        x : xr.DataArray
+            Input state DataArray with time and lead_time coordinates
+
+        Returns
+        -------
+        xr.DataArray
+            Conditioning data interpolated onto the HRRR grid
+        """
+        device = self.device
+
+        if self.conditioning_data_source is None:
+            raise RuntimeError(
+                "StormCast has been called without initializing the model's conditioning_data_source"
+            )
+
+        c: xr.DataArray = fetch_data(
+            self.conditioning_data_source,
+            time=x.coords["time"].data,
+            variable=self.conditioning_variables,
+            lead_time=x.coords["lead_time"].data,
+            device=self.device,
+            legacy=False,
+        )
+
+        # Interpolate conditioning from regular lat/lon grid to HRRR curvilinear grid
+        if cp is not None and isinstance(c.data, cp.ndarray):
+            # GPU path: bilinear interpolation using cupy, data stays on GPU
+            with cp.cuda.Device(device.index or 0):
+                data = c.data
+                src_lat = cp.asarray(c.coords["lat"].values, dtype=cp.float64)
+                src_lon = cp.asarray(c.coords["lon"].values, dtype=cp.float64)
+
+                target_lat_cp = cp.asarray(self.lat, dtype=cp.float64)
+                target_lon_cp = cp.asarray(self.lon, dtype=cp.float64)
+
+                # Ensure ascending order for searchsorted (latitude is
+                # commonly descending in weather data, e.g. 90 -> -90)
+                if src_lat[-1] < src_lat[0]:
+                    src_lat = src_lat[::-1]
+                    data = data[..., ::-1, :]
+                if src_lon[-1] < src_lon[0]:
+                    src_lon = src_lon[::-1]
+                    data = data[..., :, ::-1]
+
+                # Compute fractional indices via searchsorted (handles
+                # non-uniform spacing), src_lat and src_lon needs to be acending
+                lat_idx = cp.searchsorted(src_lat, target_lat_cp.ravel()) - 1
+                lat_idx = cp.clip(lat_idx, 0, len(src_lat) - 2)
+                lat_idx = lat_idx.reshape(target_lat_cp.shape)
+
+                lon_idx = cp.searchsorted(src_lon, target_lon_cp.ravel()) - 1
+                lon_idx = cp.clip(lon_idx, 0, len(src_lon) - 2)
+                lon_idx = lon_idx.reshape(target_lon_cp.shape)
+
+                lat0 = lat_idx
+                lon0 = lon_idx
+                lat1 = lat0 + 1
+                lon1 = lon0 + 1
+
+                # Fractional weights between grid cells
+                wlat = (target_lat_cp - src_lat[lat0]) / (src_lat[lat1] - src_lat[lat0])
+                wlon = (target_lon_cp - src_lon[lon0]) / (src_lon[lon1] - src_lon[lon0])
+                wlat = cp.clip(wlat, 0.0, 1.0)
+                wlon = cp.clip(wlon, 0.0, 1.0)
+
+                interp_data = (
+                    data[..., lat0, lon0] * (1 - wlat) * (1 - wlon)
+                    + data[..., lat0, lon1] * (1 - wlat) * wlon
+                    + data[..., lat1, lon0] * wlat * (1 - wlon)
+                    + data[..., lat1, lon1] * wlat * wlon
+                )
+
+            c = xr.DataArray(
+                data=interp_data,
+                dims=["time", "lead_time", "variable", "hrrr_y", "hrrr_x"],
+                coords={
+                    "time": c.coords["time"],
+                    "lead_time": c.coords["lead_time"],
+                    "variable": c.coords["variable"],
+                    "hrrr_y": self.hrrr_y,
+                    "hrrr_x": self.hrrr_x,
+                    "lat": (["hrrr_y", "hrrr_x"], self.lat),
+                    "lon": (["hrrr_y", "hrrr_x"], self.lon),
+                },
+            )
+        else:
+            # CPU path: use xarray's built-in interpolation
+            target_lat = xr.DataArray(self.lat, dims=["hrrr_y", "hrrr_x"])
+            target_lon = xr.DataArray(self.lon, dims=["hrrr_y", "hrrr_x"])
+            c = c.interp(lat=target_lat, lon=target_lon, method="linear")
+            c = c.assign_coords(
+                hrrr_y=("hrrr_y", self.hrrr_y),
+                hrrr_x=("hrrr_x", self.hrrr_x),
+                lat=(["hrrr_y", "hrrr_x"], self.lat),
+                lon=(["hrrr_y", "hrrr_x"], self.lon),
+            )
+
+        return c
+
+    def _to_output_dataarray(
+        self,
+        x_tensor: torch.Tensor,
+        output_coords: tuple[CoordSystem],
+    ) -> xr.DataArray:
+        """Convert output tensor to xr.DataArray with HRRR grid coordinates.
+
+        Parameters
+        ----------
+        x_tensor : torch.Tensor
+            Output tensor from _forward
+        output_coords : tuple[CoordSystem]
+            Output coordinate system from output_coords()
+
+        Returns
+        -------
+        xr.DataArray
+            Output DataArray with cupy backend on GPU or numpy on CPU
+        """
+        device = self.device
+        (oc,) = output_coords
+        if device.type == "cuda" and cp is not None:
+            with cp.cuda.Device(device.index or 0):
+                out_data = cp.asarray(x_tensor.detach())
+        else:
+            out_data = x_tensor.detach().cpu().numpy()
+
+        return xr.DataArray(
+            data=out_data,
+            dims=list(oc.keys()),
+            coords={
+                k: ((["hrrr_y", "hrrr_x"], v) if k in ("lat", "lon") else v)
+                for k, v in oc.items()
+            }
+            | {
+                "lat": (["hrrr_y", "hrrr_x"], self.lat),
+                "lon": (["hrrr_y", "hrrr_x"], self.lon),
+            },
+        )
+
+    # NOTE: @torch.inference_mode() is intentionally omitted here.
+    # DPS guidance requires gradient computation through the denoiser for
+    # the score correction step; inference_mode would disable those gradients.
+    def __call__(
+        self,
+        x: xr.DataArray,
+        obs: pd.DataFrame | None,
+    ) -> xr.DataArray:
+        """Runs assimilation model 1 step.
+
+        Parameters
+        ----------
+        x : xr.DataArray
+            Input state on the HRRR curvilinear grid
+        obs : pd.DataFrame | None
+            Sparse observations DataFrame, or None for no assimilation
+
+        Returns
+        -------
+        xr.DataArray
+            Output state one time-step into the future
+
+        Raises
+        ------
+        RuntimeError
+            If conditioning data source is not initialized
+        """
+        if self.conditioning_data_source is None:
+            raise RuntimeError(
+                "StormCast has been called without initializing the model's conditioning_data_source"
+            )
+
+        device = self.device
+        c = self._fetch_and_interp_conditioning(x)
+
+        x_coords = OrderedDict({dim: x.coords[dim].values for dim in x.dims})
+        output_coords = self.output_coords((x_coords,))
+
+        x_tensor = torch.as_tensor(x.data)
+        c_tensor = torch.as_tensor(c.data)
+
+        for j, t in enumerate(x.coords["time"].data):
+            obs_time = t + output_coords[0]["lead_time"][0]
+            y_obs, mask = self._build_obs_tensors(obs, obs_time, device)
+            x_tensor[j, :] = self._forward(x_tensor[j, :], c_tensor[j, :], y_obs, mask)
+
+        return self._to_output_dataarray(x_tensor, output_coords)
+
+    def create_generator(
+        self, x: xr.DataArray
+    ) -> Generator[xr.DataArray, pd.DataFrame | None, None]:
+        """Creates a generator for iterative forecast with data assimilation.
+
+        The generator yields forecast states and receives observation DataFrames
+        via ``send()``. At each step, conditioning data is fetched, observations
+        are mapped to the HRRR grid, and the diffusion model produces the next
+        forecast step.
+
+        Parameters
+        ----------
+        x : xr.DataArray
+            Initial state on the HRRR curvilinear grid
+
+        Yields
+        ------
+        xr.DataArray
+            Forecast state at each time step
+
+        Receives
+        --------
+        pd.DataFrame | None
+            Observations sent via ``generator.send()``. Pass ``None`` for
+            steps without assimilation.
+
+        Example
+        -------
+        >>> gen = model.create_generator(x0)
+        >>> state = next(gen)           # yields initial state x0
+        >>> state = gen.send(obs_df)    # step 1 with observations
+        >>> state = gen.send(None)      # step 2 without observations
+        """
+        if self.conditioning_data_source is None:
+            raise RuntimeError(
+                "StormCast has been called without initializing the model's "
+                "conditioning_data_source"
+            )
+
+        # Yield the initial state so the caller can inspect it
+        obs = yield x
+
+        try:
+            while True:
+                # Fetch and interpolate conditioning onto HRRR grid
+                c = self._fetch_and_interp_conditioning(x)
+
+                # Compute output coords (advances lead_time by 1h)
+                x_coords = OrderedDict({dim: x.coords[dim].values for dim in x.dims})
+                output_coords = self.output_coords((x_coords,))
+
+                # Zero-copy conversion to torch tensors
+                x_tensor = torch.as_tensor(x.data)
+                c_tensor = torch.as_tensor(c.data)
+
+                # Run forward with observations
+                for j, t in enumerate(x.coords["time"].data):
+                    obs_time = t + output_coords[0]["lead_time"][0]
+                    y_obs, mask = self._build_obs_tensors(obs, obs_time, self.device)
+                    x_tensor[j] = self._forward(x_tensor[j], c_tensor[j], y_obs, mask)
+
+                # Build output DataArray and use as next input
+                x = self._to_output_dataarray(x_tensor, output_coords)
+
+                # Yield forecast result and wait for next observations
+                obs = yield x
+
+        except GeneratorExit:
+            logger.info("StormCast SDA clean up")
diff --git a/earth2studio/models/da/utils.py b/earth2studio/models/da/utils.py
new file mode 100644
index 000000000..c0012e195
--- /dev/null
+++ b/earth2studio/models/da/utils.py
@@ -0,0 +1,167 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+import torch
+from loguru import logger
+
+try:
+    import cudf
+except ImportError:
+    cudf = None
+
+
+def validate_observation_fields(
+    observation: pd.DataFrame | cudf.DataFrame, required_fields: list[str]
+) -> None:
+    """Validate that required fields are present as columns in the DataFrame.
+
+    Parameters
+    ----------
+    observation : pd.DataFrame | cudf.DataFrame
+        DataFrame observation to validate
+    required_fields : list[str]
+        List of required field/column names
+
+    Raises
+    ------
+    ValueError
+        If any required fields are missing from the DataFrame columns
+    """
+    missing_fields = [
+        field for field in required_fields if field not in observation.columns
+    ]
+    if missing_fields:
+        raise ValueError(
+            f"DataFrame missing required fields: {missing_fields}. "
+            f"Available columns: {list(observation.columns)}"
+        )
+
+
+def filter_time_range(
+    df: pd.DataFrame | cudf.DataFrame,
+    request_time: np.datetime64 | datetime | str,
+    tolerance: tuple[np.timedelta64, np.timedelta64],
+    time_column: str = "time",
+) -> pd.DataFrame | cudf.DataFrame:
+    """Filter DataFrame rows where time column is within the specified tolerance range.
+
+    Filters the DataFrame to include only rows where the time column value is within
+    [request_time + lower_bound, request_time + upper_bound]. Ensures the time column
+    is of dtype datetime64[ns].
+
+    Parameters
+    ----------
+    df : pd.DataFrame | cudf.DataFrame
+        DataFrame to filter. Can be pandas or cudf DataFrame.
+    request_time : np.datetime64 | datetime | str
+        Reference time for filtering. Observations within the tolerance window
+        around this time will be included.
+    tolerance : tuple[np.timedelta64, np.timedelta64]
+        Tuple of (lower_bound, upper_bound) time deltas defining the tolerance window.
+    time_column : str, optional
+        Name of the time column in the DataFrame, by default "time"
+
+    Returns
+    -------
+    pd.DataFrame | cudf.DataFrame
+        Filtered DataFrame containing only rows within the time tolerance range.
+        Returns the same DataFrame type as the input (pandas or cudf).
+
+    Raises
+    ------
+    KeyError
+        If the time_column is not present in the DataFrame
+    """
+    if time_column not in df.columns:
+        raise KeyError(
+            f"Time column '{time_column}' not found in DataFrame. "
+            f"Available columns: {list(df.columns)}"
+        )
+
+    request_time_ns = np.datetime64(request_time, "ns")
+
+    # Ensure time column is datetime64[ns]
+    time_series = df[time_column]
+    if time_series.dtype != "datetime64[ns]":
+        df = df.copy()
+        # Use cudf methods if it's a cudf DataFrame, otherwise use pandas
+        if cudf is not None and isinstance(df, cudf.DataFrame):
+            df[time_column] = cudf.to_datetime(time_series).astype("datetime64[ns]")
+        else:
+            df[time_column] = pd.to_datetime(time_series).astype("datetime64[ns]")
+
+    # Calculate time bounds
+    lower_bound, upper_bound = tolerance
+    time_min = request_time_ns + lower_bound
+    time_max = request_time_ns + upper_bound
+
+    time_mask = (df[time_column] >= time_min) & (df[time_column] <= time_max)
+    return df[time_mask]
+
+
+def dfseries_to_torch(
+    series: pd.Series | cudf.Series,
+    dtype: torch.dtype = torch.float32,
+    device: torch.device | str = "cpu",
+) -> torch.Tensor:
+    """Convert a DataFrame series to a torch tensor with zero-copy for cudf.
+
+    If the series is from a cudf DataFrame, uses dlpack for zero-copy transfer.
+    If the series is from pandas but target device is GPU, transfers the data
+    and warns that cudf is not being used.
+
+    Parameters
+    ----------
+    series : pd.Series | cudf.Series
+        Series to convert to torch tensor. Can be pandas or cudf Series.
+    dtype : torch.dtype, optional
+        Desired dtype for the tensor, by default torch.float32
+    device : torch.device | str, optional
+        Target device for the tensor, by default "cpu"
+
+    Returns
+    -------
+    torch.Tensor
+        Torch tensor with the series data on the specified device
+
+    Raises
+    ------
+    ImportError
+        If cudf is required but not available
+    """
+    device = torch.device(device)
+
+    # Check if series is from cudf
+    if cudf is not None and isinstance(series, cudf.Series):
+        # Use dlpack for zero-copy transfer from cudf to torch
+        return torch.from_dlpack(series.values).to(dtype=dtype, device=device)
+
+    # Handle pandas Series
+    if device.type == "cuda":
+        # Warn that cudf is not being used for GPU transfer
+        logger.warning(
+            "Converting pandas Series to GPU tensor. Consider installing cudf "
+            "for zero-copy transfer and better performance."
+        )
+        return torch.tensor(series.values, dtype=dtype, device=device)
+
+    # CPU case - standard conversion
+    return torch.tensor(series.values, dtype=dtype, device=device)
diff --git a/earth2studio/models/dx/cbottle_sr.py b/earth2studio/models/dx/cbottle_sr.py
index 3edcb0c71..b82321d2e 100644
--- a/earth2studio/models/dx/cbottle_sr.py
+++ b/earth2studio/models/dx/cbottle_sr.py
@@ -388,7 +388,6 @@ def load_model(
         sigma_max: int = 800,
         seed: int | None = None,
         distilled_model: bool = False,
-        device: str = "cpu",
     ) -> DiagnosticModel:
         """Load diagnostic model from package
 
@@ -413,8 +412,6 @@ def load_model(
         distilled_model : bool, optional
             Whether to use the distilled model, If True, the distilled helper is used,
             enabling generation with fewer sampler steps, by default False
-        device : str
-            Device to load model onto, by default cpu
 
         Returns
         -------
@@ -437,10 +434,10 @@ def load_model(
 
         if distilled_model:
             sr_model = model_cls.from_pretrained(
-                state_path, window_function="KBD", window_alpha=1.0, device=device
+                state_path, window_function="KBD", window_alpha=1.0
             )
         else:
-            sr_model = model_cls.from_pretrained(state_path, device=device)
+            sr_model = model_cls.from_pretrained(state_path)
 
         return cls(
             sr_model,
diff --git a/earth2studio/models/dx/corrdiff.py b/earth2studio/models/dx/corrdiff.py
index 0eff57053..9186714f3 100644
--- a/earth2studio/models/dx/corrdiff.py
+++ b/earth2studio/models/dx/corrdiff.py
@@ -45,21 +45,28 @@
 from earth2studio.utils.type import CoordSystem
 
 try:
-    from physicsnemo.models import Module as PhysicsNemoModule
-    from physicsnemo.utils.corrdiff import (
+    from physicsnemo import Module as PhysicsNemoModule
+    from physicsnemo.diffusion.generate.legacy_generate import (
         diffusion_step,
         regression_step,
     )
-    from physicsnemo.utils.generative import (
-        StackedRandomGenerator,
+    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecondSR
+    from physicsnemo.diffusion.samplers.legacy_deterministic_sampler import (
         deterministic_sampler,
+    )
+    from physicsnemo.diffusion.samplers.legacy_stochastic_sampler import (
         stochastic_sampler,
     )
+    from physicsnemo.diffusion.utils import StackedRandomGenerator
+    from physicsnemo.models.diffusion_unets import UNet
 except ImportError:
     OptionalDependencyFailure("corrdiff")
     PhysicsNemoModule = None
     StackedRandomGenerator = None
     deterministic_sampler = None
+    stochastic_sampler = None
+    EDMPrecondSR = None
+    UNet = None
 
 
 @check_optional_dependencies()
@@ -1394,7 +1401,7 @@ def load_model(cls, package: Package, device: str | None = None) -> DiagnosticMo
         with zipfile.ZipFile(checkpoint_zip, "r") as zip_ref:
             zip_ref.extractall(checkpoint_zip.parent)
 
-        residual = PhysicsNemoModule.from_checkpoint(
+        residual = EDMPrecondSR.from_checkpoint(
             str(
                 checkpoint_zip.parent
                 / Path("corrdiff_inference_package/checkpoints/diffusion.mdlus")
@@ -1407,7 +1414,7 @@ def load_model(cls, package: Package, device: str | None = None) -> DiagnosticMo
             residual = residual.to(device)
         residual = residual.to(memory_format=torch.channels_last)
 
-        regression = PhysicsNemoModule.from_checkpoint(
+        regression = UNet.from_checkpoint(
             str(
                 checkpoint_zip.parent
                 / Path("corrdiff_inference_package/checkpoints/regression.mdlus")
diff --git a/earth2studio/models/dx/corrdiff_cmip6.py b/earth2studio/models/dx/corrdiff_cmip6.py
index e8bbcabee..1e83cd9c2 100644
--- a/earth2studio/models/dx/corrdiff_cmip6.py
+++ b/earth2studio/models/dx/corrdiff_cmip6.py
@@ -37,14 +37,20 @@
 from earth2studio.utils.type import CoordSystem, LeadTimeArray
 
 try:
-    from physicsnemo.models import Module as PhysicsNemoModule
-    from physicsnemo.utils.corrdiff import diffusion_step, regression_step
+    from physicsnemo.diffusion.generate.legacy_generate import (
+        diffusion_step,
+        regression_step,
+    )
+    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecondSuperResolution
+    from physicsnemo.models.diffusion_unets import UNet
     from physicsnemo.utils.zenith_angle import cos_zenith_angle
 except ImportError:  # pragma: no cover
     OptionalDependencyFailure("corrdiff")
     diffusion_step = None  # type: ignore[assignment]
     regression_step = None  # type: ignore[assignment]
     cos_zenith_angle = None  # type: ignore[assignment]
+    UNet = None  # type: ignore[assignment]
+    EDMPrecondSuperResolution = None  # type: ignore[assignment]
 
 
 class CorrDiffCMIP6(CorrDiff):
@@ -430,16 +436,14 @@ def load_model(
 
         # Load the base CorrDiff model from the package.
         residual = (
-            PhysicsNemoModule.from_checkpoint(
+            EDMPrecondSuperResolution.from_checkpoint(
                 package.resolve("diffusion.mdlus"), strict=False
             )
             .eval()
             .to(device)
         )
         regression = (
-            PhysicsNemoModule.from_checkpoint(
-                package.resolve("regression.mdlus"), strict=False
-            )
+            UNet.from_checkpoint(package.resolve("regression.mdlus"), strict=False)
             .eval()
             .to(device)
         )
diff --git a/earth2studio/models/nn/afno_ssrd.py b/earth2studio/models/nn/afno_ssrd.py
index 5b2e180bb..eba9e7aa7 100644
--- a/earth2studio/models/nn/afno_ssrd.py
+++ b/earth2studio/models/nn/afno_ssrd.py
@@ -18,8 +18,8 @@
 import physicsnemo  # noqa: F401 for docs
 import torch
 import torch.nn as nn
+from physicsnemo import Module
 from physicsnemo.models.afno.afno import Block
-from physicsnemo.models.module import Module
 
 Tensor = torch.Tensor
 
diff --git a/earth2studio/models/nn/atlas.py b/earth2studio/models/nn/atlas.py
index 3346d3cbf..671bebb7a 100644
--- a/earth2studio/models/nn/atlas.py
+++ b/earth2studio/models/nn/atlas.py
@@ -45,7 +45,9 @@
 
 @cache
 def get_isht(nlat, nlon, grid="equiangular", norm="ortho", device=None):
-    return InverseRealSHT(nlat, nlon, grid=grid, norm=norm).to(device=device)
+    return InverseRealSHT(
+        nlat, nlon, lmax=nlat, mmax=nlon // 2 + 1, grid=grid, norm=norm
+    ).to(device=device)
 
 
 def spherical_white_noise(shape, scale=3.56, device=None, studentt_deg=None):
@@ -55,19 +57,21 @@ def spherical_white_noise(shape, scale=3.56, device=None, studentt_deg=None):
     if len(shape) < 2:
         raise ValueError(f"Shape must have at least 2 dimensions, got {shape}")
 
+    isht = get_isht(shape[-2], shape[-1], device=device)
+    lmax = isht.lmax
+    mmax = isht.mmax
+
     if studentt_deg is None:
-        noise = torch.randn(shape[0:-1] + [(shape[-1] // 2) + 1] + [2], device=device)
+        noise = torch.randn(shape[0:-2] + [lmax, mmax] + [2], device=device)
     else:
         dist = torch.distributions.studentT.StudentT(studentt_deg)
-        noise = dist.sample(shape[0:-1] + [(shape[-1] // 2) + 1] + [2]).to(device)
+        noise = dist.sample(shape[0:-2] + [lmax, mmax] + [2]).to(device)
 
     noise = (1.0 / math.sqrt(float(shape[-1] * shape[-2]))) * scale * noise
     noise = torch.view_as_complex(noise)
 
     noise = torch.tril(noise)
 
-    isht = get_isht(shape[-2], shape[-1], device=device)
-
     with torch.amp.autocast("cuda", enabled=False):
         return isht(noise)
 
@@ -1305,12 +1309,9 @@ def forward(
         # Conditioning
         t = self.t_embedder(t)
 
-        # print(t.shape)
-
         if self.embedding_table is not None and label is not None:
             t = t + self.embedding_table(label)
 
-        # print(t.shape)
         # Apply DiT blocks
         for j, block in enumerate(self.blocks):
             if j >= self.checkpoint:
diff --git a/earth2studio/models/nn/stormscope_util.py b/earth2studio/models/nn/stormscope_util.py
index 729005397..4a9c75f5a 100644
--- a/earth2studio/models/nn/stormscope_util.py
+++ b/earth2studio/models/nn/stormscope_util.py
@@ -18,6 +18,7 @@
 
 import torch
 import torch.nn as nn
+from loguru import logger
 
 from earth2studio.utils.imports import (
     OptionalDependencyFailure,
@@ -25,7 +26,7 @@
 )
 
 try:
-    from physicsnemo.experimental.models.dit.dit import DiT as PNM_DiT
+    from physicsnemo.models import DiT as PNM_DiT
 except ImportError:
     OptionalDependencyFailure("stormscope")
     PNM_DiT = None  # type: ignore[assignment]
@@ -78,13 +79,13 @@ def __init__(
             self.p_min = p_min
             self.x_offset = x_offset
             self.slope = slope
-            print(f"sigma_max_dropout: {self.sigma_max_dropout}")
-            print(f"sigma_min_dropout: {self.sigma_min_dropout}")
-            print(f"dropout_function_type: {self.dropout_function_type}")
-            print(f"p_max: {self.p_max}")
-            print(f"p_min: {self.p_min}")
-            print(f"x_offset: {self.x_offset}")
-            print(f"slope: {self.slope}")
+            logger.debug(f"sigma_max_dropout: {self.sigma_max_dropout}")
+            logger.debug(f"sigma_min_dropout: {self.sigma_min_dropout}")
+            logger.debug(f"dropout_function_type: {self.dropout_function_type}")
+            logger.debug(f"p_max: {self.p_max}")
+            logger.debug(f"p_min: {self.p_min}")
+            logger.debug(f"x_offset: {self.x_offset}")
+            logger.debug(f"slope: {self.slope}")
         else:
             self.noise_dependent_dropout = False
 
diff --git a/earth2studio/models/px/aifsens.py b/earth2studio/models/px/aifsens.py
index 4f1f48b8e..3168343e9 100644
--- a/earth2studio/models/px/aifsens.py
+++ b/earth2studio/models/px/aifsens.py
@@ -818,8 +818,8 @@ def _fill_input(self, x: torch.Tensor, coords: CoordSystem) -> torch.Tensor:
 
         batch, time, lead, _, height, width = x.shape
 
-        # Prepare empty output tensor with VARIABLE dimension
-        out = torch.empty(
+        # Prepare output tensor with VARIABLE dimension (zeros to avoid uninitialised memory)
+        out = torch.zeros(
             (batch, time, lead, len(VARIABLES), height, width),
             device=x.device,
         )
@@ -848,6 +848,7 @@ def _fill_input(self, x: torch.Tensor, coords: CoordSystem) -> torch.Tensor:
         keep = torch.isin(
             all_indices, self.model.data_indices.data.output.forcing, invert=True
         )
+
         out = out[:, :, :, keep, ...]
 
         # Update coordinates with remaining variable names
diff --git a/earth2studio/models/px/cbottle_video.py b/earth2studio/models/px/cbottle_video.py
index 93b12d7a2..4453fe0ea 100644
--- a/earth2studio/models/px/cbottle_video.py
+++ b/earth2studio/models/px/cbottle_video.py
@@ -16,7 +16,7 @@
 
 from collections import OrderedDict
 from collections.abc import Generator, Iterator
-from datetime import datetime
+from datetime import datetime, timezone
 from enum import IntEnum, StrEnum
 
 import numpy as np
@@ -376,14 +376,20 @@ def reorder(x: torch.Tensor) -> torch.Tensor:
 
         # Set up time tensors
         times0 = [
-            datetime.fromtimestamp(t.astype("datetime64[s]").astype(int))
+            datetime.fromtimestamp(
+                t.astype("datetime64[s]").astype(int), tz=timezone.utc
+            )
             for t in times.reshape(-1)
         ]
         second_of_day = np.array(
             [(t.hour * 3600) + (t.minute * 60) + t.second for t in times0]
         ).reshape(times.shape)
         day_of_year = np.array(
-            [(t - datetime(t.year, 1, 1)).total_seconds() / (86400.0) for t in times0]
+            [
+                (t - datetime(t.year, 1, 1, tzinfo=timezone.utc)).total_seconds()
+                / (86400.0)
+                for t in times0
+            ]
         ).reshape(times.shape)
         second_of_day = torch.tensor(second_of_day.astype(np.float32), device=device)
         day_of_year = torch.tensor(day_of_year.astype(np.float32), device=device)
diff --git a/earth2studio/models/px/dlesym.py b/earth2studio/models/px/dlesym.py
index 8c4be9b29..bcdbbb4d1 100644
--- a/earth2studio/models/px/dlesym.py
+++ b/earth2studio/models/px/dlesym.py
@@ -36,7 +36,7 @@
 try:
     import earth2grid
     from omegaconf import OmegaConf
-    from physicsnemo.models import Module
+    from physicsnemo import Module
     from physicsnemo.utils.insolation import insolation
 except ImportError:
     OptionalDependencyFailure("dlesym")
diff --git a/earth2studio/models/px/fcn.py b/earth2studio/models/px/fcn.py
index 17588ec7c..e25d810e1 100644
--- a/earth2studio/models/px/fcn.py
+++ b/earth2studio/models/px/fcn.py
@@ -14,10 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import zipfile
 from collections import OrderedDict
 from collections.abc import Generator, Iterator
-from pathlib import Path
 
 import numpy as np
 import torch
@@ -81,7 +79,7 @@ class FCN(torch.nn.Module, AutoModelMixin, PrognosticMixin):
     paper. For additional information see the following resources:
 
     - https://arxiv.org/abs/2202.11214
-    - https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_fcn
+    - https://huggingface.co/nvidia/fourcastnet1
 
     Parameters
     ----------
@@ -177,7 +175,7 @@ def __str__(
     def load_default_package(cls) -> Package:
         """Load prognostic package"""
         return Package(
-            "ngc://models/nvidia/modulus/modulus_fcn@v0.2",
+            "hf://nvidia/fourcastnet1@c67a63995f6c8e0e557eb3d791f32f437e9b02d5",
             cache_options={
                 "cache_storage": Package.default_cache("fcn"),
                 "same_names": True,
@@ -191,20 +189,16 @@ def load_model(
         package: Package,
     ) -> PrognosticModel:
         """Load prognostic from package"""
-        fcn_zip = Path(package.resolve("fcn.zip"))
-        # Have to manually unzip here. Should not zip checkpoints in the future
-        with zipfile.ZipFile(fcn_zip, "r") as zip_ref:
-            zip_ref.extractall(fcn_zip.parent)
+        try:
+            package.resolve("config.json")  # HF tracking download statistics
+        except FileNotFoundError:
+            pass
 
-        model = AFNO.from_checkpoint(str(fcn_zip.parent / Path("fcn/fcn.mdlus")))
+        model = AFNO.from_checkpoint(package.resolve("fcn.mdlus"))
         model.eval()
 
-        local_center = torch.Tensor(
-            np.load(str(fcn_zip.parent / Path("fcn/global_means.npy")))
-        )
-        local_std = torch.Tensor(
-            np.load(str(fcn_zip.parent / Path("fcn/global_stds.npy")))
-        )
+        local_center = torch.Tensor(np.load(package.resolve("global_means.npy")))
+        local_std = torch.Tensor(np.load(package.resolve("global_stds.npy")))
         return cls(model, center=local_center, scale=local_std)
 
     @torch.inference_mode()
diff --git a/earth2studio/models/px/graphcast_operational.py b/earth2studio/models/px/graphcast_operational.py
index cd036fa25..e29881d31 100644
--- a/earth2studio/models/px/graphcast_operational.py
+++ b/earth2studio/models/px/graphcast_operational.py
@@ -237,7 +237,7 @@ def __init__(
                     ]
                 ),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(-90, 90, 721, endpoint=True),
+                "lat": np.linspace(90, -90, 721, endpoint=True),
                 "lon": np.linspace(0, 360, 1440, endpoint=False),
             }
         )
@@ -248,7 +248,7 @@ def __init__(
                 "time": np.empty(0),
                 "lead_time": np.array([np.timedelta64(6, "h")]),
                 "variable": np.array(VARIABLES + ["tp06"]),
-                "lat": np.linspace(-90, 90, 721, endpoint=True),
+                "lat": np.linspace(90, -90, 721, endpoint=True),
                 "lon": np.linspace(0, 360, 1440, endpoint=False),
             }
         )
@@ -456,7 +456,7 @@ def _default_generator(
         # Get device
         device = x.device
 
-        # first batch has 2 times
+        # first batch has 2 times (lead_times)
         coords_out["lead_time"] = coords["lead_time"][1:]
         # Add on zeros slice for tp06
         out = torch.cat(
@@ -469,8 +469,11 @@ def _default_generator(
             # Forward is identity operator
             coords = self.output_coords(coords)
 
-            # Get next prediction
-            x = self.iterator_result_to_tensor(next(self.iterator))
+            # Get next prediction from all time iterators
+            results = [
+                self.iterator_result_to_tensor(next(it)) for it in self.iterators
+            ]
+            x = torch.cat(results, dim=1) if len(results) > 1 else results[0]
 
             # Rear hook
             x, coords = self.rear_hook(x, coords)
@@ -503,24 +506,37 @@ def create_iterator(
         """
 
         with jax.default_device(self.get_jax_device_from_tensor(x)):
-            batch, target_lead_times = self.from_dataarray_to_dataset(
-                xr.DataArray(x.cpu(), coords=coords), 6
-            )
+            # Create a separate JAX iterator for each init time
+            # (rollout._get_next_inputs only supports single time)
+            time_dim = list(coords.keys()).index("time")
+            n_times = len(coords["time"])
+            self.iterators = []
+
+            for t in range(n_times):
+                x_t = x.narrow(time_dim, t, 1)
+                coords_t = coords.copy()
+                coords_t["time"] = coords["time"][t : t + 1]
+
+                batch, target_lead_times = self.from_dataarray_to_dataset(
+                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
+                )
 
-            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                batch,
-                target_lead_times=target_lead_times,
-                **dataclasses.asdict(self.ckpt.task_config),
-            )
+                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                    batch,
+                    target_lead_times=target_lead_times,
+                    **dataclasses.asdict(self.ckpt.task_config),
+                )
 
-            self.iterator = self._chunked_prediction_generator(
-                predictor_fn=self.run_forward,
-                rng=self.prng_key,
-                inputs=inputs,
-                targets_template=targets * np.nan,
-                batch=batch,
-                forcings=forcings,
-            )
+                self.iterators.append(
+                    self._chunked_prediction_generator(
+                        predictor_fn=self.run_forward,
+                        rng=self.prng_key,
+                        inputs=inputs,
+                        targets_template=targets * np.nan,
+                        batch=batch,
+                        forcings=forcings,
+                    )
+                )
 
             yield from self._default_generator(x, coords)
 
@@ -561,7 +577,9 @@ def iterator_result_to_tensor(self, dataset: xr.Dataset) -> torch.Tensor:
                 .T.transpose(..., "time", "lead_time", "variable", "lat", "lon")
             )
 
-        return torch.from_numpy(dataarray.to_numpy().copy())
+        out = torch.from_numpy(dataarray.to_numpy().copy())
+        out = out.flip(-2)  # Flip lat from ascending (-90->90, JAX native) to (90->-90)
+        return out
 
     @staticmethod
     def get_jax_device_from_tensor(x: torch.Tensor) -> "jax.Device":
@@ -600,24 +618,35 @@ def __call__(
             # Map lat and lon if needed
             x, coords = map_coords(x, coords, self.input_coords())
 
-            data, target_lead_times = self.from_dataarray_to_dataset(
-                xr.DataArray(x.cpu(), coords=coords), 6
-            )
+            # Loop over time dimension (JAX model supports single init time only)
+            time_dim = list(coords.keys()).index("time")
+            n_times = len(coords["time"])
+            results = []
+            for t in range(n_times):
+                x_t = x.narrow(time_dim, t, 1)
+                coords_t = coords.copy()
+                coords_t["time"] = coords["time"][t : t + 1]
+
+                data, target_lead_times = self.from_dataarray_to_dataset(
+                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
+                )
 
-            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                data,
-                target_lead_times=target_lead_times,
-                **dataclasses.asdict(self.ckpt.task_config),
-            )
+                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                    data,
+                    target_lead_times=target_lead_times,
+                    **dataclasses.asdict(self.ckpt.task_config),
+                )
 
-            predictions = rollout.chunked_prediction(
-                self.run_forward,
-                rng=self.prng_key,
-                inputs=inputs,
-                targets_template=targets * np.nan,
-                forcings=forcings,
-            )
-            out = self.iterator_result_to_tensor(predictions)
+                predictions = rollout.chunked_prediction(
+                    self.run_forward,
+                    rng=self.prng_key,
+                    inputs=inputs,
+                    targets_template=targets * np.nan,
+                    forcings=forcings,
+                )
+                results.append(self.iterator_result_to_tensor(predictions))
+
+            out = torch.cat(results, dim=1) if n_times > 1 else results[0]
             output_coords = self.output_coords(coords)
 
             # Convert to device
diff --git a/earth2studio/models/px/graphcast_small.py b/earth2studio/models/px/graphcast_small.py
index 0650df74d..0afe99bdf 100644
--- a/earth2studio/models/px/graphcast_small.py
+++ b/earth2studio/models/px/graphcast_small.py
@@ -169,7 +169,7 @@ class GraphCastSmall(torch.nn.Module, AutoModelMixin, PrognosticMixin):
     A smaller, low-resolution version of GraphCast (1 degree resolution, 13 pressure
     levels and a smaller mesh), trained on ERA5 data from 1979 to 2015. This model is
     useful for running with lower memory and compute constraints while maintaining good
-    forecast skill. The model operates on a 1-degree lat-lon grid (south-pole including)
+    forecast skill. The model operates on a 1-degree lat-lon grid (pole including)
     equirectangular grid with 85 variables including:
 
     - Surface variables (2m temperature, 10m winds, etc.)
@@ -238,7 +238,7 @@ def __init__(
                     ]
                 ),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(-90, 90, 181, endpoint=True),
+                "lat": np.linspace(90, -90, 181, endpoint=True),
                 "lon": np.linspace(0, 360, 360, endpoint=False),
             }
         )
@@ -249,7 +249,7 @@ def __init__(
                 "time": np.empty(0),
                 "lead_time": np.array([np.timedelta64(6, "h")]),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(-90, 90, 181, endpoint=True),
+                "lat": np.linspace(90, -90, 181, endpoint=True),
                 "lon": np.linspace(0, 360, 360, endpoint=False),
             }
         )
@@ -457,7 +457,7 @@ def _default_generator(
         # Get device
         device = x.device
 
-        # first batch has 2 times
+        # first batch has 2 times (lead_times)
         coords_out = coords.copy()
         coords_out["lead_time"] = coords["lead_time"][1:]
         yield x[:, :, 1:, ...], coords_out
@@ -467,8 +467,11 @@ def _default_generator(
             # Forward is identity operator
             coords = self.output_coords(coords)
 
-            # Get next prediction
-            x = self.iterator_result_to_tensor(next(self.iterator))
+            # Get next prediction from all time iterators
+            results = [
+                self.iterator_result_to_tensor(next(it)) for it in self.iterators
+            ]
+            x = torch.cat(results, dim=1) if len(results) > 1 else results[0]
 
             # Rear hook
             x, coords = self.rear_hook(x, coords)
@@ -501,24 +504,37 @@ def create_iterator(
         """
 
         with jax.default_device(self.get_jax_device_from_tensor(x)):
-            batch, target_lead_times = self.from_dataarray_to_dataset(
-                xr.DataArray(x.cpu(), coords=coords), 6
-            )
+            # Create a separate JAX iterator for each init time
+            # (rollout._get_next_inputs only supports single time)
+            time_dim = list(coords.keys()).index("time")
+            n_times = len(coords["time"])
+            self.iterators = []
+
+            for t in range(n_times):
+                x_t = x.narrow(time_dim, t, 1)
+                coords_t = coords.copy()
+                coords_t["time"] = coords["time"][t : t + 1]
+
+                batch, target_lead_times = self.from_dataarray_to_dataset(
+                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
+                )
 
-            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                batch,
-                target_lead_times=target_lead_times,
-                **dataclasses.asdict(self.ckpt.task_config),
-            )
+                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                    batch,
+                    target_lead_times=target_lead_times,
+                    **dataclasses.asdict(self.ckpt.task_config),
+                )
 
-            self.iterator = self._chunked_prediction_generator(
-                predictor_fn=self.run_forward,
-                rng=self.prng_key,
-                inputs=inputs,
-                targets_template=targets * np.nan,
-                batch=batch,
-                forcings=forcings,
-            )
+                self.iterators.append(
+                    self._chunked_prediction_generator(
+                        predictor_fn=self.run_forward,
+                        rng=self.prng_key,
+                        inputs=inputs,
+                        targets_template=targets * np.nan,
+                        batch=batch,
+                        forcings=forcings,
+                    )
+                )
 
             yield from self._default_generator(x, coords)
 
@@ -559,7 +575,9 @@ def iterator_result_to_tensor(self, dataset: xr.Dataset) -> torch.Tensor:
                 .T.transpose(..., "time", "lead_time", "variable", "lat", "lon")
             )
 
-        return torch.from_numpy(dataarray.to_numpy().copy())
+        out = torch.from_numpy(dataarray.to_numpy().copy())
+        out = out.flip(-2)  # Flip lat from ascending (-90->90, JAX native) to (90->-90)
+        return out
 
     @staticmethod
     def get_jax_device_from_tensor(x: torch.Tensor) -> "jax.Device":
@@ -598,24 +616,35 @@ def __call__(
             # Map lat and lon if needed
             x, coords = map_coords(x, coords, self.input_coords())
 
-            data, target_lead_times = self.from_dataarray_to_dataset(
-                xr.DataArray(x.cpu(), coords=coords), 6
-            )
+            # Loop over time dimension (JAX model supports single init time only)
+            time_dim = list(coords.keys()).index("time")
+            n_times = len(coords["time"])
+            results = []
+            for t in range(n_times):
+                x_t = x.narrow(time_dim, t, 1)
+                coords_t = coords.copy()
+                coords_t["time"] = coords["time"][t : t + 1]
+
+                data, target_lead_times = self.from_dataarray_to_dataset(
+                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
+                )
 
-            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                data,
-                target_lead_times=target_lead_times,
-                **dataclasses.asdict(self.ckpt.task_config),
-            )
+                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                    data,
+                    target_lead_times=target_lead_times,
+                    **dataclasses.asdict(self.ckpt.task_config),
+                )
 
-            predictions = rollout.chunked_prediction(
-                self.run_forward,
-                rng=self.prng_key,
-                inputs=inputs,
-                targets_template=targets * np.nan,
-                forcings=forcings,
-            )
-            out = self.iterator_result_to_tensor(predictions)
+                predictions = rollout.chunked_prediction(
+                    self.run_forward,
+                    rng=self.prng_key,
+                    inputs=inputs,
+                    targets_template=targets * np.nan,
+                    forcings=forcings,
+                )
+                results.append(self.iterator_result_to_tensor(predictions))
+
+            out = torch.cat(results, dim=1) if n_times > 1 else results[0]
             output_coords = self.output_coords(coords)
 
             # Convert to device
diff --git a/earth2studio/models/px/stormcast.py b/earth2studio/models/px/stormcast.py
index dea2ec454..5a0d2aa73 100644
--- a/earth2studio/models/px/stormcast.py
+++ b/earth2studio/models/px/stormcast.py
@@ -43,11 +43,15 @@
 
 try:
     from omegaconf import OmegaConf
-    from physicsnemo.models import Module as PhysicsNemoModule
-    from physicsnemo.utils.generative import deterministic_sampler
+    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecond
+    from physicsnemo.diffusion.samplers.legacy_deterministic_sampler import (
+        deterministic_sampler,
+    )
+    from physicsnemo.models.diffusion_unets import StormCastUNet
 except ImportError:
     OptionalDependencyFailure("stormcast")
-    PhysicsNemoModule = None
+    StormCastUNet = None
+    EDMPrecond = None
     OmegaConf = None
     deterministic_sampler = None
 
@@ -287,11 +291,14 @@ def load_model(
         # load model registry:
         config = OmegaConf.load(package.resolve("model.yaml"))
 
-        regression = PhysicsNemoModule.from_checkpoint(
-            package.resolve("StormCastUNet.0.0.mdlus")
+        # TODO: remove strict=False once checkpoints/imports updated to new diffusion API
+        regression = StormCastUNet.from_checkpoint(
+            package.resolve("StormCastUNet.0.0.mdlus"),
+            strict=False,
         )
-        diffusion = PhysicsNemoModule.from_checkpoint(
-            package.resolve("EDMPrecond.0.0.mdlus")
+        diffusion = EDMPrecond.from_checkpoint(
+            package.resolve("EDMPrecond.0.0.mdlus"),
+            strict=False,
         )
 
         # Load metadata: means, stds, grid
diff --git a/earth2studio/models/px/stormscope.py b/earth2studio/models/px/stormscope.py
index 9e97aed93..803f96e7c 100644
--- a/earth2studio/models/px/stormscope.py
+++ b/earth2studio/models/px/stormscope.py
@@ -46,7 +46,7 @@
 from earth2studio.utils.type import CoordSystem
 
 try:
-    from physicsnemo.models import Module
+    from physicsnemo import Module
     from physicsnemo.utils.zenith_angle import cos_zenith_angle
 except ImportError:
     OptionalDependencyFailure("stormscope")
diff --git a/earth2studio/perturbation/gaussian.py b/earth2studio/perturbation/gaussian.py
index 4270169cd..5809db6d9 100644
--- a/earth2studio/perturbation/gaussian.py
+++ b/earth2studio/perturbation/gaussian.py
@@ -232,16 +232,18 @@ def __init__(
         self.isht = InverseRealSHT(
             self.nlat, 2 * self.nlat, grid=grid, norm="backward"
         ).to(dtype=dtype)
+        self.lmax = self.isht.lmax
+        self.mmax = self.isht.mmax
 
         r_earth = 6.371e6
         # kT is defined on slide 7
         self.kT = (length_scale / r_earth) ** 2 / 2
-        F0 = self.calculateF0(self.sigma, self.phi, self.nlat, self.kT)
+        F0 = self.calculateF0(self.sigma, self.phi, self.lmax, self.kT)
 
         prods = (
-            torch.tensor([j * (j + 1) for j in range(0, self.nlat)])
-            .view(self.nlat, 1)
-            .repeat(1, self.nlat + 1)
+            torch.tensor([j * (j + 1) for j in range(0, self.lmax)])
+            .view(self.lmax, 1)
+            .repeat(1, self.mmax)
         )
 
         sigma_n = torch.tril(torch.exp(-self.kT * prods / 2) * F0)
@@ -260,7 +262,7 @@ def __init__(
         # in the call function
         self.gaussian_noise = torch.distributions.normal.Normal(self.mean, self.var)
         xi = self.gaussian_noise.sample(
-            torch.Size((self.N, self.nlat, self.nlat + 1, 2))
+            torch.Size((self.N, self.lmax, self.mmax, 2))
         ).squeeze()
         xi = torch.view_as_complex(xi)
 
@@ -271,13 +273,13 @@ def __init__(
         self.register_buffer("coeff", coeff)
 
     def calculateF0(
-        self, sigma: float, phi: float, nlat: int, kT: float
+        self, sigma: float, phi: float, lmax: int, kT: float
     ) -> torch.Tensor:
         """This function scales the coefficients such that their grid-point standarddeviation is sigma.
         sigma is the desired variance phi is a np.exp(-dt/time_scale)
         """
         numerator = sigma**2 * (1 - (phi**2))
-        wavenumbers = torch.arange(1, nlat)
+        wavenumbers = torch.arange(1, lmax)
         denominator = (2 * wavenumbers + 1) * torch.exp(
             -kT * wavenumbers * (wavenumbers + 1)
         )
@@ -298,7 +300,7 @@ def forward(self, x: torch.Tensor, time: np.datetime64 = None) -> torch.Tensor:
 
             # Sample Gaussian noise. # TODO why??? for next step maybe?
             xi = self.gaussian_noise.sample(
-                torch.Size((self.N, self.nlat, self.nlat + 1, 2))
+                torch.Size((self.N, self.lmax, self.mmax, 2))
             ).squeeze()
             xi = torch.view_as_complex(xi)
 
diff --git a/earth2studio/perturbation/spherical.py b/earth2studio/perturbation/spherical.py
index 995d53f81..29ce383ea 100644
--- a/earth2studio/perturbation/spherical.py
+++ b/earth2studio/perturbation/spherical.py
@@ -203,12 +203,14 @@ def __init__(
             .to(dtype=dtype)
             .to(device=device)
         )
+        self.lmax = self.isht.lmax
+        self.mmax = self.isht.mmax
 
         # Square root of the eigenvalues of C.
         sqrt_eig = (
-            torch.tensor([j * (j + 1) for j in range(self.nlat)], device=device)
-            .view(self.nlat, 1)
-            .repeat(1, self.nlat + 1)
+            torch.tensor([j * (j + 1) for j in range(self.lmax)], device=device)
+            .view(self.lmax, 1)
+            .repeat(1, self.mmax)
         )
         sqrt_eig = torch.tril(
             sigma * (((sqrt_eig / radius**2) + tau**2) ** (-alpha / 2.0))
@@ -232,7 +234,7 @@ def forward(self, N: int, xi: torch.Tensor | None = None) -> torch.Tensor:
         N : int
             Number of functions to sample.
         xi : torch.Tensor, default is None
-            Noise is a complex tensor of size (N, nlat, nlat+1).
+            Noise is a complex tensor of size (N, lmax, mmax).
             If None, new Gaussian noise is sampled.
             If xi is provided, N is ignored.
 
@@ -246,7 +248,7 @@ def forward(self, N: int, xi: torch.Tensor | None = None) -> torch.Tensor:
         if xi is None:
             gaussian_noise = torch.distributions.normal.Normal(self.mean, self.var)
             xi = gaussian_noise.sample(
-                torch.Size((N, self.nlat, self.nlat + 1, 2))
+                torch.Size((N, self.lmax, self.mmax, 2))
             ).squeeze()
             xi = torch.view_as_complex(xi)
 
diff --git a/earth2studio/run.py b/earth2studio/run.py
index 562a5d082..706acaa93 100644
--- a/earth2studio/run.py
+++ b/earth2studio/run.py
@@ -23,7 +23,7 @@
 from loguru import logger
 from tqdm import tqdm
 
-from earth2studio.data import DataSource, fetch_data
+from earth2studio.data import DataSource, ForecastSource, fetch_data
 from earth2studio.io import IOBackend
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
@@ -158,7 +158,7 @@ def diagnostic(
     nsteps: int,
     prognostic: PrognosticModel,
     diagnostic: DiagnosticModel,
-    data: DataSource,
+    data: DataSource | ForecastSource,
     io: IOBackend,
     output_coords: CoordSystem = OrderedDict({}),
     device: torch.device | None = None,
@@ -178,7 +178,7 @@ def diagnostic(
         Prognostic model
     diagnostic: DiagnosticModel
         Diagnostic model, must be on same coordinate axis as prognostic
-    data : DataSource
+    data : DataSource | ForecastSource
         Data source
     io : IOBackend
         IO object
@@ -401,9 +401,10 @@ def ensemble(
 
         # Expand x, coords for ensemble
         mini_batch_size = min(batch_size, nensemble - batch_id)
-        coords = {
-            "ensemble": np.arange(batch_id, batch_id + mini_batch_size)
-        } | coords0.copy()
+        coords = (
+            OrderedDict({"ensemble": np.arange(batch_id, batch_id + mini_batch_size)})
+            | coords0.copy()
+        )
 
         # Unsqueeze x for batching ensemble
         x = x.unsqueeze(0).repeat(mini_batch_size, *([1] * x.ndim))
diff --git a/earth2studio/serve/client/__init__.py b/earth2studio/serve/client/__init__.py
new file mode 100644
index 000000000..0df5e8698
--- /dev/null
+++ b/earth2studio/serve/client/__init__.py
@@ -0,0 +1,15 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/earth2studio/serve/client/client.py b/earth2studio/serve/client/client.py
new file mode 100644
index 000000000..ecbaa6eec
--- /dev/null
+++ b/earth2studio/serve/client/client.py
@@ -0,0 +1,550 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import io
+import json
+import time
+from typing import Any, cast
+from urllib.parse import urljoin
+
+import requests  # type: ignore[import-untyped]
+from requests.adapters import HTTPAdapter  # type: ignore[import-untyped]
+from requests.packages.urllib3.util.retry import Retry  # type: ignore[import-untyped]
+
+from earth2studio.serve.client.exceptions import (
+    APIConnectionError as ClientConnectionError,
+)
+from earth2studio.serve.client.exceptions import (
+    BadRequestError,
+    Earth2StudioAPIError,
+    InferenceRequestNotFoundError,
+    InternalServerError,
+    RequestTimeoutError,
+)
+from earth2studio.serve.client.models import (
+    HealthStatus,
+    InferenceRequest,
+    InferenceRequestResponse,
+    InferenceRequestResults,
+    InferenceRequestStatus,
+    RequestStatus,
+    StorageType,
+)
+
+
+class Earth2StudioClient:
+    """
+    Python client for Earth2Studio REST API.
+
+    Parameters
+    ----------
+    base_url : str, optional
+        Base URL of the Earth2Studio API server. Default is "http://localhost:8000".
+    workflow_name : str, optional
+        Name of the workflow to use. Default is "deterministic_earth2_workflow".
+    timeout : float, optional
+        Request timeout in seconds. Default is 30.0.
+    max_retries : int, optional
+        Maximum number of retries for failed requests. Default is 3.
+    retry_backoff_factor : float, optional
+        Backoff factor for retries. Default is 0.3.
+    token : str, optional
+        Optional authentication token (sent as Bearer token in Authorization header).
+
+    Examples
+    --------
+    >>> client = Earth2StudioClient(base_url="http://localhost:8000")
+    >>> health = client.health_check()
+    >>> print(health.status)
+    healthy
+
+    With authentication:
+
+    >>> client = Earth2StudioClient(base_url="http://localhost:8000", token="your-token")
+    """
+
+    def __init__(
+        self,
+        base_url: str = "http://localhost:8000",
+        workflow_name: str = "deterministic_earth2_workflow",
+        timeout: float = 30.0,
+        max_retries: int = 3,
+        retry_backoff_factor: float = 0.3,
+        token: str | None = None,
+    ):
+        self.base_url = base_url.rstrip("/")
+        self.workflow_name = workflow_name
+        self.timeout = timeout
+        self.token = token
+
+        # Create session with retry strategy
+        self.session = requests.Session()
+        retry_strategy = Retry(
+            total=max_retries,
+            backoff_factor=retry_backoff_factor,
+            status_forcelist=[429, 500, 502, 503, 504],
+        )
+        adapter = HTTPAdapter(max_retries=retry_strategy)
+        self.session.mount("http://", adapter)
+        self.session.mount("https://", adapter)
+
+        # Set default headers
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+        }
+        # Add authorization header if token is provided
+        if token:
+            headers["Authorization"] = f"Bearer {token}"
+        self.session.headers.update(headers)
+
+    def _make_request(
+        self,
+        method: str,
+        endpoint: str,
+        json_data: dict | None = None,
+        params: dict | None = None,
+        return_response: bool = False,
+        stream: bool = False,
+        timeout: float | None = None,
+    ) -> dict | requests.Response:
+        """
+        Make an HTTP request with error handling.
+
+        Parameters
+        ----------
+        method : str
+            HTTP method (GET, POST, etc.).
+        endpoint : str
+            API endpoint path.
+        json_data : dict, optional
+            JSON data for request body.
+        params : dict, optional
+            Query parameters.
+        return_response : bool, optional
+            If True, return raw Response object instead of parsed JSON.
+        stream : bool, optional
+            If True, enable streaming for large responses.
+        timeout : float, optional
+            Timeout in seconds. If None, uses client default.
+
+        Returns
+        -------
+        dict or requests.Response
+            Parsed JSON response (if return_response=False), or raw Response
+            object (if return_response=True).
+
+        Raises
+        ------
+        RequestTimeoutError
+            If the request times out.
+        APIConnectionError
+            If the connection fails.
+        Earth2StudioAPIError
+            For other request failures or non-2xx responses.
+        """
+        url = urljoin(self.base_url + "/", endpoint.lstrip("/"))
+        timeout = self.timeout if timeout is None else timeout
+        try:
+            response = self.session.request(
+                method=method,
+                url=url,
+                json=json_data,
+                params=params,
+                timeout=timeout,
+                stream=stream,
+            )
+        except requests.exceptions.Timeout:
+            raise RequestTimeoutError(
+                f"Request to {url} timed out after {timeout} seconds"
+            )
+        except requests.exceptions.ConnectionError as e:
+            raise ClientConnectionError(f"Failed to connect to {url}: {str(e)}")
+        except requests.exceptions.RequestException as e:
+            raise Earth2StudioAPIError(f"Request failed: {str(e)}")
+
+        # Handle HTTP error status codes
+        if response.status_code == 400:
+            error_data = self._parse_error_response(response)
+            raise BadRequestError(
+                error_data.get("error", "Bad request"),
+                details=error_data.get("details"),
+            )
+        elif response.status_code == 404:
+            error_data = self._parse_error_response(response)
+            raise InferenceRequestNotFoundError(
+                error_data.get("error", "Not found"), details=error_data.get("details")
+            )
+        elif response.status_code == 500:
+            error_data = self._parse_error_response(response)
+            raise InternalServerError(
+                error_data.get("error", "Internal server error"),
+                details=error_data.get("details"),
+            )
+        elif not response.ok:
+            error_data = self._parse_error_response(response)
+            raise Earth2StudioAPIError(
+                error_data.get("error", f"HTTP {response.status_code} error"),
+                status_code=response.status_code,
+                details=error_data.get("details"),
+            )
+
+        # Return raw response if requested (for binary content like zip files)
+        if return_response:
+            return response
+
+        # Otherwise parse as JSON (default behavior)
+        try:
+            return response.json()
+        except json.JSONDecodeError:
+            raise Earth2StudioAPIError("Invalid JSON response from server")
+
+    def _parse_error_response(self, response: requests.Response) -> dict:
+        """
+        Parse error response JSON, fallback to dict with error text.
+
+        Parameters
+        ----------
+        response : requests.Response
+            HTTP response from the API.
+
+        Returns
+        -------
+        dict
+            Parsed JSON body, or {"error": ...} if JSON decode fails.
+        """
+        try:
+            return response.json()
+        except json.JSONDecodeError:
+            return {"error": response.text or f"HTTP {response.status_code} error"}
+
+    def health_check(self) -> HealthStatus:
+        """
+        Check if the API is running and healthy.
+
+        Returns
+        -------
+        HealthStatus
+            Object with status and timestamp.
+
+        Raises
+        ------
+        Earth2StudioAPIError
+            If the API is not accessible.
+        """
+        response_data = self._make_request("GET", "/health")
+        return HealthStatus.from_dict(cast(dict[str, Any], response_data))
+
+    def submit_inference_request(
+        self, request: InferenceRequest
+    ) -> InferenceRequestResponse:
+        """
+        Submit an inference request to be processed.
+
+        Parameters
+        ----------
+        request : InferenceRequest
+            InferenceRequest object with workflow configuration.
+
+        Returns
+        -------
+        InferenceRequestResponse
+            Response with execution ID and initial status.
+
+        Raises
+        ------
+        BadRequestError
+            If the request parameters are invalid.
+        Earth2StudioAPIError
+            For other API errors.
+        """
+        response_data = self._make_request(
+            "POST", f"/v1/infer/{self.workflow_name}", json_data=request.to_dict()
+        )
+        return InferenceRequestResponse.from_dict(cast(dict[str, Any], response_data))
+
+    def get_request_status(self, request_id: str) -> InferenceRequestStatus:
+        """
+        Get the current status of an inference request.
+
+        Parameters
+        ----------
+        request_id : str
+            Unique identifier for the inference request.
+
+        Returns
+        -------
+        InferenceRequestStatus
+            Current progress and status.
+
+        Raises
+        ------
+        InferenceRequestNotFoundError
+            If the request ID is not found.
+        Earth2StudioAPIError
+            For other API errors.
+        """
+        response_data = self._make_request(
+            "GET", f"/v1/infer/{self.workflow_name}/{request_id}/status"
+        )
+        return InferenceRequestStatus.from_dict(cast(dict[str, Any], response_data))
+
+    def get_request_results(
+        self,
+        request_id: str,
+        timeout: float | None = None,
+    ) -> InferenceRequestResults:
+        """
+        Get the results of a completed inference request by downloading the results.
+
+        Parameters
+        ----------
+        request_id : str
+            Unique identifier for the inference request.
+        timeout : float, optional
+            Timeout in seconds for the download request. If None, uses client default.
+
+        Returns
+        -------
+        InferenceRequestResults
+            Results with output file paths. Use result_paths() to get paths.
+
+        Raises
+        ------
+        InferenceRequestNotFoundError
+            If the request ID is not found.
+        Earth2StudioAPIError
+            For other API errors (including 202 if request is still processing).
+
+        Notes
+        -----
+        If the request is still processing, the API returns 202 and this method
+        raises Earth2StudioAPIError. Use get_request_status() to check completion first.
+        """
+        # Use _make_request with return_response=True to get raw response for zip file
+        response = cast(
+            requests.Response,
+            self._make_request(
+                method="GET",
+                endpoint=f"/v1/infer/{self.workflow_name}/{request_id}/results",
+                return_response=True,
+                timeout=timeout,
+            ),
+        )
+
+        # Handle 202 status specially (request still processing)
+        if response.status_code == 202:
+            try:
+                error_data = response.json()
+                raise Earth2StudioAPIError(
+                    error_data.get("message", "Request is still processing"),
+                    status_code=202,
+                    details=error_data.get("status"),
+                )
+            except json.JSONDecodeError:
+                raise Earth2StudioAPIError(
+                    "Request is still processing", status_code=202
+                )
+
+        response_data = response.json()
+        return InferenceRequestResults.from_dict(response_data)
+
+    def wait_for_completion(
+        self,
+        request_id: str,
+        poll_interval: float = 5.0,
+        timeout: float | None = None,
+    ) -> InferenceRequestResults:
+        """
+        Wait for an inference request to complete and return results.
+
+        Parameters
+        ----------
+        request_id : str
+            Unique identifier for the inference request.
+        poll_interval : float, optional
+            How often to check status in seconds. Default is 5.0.
+        timeout : float, optional
+            Maximum time to wait in seconds. None for no timeout.
+
+        Returns
+        -------
+        InferenceRequestResults
+            Results when the request completes successfully.
+
+        Raises
+        ------
+        RequestTimeoutError
+            If timeout is reached.
+        Earth2StudioAPIError
+            If the request fails or other API errors occur.
+        """
+        start_time = time.time()
+
+        while True:
+            status = self.get_request_status(request_id)
+
+            if status.status == RequestStatus.COMPLETED:
+                return self.get_request_results(request_id, timeout)
+            elif status.status == RequestStatus.FAILED:
+                raise Earth2StudioAPIError(
+                    f"Inference request {request_id} failed: {status.error_message}"
+                )
+            elif status.status == RequestStatus.CANCELLED:
+                raise Earth2StudioAPIError("Inference request was cancelled")
+
+            # Check timeout (timeout=0.0 means fail immediately if not complete)
+            if timeout is not None and (time.time() - start_time) > timeout:
+                raise RequestTimeoutError(
+                    f"Request {request_id} did not complete within {timeout} seconds"
+                )
+
+            time.sleep(poll_interval)
+
+    def run_inference_sync(
+        self,
+        request: InferenceRequest,
+        poll_interval: float = 5.0,
+        timeout: float | None = None,
+    ) -> InferenceRequestResults:
+        """
+        Submit an inference request and wait for it to complete.
+
+        Convenience method that combines submit_inference_request() and
+        wait_for_completion().
+
+        Parameters
+        ----------
+        request : InferenceRequest
+            InferenceRequest object with workflow configuration.
+        poll_interval : float, optional
+            How often to check status in seconds. Default is 5.0.
+        timeout : float, optional
+            Maximum time to wait in seconds. None for no timeout.
+
+        Returns
+        -------
+        InferenceRequestResults
+            Results when the request completes successfully.
+        """
+        response = self.submit_inference_request(request)
+        return self.wait_for_completion(response.execution_id, poll_interval, timeout)
+
+    def result_root_path(self, result: InferenceRequestResults) -> str:
+        """
+        Get the root URL path for accessing result files.
+
+        Parameters
+        ----------
+        result : InferenceRequestResults
+            Result object returned by the request.
+
+        Returns
+        -------
+        str
+            Root path URL for result files (e.g. "/v1/infer/workflow_name/exec_123/results/").
+        """
+        return f"/v1/infer/{self.workflow_name}/{result.request_id}/results/"
+
+    def download_result(
+        self,
+        result: InferenceRequestResults,
+        path: str,
+        timeout: float | None = None,
+    ) -> io.BytesIO:
+        """
+        Download a result file from the server or S3.
+
+        Parameters
+        ----------
+        result : InferenceRequestResults
+            Result object containing request metadata.
+        path : str
+            Relative path to the result file (as returned in result_paths).
+        timeout : float, optional
+            Timeout in seconds for the download. If None, uses client default.
+
+        Returns
+        -------
+        io.BytesIO
+            In-memory buffer containing the downloaded file content.
+
+        Raises
+        ------
+        Earth2StudioAPIError
+            If the download fails or other API errors occur.
+        """
+        if result.storage_type == StorageType.S3:
+            if not result.signed_url:
+                raise Earth2StudioAPIError("S3 storage type requires a signed URL")
+
+            from earth2studio.serve.client.fsspec_utils import (
+                create_cloudfront_mapper,
+            )
+
+            mapper = create_cloudfront_mapper(result.signed_url, zarr_path="")
+            # strip out the prefix execution_id from the path
+            parts = path.split("/")
+            if len(parts) < 2:
+                raise Earth2StudioAPIError(
+                    f"Expected S3 result path to include an execution-id prefix, got: {path!r}"
+                )
+            path = "/".join(parts[1:])
+            content = mapper.fs.cat_file(path)
+            return io.BytesIO(content)
+
+        # Default: download from server
+        response = cast(
+            requests.Response,
+            self._make_request(
+                method="GET",
+                endpoint=f"{self.result_root_path(result)}{path}",
+                return_response=True,
+                stream=True,
+                timeout=timeout,
+            ),
+        )
+        # TODO: add support for downloading to tmp file to avoid OOM
+        return io.BytesIO(response.content)
+
+    def close(self) -> None:
+        """
+        Close the underlying HTTP session.
+        """
+        self.session.close()  # type: ignore[no-untyped-call]
+
+    def __enter__(self) -> "Earth2StudioClient":
+        """
+        Context manager entry.
+
+        Returns
+        -------
+        Earth2StudioClient
+            self.
+        """
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: object,
+    ) -> None:
+        """
+        Context manager exit. Closes the HTTP session.
+        """
+        self.close()
diff --git a/earth2studio/serve/client/e2client.py b/earth2studio/serve/client/e2client.py
new file mode 100644
index 000000000..59a17aebf
--- /dev/null
+++ b/earth2studio/serve/client/e2client.py
@@ -0,0 +1,479 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from collections import OrderedDict
+from collections.abc import Iterator
+from typing import Any, Literal
+from urllib.parse import urljoin
+
+import numpy as np
+import torch
+
+try:
+    import aiohttp
+except ImportError:
+    aiohttp = None  # type: ignore[assignment]
+import xarray as xr
+
+from earth2studio.data import (  # type: ignore[import-untyped]
+    InferenceOutputSource,
+    fetch_data,
+)
+from earth2studio.models.auto import AutoModelMixin  # type: ignore[import-untyped]
+from earth2studio.models.px.utils import PrognosticMixin  # type: ignore[import-untyped]
+from earth2studio.serve.client import fsspec_utils
+from earth2studio.serve.client.client import Earth2StudioClient
+from earth2studio.serve.client.exceptions import Earth2StudioAPIError
+from earth2studio.serve.client.models import (
+    InferenceRequest,
+    InferenceRequestResults,
+    StorageType,
+)
+from earth2studio.utils.type import CoordSystem  # type: ignore[import-untyped]
+
+
+class RemoteEarth2Workflow:
+    """
+    Remote inference workflow client for Earth2Studio API.
+
+    Provides Earth2Studio-compatible interface for running inference on a remote
+    Earth2Studio API server. Supports both direct calls and iterator-based access.
+
+    Parameters
+    ----------
+    base_url : str
+        URL of the Earth2Studio API server.
+    workflow_name : str
+        Name of the workflow to execute on the server.
+    device : str or torch.device, optional
+        Device for tensor operations (e.g. "cuda", "cpu"). Default from CUDA availability.
+    xr_args : dict, optional
+        Additional arguments passed to xarray.open_dataset / xarray.open_zarr.
+    **client_kwargs : Any
+        Additional arguments passed to Earth2StudioClient (e.g. token for authentication).
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        workflow_name: str,
+        device: str | torch.device | None = None,
+        xr_args: dict[str, Any] | None = None,
+        **client_kwargs: Any,
+    ) -> None:
+        self.base_url = base_url
+        self.client = Earth2StudioClient(
+            base_url=base_url, workflow_name=workflow_name, **client_kwargs
+        )
+        self.workflow_name = workflow_name
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = device
+        self.xr_args = xr_args.copy() if xr_args else {}
+
+    def __call__(self, **kwargs: Any) -> "RemoteEarth2WorkflowResult":
+        """
+        Execute inference request and return result object for accessing outputs.
+
+        Parameters
+        ----------
+        **kwargs : Any
+            Workflow parameters for the inference request.
+
+        Returns
+        -------
+        RemoteEarth2WorkflowResult
+            Result object for accessing inference outputs.
+        """
+        request = InferenceRequest(parameters=kwargs.copy())
+        response = self.client.submit_inference_request(request)
+        return RemoteEarth2WorkflowResult(self, response.execution_id)
+
+    def to(self, device: torch.device | str) -> "RemoteEarth2Workflow":
+        """
+        Move workflow to specified device.
+
+        Parameters
+        ----------
+        device : torch.device or str
+            Target device ("cuda", "cpu", or torch.device).
+
+        Returns
+        -------
+        RemoteEarth2Workflow
+            self for method chaining.
+        """
+        self.device = device
+        return self
+
+
+class RemoteEarth2WorkflowResult:
+    """
+    Result object for a remote inference request.
+
+    Provides methods to access inference results as datasets, data sources, or iterators.
+    The result is fetched lazily; methods wait for completion if needed.
+
+    Parameters
+    ----------
+    workflow : RemoteEarth2Workflow
+        Parent RemoteEarth2Workflow instance.
+    execution_id : str
+        Unique identifier for the inference execution.
+    """
+
+    def __init__(self, workflow: RemoteEarth2Workflow, execution_id: str) -> None:
+        """
+        Store the parent workflow and execution id; result is fetched lazily.
+
+        Parameters
+        ----------
+        workflow : RemoteEarth2Workflow
+            Parent workflow instance.
+        execution_id : str
+            Unique identifier for the inference execution.
+        """
+        self.workflow = workflow
+        self.execution_id = execution_id
+        self._result: InferenceRequestResults | None = None
+
+    def _get_result(self) -> InferenceRequestResults:
+        """
+        Return cached result or wait for completion and cache it.
+
+        Returns
+        -------
+        InferenceRequestResults
+            Cached or newly fetched inference results.
+        """
+        if self._result is None:
+            self._result = self.workflow.client.wait_for_completion(self.execution_id)
+        return self._result
+
+    def as_dataset(self) -> xr.Dataset:
+        """
+        Wait for inference to complete and return output dataset.
+
+        Returns
+        -------
+        xr.Dataset
+            Xarray Dataset containing the inference result data.
+
+        Raises
+        ------
+        Earth2StudioAPIError
+            If the request did not return any outputs.
+        ValueError
+            If the result file format is not .zarr or .nc.
+        """
+        request_result = self._get_result()
+        result_paths = request_result.result_paths()
+        if not result_paths:
+            raise Earth2StudioAPIError("The request did not return any outputs.")
+        result_path = result_paths[0]
+
+        if result_path.endswith(".zarr"):
+            if request_result.storage_type == StorageType.S3:
+                # Extract zarr path without execution_id prefix (first path component)
+                zarr_path = "/".join(result_path.split("/")[1:])
+                mapper = fsspec_utils.get_mapper(request_result, zarr_path)
+                ds = xr.open_zarr(mapper, consolidated=True, **self.workflow.xr_args)
+            elif request_result.storage_type == StorageType.SERVER:
+                result_url = urljoin(
+                    self.workflow.base_url + "/",
+                    (
+                        self.workflow.client.result_root_path(request_result)
+                        + result_path
+                    ).lstrip("/"),
+                )
+                # Pass auth token and longer timeout for HTTP requests to zarr store
+                xr_kwargs = dict(self.workflow.xr_args)
+                storage_options = dict(xr_kwargs.pop("storage_options", {}))
+                if self.workflow.client.token:
+                    headers = dict(storage_options.get("headers", {}))
+                    headers["Authorization"] = f"Bearer {self.workflow.client.token}"
+                    storage_options["headers"] = headers
+                # Use at least 300s timeout for zarr reads (fsspec/aiohttp)
+                zarr_timeout = max(300.0, self.workflow.client.timeout)
+                client_kwargs = dict(storage_options.get("client_kwargs", {}))
+                if aiohttp is not None and "timeout" not in client_kwargs:
+                    client_kwargs["timeout"] = aiohttp.ClientTimeout(total=zarr_timeout)
+                storage_options["client_kwargs"] = client_kwargs
+                ds = xr.open_zarr(
+                    result_url,
+                    consolidated=True,
+                    storage_options=storage_options or None,
+                    **xr_kwargs,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported storage type: {request_result.storage_type}"
+                )
+        elif result_path.endswith(".nc"):
+            # TODO: support OpenDAP in the future for remote NetCDF4 access?
+            result_data = self.workflow.client.download_result(
+                request_result, result_path
+            )
+            ds = xr.open_dataset(result_data, engine="netcdf4", **self.workflow.xr_args)
+        else:
+            raise ValueError(
+                f"Unsupported result file format: {result_path!r}. "
+                "Only .zarr and .nc are supported for as_dataset()."
+            )
+
+        return ds
+
+    def as_data_source(self) -> InferenceOutputSource:
+        """
+        Wait for inference to complete and return as DataSource.
+
+        Returns
+        -------
+        DataSource
+            Data source wrapper for the inference results.
+        """
+        ds = self.as_dataset()
+        return InferenceOutputSource(ds)
+
+    def as_model(
+        self, iter_coord: Literal["time", "lead_time"] = "lead_time"
+    ) -> "InferenceOutputModel":
+        """
+        Create model wrapper over inference results for iteration by time step.
+
+        Parameters
+        ----------
+        iter_coord : {"time", "lead_time"}, optional
+            Coordinate to iterate over. Default is "lead_time".
+
+        Returns
+        -------
+        InferenceOutputModel
+            Model that yields (tensor, coordinate_system) per time step.
+        """
+        data_source = self.as_data_source()
+        return InferenceOutputModel(
+            data_source=data_source, iter_coord=iter_coord, device=self.workflow.device
+        )
+
+
+def _convert_time_to_lead_time(
+    x: torch.Tensor, coords: CoordSystem, start_time: np.datetime64
+) -> tuple[torch.Tensor, CoordSystem]:
+    """
+    Convert time coordinate to lead_time coordinate.
+
+    Transforms absolute time coordinates to lead times relative to start_time,
+    adding a time dimension with the start time.
+
+    Parameters
+    ----------
+    x : torch.Tensor
+        Input tensor.
+    coords : CoordSystem
+        Coordinate system containing time coordinates.
+    start_time : np.datetime64
+        Reference start time for lead time calculation.
+
+    Returns
+    -------
+    tuple[torch.Tensor, CoordSystem]
+        Transformed tensor and updated coordinate system.
+    """
+    coords = coords.copy()
+    time = coords["time"]
+    dims = list(coords.keys())
+    time_dim = dims.index("time")
+    if "lead_time" not in dims:
+        raise ValueError(
+            "Cannot convert to lead_time: 'lead_time' coordinate not found in "
+            f"fetch_data output. Available coords: {dims}"
+        )
+    lead_time_dim = dims.index("lead_time")
+    lead_time = time - start_time
+    coords["time"] = np.array([start_time])
+    coords["lead_time"] = lead_time
+    x = x.transpose(time_dim, lead_time_dim)
+    return (x, coords)
+
+
+class InferenceOutputModel(AutoModelMixin, PrognosticMixin):
+    """
+    Prognostic model wrapper for inference output data sources.
+
+    Wraps an InferenceOutputSource to provide a prognostic model interface that
+    can be used in Earth2Studio workflows. Pre-computed inference results are
+    consumed as if generated by a live model.
+
+    Parameters
+    ----------
+    data_source : InferenceOutputSource
+        Pre-computed inference data source.
+    iter_coord : {"time", "lead_time"}, optional
+        Coordinate to iterate over. Default is "lead_time".
+    variables : list[str], optional
+        Variable names to include. If None, uses all from the data source.
+    device : torch.device or str, optional
+        Device for tensor operations (e.g. "cuda", "cpu"). Default is "cpu".
+    """
+
+    def __init__(
+        self,
+        data_source: InferenceOutputSource,
+        iter_coord: Literal["time", "lead_time"] = "lead_time",
+        variables: list[str] | None = None,
+        device: torch.device | str = "cpu",
+    ):
+        self.data_source = data_source
+        self.iter_coord = iter_coord
+        if variables is None:
+            variables = self.data_source.da.coords["variable"]
+        self.variables = np.array(variables)
+        self.device = device
+
+    def input_coords(self) -> CoordSystem:
+        """
+        Return empty input coordinate system.
+
+        This model reads from a pre-computed data source and requires no input
+        coordinates.
+
+        Returns
+        -------
+        CoordSystem
+            Empty ordered dictionary of coordinates.
+        """
+        return OrderedDict(
+            {
+                "batch": np.empty(0),
+                "time": np.empty(0),
+                "lead_time": np.array([np.timedelta64(0, "h")]),
+                "variable": np.array([]),
+                "lat": np.empty(0),
+                "lon": np.empty(0),
+            }
+        )
+
+    def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
+        """
+        Generate output coordinate system based on data source coordinates.
+
+        Constructs the coordinate system for output tensors. Time step is inferred
+        from the first two time coordinates if available, otherwise defaults to 6 hours.
+
+        Parameters
+        ----------
+        input_coords : CoordSystem
+            Input coordinate system (unused; kept for interface compatibility).
+
+        Returns
+        -------
+        CoordSystem
+            Coordinate system with batch, time, lead_time, variable, lat, lon.
+        """
+        time_coord = self.data_source.da.coords["time"][:2].values
+        if len(time_coord) >= 2:
+            time_step = time_coord[1] - time_coord[0]
+        else:
+            # use a placeholder if we only have one time step of data
+            time_step = np.timedelta64(6, "h")
+        output_coords = OrderedDict(
+            {
+                "batch": np.empty(0),
+                "time": np.empty(0),
+                "lead_time": np.array([time_step]),
+                "variable": self.variables,
+                "lat": self.data_source.da.coords["lat"].values,
+                "lon": self.data_source.da.coords["lon"].values,
+            }
+        )
+        return output_coords
+
+    def to(self, device: torch.device | str) -> "InferenceOutputModel":
+        """
+        Move model to specified device.
+
+        Parameters
+        ----------
+        device : torch.device or str
+            Target device ("cuda", "cpu", or torch.device).
+
+        Returns
+        -------
+        InferenceOutputModel
+            self for method chaining.
+        """
+        self.device = device
+        return self
+
+    def __call__(
+        self, x: torch.Tensor | None = None, coords: CoordSystem | None = None
+    ) -> tuple[torch.Tensor, CoordSystem]:
+        """
+        Execute single time-step from the data source.
+
+        Returns the first available time step. Input parameters are ignored;
+        data is read from the pre-computed data source.
+
+        Parameters
+        ----------
+        x : torch.Tensor, optional
+            Input tensor (unused; kept for interface compatibility).
+        coords : CoordSystem, optional
+            Input coordinate system (unused; kept for interface compatibility).
+
+        Returns
+        -------
+        tuple[torch.Tensor, CoordSystem]
+            Output tensor and coordinate system for the first time step.
+        """
+        return next(self.create_iterator(x, coords))
+
+    def create_iterator(
+        self, x: torch.Tensor | None = None, coords: CoordSystem | None = None
+    ) -> Iterator[tuple[torch.Tensor, CoordSystem]]:
+        """
+        Create iterator over time steps from the data source.
+
+        Iterates through all available time steps, optionally converting time
+        coordinates to lead_time relative to the first time step.
+
+        Parameters
+        ----------
+        x : torch.Tensor, optional
+            Input tensor (unused; kept for interface compatibility).
+        coords : CoordSystem, optional
+            Input coordinate system (unused; kept for interface compatibility).
+
+        Yields
+        ------
+        tuple[torch.Tensor, CoordSystem]
+            (tensor, coordinate_system) for each time step in the data source.
+        """
+        times = self.data_source.da.coords["time"].values
+        start_time = times[0]
+        for time in times:
+            (x, coords) = fetch_data(
+                self.data_source,
+                time=np.array([time]),
+                variable=self.variables,
+                device=self.device,
+            )
+            if self.iter_coord == "lead_time":
+                (x, coords) = _convert_time_to_lead_time(x, coords, start_time)
+
+            yield (x, coords)
diff --git a/earth2studio/serve/client/exceptions.py b/earth2studio/serve/client/exceptions.py
new file mode 100644
index 000000000..f3c2de5c4
--- /dev/null
+++ b/earth2studio/serve/client/exceptions.py
@@ -0,0 +1,137 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class Earth2StudioAPIError(Exception):
+    """Base exception for Earth2Studio API errors.
+
+    Parameters
+    ----------
+    message : str
+        Human-readable error message.
+    status_code : int, optional
+        HTTP status code if the error came from an API response.
+    details : str, optional
+        Additional error details.
+    """
+
+    def __init__(
+        self,
+        message: str,
+        status_code: int | None = None,
+        details: str | None = None,
+    ):
+        super().__init__(message)
+        self.message = message
+        self.status_code = status_code
+        self.details = details
+
+    def __str__(self) -> str:
+        """
+        Return a human-readable error string.
+
+        Returns
+        -------
+        str
+            Error string including message, status code (if set), and details (if set).
+        """
+        error_msg = f"Earth2Studio API Error: {self.message}"
+        if self.status_code:
+            error_msg += f" (HTTP {self.status_code})"
+        if self.details:
+            error_msg += f" - {self.details}"
+        return error_msg
+
+
+class BadRequestError(Earth2StudioAPIError):
+    """Exception raised for HTTP 400 Bad Request errors."""
+
+    def __init__(self, message: str, details: str | None = None):
+        """
+        Initialize the exception.
+
+        Parameters
+        ----------
+        message : str
+            Human-readable error message.
+        details : str, optional
+            Additional error details.
+        """
+        super().__init__(message, status_code=400, details=details)
+
+
+class InferenceRequestNotFoundError(Earth2StudioAPIError):
+    """Exception raised for HTTP 404 Not Found errors when inference request is not found."""
+
+    def __init__(self, message: str, details: str | None = None):
+        """
+        Initialize the exception.
+
+        Parameters
+        ----------
+        message : str
+            Human-readable error message.
+        details : str, optional
+            Additional error details.
+        """
+        super().__init__(message, status_code=404, details=details)
+
+
+class InternalServerError(Earth2StudioAPIError):
+    """Exception raised for HTTP 500 Internal Server errors."""
+
+    def __init__(self, message: str, details: str | None = None):
+        """
+        Initialize the exception.
+
+        Parameters
+        ----------
+        message : str
+            Human-readable error message.
+        details : str, optional
+            Additional error details.
+        """
+        super().__init__(message, status_code=500, details=details)
+
+
+class RequestTimeoutError(Earth2StudioAPIError):
+    """Exception raised when a request times out."""
+
+    def __init__(self, message: str = "Request timed out"):
+        """
+        Initialize the exception.
+
+        Parameters
+        ----------
+        message : str, optional
+            Human-readable error message. Default is "Request timed out".
+        """
+        super().__init__(message)
+
+
+class APIConnectionError(Earth2StudioAPIError):
+    """Exception raised when connection to the API fails."""
+
+    def __init__(self, message: str = "Failed to connect to Earth2Studio API"):
+        """
+        Initialize the exception.
+
+        Parameters
+        ----------
+        message : str, optional
+            Human-readable error message. Default is "Failed to connect to Earth2Studio API".
+        """
+        super().__init__(message)
diff --git a/earth2studio/serve/client/fsspec_utils.py b/earth2studio/serve/client/fsspec_utils.py
new file mode 100644
index 000000000..c121e7108
--- /dev/null
+++ b/earth2studio/serve/client/fsspec_utils.py
@@ -0,0 +1,302 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Any, NoReturn
+from urllib.parse import parse_qs, urlencode, urlparse
+
+import fsspec  # type: ignore[import-untyped]
+
+from earth2studio.serve.client.models import InferenceRequestResults, StorageType
+
+
+class SignedURLFileSystem(fsspec.AbstractFileSystem):
+    """
+    FSSpec filesystem that appends signed params to requests.
+
+    Wraps a base HTTP filesystem and URL; converts 403 responses to
+    FileNotFoundError for compatibility with consumers like Zarr.
+    """
+
+    def __init__(
+        self, base_fs: Any, query_params: dict[str, str], base_url: str
+    ) -> None:
+        """
+        Wrap a base filesystem and URL with query params for signed requests.
+
+        Parameters
+        ----------
+        base_fs : Any
+            Base fsspec filesystem (e.g. HTTP).
+        query_params : dict[str, str]
+            Query parameters to append (e.g. Policy, Signature, Key-Pair-Id).
+        base_url : str
+            Base URL for the store.
+        """
+        super().__init__()
+        self._fs = base_fs
+        self._query_params = query_params
+        self._base_url = base_url
+        self._query_string = urlencode(query_params, safe="~")
+
+    def _make_signed_path(self, path: str) -> str:
+        """
+        Return the path with signed query parameters appended.
+
+        Parameters
+        ----------
+        path : str
+            Relative or absolute path/URL.
+
+        Returns
+        -------
+        str
+            Full URL with signed query string.
+        """
+        if path.startswith("http"):
+            full_url = path
+        else:
+            clean_path = path.lstrip("/")
+            full_url = (
+                f"{self._base_url}/{clean_path}" if clean_path else self._base_url
+            )
+        separator = "&" if "?" in full_url else "?"
+        return f"{full_url}{separator}{self._query_string}"
+
+    def _handle_403(self, e: BaseException, path: str) -> "NoReturn":
+        """
+        Convert 403 errors to FileNotFoundError; re-raise others.
+
+        Parameters
+        ----------
+        e : BaseException
+            Caught exception.
+        path : str
+            Path that was accessed (for error message).
+        """
+        error_str = str(e).lower()
+        if "403" in str(e) or "forbidden" in error_str:
+            raise FileNotFoundError(f"File not found: {path}") from None
+        raise e
+
+    def _open(self, path: str, mode: str = "rb", **kwargs: Any) -> Any:
+        """
+        Open a path with signed URL; 403 is converted to FileNotFoundError.
+
+        Parameters
+        ----------
+        path : str
+            Path or URL to open.
+        mode : str, optional
+            Open mode (e.g. "rb"). Default is "rb".
+        **kwargs : Any
+            Passed to the underlying filesystem.
+
+        Returns
+        -------
+        Any
+            File-like object from the base filesystem.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the server returns 403 (forbidden).
+        """
+        try:
+            return self._fs._open(self._make_signed_path(path), mode=mode, **kwargs)
+        except Exception as e:
+            self._handle_403(e, path)
+
+    def cat_file(
+        self, path: str, start: int | None = None, end: int | None = None, **kwargs: Any
+    ) -> Any:
+        """
+        Read file contents with signed URL; 403 is converted to FileNotFoundError.
+
+        Parameters
+        ----------
+        path : str
+            Path or URL to read.
+        start : int, optional
+            Start byte offset.
+        end : int, optional
+            End byte offset.
+        **kwargs : Any
+            Passed to the underlying filesystem.
+
+        Returns
+        -------
+        Any
+            File contents (typically bytes).
+
+        Raises
+        ------
+        FileNotFoundError
+            If the server returns 403.
+        """
+        try:
+            return self._fs.cat_file(
+                self._make_signed_path(path), start=start, end=end, **kwargs
+            )
+        except Exception as e:
+            self._handle_403(e, path)
+
+    def _cat_file(
+        self, path: str, start: int | None = None, end: int | None = None, **kwargs: Any
+    ) -> Any:
+        """
+        Async cat_file used by Zarr; delegates to cat_file.
+
+        Parameters
+        ----------
+        path : str
+            Path or URL to read.
+        start : int, optional
+            Start byte offset.
+        end : int, optional
+            End byte offset.
+        **kwargs : Any
+            Passed to cat_file.
+
+        Returns
+        -------
+        Any
+            File contents.
+        """
+        return self.cat_file(path, start=start, end=end, **kwargs)
+
+    def info(self, path: str, **kwargs: Any) -> Any:
+        """
+        Return metadata for path with signed URL; 403 becomes FileNotFoundError.
+
+        Parameters
+        ----------
+        path : str
+            Path or URL.
+        **kwargs : Any
+            Passed to the underlying filesystem.
+
+        Returns
+        -------
+        Any
+            Metadata dict for the path.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the server returns 403.
+        """
+        try:
+            return self._fs.info(self._make_signed_path(path), **kwargs)
+        except Exception as e:
+            self._handle_403(e, path)
+
+    def exists(self, path: str, **kwargs: Any) -> bool:
+        """
+        Return True if path exists; 403 is treated as not found.
+
+        Parameters
+        ----------
+        path : str
+            Path or URL to check.
+        **kwargs : Any
+            Passed to the underlying filesystem.
+
+        Returns
+        -------
+        bool
+            True if path exists, False if not found or 403.
+        """
+        try:
+            return self._fs.exists(self._make_signed_path(path), **kwargs)
+        except Exception as e:
+            try:
+                self._handle_403(e, path)
+            except FileNotFoundError:
+                return False
+
+
+def create_cloudfront_mapper(signed_url: str, zarr_path: str = "") -> Any:
+    """
+    Create an fsspec mapper for a CloudFront signed Zarr URL.
+
+    Parameters
+    ----------
+    signed_url : str
+        CloudFront signed URL with Policy, Signature, and Key-Pair-Id query params.
+    zarr_path : str, optional
+        Subpath within the signed URL to the Zarr store. Default is "".
+
+    Returns
+    -------
+    fsspec.mapping.FSMap
+        Mapper suitable for use with xarray.open_zarr().
+    """
+    # Parse the URL
+    parsed = urlparse(signed_url)
+
+    # Extract query parameters
+    query_params = {k: v[0] for k, v in parse_qs(parsed.query).items()}
+
+    # Get base path (remove trailing /* if present)
+    base_path = parsed.path.rstrip("/*").rstrip("*")
+
+    # Reconstruct base URL
+    if zarr_path != "":
+        base_url = f"{parsed.scheme}://{parsed.netloc}{base_path}/{zarr_path}"
+    else:
+        base_url = f"{parsed.scheme}://{parsed.netloc}{base_path}"
+
+    # Create HTTP filesystem
+    fs = fsspec.filesystem("https")
+
+    signed_fs = SignedURLFileSystem(fs, query_params, base_url)
+    mapper = fsspec.mapping.FSMap(root="", fs=signed_fs, check=False, create=False)
+
+    return mapper
+
+
+def get_mapper(
+    request_result: InferenceRequestResults, zarr_path: str = ""
+) -> Any | None:
+    """
+    Return an fsspec mapper for the result, or None for SERVER storage.
+
+    Parameters
+    ----------
+    request_result : InferenceRequestResults
+        Inference result containing storage type and optional signed_url.
+    zarr_path : str, optional
+        Subpath to the Zarr store within the result. Default is "".
+
+    Returns
+    -------
+    fsspec.mapping.FSMap or None
+        Mapper for S3/signed URL storage, or None for SERVER storage.
+
+    Raises
+    ------
+    ValueError
+        If storage type is S3 but signed_url is missing, or storage type is unsupported.
+    """
+    if request_result.storage_type == StorageType.S3:
+        if request_result.signed_url is None:
+            raise ValueError("S3 storage type requires a signed URL")
+        return create_cloudfront_mapper(request_result.signed_url, zarr_path)
+    elif request_result.storage_type == StorageType.SERVER:
+        return None
+    else:
+        raise ValueError(f"Unsupported storage type: {request_result.storage_type}")
diff --git a/earth2studio/serve/client/models.py b/earth2studio/serve/client/models.py
new file mode 100644
index 000000000..60e3f9234
--- /dev/null
+++ b/earth2studio/serve/client/models.py
@@ -0,0 +1,314 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import json
+from dataclasses import dataclass
+from datetime import date, datetime, timedelta
+from enum import Enum
+from typing import Any
+
+import numpy as np
+
+
+class StorageType(str, Enum):
+    """Storage type for inference results."""
+
+    SERVER = "server"
+    S3 = "s3"
+
+
+class RequestStatus(Enum):
+    """Status values for inference requests."""
+
+    # Initial statuses
+    ACCEPTED = "accepted"
+    QUEUED = "queued"
+
+    # Processing statuses
+    RUNNING = "running"
+
+    # Final statuses
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+    PENDING_RESULTS = "pending_results"
+
+
+@dataclass
+class InferenceRequest:
+    """Main inference request payload."""
+
+    parameters: dict[str, Any] | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert to dictionary for JSON serialization.
+
+        Returns
+        -------
+        dict[str, Any]
+            Dictionary suitable for JSON request body (e.g. {"parameters": ...}).
+        """
+        # use custom JSON encoder first to convert objects that are normally not serializable
+        parameters = json.loads(
+            json.dumps(self.parameters, default=InferenceRequest.json_serial)
+        )
+        return {"parameters": parameters}
+
+    @staticmethod
+    def json_serial(obj: Any) -> Any:
+        """
+        JSON serializer for objects not serializable by default json code.
+
+        Parameters
+        ----------
+        obj : Any
+            Object to serialize (numpy array, datetime, timedelta, etc.).
+
+        Returns
+        -------
+        Any
+            JSON-serializable representation of the object.
+        """
+        if isinstance(obj, np.ndarray):
+            return list(obj)
+        elif isinstance(obj, (datetime, date)):
+            return obj.isoformat()
+        elif isinstance(obj, timedelta):
+            return obj.total_seconds()
+        elif isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.datetime64):
+            return str(obj)
+        elif isinstance(obj, np.timedelta64):
+            return obj / np.timedelta64(1, "s")  # seconds as float
+        else:
+            raise TypeError(
+                f"Object of type {type(obj).__name__} is not JSON serializable"
+            )
+
+
+@dataclass
+class InferenceRequestResponse:
+    """Response when submitting an inference request."""
+
+    execution_id: str
+    status: RequestStatus
+    message: str
+    timestamp: datetime
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestResponse":
+        """
+        Build an InferenceRequestResponse from an API response dict.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            API response dictionary containing execution_id, status, message, timestamp.
+
+        Returns
+        -------
+        InferenceRequestResponse
+            Constructed response instance.
+        """
+        return cls(
+            execution_id=data["execution_id"],
+            status=RequestStatus(data["status"]),
+            message=data["message"],
+            timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
+        )
+
+
+@dataclass
+class ProgressInfo:
+    """Progress information for inference requests."""
+
+    progress: str
+    current_step: int
+    total_steps: int
+
+
+@dataclass
+class InferenceRequestStatus:
+    """Status information for an inference request."""
+
+    execution_id: str
+    status: RequestStatus
+    progress: ProgressInfo | None
+    error_message: str | None
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestStatus":
+        """
+        Build an InferenceRequestStatus from an API response dict.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            API response dictionary containing execution_id, status, progress, error_message.
+
+        Returns
+        -------
+        InferenceRequestStatus
+            Constructed status instance.
+        """
+        progress_data = data.get("progress")
+        progress = (
+            ProgressInfo(
+                progress=progress_data["progress"],
+                current_step=progress_data["current_step"],
+                total_steps=progress_data["total_steps"],
+            )
+            if progress_data is not None
+            else None
+        )
+
+        return cls(
+            execution_id=data["execution_id"],
+            status=RequestStatus(data["status"]),
+            progress=progress,
+            error_message=data.get("error_message"),
+        )
+
+
+@dataclass
+class OutputFile:
+    """Information about an output file."""
+
+    path: str
+    size: int
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "OutputFile":
+        """
+        Build an OutputFile from an API response dict.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            API response dictionary containing path and size.
+
+        Returns
+        -------
+        OutputFile
+            Constructed output file instance.
+        """
+        return cls(path=data["path"], size=data["size"])
+
+
+@dataclass
+class InferenceRequestResults:
+    """Results of a completed inference request."""
+
+    request_id: str
+    status: RequestStatus
+    output_files: list[OutputFile]
+    completion_time: datetime | None
+    execution_time_seconds: float | None = None
+    storage_type: StorageType = StorageType.SERVER
+    signed_url: str | None = None
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestResults":
+        """
+        Build InferenceRequestResults from an API response dict.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            API response dictionary containing request_id, status, output_files,
+            completion_time, and optional execution_time_seconds, storage_type, signed_url.
+
+        Returns
+        -------
+        InferenceRequestResults
+            Constructed results instance.
+        """
+        output_files = [
+            OutputFile.from_dict(file_data) for file_data in data["output_files"]
+        ]
+        completion_timestamp = data.get("completion_time")
+        completion_time = (
+            datetime.fromisoformat(completion_timestamp.replace("Z", "+00:00"))
+            if completion_timestamp is not None
+            else None
+        )
+
+        return cls(
+            request_id=data["request_id"],
+            status=RequestStatus(data["status"]),
+            output_files=output_files,
+            completion_time=completion_time,
+            execution_time_seconds=data.get("execution_time_seconds"),
+            storage_type=StorageType(
+                data.get("storage_type", StorageType.SERVER.value)
+            ),
+            signed_url=data.get("signed_url"),
+        )
+
+    def result_paths(self) -> list[str]:
+        """
+        Get paths to result data files found in the request response.
+
+        Returns
+        -------
+        list[str]
+            List of result paths (e.g. zarr roots or .nc files). Append to the
+            server results URL to download or open.
+        """
+        # locate the root of zarr store
+        zarr_paths = {
+            f.path
+            for f in self.output_files
+            if (".zarr/" in f.path) or f.path.endswith(".zarr")
+        }
+        zarr_paths_sorted = sorted(
+            {path[: path.find(".zarr") + len(".zarr")] for path in zarr_paths}
+        )
+        netcdf_paths = [f.path for f in self.output_files if f.path.endswith(".nc")]
+        return zarr_paths_sorted + netcdf_paths
+
+
+@dataclass
+class HealthStatus:
+    """Health status response."""
+
+    status: str
+    timestamp: datetime
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "HealthStatus":
+        """
+        Build a HealthStatus from an API response dict.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            API response dictionary containing status and timestamp.
+
+        Returns
+        -------
+        HealthStatus
+            Constructed health status instance.
+        """
+        return cls(
+            status=data["status"],
+            timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
+        )
diff --git a/serve/inferenceserver/api_server/workflow/__init__.py b/earth2studio/serve/server/__init__.py
similarity index 62%
rename from serve/inferenceserver/api_server/workflow/__init__.py
rename to earth2studio/serve/server/__init__.py
index 91d3cf418..475e291bf 100644
--- a/serve/inferenceserver/api_server/workflow/__init__.py
+++ b/earth2studio/serve/server/__init__.py
@@ -14,16 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Workflow package for custom workflows.
-
-This package provides the base classes and functionality for creating custom workflows
-that can be served as REST APIs.
-"""
-
 from typing import Any
 
-from .workflow import (
+from earth2studio.serve.server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
@@ -37,26 +30,9 @@
 )
 
 
-# Lazy import for Earth2Workflow to avoid requiring torch/earth2studio dependencies
-# unless actually needed
 def __getattr__(name: str) -> Any:
     if name == "Earth2Workflow":
-        from .e2workflow import Earth2Workflow
+        from earth2studio.serve.server import e2workflow
 
-        return Earth2Workflow
+        return e2workflow.Earth2Workflow
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-__all__ = [
-    "Workflow",
-    "WorkflowParameters",
-    "WorkflowProgress",
-    "WorkflowResult",
-    "WorkflowStatus",
-    "WorkflowRegistry",
-    "workflow_registry",
-    "get_workflow_config",
-    "register_all_workflows",
-    "parse_workflow_directories_from_env",
-    "Earth2Workflow",
-]
diff --git a/serve/inferenceserver/api_server/cleanup_daemon.py b/earth2studio/serve/server/cleanup_daemon.py
similarity index 82%
rename from serve/inferenceserver/api_server/cleanup_daemon.py
rename to earth2studio/serve/server/cleanup_daemon.py
index 689f88d2f..4a0b29498 100644
--- a/serve/inferenceserver/api_server/cleanup_daemon.py
+++ b/earth2studio/serve/server/cleanup_daemon.py
@@ -14,13 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Cleanup Daemon for API Server
-
-This daemon runs as a separate process and periodically cleans up expired
-inference results based on the configured TTL (time-to-live).
-"""
-
 import json
 import logging
 import shutil
@@ -34,36 +27,21 @@
 
 import redis  # type: ignore[import-untyped]
 
-from api_server.config import get_config, get_config_manager
-from api_server.workflow import WorkflowStatus
+from earth2studio.serve.server.config import get_config, get_config_manager
+from earth2studio.serve.server.workflow import WorkflowStatus
 
-# Get configuration
-config = get_config()
+# Configure logging (config is obtained in main())
 config_manager = get_config_manager()
-
-# Configure logging
 config_manager.setup_logging()
 logger = logging.getLogger(__name__)
 
-# Path configuration from config
-DEFAULT_OUTPUT_DIR = Path(config.paths.default_output_dir)
-RESULTS_ZIP_DIR = Path(config.paths.results_zip_dir)
-
-# Global flag for graceful shutdown
-shutdown_flag = False
-
-
-def signal_handler(signum: int, frame: Any) -> None:
-    """Handle shutdown signals gracefully"""
-    global shutdown_flag
-    logger.info(f"Received signal {signum}, initiating graceful shutdown...")
-    shutdown_flag = True
-
 
 def _delete_result_files(
     redis_client: redis.Redis,
     result_id: str,
     search_id: str,
+    default_output_dir: Path,
+    results_zip_dir: Path,
     workflow_name: str | None = None,
 ) -> None:
     """
@@ -73,19 +51,21 @@ def _delete_result_files(
         redis_client: Redis client
         result_id: ID to use for zip file lookup (request_id or combined_id)
         search_id: ID to search for in directory names
+        default_output_dir: Base directory for raw result outputs
+        results_zip_dir: Directory for result zip files and metadata
         workflow_name: Optional workflow name for workflow-specific subdirectory checking
     """
     # Delete zip file
     zip_key = f"inference_request_zips:{result_id}:zip_file"
     zip_filename = redis_client.get(zip_key)
     if zip_filename:
-        zip_path = RESULTS_ZIP_DIR / zip_filename
+        zip_path = results_zip_dir / zip_filename
         if zip_path.exists():
             zip_path.unlink()
             logger.info(f"Deleted zip file: {zip_path}")
 
     # Delete metadata file (metadata_{request_id}.json)
-    for item in RESULTS_ZIP_DIR.iterdir():
+    for item in results_zip_dir.iterdir():
         if (
             item.is_file()
             and item.name.startswith("metadata")
@@ -95,20 +75,20 @@ def _delete_result_files(
             logger.info(f"Deleted metadata file: {item}")
 
     # Delete raw results directory (search for directories containing search_id)
-    for item in DEFAULT_OUTPUT_DIR.iterdir():
+    for item in default_output_dir.iterdir():
         if item.is_dir() and search_id in item.name:
             shutil.rmtree(item)
             logger.info(f"Deleted raw results directory: {item}")
 
     # Also check subdirectories recursively for results
-    for item in RESULTS_ZIP_DIR.iterdir():
+    for item in results_zip_dir.iterdir():
         if item.is_dir() and search_id in item.name:
             shutil.rmtree(item)
             logger.info(f"Deleted results from zip dir: {item}")
 
     # Check in workflow-specific subdirectories if workflow_name is provided
     if workflow_name:
-        workflow_dir = DEFAULT_OUTPUT_DIR / workflow_name
+        workflow_dir = default_output_dir / workflow_name
         if workflow_dir.exists():
             for item in workflow_dir.iterdir():
                 if item.is_dir() and search_id in item.name:
@@ -121,6 +101,7 @@ def _process_expired_key(
     key: str,
     current_time: datetime,
     results_ttl_hours: float,
+    retention_ttl: int,
     expected_status: str,
     get_end_time_field: str,
     delete_files_func: Callable[[redis.Redis, str], None],
@@ -134,6 +115,7 @@ def _process_expired_key(
         key: Redis key to process
         current_time: Current timestamp for age calculation
         results_ttl_hours: TTL threshold in hours
+        retention_ttl: Redis TTL in seconds for the key after marking expired
         expected_status: Expected status value for completed items
         get_end_time_field: Primary field name for end time
         delete_files_func: Function to call for deleting files
@@ -180,7 +162,7 @@ def _process_expired_key(
         data["status"] = WorkflowStatus.EXPIRED
         redis_client.setex(
             key,
-            config.redis.retention_ttl,
+            retention_ttl,
             json.dumps(data),
         )
 
@@ -189,7 +171,13 @@ def _process_expired_key(
     return False
 
 
-def cleanup_expired_results(redis_client: redis.Redis) -> None:
+def cleanup_expired_results(
+    redis_client: redis.Redis,
+    results_ttl_hours: float,
+    retention_ttl: int,
+    default_output_dir: Path,
+    results_zip_dir: Path,
+) -> None:
     """
     Check Redis for expired results and clean them up.
 
@@ -198,8 +186,6 @@ def cleanup_expired_results(redis_client: redis.Redis) -> None:
     2. Deletes both raw result files and zip files
     3. Updates the status to "expired"
     """
-    results_ttl_hours = config.server.results_ttl_hours
-
     logger.info("Running cleanup watchdog check...")
     current_time = datetime.now(timezone.utc)
 
@@ -231,6 +217,8 @@ def delete_func(
                         rc,
                         result_id=_combined_id,
                         search_id=_execution_id,
+                        default_output_dir=default_output_dir,
+                        results_zip_dir=results_zip_dir,
                         workflow_name=_workflow_name,
                     )
 
@@ -239,6 +227,7 @@ def delete_func(
                     key=key,
                     current_time=current_time,
                     results_ttl_hours=results_ttl_hours,
+                    retention_ttl=retention_ttl,
                     expected_status=WorkflowStatus.COMPLETED,
                     get_end_time_field="end_time",
                     delete_files_func=delete_func,
@@ -257,7 +246,16 @@ def delete_func(
 
 def main() -> None:
     """Main daemon loop"""
-    global shutdown_flag
+    config = get_config()
+    default_output_dir = Path(config.paths.default_output_dir)
+    results_zip_dir = Path(config.paths.results_zip_dir)
+
+    state: dict[str, bool] = {"shutdown": False}
+
+    def signal_handler(signum: int, frame: Any) -> None:
+        """Handle shutdown signals gracefully."""
+        logger.info(f"Received signal {signum}, initiating graceful shutdown...")
+        state["shutdown"] = True
 
     # Set up signal handlers
     signal.signal(signal.SIGINT, signal_handler)
@@ -287,15 +285,21 @@ def main() -> None:
 
     # Main daemon loop
     try:
-        while not shutdown_flag:
+        while not state["shutdown"]:
             try:
-                cleanup_expired_results(redis_client)
+                cleanup_expired_results(
+                    redis_client,
+                    results_ttl_hours=config.server.results_ttl_hours,
+                    retention_ttl=config.redis.retention_ttl,
+                    default_output_dir=default_output_dir,
+                    results_zip_dir=results_zip_dir,
+                )
             except Exception as e:
                 logger.exception(f"Error in cleanup cycle: {e}")
 
             # Sleep for the configured interval, checking shutdown flag periodically
             sleep_remaining = config.server.cleanup_watchdog_sec
-            while sleep_remaining > 0 and not shutdown_flag:
+            while sleep_remaining > 0 and not state["shutdown"]:
                 sleep_time = min(5, sleep_remaining)  # Check every 5 seconds
                 time.sleep(sleep_time)
                 sleep_remaining -= sleep_time
diff --git a/serve/inferenceserver/api_server/config.py b/earth2studio/serve/server/config.py
similarity index 95%
rename from serve/inferenceserver/api_server/config.py
rename to earth2studio/serve/server/config.py
index fc1f86fee..69bc6a353 100644
--- a/serve/inferenceserver/api_server/config.py
+++ b/earth2studio/serve/server/config.py
@@ -14,14 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Configuration Management for Earth2Studio API Server
-
-This module provides centralized configuration management using Hydra.
-All configuration values are defined here with sensible defaults and can be
-overridden via YAML files or command-line arguments.
-"""
-
 import logging
 import os
 from dataclasses import dataclass, field
@@ -206,8 +198,13 @@ def __init__(self) -> None:
     def _initialize_config(self) -> None:
         """Initialize configuration from Hydra"""
         try:
-            # Get config directory
-            config_dir = Path(__file__).parent / "conf"
+            # Prefer CONFIG_DIR env var (required when package is installed without repo layout)
+            config_dir_env = os.environ.get("CONFIG_DIR")
+            if config_dir_env:
+                config_dir = Path(config_dir_env)
+            else:
+                _repo_root = Path(__file__).resolve().parent.parent.parent.parent
+                config_dir = _repo_root / "serve" / "server" / "conf"
 
             # Clear any existing Hydra instance
             GlobalHydra.instance().clear()
@@ -517,10 +514,6 @@ def workflow_config(self) -> dict[str, Any]:
         return self._workflow_config
 
 
-# Global configuration instance
-_config_manager: ConfigManager | None = None
-
-
 def get_config() -> AppConfig:
     """
     Get the global configuration instance.
@@ -529,7 +522,7 @@ def get_config() -> AppConfig:
         AppConfig: The application configuration
 
     Example:
-        >>> from api_server.config import get_config
+        >>> from earth2studio.serve.server.config import get_config
         >>> config = get_config()
         >>> print(config.redis.host)
         'localhost'
@@ -539,27 +532,23 @@ def get_config() -> AppConfig:
 
 def get_config_manager() -> ConfigManager:
     """
-    Get the global configuration manager instance.
+    Get the configuration manager instance (singleton).
 
     Returns:
         ConfigManager: The configuration manager
 
     Example:
-        >>> from api_server.config import get_config_manager
+        >>> from earth2studio.serve.server.config import get_config_manager
         >>> manager = get_config_manager()
         >>> manager.setup_logging()
     """
-    global _config_manager
-    if _config_manager is None:
-        _config_manager = ConfigManager()
-    return _config_manager
+    return ConfigManager()
 
 
 # Convenience function to reset config (mainly for testing)
 def reset_config() -> None:
-    """Reset the global configuration instance (mainly for testing)"""
-    global _config_manager
-    _config_manager = None
+    """Reset the configuration manager singleton (mainly for testing)."""
+    ConfigManager._instance = None
     GlobalHydra.instance().clear()
 
 
@@ -571,7 +560,7 @@ def get_workflow_config(name: str) -> dict[str, Any]:
         dict[str, Any]: The workflow configuration
 
     Example:
-        >>> from api_server.config import get_workflow_config
+        >>> from earth2studio.serve.server.config import get_workflow_config
         >>> config = get_workflow_config("deterministic_earth2_workflow")
         >>> print(config["model_type"])
         'fcn'
diff --git a/serve/inferenceserver/api_server/cpu_worker.py b/earth2studio/serve/server/cpu_worker.py
similarity index 98%
rename from serve/inferenceserver/api_server/cpu_worker.py
rename to earth2studio/serve/server/cpu_worker.py
index 99ac82099..78248ad6f 100644
--- a/serve/inferenceserver/api_server/cpu_worker.py
+++ b/earth2studio/serve/server/cpu_worker.py
@@ -29,6 +29,7 @@
     "process_finalize_metadata",
 ]
 
+
 import json
 import logging
 import zipfile
@@ -40,8 +41,8 @@
 import redis  # type: ignore[import-untyped]
 
 # Import configuration
-from api_server.config import get_config, get_config_manager
-from api_server.utils import (
+from earth2studio.serve.server.config import get_config, get_config_manager
+from earth2studio.serve.server.utils import (
     get_inference_request_metadata_key,
     get_inference_request_output_path_key,
     get_inference_request_zip_key,
@@ -49,7 +50,7 @@
     get_signed_url_key,
     queue_next_stage,
 )
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     WorkflowResult,
     WorkflowStatus,
     workflow_registry,
@@ -76,7 +77,7 @@
 
 # Register custom workflows in the CPU worker process
 try:
-    from api_server.workflow import register_all_workflows
+    from earth2studio.serve.server.workflow import register_all_workflows
 
     register_all_workflows(redis_client)
     logger.info("Custom workflows registered successfully in CPU worker process")
@@ -327,14 +328,14 @@ def create_results_zip(
         file_manifest = build_file_manifest(output_path)
 
         if not output_path.exists():
-            logger.warning(f"Output path {output_path} does not exist")
+            log.warning(f"Output path {output_path} does not exist")
 
         # Create zip file only if requested
         if create_zip:
             if zip_filename is None:
                 zip_filename = f"{request_id}"
             zip_path = results_zip_dir / zip_filename
-            logger.info(f"Creating results zip file: {zip_path}")
+            log.info(f"Creating results zip file: {zip_path}")
 
             with zipfile.ZipFile(
                 zip_path, "w", zipfile.ZIP_DEFLATED, compresslevel=6
@@ -368,9 +369,9 @@ def create_results_zip(
                     FileManifestEntry(path=zip_filename, size=zip_size)
                 )
 
-                logger.info(f"Created zip file {zip_filename} ({zip_size} bytes)")
+                log.info(f"Created zip file {zip_filename} ({zip_size} bytes)")
         else:
-            logger.info(
+            log.info(
                 f"Skipping zip creation for {request_id}, manifest has {len(file_manifest)} files"
             )
             zip_filename = None  # No zip file created
@@ -413,7 +414,7 @@ def create_results_zip(
             config.redis.retention_ttl,
             str(results_zip_dir),
         )
-        logger.info(f"Stored pending metadata in Redis for {request_id}")
+        log.info(f"Stored pending metadata in Redis for {request_id}")
 
         # Store zip filename association in Redis (only if zip was created)
         if zip_filename:
@@ -559,7 +560,7 @@ def process_object_storage_upload(
             config.object_storage.bucket
             or config.object_storage.storage_type == "azure"
         ):
-            from api_server.object_storage import (
+            from earth2studio.serve.server.object_storage import (
                 MSCObjectStorage,
                 ObjectStorageError,
             )
@@ -573,7 +574,7 @@ def process_object_storage_upload(
                 )
 
             # Create S3 storage instance
-            storage_kwargs = {
+            storage_kwargs: dict[str, Any] = {
                 "bucket": config.object_storage.bucket
                 or config.object_storage.azure_container_name
                 or "",
@@ -1024,7 +1025,6 @@ def process_finalize_metadata(
     Returns:
         Dict containing result info, None on critical failure
     """
-    from api_server.workflow import WorkflowStatus, workflow_registry
 
     request_id = f"{workflow_name}:{execution_id}"
     logger.info(f"Processing finalize metadata for {request_id}")
@@ -1039,7 +1039,6 @@ def process_finalize_metadata(
     storage_info_json = redis_client.get(storage_info_key)
 
     if not pending_metadata_json or not results_zip_dir_str:
-        logger.error(f"Pending metadata not found in Redis for {request_id}")
         return fail_workflow(
             workflow_name,
             execution_id,
diff --git a/serve/inferenceserver/api_server/workflow/e2workflow.py b/earth2studio/serve/server/e2workflow.py
similarity index 97%
rename from serve/inferenceserver/api_server/workflow/e2workflow.py
rename to earth2studio/serve/server/e2workflow.py
index 3e52df372..e71719de5 100644
--- a/serve/inferenceserver/api_server/workflow/e2workflow.py
+++ b/earth2studio/serve/server/e2workflow.py
@@ -24,12 +24,16 @@
 import zarr
 from pydantic import Field, create_model
 
-from api_server.config import get_config
 from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
+from earth2studio.serve.server.config import get_config
+from earth2studio.serve.server.workflow import (
+    Workflow,
+    WorkflowConfig,
+    WorkflowParameters,
+    WorkflowProgress,
+)
 from earth2studio.utils.type import CoordSystem
 
-from .workflow import Workflow, WorkflowConfig, WorkflowParameters, WorkflowProgress
-
 
 def _convert_param(param: inspect.Parameter, typeinfo: type) -> tuple[type, Field]:
     """Convert parameter to Pydantic Field declaration with validation constraints"""
@@ -228,7 +232,7 @@ class BackendProgress:
     def __init__(
         self,
         io: IOBackend,
-        workflow: Workflow,
+        workflow: "Earth2Workflow",
         execution_id: str,
         progress_dim: str = "lead_time",
     ) -> None:
diff --git a/serve/inferenceserver/api_server/main.py b/earth2studio/serve/server/main.py
similarity index 83%
rename from serve/inferenceserver/api_server/main.py
rename to earth2studio/serve/server/main.py
index 96905a618..3673c9c96 100644
--- a/serve/inferenceserver/api_server/main.py
+++ b/earth2studio/serve/server/main.py
@@ -15,15 +15,16 @@
 # limitations under the License.
 
 """
-Earth2Studio REST API Server
+Earth2Studio REST API server.
 
-This module provides a REST API interface for running Earth2Studio workflows
-including deterministic, diagnostic, ensemble, and custom inference workflows.
+Provides FastAPI endpoints for workflow execution, status, and result retrieval,
+with Redis and RQ for job queuing and Prometheus metrics.
 """
 
 import asyncio
 import json
 import logging
+import os
 import time
 import uuid
 from collections.abc import AsyncGenerator
@@ -35,6 +36,7 @@
 import aiofiles  # type: ignore[import-untyped]
 import redis as redis_sync  # type: ignore[import-untyped]  # For RQ (synchronous)
 import redis.asyncio as redis  # type: ignore[import-untyped]
+import uvicorn
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import Response, StreamingResponse
@@ -43,8 +45,8 @@
 from rq import Queue
 
 # Import configuration
-from api_server.config import get_config, get_config_manager
-from api_server.utils import (
+from earth2studio.serve.server.config import get_config, get_config_manager
+from earth2studio.serve.server.utils import (
     create_file_stream,
     get_inference_request_output_path_key,
     get_inference_request_zip_key,
@@ -52,7 +54,7 @@
 )
 
 # Import workflow registry
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     WorkflowResult,
     WorkflowStatus,
     workflow_registry,
@@ -80,13 +82,16 @@ def check_admission_control() -> None:
     """
     Check if the inference request can be admitted based on queue sizes.
 
-    This function checks all relevant queues (inference, result zip, object storage,
-    and finalize metadata) to ensure none of them are at capacity before allowing
+    Checks all relevant queues (inference, result zip, object storage,
+    and finalize metadata) to ensure none are at capacity before allowing
     a new request to be enqueued.
 
-    Raises:
-        HTTPException: 429 status if any queue is full, indicating the service
-            is temporarily unavailable and the client should retry later.
+    Raises
+    ------
+    HTTPException
+        429 if any queue is full (service temporarily unavailable; retry later).
+        503 if Redis is not initialized.
+        500 if queue status cannot be determined.
     """
     queue_names = [
         config.queue.name,
@@ -124,14 +129,18 @@ def get_queue_position(job_id: str) -> int | None:
     """
     Get the position of a job in the queue.
 
-    Uses RQ's Queue.job_ids property which correctly returns the job IDs
+    Uses RQ's Queue.job_ids property, which correctly returns job IDs
     including custom job_id values set during enqueue.
 
-    Args:
-        job_id: The ID of the job to find
+    Parameters
+    ----------
+    job_id : str
+        The ID of the job to find.
 
-    Returns:
-        Position in queue (0-indexed), or None if not found in queue
+    Returns
+    -------
+    int or None
+        Position in queue (0-indexed), or None if not found (e.g. picked up by worker).
     """
     if not inference_queue:
         logger.warning("Inference queue not available for position lookup")
@@ -167,7 +176,12 @@ def get_queue_position(job_id: str) -> int | None:
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
-    """Lifespan event handler for application startup and shutdown"""
+    """
+    Lifespan context manager for FastAPI application startup and shutdown.
+
+    On startup: connects to Redis (async and sync), initializes the RQ inference
+    queue, and registers custom workflows. On shutdown: closes Redis connections.
+    """
     # Startup
     global redis_client, redis_sync_client, inference_queue
     try:
@@ -211,7 +225,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 
         # Register custom workflows
         try:
-            from api_server.workflow import register_all_workflows
+            from earth2studio.serve.server.workflow import register_all_workflows
 
             register_all_workflows(redis_sync_client)
             logger.info("Custom workflows registered successfully")
@@ -265,10 +279,30 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 @app.get("/health")
 @app.get("/readiness")
 async def health_check() -> dict[str, str]:
-    """Health check endpoint using status script"""
+    """
+    Health and readiness check endpoint.
+
+    Runs the status script and returns overall health based on its exit code.
+
+    Returns
+    -------
+    dict
+        Keys ``status`` (e.g. "healthy"/"unhealthy") and ``timestamp`` (ISO format).
+
+    Raises
+    ------
+    HTTPException
+        503 if status is unhealthy; 500 if the check fails.
+    """
     try:
         # Run the status script and check exit code
-        script_path = Path(__file__).parent.parent / "scripts" / "status.sh"
+        # Prefer SCRIPT_DIR env var (required when package is installed without repo layout)
+        script_dir_env = os.environ.get("SCRIPT_DIR")
+        if script_dir_env:
+            script_path = Path(script_dir_env) / "status.sh"
+        else:
+            _repo_root = Path(__file__).resolve().parent.parent.parent.parent
+            script_path = _repo_root / "serve" / "server" / "scripts" / "status.sh"
         process = await asyncio.create_subprocess_exec(
             str(script_path),
             stdout=asyncio.subprocess.PIPE,
@@ -302,13 +336,32 @@ async def health_check() -> dict[str, str]:
 # Azure ML uses this to see if the container is alive
 @app.get("/liveness")
 def liveness() -> dict[str, str]:
-    """Liveness probe for Azure ML; returns alive status to indicate container is running."""
+    """
+    Liveness probe for Azure ML.
+
+    Returns
+    -------
+    dict
+        ``status`` key set to "alive" to indicate the container is running.
+    """
     return {"status": "alive"}
 
 
 @app.get("/metrics")
 async def get_metrics() -> Response:
-    """Get Prometheus metrics"""
+    """
+    Expose Prometheus metrics.
+
+    Returns
+    -------
+    Response
+        Response with Prometheus text format and appropriate content type.
+
+    Raises
+    ------
+    HTTPException
+        500 if metrics generation fails.
+    """
     try:
         metrics_data = generate_latest()
         return Response(content=metrics_data, media_type=CONTENT_TYPE_LATEST)
@@ -327,7 +380,14 @@ async def get_metrics() -> Response:
 @app.get("/v1/workflows")
 @app.get("/v1/infer/workflows")
 async def list_workflows() -> dict[str, dict[str, str]]:
-    """List all available workflows (only exposed workflows)"""
+    """
+    List all available workflows.
+
+    Returns
+    -------
+    dict
+        Single key ``workflows`` mapping workflow name to description.
+    """
     workflows = workflow_registry.list_workflows(exposed_only=True)
     return {"workflows": workflows}
 
@@ -338,15 +398,24 @@ async def get_workflow_schema(workflow_name: str) -> dict[str, Any]:
     """
     Get the OpenAPI JSON schema for a workflow's parameters.
 
-    For workflows derived from Workflow class, the schema is generated from
-    the WorkflowParameters class. For workflows derived from Earth2Workflow class,
-    the schema is auto-generated from the __call__ signature via AutoParameters.
+    For Workflow subclasses the schema comes from WorkflowParameters; for
+    Earth2Workflow subclasses it is generated from the __call__ signature via
+    AutoParameters.
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the workflow.
 
-    Args:
-        workflow_name: Name of the workflow
+    Returns
+    -------
+    dict
+        JSON schema for the workflow parameters (OpenAPI-compatible).
 
-    Returns:
-        JSON schema for the workflow parameters
+    Raises
+    ------
+    HTTPException
+        404 if workflow not found; 500 if schema generation fails.
     """
     # Check if workflow exists and is exposed
     workflow_class = workflow_registry.get_workflow_class(workflow_name)
@@ -386,7 +455,14 @@ async def get_workflow_schema(workflow_name: str) -> dict[str, Any]:
 
 
 class WorkflowExecutionRequest(BaseModel):
-    """Request model for workflow execution"""
+    """
+    Request body for workflow execution.
+
+    Attributes
+    ----------
+    parameters : dict
+        Workflow-specific parameters (validated by the workflow's parameter class).
+    """
 
     model_config = {"extra": "forbid"}
 
@@ -396,7 +472,24 @@ class WorkflowExecutionRequest(BaseModel):
 
 
 class WorkflowExecutionResponse(BaseModel):
-    """Response model for workflow execution"""
+    """
+    Response after submitting a workflow execution.
+
+    Attributes
+    ----------
+    workflow_name : str
+        Name of the workflow.
+    execution_id : str
+        Unique execution identifier.
+    status : str
+        Current status (e.g. QUEUED).
+    position : int or None
+        Queue position (0-indexed); set when status is QUEUED.
+    message : str
+        Human-readable message.
+    timestamp : str
+        ISO format timestamp.
+    """
 
     workflow_name: str
     execution_id: str
@@ -412,8 +505,31 @@ class WorkflowExecutionResponse(BaseModel):
 async def execute_workflow(
     workflow_name: str, request: WorkflowExecutionRequest
 ) -> WorkflowExecutionResponse:
-    """Execute a custom workflow"""
-    # Check if workflow exists and is exposed
+    """
+    Enqueue a custom workflow for execution.
+
+    Validates parameters, performs admission control, persists execution data
+    in Redis, and enqueues the job to the RQ inference queue.
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the registered workflow.
+    request : WorkflowExecutionRequest
+        Request body containing workflow parameters.
+
+    Returns
+    -------
+    WorkflowExecutionResponse
+        Execution ID, status (QUEUED), queue position, and message.
+
+    Raises
+    ------
+    HTTPException
+        404 if workflow not found; 422 if parameters invalid; 429 if queues full;
+        503 if Redis/queue not initialized; 500 on enqueue failure.
+    """
+    # Check if workflow exists and get the workflow class for validation
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
@@ -467,7 +583,7 @@ async def execute_workflow(
                 status_code=503, detail="Inference queue not initialized"
             )
         job = inference_queue.enqueue(
-            "api_server.worker.run_custom_workflow",  # New worker function
+            "earth2studio.serve.server.worker.run_custom_workflow",
             workflow_name,
             execution_id,
             validated_params_dict,
@@ -522,7 +638,26 @@ async def execute_workflow(
     "/v1/infer/{workflow_name}/{execution_id}/status", response_model=WorkflowResult
 )
 async def get_workflow_status(workflow_name: str, execution_id: str) -> WorkflowResult:
-    """Get status of a workflow execution"""
+    """
+    Get the status of a workflow execution.
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the workflow.
+    execution_id : str
+        Unique execution identifier.
+
+    Returns
+    -------
+    WorkflowResult
+        Current status, progress, timestamps, and optional queue position.
+
+    Raises
+    ------
+    HTTPException
+        404 if workflow or execution not found; 500 on server error.
+    """
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -574,7 +709,27 @@ async def get_workflow_status(workflow_name: str, execution_id: str) -> Workflow
 async def get_workflow_results(
     workflow_name: str, execution_id: str
 ) -> dict[str, Any] | StreamingResponse:
-    """Get metadata.json for a workflow execution"""
+    """
+    Get result metadata (e.g. metadata.json) for a completed workflow execution.
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the workflow.
+    execution_id : str
+        Unique execution identifier.
+
+    Returns
+    -------
+    dict
+        JSON metadata (e.g. from metadata_{workflow_name}:{execution_id}.json).
+
+    Raises
+    ------
+    HTTPException
+        202 if still queued/running/pending; 400 if expired or bad request;
+        404 if workflow, execution, or metadata file not found, or if execution failed/cancelled.
+    """
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -687,13 +842,29 @@ async def get_workflow_result_file(
     """
     Stream a specific file from the workflow execution results.
 
-    Args:
-        workflow_name: Name of the workflow
-        execution_id: Execution ID
-        filepath: Relative path to the file within the output directory
-
-    Returns:
-        Streaming response with the file contents
+    If filepath equals the request ID (workflow_name:execution_id), streams the
+    results zip file. Otherwise serves a file from the output directory (path
+    normalized and checked to prevent directory traversal).
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the workflow.
+    execution_id : str
+        Execution identifier.
+    filepath : str
+        Relative path within the output directory, or the request ID for the zip.
+
+    Returns
+    -------
+    StreamingResponse
+        File or zip contents with appropriate headers.
+
+    Raises
+    ------
+    HTTPException
+        403 on path traversal attempt; 404 if workflow, execution, file, or zip
+        not found or results not completed; 503 if Redis not initialized; 500 on error.
     """
     # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
@@ -909,8 +1080,6 @@ async def get_workflow_result_file(
 
 
 if __name__ == "__main__":
-    import uvicorn
-
     uvicorn.run(
         app,
         host=config.server.host,  # noqa: S104
diff --git a/serve/inferenceserver/api_server/object_storage.py b/earth2studio/serve/server/object_storage.py
similarity index 90%
rename from serve/inferenceserver/api_server/object_storage.py
rename to earth2studio/serve/server/object_storage.py
index 7961df4ff..42f8e07a9 100644
--- a/serve/inferenceserver/api_server/object_storage.py
+++ b/earth2studio/serve/server/object_storage.py
@@ -14,18 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Object Storage Module
-
-This module provides object storage functionality using NVIDIA Multi-Storage Client (MSC)
-with Rust backend for high-performance parallel file transfers.
-
-Example:
-    >>> from api_server.object_storage import MSCObjectStorage
-    >>> storage = MSCObjectStorage(bucket="my-bucket", region="us-east-1")
-    >>> storage.upload_directory("/path/to/local/dir", "remote/prefix")
-"""
-
 import base64
 import datetime
 import logging
@@ -41,7 +29,22 @@
 
 @dataclass
 class UploadResult:
-    """Result of an upload operation"""
+    """
+    Result of an upload operation.
+
+    Attributes
+    ----------
+    success : bool
+        Whether the upload completed without errors.
+    files_uploaded : int
+        Number of files uploaded.
+    total_bytes : int
+        Total bytes uploaded.
+    destination : str
+        Destination path or URI.
+    errors : list of str
+        List of error messages, if any.
+    """
 
     success: bool
     files_uploaded: int
@@ -61,9 +64,9 @@ class ObjectStorage(ABC):
     """
     Abstract base class for object storage operations.
 
-    This class defines the interface that all object storage implementations must follow.
-    Subclasses should implement the abstract methods to provide functionality for
-    specific cloud providers (AWS S3, GCS, Azure Blob Storage, etc.).
+    Defines the interface that all object storage implementations must follow.
+    Subclasses implement the abstract methods for specific cloud providers
+    (AWS S3, GCS, Azure Blob Storage, etc.).
     """
 
     @abstractmethod
@@ -77,20 +80,31 @@ def upload_directory(
         """
         Upload a local directory to the object store at the specified path.
 
-        Args:
-            local_directory: Path to the local directory to upload.
-            remote_prefix: The prefix/path in the bucket where files will be uploaded.
-                          Files will maintain their relative paths under this prefix.
-            recursive: If True, recursively upload all subdirectories.
-            overwrite: If True, overwrite existing files in the destination.
-
-        Returns:
-            UploadResult containing details about the upload operation.
-
-        Raises:
-            FileNotFoundError: If the local directory does not exist.
-            PermissionError: If there are permission issues with the bucket.
-            ObjectStorageError: For other storage-related errors.
+        Parameters
+        ----------
+        local_directory : str or Path
+            Path to the local directory to upload.
+        remote_prefix : str
+            Prefix/path in the bucket where files will be uploaded.
+            Files maintain their relative paths under this prefix.
+        recursive : bool, optional
+            If True, recursively upload all subdirectories. Default is True.
+        overwrite : bool, optional
+            If True, overwrite existing files in the destination. Default is True.
+
+        Returns
+        -------
+        UploadResult
+            Details about the upload operation.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the local directory does not exist.
+        PermissionError
+            If there are permission issues with the bucket.
+        ObjectStorageError
+            For other storage-related errors.
         """
         pass
 
@@ -104,12 +118,18 @@ def upload_file(
         """
         Upload a single file to the object store.
 
-        Args:
-            local_file: Path to the local file to upload.
-            remote_key: The full key/path in the bucket for the file.
-            overwrite: If True, overwrite if the file already exists.
-
-        Returns:
+        Parameters
+        ----------
+        local_file : str or Path
+            Path to the local file to upload.
+        remote_key : str
+            Full key/path in the bucket for the file.
+        overwrite : bool, optional
+            If True, overwrite if the file already exists. Default is True.
+
+        Returns
+        -------
+        bool
             True if upload was successful, False otherwise.
         """
         pass
@@ -119,10 +139,14 @@ def file_exists(self, remote_key: str) -> bool:
         """
         Check if a file exists in the object store.
 
-        Args:
-            remote_key: The full key/path in the bucket.
+        Parameters
+        ----------
+        remote_key : str
+            Full key/path in the bucket.
 
-        Returns:
+        Returns
+        -------
+        bool
             True if the file exists, False otherwise.
         """
         pass
@@ -132,10 +156,14 @@ def delete_file(self, remote_key: str) -> bool:
         """
         Delete a file from the object store.
 
-        Args:
-            remote_key: The full key/path in the bucket.
+        Parameters
+        ----------
+        remote_key : str
+            Full key/path in the bucket.
 
-        Returns:
+        Returns
+        -------
+        bool
             True if deletion was successful, False otherwise.
         """
         pass
@@ -145,15 +173,25 @@ def generate_signed_url(self, remote_key: str, expires_in: int = 86400) -> str:
         """
         Generate a signed URL for a file in the object store.
 
-        Args:
-            remote_key: The full key/path in the bucket.
-            expires_in: The number of seconds the URL will be valid for.
+        Parameters
+        ----------
+        remote_key : str
+            Full key/path in the bucket.
+        expires_in : int, optional
+            Number of seconds the URL will be valid for. Default is 86400.
+
+        Returns
+        -------
+        str
+            Signed URL string.
         """
         pass
 
 
 class ObjectStorageError(Exception):
-    """Base exception for object storage errors"""
+    """
+    Base exception for object storage errors.
+    """
 
     pass
 
@@ -632,7 +670,9 @@ def file_exists(self, remote_key: str) -> bool:
         Args:
             remote_key: The storage key to check.
 
-        Returns:
+        Returns
+        -------
+        bool
             True if the file exists, False otherwise.
         """
         try:
@@ -651,7 +691,9 @@ def delete_file(self, remote_key: str) -> bool:
         Args:
             remote_key: The storage key to delete.
 
-        Returns:
+        Returns
+        -------
+        bool
             True if successful, False otherwise.
         """
         try:
@@ -666,7 +708,19 @@ def delete_file(self, remote_key: str) -> bool:
             return False
 
     def _rsa_signer(self, message: bytes) -> bytes:
-        """Sign a message using the CloudFront private key."""
+        """
+        Sign a message using the CloudFront private key.
+
+        Parameters
+        ----------
+        message : bytes
+            Message to sign.
+
+        Returns
+        -------
+        bytes
+            RSA signature.
+        """
         from cryptography.hazmat.primitives import hashes
         from cryptography.hazmat.primitives.asymmetric import padding
         from cryptography.hazmat.primitives.serialization import load_pem_private_key
@@ -687,7 +741,19 @@ def _rsa_signer(self, message: bytes) -> bytes:
 
     @staticmethod
     def _url_safe_b64(data: bytes) -> str:
-        """Convert bytes to URL-safe base64."""
+        """
+        Convert bytes to URL-safe base64.
+
+        Parameters
+        ----------
+        data : bytes
+            Data to encode.
+
+        Returns
+        -------
+        str
+            URL-safe base64 string.
+        """
         return (
             base64.b64encode(data)
             .decode("utf-8")
@@ -826,44 +892,3 @@ def _generate_azure_sas_url(self, remote_key: str, expires_in: int) -> str:
             f"Generated Azure SAS URL for {remote_key}, expires in {expires_in}s"
         )
         return signed_url
-
-
-def get_object_storage(
-    provider: Literal["msc"] = "msc",
-    **kwargs: Any,
-) -> ObjectStorage:
-    """
-    Factory function to get an object storage instance.
-
-    This function provides a convenient way to instantiate the MSCObjectStorage
-    implementation which uses NVIDIA Multi-Storage Client with Rust backend.
-
-    Args:
-        provider: The storage provider to use. Currently only "msc" is supported.
-        **kwargs: Provider-specific configuration arguments.
-
-    Returns:
-        An ObjectStorage instance.
-
-    Raises:
-        ValueError: If an unsupported provider is specified.
-
-    Example:
-        >>> storage = get_object_storage(
-        ...     "msc",
-        ...     bucket="my-bucket",
-        ...     region="us-east-1",
-        ...     use_rust_client=True
-        ... )
-    """
-    providers = {
-        "msc": MSCObjectStorage,
-    }
-
-    if provider not in providers:
-        raise ValueError(
-            f"Unsupported storage provider: {provider}. "
-            f"Supported providers: {list(providers.keys())}"
-        )
-
-    return providers[provider](**kwargs)
diff --git a/serve/inferenceserver/api_server/utils.py b/earth2studio/serve/server/utils.py
similarity index 94%
rename from serve/inferenceserver/api_server/utils.py
rename to earth2studio/serve/server/utils.py
index 2c6d725df..1cc044caa 100644
--- a/serve/inferenceserver/api_server/utils.py
+++ b/earth2studio/serve/server/utils.py
@@ -34,7 +34,7 @@
 from fastapi import HTTPException
 from rq import Queue
 
-from api_server.config import get_config
+from earth2studio.serve.server.config import get_config
 
 logger = logging.getLogger(__name__)
 
@@ -201,7 +201,9 @@ async def create_file_stream(
 
 def queue_next_stage(
     redis_client: redis.Redis,
-    current_stage: Stage,
+    current_stage: Literal[
+        "inference", "result_zip", "object_storage", "geocatalog_ingestion"
+    ],
     workflow_name: str,
     execution_id: str,
     output_path_str: str,
@@ -232,7 +234,7 @@ def queue_next_stage(
     if current_stage == "inference":
         # Always go to result_zip stage (for manifest building), but control zip creation via config
         next_queue = "result_zip"
-        next_func = "api_server.cpu_worker.process_result_zip"
+        next_func = "earth2studio.serve.server.cpu_worker.process_result_zip"
         # Pass create_zip parameter based on config
         # Disable zip creation when object storage is enabled (files uploaded directly)
         create_zip = (
@@ -249,11 +251,13 @@ def queue_next_stage(
     elif current_stage == "result_zip":
         if config.object_storage.enabled:
             next_queue = "object_storage"
-            next_func = "api_server.cpu_worker.process_object_storage_upload"
+            next_func = (
+                "earth2studio.serve.server.cpu_worker.process_object_storage_upload"
+            )
             args = (workflow_name, execution_id, output_path_str)
         else:
             next_queue = "finalize_metadata"
-            next_func = "api_server.cpu_worker.process_finalize_metadata"
+            next_func = "earth2studio.serve.server.cpu_worker.process_finalize_metadata"
             args = (workflow_name, execution_id)
 
     elif current_stage == "object_storage":
@@ -268,7 +272,7 @@ def queue_next_stage(
 
     elif current_stage == "geocatalog_ingestion":
         next_queue = "finalize_metadata"
-        next_func = "api_server.cpu_worker.process_finalize_metadata"
+        next_func = "earth2studio.serve.server.cpu_worker.process_finalize_metadata"
         args = (workflow_name, execution_id)
 
     else:
diff --git a/serve/inferenceserver/api_server/worker.py b/earth2studio/serve/server/worker.py
similarity index 79%
rename from serve/inferenceserver/api_server/worker.py
rename to earth2studio/serve/server/worker.py
index f8ff9c601..804c6d3e0 100644
--- a/serve/inferenceserver/api_server/worker.py
+++ b/earth2studio/serve/server/worker.py
@@ -14,14 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Earth2Studio RQ Worker
-
-This module contains the worker functions for processing inference requests
-using Redis Queue (RQ). Workers run the actual inference computations.
-
-"""
-
 import logging
 import time
 from datetime import datetime, timezone
@@ -31,13 +23,13 @@
 import redis  # type: ignore[import-untyped]
 
 # Import configuration
-from api_server.config import get_config, get_config_manager
+from earth2studio.serve.server.config import get_config, get_config_manager
 
 # Import queue_next_stage utility
-from api_server.utils import queue_next_stage
+from earth2studio.serve.server.utils import queue_next_stage
 
 # Import workflow registry
-from api_server.workflow import WorkflowStatus, workflow_registry
+from earth2studio.serve.server.workflow import WorkflowStatus, workflow_registry
 
 # Get configuration
 config = get_config()
@@ -67,7 +59,7 @@
 
 # Register custom workflows in the worker process
 try:
-    from api_server.workflow import register_all_workflows
+    from earth2studio.serve.server.workflow import register_all_workflows
 
     register_all_workflows(redis_client)
     logger.info("Custom workflows registered successfully in worker process")
@@ -86,7 +78,25 @@ def get_output_path(
     workflow_type: str,
     request_id: str,
 ) -> Path:
-    """Generate output path for workflow results"""
+    """
+    Generate output path for workflow results.
+
+    Parameters
+    ----------
+    io_config : dict or None
+        I/O configuration (used for backend_type); if None, backend_type defaults to "zarr".
+    timestamp : str
+        Timestamp string used in the output directory name.
+    workflow_type : str
+        Type of workflow (e.g. workflow name).
+    request_id : str
+        Request identifier.
+
+    Returns
+    -------
+    Path
+        Path to the forecast output file (e.g. forecast.zarr).
+    """
     # Create timestamp-based subdirectory
     timestamp_str = timestamp.replace(":", "").replace("Z", "").replace("+", "")
     output_dir = DEFAULT_OUTPUT_DIR / f"{workflow_type}_{timestamp_str}_{request_id}"
@@ -99,7 +109,33 @@ def get_output_path(
 def run_custom_workflow(
     workflow_name: str, execution_id: str, parameters: dict[str, Any]
 ) -> Any:
-    """RQ Worker function to run custom workflow"""
+    """
+    RQ worker function to run a custom workflow.
+
+    Executes the workflow, updates execution status in Redis, and queues the
+    next pipeline stage (e.g. result_zip or object_storage) as configured.
+
+    Parameters
+    ----------
+    workflow_name : str
+        Name of the registered workflow to run.
+    execution_id : str
+        Unique execution identifier.
+    parameters : dict
+        Workflow input parameters (validated by the workflow's parameter type).
+
+    Returns
+    -------
+    Any
+        Result returned by the workflow's run() method.
+
+    Raises
+    ------
+    ValueError
+        If the workflow is not found or cannot be instantiated.
+    RuntimeError
+        If queuing the next pipeline stage fails.
+    """
     # Create logger adapter with execution_id for automatic log prefixing
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -112,11 +148,13 @@ def run_custom_workflow(
 
     # Create workflow instance for execution
     custom_workflow = workflow_registry.get(workflow_name, redis_client=redis_client)
+    if custom_workflow is None:
+        raise ValueError(f"Custom workflow '{workflow_name}' could not be instantiated")
 
     try:
         start_timestamp = time.time()
         start_time = datetime.now(timezone.utc).isoformat()
-        updates = {
+        updates: dict[str, Any] = {
             "status": WorkflowStatus.RUNNING,
             "start_time": start_time,
         }
diff --git a/serve/inferenceserver/api_server/workflow/workflow.py b/earth2studio/serve/server/workflow.py
similarity index 75%
rename from serve/inferenceserver/api_server/workflow/workflow.py
rename to earth2studio/serve/server/workflow.py
index 8e786c34b..c262bdefd 100644
--- a/serve/inferenceserver/api_server/workflow/workflow.py
+++ b/earth2studio/serve/server/workflow.py
@@ -14,12 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Workflow module
-
-This module provides the base classes and functionality for custom workflows.
-"""
-
 import importlib.util
 import json
 import logging
@@ -31,10 +25,10 @@
 from typing import Any, Union
 
 import redis  # type: ignore[import-untyped]
-from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, ValidationInfo, field_validator
 
 # Import configuration
-from api_server.config import (
+from earth2studio.serve.server.config import (
     get_config,
     get_config_manager,
     get_workflow_config,
@@ -50,7 +44,7 @@
 
 
 class WorkflowStatus:
-    """Workflow execution status constants"""
+    """Workflow execution status constants."""
 
     QUEUED = "queued"
     RUNNING = "running"
@@ -62,7 +56,7 @@ class WorkflowStatus:
 
 
 class WorkflowArgsBase(BaseModel):
-    """Base parameters for workflow execution"""
+    """Base parameters for workflow execution."""
 
     # Add strict validation - reject unknown fields
     model_config = ConfigDict(extra="forbid")
@@ -78,14 +72,20 @@ def validate(
         It ensures that all required fields are present, types are correct, and any
         custom validators defined in the parameter class are executed.
 
-        Args:
-            data: Input data as dict or WorkflowArgsBase instance
+        Parameters
+        ----------
+        data : dict or WorkflowArgsBase
+            Input data as dict or WorkflowArgsBase instance.
 
-        Returns:
-            Validated WorkflowArgsBase instance
+        Returns
+        -------
+        WorkflowArgsBase
+            Validated WorkflowArgsBase instance.
 
-        Raises:
-            ValueError: If validation fails
+        Raises
+        ------
+        ValueError
+            If validation fails.
         """
         if isinstance(data, cls):
             # Already the correct type, re-validate to ensure all constraints are met
@@ -109,24 +109,34 @@ class WorkflowParameters(WorkflowArgsBase):
 
     @field_validator("forecast_times", "start_time", mode="before", check_fields=False)
     @classmethod
-    def validate_datetime_list(cls, v: Any, info: Any) -> Any:
+    def validate_datetime_list(
+        cls: type["WorkflowParameters"],
+        v: list[str] | list[datetime] | str | datetime | None,
+        info: ValidationInfo,
+    ) -> list[str] | list[datetime] | str | datetime | None:
         """
         Validate that forecast_times and start_time are in valid ISO 8601 datetime format.
 
         This validator handles both string lists (forecast_times) and can validate
         datetime objects or ISO 8601 strings (start_time).
-
         Requires full datetime format with 'T' separator (not just date) for strings.
 
-        Args:
-            v: The value (can be list of strings/datetime objects, single value, or None)
-            info: Validation context info
+        Parameters
+        ----------
+        v : list of str or datetime, or single value, or None
+            The value (can be list of strings/datetime objects, single value, or None).
+        info : ValidationInfo
+            Pydantic validation context (e.g. field name).
 
-        Returns:
-            The validated value
+        Returns
+        -------
+        list of str or list of datetime or str or datetime or None
+            The validated value (same shape as input).
 
-        Raises:
-            ValueError: If any value is not in valid ISO 8601 datetime format
+        Raises
+        ------
+        ValueError
+            If any value is not in valid ISO 8601 datetime format.
         """
         from datetime import datetime
 
@@ -178,7 +188,7 @@ def validate_datetime_list(cls, v: Any, info: Any) -> Any:
 
 class WorkflowConfig(WorkflowArgsBase):
     """
-    Specialize in case we want to differentiate parameters and config handling  later.
+    Specialize in case we want to differentiate parameters and config handling later.
     """
 
     pass
@@ -191,38 +201,46 @@ class WorkflowProgress(BaseModel):
     This class provides standard progress tracking fields that can be extended
     by custom workflows to include additional tracking information.
 
-    Attributes:
-        progress: Human-readable progress message
-        current_step: Current step number in the workflow
-        total_steps: Total number of steps in the workflow
-        error_message: Error message to report specific errors when workflow fails
-
-    Example:
-        Basic usage:
-            progress = WorkflowProgress(
-                progress="Processing data...",
-                current_step=3,
-                total_steps=10
-            )
-
-        Error reporting:
-            progress = WorkflowProgress(
-                progress="Failed!",
-                error_message="Connection timeout: Could not reach data source"
-            )
-
-        Extended for custom workflow:
-            class MyCustomProgress(WorkflowProgress):
-                data_processed_gb: float = 0.0
-                error_count: int = 0
-
-            progress = MyCustomProgress(
-                progress="Processing batch 5",
-                current_step=5,
-                total_steps=20,
-                data_processed_gb=150.5,
-                error_count=2
-            )
+    Attributes
+    ----------
+    progress : str or None
+        Human-readable progress message.
+    current_step : int or None
+        Current step number in the workflow.
+    total_steps : int or None
+        Total number of steps in the workflow.
+    error_message : str or None
+        Error message to report specific errors when workflow fails.
+
+    Examples
+    --------
+    Basic usage:
+
+    >>> progress = WorkflowProgress(
+    ...     progress="Processing data...",
+    ...     current_step=3,
+    ...     total_steps=10
+    ... )
+
+    Error reporting:
+
+    >>> progress = WorkflowProgress(
+    ...     progress="Failed!",
+    ...     error_message="Connection timeout: Could not reach data source"
+    ... )
+
+    Extended for custom workflow:
+
+    >>> class MyCustomProgress(WorkflowProgress):
+    ...     data_processed_gb: float = 0.0
+    ...     error_count: int = 0
+    >>> progress = MyCustomProgress(
+    ...     progress="Processing batch 5",
+    ...     current_step=5,
+    ...     total_steps=20,
+    ...     data_processed_gb=150.5,
+    ...     error_count=2
+    ... )
     """
 
     progress: str | None = None
@@ -232,7 +250,7 @@ class MyCustomProgress(WorkflowProgress):
 
 
 class WorkflowResult(BaseModel):
-    """Base result structure for workflow execution"""
+    """Base result structure for workflow execution."""
 
     workflow_name: str
     execution_id: str
@@ -276,14 +294,16 @@ def __init__(self) -> None:
         """
         Initialize workflow.
 
-        Note: name and description are set by the registry during workflow instantiation.
+        Notes
+        -----
+        name and description are set by the registry during workflow instantiation.
         """
         self.redis_client: redis.Redis | None = None
         self.output_dir = Path(config.paths.default_output_dir)
         self.output_dir.mkdir(parents=True, exist_ok=True)
 
     def set_redis_client(self, redis_client: redis.Redis) -> None:
-        """Set Redis client for state management"""
+        """Set Redis client for state management."""
         self.redis_client = redis_client
 
     @classmethod
@@ -298,19 +318,26 @@ def validate_parameters(
         Pydantic field validation and model validation. Subclasses should implement
         this method to specify their parameter type.
 
-        Args:
-            parameters: Input parameters as dict or WorkflowParameters instance
+        Parameters
+        ----------
+        parameters : dict or WorkflowParameters
+            Input parameters as dict or WorkflowParameters instance.
 
-        Returns:
-            Validated WorkflowParameters instance of the workflow-specific type
+        Returns
+        -------
+        WorkflowParameters
+            Validated WorkflowParameters instance of the workflow-specific type.
 
-        Raises:
-            ValueError: If validation fails
+        Raises
+        ------
+        ValueError
+            If validation fails.
 
-        Example:
-            @classmethod
-            def validate_parameters(cls, parameters):
-                return MyWorkflowParameters.validate(parameters)
+        Examples
+        --------
+        >>> @classmethod
+        ... def validate_parameters(cls, parameters):
+        ...     return MyWorkflowParameters.validate(parameters)
         """
         raise NotImplementedError(
             "Subclasses must implement validate_parameters() method"
@@ -326,12 +353,17 @@ def run(
         This method should implement the actual workflow logic.
         It will be called when a POST request is made to /v1/infer/{workflow_name}.
 
-        Args:
-            parameters: Input parameters for the workflow (dict or WorkflowParameters)
-            execution_id: Unique execution identifier
+        Parameters
+        ----------
+        parameters : dict or WorkflowParameters
+            Input parameters for the workflow.
+        execution_id : str
+            Unique execution identifier.
 
-        Returns:
-            Dictionary containing workflow results
+        Returns
+        -------
+        dict
+            Dictionary containing workflow results.
         """
         raise NotImplementedError("Subclasses must implement run() method")
 
@@ -344,41 +376,49 @@ def update_execution_data(
         This method can be called by workflow implementations to update specific
         fields in the execution data without replacing the entire record.
 
-        Args:
-            execution_id: Unique execution identifier
-            updates: Dictionary of fields to update, or WorkflowProgress instance.
-                    If a WorkflowProgress object is provided, it will update the
-                    'progress' field of WorkflowResult. Dict updates can include
-                    any WorkflowResult fields including 'progress', 'metadata', etc.
-
-        Raises:
-            RuntimeError: If Redis client is not set
-
-        Example:
-            # Using dict for general updates
-            self.update_execution_data(execution_id, {
-                "metadata": {"custom_field": "value"}
-            })
-
-            # Using WorkflowProgress for progress updates
-            progress = WorkflowProgress(
-                progress="Processing data...",
-                current_step=3,
-                total_steps=10
-            )
-            self.update_execution_data(execution_id, progress)
-
-            # Using custom WorkflowProgress subclass
-            class MyProgress(WorkflowProgress):
-                data_processed_gb: float = 0.0
-
-            progress = MyProgress(
-                progress="Batch processing",
-                current_step=5,
-                total_steps=20,
-                data_processed_gb=150.5
-            )
-            self.update_execution_data(execution_id, progress)
+        Parameters
+        ----------
+        execution_id : str
+            Unique execution identifier.
+        updates : dict or WorkflowProgress
+            Dictionary of fields to update, or WorkflowProgress instance.
+            If a WorkflowProgress object is provided, it will update the
+            'progress' field of WorkflowResult. Dict updates can include
+            any WorkflowResult fields including 'progress', 'metadata', etc.
+
+        Raises
+        ------
+        RuntimeError
+            If Redis client is not set.
+
+        Examples
+        --------
+        Using dict for general updates:
+
+        >>> self.update_execution_data(execution_id, {
+        ...     "metadata": {"custom_field": "value"}
+        ... })
+
+        Using WorkflowProgress for progress updates:
+
+        >>> progress = WorkflowProgress(
+        ...     progress="Processing data...",
+        ...     current_step=3,
+        ...     total_steps=10
+        ... )
+        >>> self.update_execution_data(execution_id, progress)
+
+        Using custom WorkflowProgress subclass:
+
+        >>> class MyProgress(WorkflowProgress):
+        ...     data_processed_gb: float = 0.0
+        >>> progress = MyProgress(
+        ...     progress="Batch processing",
+        ...     current_step=5,
+        ...     total_steps=20,
+        ...     data_processed_gb=150.5
+        ... )
+        >>> self.update_execution_data(execution_id, progress)
         """
         if not self.redis_client:
             raise RuntimeError("Redis client not set. Call set_redis_client() first.")
@@ -414,11 +454,15 @@ def get_output_path(self, execution_id: str) -> Path:
         output directory path for a specific execution. The directory is automatically
         created if it doesn't exist.
 
-        Args:
-            execution_id: Unique execution identifier
+        Parameters
+        ----------
+        execution_id : str
+            Unique execution identifier.
 
-        Returns:
-            Path object pointing to the output directory
+        Returns
+        -------
+        Path
+            Path object pointing to the output directory.
         """
         workflow_dir = self.output_dir / self.name / execution_id
         workflow_dir.mkdir(parents=True, exist_ok=True)
@@ -434,13 +478,19 @@ def _get_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Args:
-            redis_client: Redis client instance
-            workflow_name: Name of the workflow
-            execution_id: Unique execution identifier
+        Parameters
+        ----------
+        redis_client : redis.Redis
+            Redis client instance.
+        workflow_name : str
+            Name of the workflow.
+        execution_id : str
+            Unique execution identifier.
 
-        Returns:
-            WorkflowResult with execution data
+        Returns
+        -------
+        WorkflowResult
+            Execution data.
         """
         try:
             # Get execution data from Redis
@@ -477,11 +527,16 @@ def _save_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Args:
-            redis_client: Redis client instance
-            workflow_name: Name of the workflow
-            execution_id: Unique execution identifier
-            data: WorkflowResult to save
+        Parameters
+        ----------
+        redis_client : redis.Redis
+            Redis client instance.
+        workflow_name : str
+            Name of the workflow.
+        execution_id : str
+            Unique execution identifier.
+        data : WorkflowResult
+            WorkflowResult to save.
         """
         try:
             redis_client.setex(
@@ -508,11 +563,16 @@ def _update_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Args:
-            redis_client: Redis client instance
-            workflow_name: Name of the workflow
-            execution_id: Unique execution identifier
-            updates: Dictionary of fields to update
+        Parameters
+        ----------
+        redis_client : redis.Redis
+            Redis client instance.
+        workflow_name : str
+            Name of the workflow.
+        execution_id : str
+            Unique execution identifier.
+        updates : dict
+            Dictionary of fields to update.
         """
         try:
             # Get current data
@@ -535,7 +595,7 @@ def _update_execution_data(
 
 
 def json_serial(obj: Any) -> Any:
-    """JSON serializer for objects not serializable by default json code"""
+    """JSON serializer for objects not serializable by default json code."""
 
     if isinstance(obj, (datetime, date)):
         return obj.isoformat()
@@ -545,7 +605,7 @@ def json_serial(obj: Any) -> Any:
 
 
 class WorkflowRegistry:
-    """Registry for managing custom workflows"""
+    """Registry for managing custom workflows."""
 
     def __init__(self, **config: Any) -> None:
         self._workflows: dict[str, type[Workflow]] = {}
@@ -555,15 +615,22 @@ def register(self, workflow_class: type[Workflow]) -> type[Workflow]:
         """
         Register a workflow class.
 
-        Args:
-            workflow_class: Workflow class (not instance) to register
+        Parameters
+        ----------
+        workflow_class : type[Workflow]
+            Workflow class (not instance) to register.
 
-        Returns:
-            workflow_class (enabling the use of ``register`` as a decorator)
+        Returns
+        -------
+        type[Workflow]
+            workflow_class (enabling the use of ``register`` as a decorator).
 
-        Raises:
-            TypeError: If workflow_class is not a class or not a Workflow subclass
-            ValueError: If workflow name is already registered
+        Raises
+        ------
+        TypeError
+            If workflow_class is not a class or not a Workflow subclass.
+        ValueError
+            If workflow name is already registered.
         """
         # Skip registration in a non-API environment
         api_server_running = os.environ.get("EARTH2STUDIO_API_ACTIVE", "0") == "1"
@@ -598,7 +665,7 @@ def register(self, workflow_class: type[Workflow]) -> type[Workflow]:
         return workflow_class
 
     def unregister(self, name: str) -> None:
-        """Unregister a workflow"""
+        """Unregister a workflow."""
         if name not in self._workflows:
             raise ValueError(f"Workflow '{name}' is not registered")
 
@@ -612,11 +679,15 @@ def get_workflow_class(self, name: str) -> type[Workflow] | None:
         """
         Get a workflow class by name.
 
-        Args:
-            name: Name of the workflow
+        Parameters
+        ----------
+        name : str
+            Name of the workflow.
 
-        Returns:
-            Workflow class or None if not found
+        Returns
+        -------
+        type[Workflow] or None
+            Workflow class or None if not found.
         """
         return self._workflows.get(name)
 
@@ -629,12 +700,17 @@ def get(
         Creates a new instance on first call and caches it for subsequent calls.
         The instance is automatically initialized with the registered name and description.
 
-        Args:
-            name: Name of the workflow
-            redis_client: Optional Redis client to set on the instance
+        Parameters
+        ----------
+        name : str
+            Name of the workflow.
+        redis_client : redis.Redis or None, optional
+            Optional Redis client to set on the instance.
 
-        Returns:
-            Cached workflow instance or None if not found
+        Returns
+        -------
+        Workflow or None
+            Cached workflow instance or None if not found.
         """
         workflow_class = self.get_workflow_class(name)
         if workflow_class is None:
@@ -677,7 +753,7 @@ def is_workflow_exposed(self, workflow_name: str) -> bool:
         Returns:
             True if workflow should be exposed, False otherwise
         """
-        from api_server.config import get_config
+        from earth2studio.serve.server.config import get_config
 
         config = get_config()
         exposed_workflows = config.workflow_exposure.exposed_workflows
@@ -703,7 +779,7 @@ def list_workflows(self, exposed_only: bool = True) -> dict[str, str]:
             Dictionary mapping workflow names to descriptions
         """
         if exposed_only:
-            from api_server.config import get_config
+            from earth2studio.serve.server.config import get_config
 
             config = get_config()
             exposed_workflows = config.workflow_exposure.exposed_workflows
@@ -734,16 +810,24 @@ def discover_and_register_from_directories(
         """
         Discover and register workflows from specified directories.
 
-        Args:
-            workflow_dirs: List of directory paths to search for workflow modules
-            include_builtin: Whether to automatically include built-in example_workflows
+        Parameters
+        ----------
+        workflow_dirs : list
+            List of directory paths to search for workflow modules.
+        include_builtin : bool, optional
+            Whether to automatically include built-in example_workflows. Default is True.
 
-        Returns:
-            tuple: (successful_imports, failed_imports) counts
+        Returns
+        -------
+        tuple
+            (successful_imports, failed_imports) counts.
         """
-        # Always include built-in workflows if requested
+        # Always include built-in workflows if requested (serve/server/example_workflows)
         if include_builtin:
-            builtin_workflows_dir = Path(__file__).parent.parent / "example_workflows"
+            _repo_root = Path(__file__).resolve().parent.parent.parent.parent
+            builtin_workflows_dir = (
+                _repo_root / "serve" / "server" / "example_workflows"
+            )
             already_included = builtin_workflows_dir in {Path(d) for d in workflow_dirs}
             if builtin_workflows_dir.exists() and not already_included:
                 # Add to the beginning so built-in workflows are discovered first
@@ -829,8 +913,10 @@ def auto_register_workflows(self, redis_client: redis.Redis) -> None:
 
         This is the main entry point for workflow registration during server startup.
 
-        Args:
-            redis_client: Redis client instance to set on registered workflows
+        Parameters
+        ----------
+        redis_client : redis.Redis
+            Redis client instance to set on registered workflows.
         """
         try:
             logger.info("=" * 60)
@@ -878,8 +964,10 @@ def parse_workflow_directories_from_env() -> list[str]:
     - Comma-separated list of directories
     - Colon-separated list of directories
 
-    Returns:
-        list: List of directory paths to search for workflows
+    Returns
+    -------
+    list of str
+        List of directory paths to search for workflows.
     """
     workflow_dirs = []
 
@@ -918,13 +1006,16 @@ def register_all_workflows(redis_client: redis.Redis) -> None:
     1. Built-in example_workflows directory (always included)
     2. User directories specified via WORKFLOW_DIR environment variable
 
-    Args:
-        redis_client: Redis client instance to set on registered workflows
-
-    Example:
-        >>> import redis
-        >>> from api_server.workflow import register_all_workflows
-        >>> redis_client = redis.Redis(host='localhost', port=6379)
-        >>> register_all_workflows(redis_client)
+    Parameters
+    ----------
+    redis_client : redis.Redis
+        Redis client instance to set on registered workflows.
+
+    Examples
+    --------
+    >>> import redis
+    >>> from earth2studio.serve.server.workflow import register_all_workflows
+    >>> redis_client = redis.Redis(host='localhost', port=6379)
+    >>> register_all_workflows(redis_client)
     """
     workflow_registry.auto_register_workflows(redis_client)
diff --git a/earth2studio/utils/coords.py b/earth2studio/utils/coords.py
index 62f1d85ae..c8259f6c2 100644
--- a/earth2studio/utils/coords.py
+++ b/earth2studio/utils/coords.py
@@ -14,14 +14,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 from collections import OrderedDict
+from copy import deepcopy
 from typing import Literal
 
 import numpy as np
 import torch
+import xarray as xr
 
 from earth2studio.utils.type import CoordSystem
 
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
 
 def handshake_dim(
     input_coords: CoordSystem,
@@ -75,7 +83,7 @@ def handshake_dim(
 def handshake_coords(
     input_coords: CoordSystem,
     target_coords: CoordSystem,
-    required_dim: str,
+    required_dim: str | list[str],
 ) -> None:
     """Simple check to see if the required dimensions have the same coordinate system
 
@@ -85,8 +93,8 @@ def handshake_coords(
         Input coordinate system to validate
     target_coords : CoordSystem
         Target coordinate system
-    required_dim : str
-        Required dimension (name of coordinate)
+    required_dim : str | list[str]
+        Required dimension(s) (name of coordinate)
     Raises
     ------
     KeyError
@@ -94,27 +102,31 @@ def handshake_coords(
     ValueError
         If coordinates of required dimensions don't match
     """
-    if required_dim not in input_coords:
-        raise KeyError(
-            f"Required dimension {required_dim} not found in input coordinates"
-        )
+    if isinstance(required_dim, str):
+        required_dim = [required_dim]
 
-    if required_dim not in target_coords:
-        raise KeyError(
-            f"Required dimension {required_dim} not found in target coordinates"
-        )
+    for _required_dim in required_dim:
+        if _required_dim not in input_coords:
+            raise KeyError(
+                f"Required dimension {_required_dim} not found in input coordinates"
+            )
 
-    if input_coords[required_dim].shape != target_coords[required_dim].shape:
-        raise ValueError(
-            f"Coordinate systems for required dim {required_dim} are not the same"
-        )
+        if _required_dim not in target_coords:
+            raise KeyError(
+                f"Required dimension {_required_dim} not found in target coordinates"
+            )
 
-    if not np.all(
-        (input_coords[required_dim] == target_coords[required_dim]).flatten()
-    ):
-        raise ValueError(
-            f"Coordinate systems for required dim {required_dim} are not the same"
-        )
+        if input_coords[_required_dim].shape != target_coords[_required_dim].shape:
+            raise ValueError(
+                f"Coordinate systems for required dim {_required_dim} are not the same"
+            )
+
+        if not np.all(
+            (input_coords[_required_dim] == target_coords[_required_dim]).flatten()
+        ):
+            raise ValueError(
+                f"Coordinate systems for required dim {_required_dim} are not the same"
+            )
 
 
 def handshake_size(
@@ -306,6 +318,198 @@ def map_coords(
     return x, mapped_coords
 
 
+def map_coords_xr(
+    x: xr.DataArray,
+    output_coords: CoordSystem,
+    method: Literal["nearest"] = "nearest",
+) -> xr.DataArray:
+    """Map xarray DataArray to target coordinate system using selection or interpolation.
+
+    Maps an input DataArray to match the coordinates specified in output_coords by
+    selecting or interpolating along dimensions. Supports both numpy and cupy-backed
+    DataArrays. Empty coordinate arrays are ignored, and warnings are issued for
+    missing coordinate keys.
+
+    Parameters
+    ----------
+    x : xr.DataArray
+        Input DataArray to map. May be backed by numpy or cupy arrays.
+    output_coords : CoordSystem
+        Target coordinate system containing a subset of coordinates present in x.
+        Dimensions not in output_coords are preserved from the input.
+    method : Literal["nearest"], optional
+        Interpolation method for numeric coordinates, by default "nearest"
+
+    Returns
+    -------
+    xr.DataArray
+        Mapped DataArray with coordinates matching output_coords where specified.
+        Preserves all dimensions and coordinates not specified in output_coords.
+
+    Raises
+    ------
+    KeyError
+        If output coordinate dimension is not found in input DataArray
+    ValueError
+        If non-numeric coordinate values are not present in input coordinates
+        If interpolation method is not supported
+    """
+    result = x.copy()
+
+    # Build selection/interpolation dictionary
+    sel_dict = {}
+    interp_dict = {}
+
+    for key, value in output_coords.items():
+        # Ignore batch dimension
+        if key == "batch":
+            continue
+
+        # Skip empty arrays (free coordinate system)
+        if len(value) == 0:
+            continue
+
+        # Check if dimension exists in input
+        if key not in result.dims and key not in result.coords:
+            warnings.warn(
+                f"Coordinate key '{key}' not found in input DataArray. "
+                f"Available dims: {list(result.dims)}, "
+                f"Available coords: {list(result.coords.keys())}"
+            )
+            continue
+
+        # Get coordinate values from input DataArray
+        if key in result.coords:
+            coord_values = result.coords[key]
+        elif key in result.dims:
+            coord_values = result[key]
+        else:
+            continue  # Should not happen due to check above
+
+        coord_array = (
+            coord_values.values if hasattr(coord_values, "values") else coord_values
+        )
+
+        # Check if coordinate types are compatible
+        # Check for datetime/timedelta first (these are not numeric for comparison purposes)
+        is_datetime = np.issubdtype(value.dtype, np.datetime64) or np.issubdtype(
+            coord_array.dtype, np.datetime64
+        )
+        is_timedelta = np.issubdtype(value.dtype, np.timedelta64) or np.issubdtype(
+            coord_array.dtype, np.timedelta64
+        )
+        # Only treat as numeric if both are numeric AND neither is datetime/timedelta
+        is_numeric = (
+            not is_datetime
+            and not is_timedelta
+            and np.issubdtype(value.dtype, np.number)
+            and np.issubdtype(coord_array.dtype, np.number)
+        )
+
+        # Check if all output values are in input (exact match)
+        if is_numeric:
+            # Numeric coordinate: check if values match exactly
+            if len(value) == len(coord_array) and np.allclose(
+                value, coord_array, equal_nan=True
+            ):
+                continue  # No change needed, exact match
+
+            # Check if all values are present in input (can use selection)
+            if np.all(np.isin(value, coord_array)):
+                sel_dict[key] = value
+            else:
+                # Need interpolation for values not in input
+                # xarray's interp uses coordinate names and handles dimension mapping
+                interp_dict[key] = xr.DataArray(value, dims=[key])
+        elif is_datetime or is_timedelta:
+            # Datetime/timedelta coordinate: use direct equality comparison
+            if len(value) == len(coord_array) and np.array_equal(value, coord_array):
+                continue  # No change needed, exact match
+
+            # Check if all values are present in input (can use selection)
+            if np.all(np.isin(value, coord_array)):
+                sel_dict[key] = value
+            else:
+                # Need interpolation for datetime/timedelta values not in input
+                # xarray's interp uses coordinate names and handles dimension mapping
+                interp_dict[key] = xr.DataArray(value, dims=[key])
+        else:
+            # Non-numeric coordinate: must use selection, all values must be present
+            if not np.all(np.isin(value, coord_array)):
+                raise ValueError(
+                    f"For non-numeric coordinate '{key}', all values of output coords "
+                    f"must be in the input coordinates. Some elements of {value} are "
+                    f"not in {coord_array}."
+                )
+            sel_dict[key] = value
+
+    # Apply selection first (exact matches)
+    if sel_dict:
+        result = result.sel(sel_dict)
+
+    # Apply nearest-neighbor interpolation per dimension using torch
+    if interp_dict:
+        if method != "nearest":
+            raise ValueError(f"Interpolation method '{method}' not supported")
+
+        data = result.data
+        is_cupy = cp is not None and isinstance(data, cp.ndarray)
+        dims = list(result.dims)
+        new_coords = dict(result.coords)
+
+        for key, target_da in interp_dict.items():
+            dim_idx = dims.index(key)
+            src_raw = np.asarray(result.coords[key].values)
+            tgt_raw = np.asarray(target_da.values)
+
+            sort_order = np.argsort(src_raw)
+            src_sorted = src_raw[sort_order]
+            idx_sorted = np.searchsorted(src_sorted, tgt_raw)
+            idx_sorted = np.clip(idx_sorted, 1, len(src_sorted) - 1)
+            left = np.abs(tgt_raw - src_sorted[idx_sorted - 1])
+            right = np.abs(tgt_raw - src_sorted[idx_sorted])
+            idx_sorted = np.where(left <= right, idx_sorted - 1, idx_sorted)
+
+            # Map back to original unsorted indices
+            idx = sort_order[idx_sorted]
+
+            # Index into the data array along this dimension
+            if is_cupy:
+                idx_arr = cp.asarray(idx)
+                data = cp.take(data, idx_arr, axis=dim_idx)
+            else:
+                data = np.take(data, idx, axis=dim_idx)
+
+            # Update the interpolated dimension coordinate
+            new_coords[key] = (key, tgt_raw)
+
+            # Re-index any non-dimension coordinates that depend on this dim
+            for cname, cval in list(new_coords.items()):
+                if cname == key:
+                    continue
+                if isinstance(cval, xr.Variable):
+                    c_dims = cval.dims
+                    c_data = cval.values
+                elif isinstance(cval, xr.DataArray):
+                    c_dims = cval.dims
+                    c_data = cval.values
+                elif isinstance(cval, tuple) and len(cval) == 2:
+                    c_dims, c_data = cval
+                    if isinstance(c_dims, str):
+                        c_dims = (c_dims,)
+                else:
+                    continue
+
+                if key in c_dims:
+                    ax = list(c_dims).index(key)
+                    c_data = np.take(np.asarray(c_data), idx, axis=ax)
+                    new_coords[cname] = (c_dims, c_data)
+
+        result = xr.DataArray(data=data, dims=dims, coords=new_coords)
+
+    return result
+
+
 def split_coords(
     x: torch.Tensor, coords: CoordSystem, dim: str = "variable"
 ) -> tuple[list[torch.Tensor], CoordSystem, np.ndarray]:
@@ -429,3 +633,166 @@ def convert_multidim_to_singledim(
             i += j + 1
 
     return CoordSystem(adjusted_coords), mapping
+
+
+def tile_coords(
+    x: torch.Tensor, coords: CoordSystem, target_coords: CoordSystem
+) -> tuple[torch.Tensor, CoordSystem]:
+    """Tile tensor x to match dimensions in target_coords that don't exist in coords.
+
+    This function tiles the input tensor to match leading dimensions from target_coords
+    that are not present in coords. Dimensions that exist in both coords and target_coords
+    are ignored in target_coords and use the values from coords instead.
+
+    Parameters
+    ----------
+    x : torch.Tensor
+        Source tensor to be tiled
+    coords : CoordSystem
+        Coordinate system for x tensor
+    target_coords : CoordSystem
+        Target coordinate system. Dimensions that exist in coords are ignored.
+
+    Returns
+    -------
+    tuple[torch.Tensor, CoordSystem]
+        Tuple containing the tiled tensor and updated coordinate system
+
+    Examples
+    --------
+    Tiling a tensor to match additional dimensions from target_coords
+
+    >>> from earth2studio.utils.coords import tile_coords
+    >>> from collections import OrderedDict
+    >>> import torch
+    >>> import numpy as np
+    >>>
+    >>> x = torch.randn(3, 4)
+    >>> coords = OrderedDict({
+    ...     "variable": np.array(["a", "b", "c"]),
+    ...     "time": np.array([0, 1, 2, 3])
+    ... })
+    >>>
+    >>> target_coords = OrderedDict({
+    ...     "batch": np.array([0, 1]),
+    ...     "ensemble": np.array([0, 1, 2]),
+    ...     "variable": np.array(["x", "y"]),  # Ignored, uses coords value
+    ...     "time": np.array([10, 20, 30, 40])  # Ignored, uses coords value
+    ... })
+    >>>
+    >>> # Tile x to match batch and ensemble dimensions
+    >>> x_tiled, out_coords = tile_coords(x, coords, target_coords)
+    >>> x_tiled.shape
+    torch.Size([2, 3, 3, 4])
+    >>> list(out_coords.keys())
+    ['batch', 'ensemble', 'variable', 'time']
+    """
+    coords_keys = set(coords.keys())
+    leading_dims = OrderedDict()
+    common_dims = []
+
+    for key, val in target_coords.items():
+        if key not in coords_keys:
+            leading_dims[key] = val
+        else:
+            common_dims.append(key)
+
+    # Validate that all common dims are at the end of target_coords
+    if common_dims:
+        target_keys = list(target_coords.keys())
+        coords_keys_list = list(coords.keys())
+        if target_keys[-len(common_dims) :] != coords_keys_list:
+            raise ValueError(
+                f"All common dimensions must appear at the end of target_coords. "
+                f"Common dimensions: {coords_keys_list}, "
+                f"target_coords trailing keys: {target_keys[-len(common_dims):]}, "
+                f"target_coords order: {target_keys}"
+            )
+
+    n_lead = len(leading_dims)
+
+    out_coords = deepcopy(leading_dims)
+    for key, val in coords.items():
+        out_coords[key] = val
+
+    # add leading size-1 dims so tile has one rep per dimension, then tile to match target
+    reps = [len(dim) for dim in leading_dims.values()] + [1] * len(x.shape)
+    x_tiled = x.view(*([1] * n_lead), *x.shape).tile(reps)
+
+    return x_tiled, out_coords
+
+
+def cat_coords(
+    tensors: tuple[torch.Tensor, ...],
+    coords: tuple[CoordSystem, ...],
+    dim: str = "variable",
+) -> tuple[torch.Tensor, CoordSystem]:
+    """
+    concatenate data along coordinate dimension.
+
+    Parameters
+    ----------
+    tensors : tuple[torch.Tensor, ...]
+        Tuple of input tensors to concatenate
+    coords : tuple[CoordSystem, ...]
+        Tuple of OrderedDicts representing coordinate systems for each tensor
+    dim : str
+        name of dimension along which to concatenate
+
+    Returns
+    -------
+    tuple[torch.Tensor, CoordSystem]
+        Tuple containing output tensor and coordinate OrderedDict from
+        concatenated data.
+
+    Raises
+    ------
+    ValueError
+        If tensors and coords have different lengths
+        If input tensors have different dimension names
+        If non-concatenation dimensions don't match across inputs
+    KeyError
+        If concatenation dimension is not found in all coordinate systems
+    """
+    if len(tensors) != len(coords):
+        raise ValueError(
+            f"tensors and coords must have the same length, got {len(tensors)} tensors and {len(coords)} coords"
+        )
+
+    if len(tensors) == 0:
+        raise ValueError("at least one tensor and coord must be provided")
+
+    if len(tensors) == 1:
+        return tensors[0], deepcopy(coords[0])
+
+    # Use first coord as reference
+    ref_coord = coords[0]
+
+    # make sure cat dim is present in all tensors
+    handshake_dim(ref_coord, dim)
+
+    # make sure all tensors have the same dimension names
+    ref_keys = list(ref_coord.keys())
+    for i, coord in enumerate(coords[1:], start=1):
+        handshake_dim(coord, dim)
+        if not list(coord.keys()) == ref_keys:
+            raise ValueError(
+                f"all input tensors must have the same dimension names. "
+                f"Tensor 0 has {ref_keys}, tensor {i} has {list(coord.keys())}"
+            )
+
+    # make sure all the other dimensions are of equal length
+    other_dims = list(ref_keys)
+    other_dims.remove(dim)
+    for i, coord in enumerate(coords[1:], start=1):
+        handshake_coords(ref_coord, coord, other_dims)
+
+    # assemble output coords
+    coz = deepcopy(ref_coord)
+    coz[dim] = np.concatenate([coord[dim] for coord in coords])
+
+    # concatenate tensors
+    dim_index = list(coz).index(dim)
+    zz = torch.cat(tensors, dim=dim_index)
+
+    return zz, coz
diff --git a/earth2studio/utils/time.py b/earth2studio/utils/time.py
index e2b823892..e58b70cc0 100644
--- a/earth2studio/utils/time.py
+++ b/earth2studio/utils/time.py
@@ -17,7 +17,7 @@
 
 import numpy as np
 
-from earth2studio.utils.type import LeadTimeArray, TimeArray
+from earth2studio.utils.type import LeadTimeArray, TimeArray, TimeTolerance
 
 
 def timearray_to_datetime(time: TimeArray) -> list[datetime]:
@@ -95,3 +95,64 @@ def to_time_array(time: list[str] | list[datetime] | TimeArray) -> TimeArray:
             )
 
     return np.array(output).astype("datetime64[ns]")
+
+
+def normalize_time_tolerance(
+    tolerance: TimeTolerance,
+) -> tuple[np.timedelta64, np.timedelta64]:
+    """Normalize a TimeTolerance to a tuple of (lower_bound, upper_bound) as np.timedelta64.
+
+    Parameters
+    ----------
+    tolerance : TimeTolerance
+        Time tolerance value. Can be:
+        - A single timedelta or np.timedelta64 (interpreted as symmetric +/- bounds)
+        - A tuple of (timedelta, timedelta) or (np.timedelta64, np.timedelta64)
+          (interpreted as (lower_bound, upper_bound))
+
+    Returns
+    -------
+    tuple[np.timedelta64, np.timedelta64]
+        Tuple of (lower_bound, upper_bound) as np.timedelta64 values.
+        For a single tolerance value, returns (-tolerance, +tolerance).
+        For a tuple, returns the normalized bounds.
+
+    Examples
+    --------
+    >>> normalize_time_tolerance(np.timedelta64(1, "h"))
+    (numpy.timedelta64(-3600,'s'), numpy.timedelta64(3600,'s'))
+
+    >>> normalize_time_tolerance((np.timedelta64(-2, "h"), np.timedelta64(3, "h")))
+    (numpy.timedelta64(-7200,'s'), numpy.timedelta64(10800,'s'))
+    """
+    # Handle single value (symmetric tolerance)
+    if isinstance(tolerance, (timedelta, np.timedelta64)):
+        tol = np.timedelta64(tolerance)
+        return (-tol, tol)
+
+    # Handle tuple
+    if isinstance(tolerance, tuple):
+        if len(tolerance) != 2:
+            raise ValueError(
+                f"TimeTolerance tuple must have 2 elements, got {len(tolerance)}"
+            )
+
+        lower, upper = tolerance
+
+        # Convert both to np.timedelta64
+        lower_td = np.timedelta64(lower)
+        upper_td = np.timedelta64(upper)
+
+        # Ensure lower <= upper
+        if lower_td > upper_td:
+            raise ValueError(
+                f"TimeTolerance tuple must have lower_bound <= upper_bound, "
+                f"got ({lower_td}, {upper_td})"
+            )
+
+        return (lower_td, upper_td)
+
+    raise TypeError(
+        f"TimeTolerance must be timedelta, np.timedelta64, or tuple of 2, "
+        f"got {type(tolerance)}"
+    )
diff --git a/earth2studio/utils/type.py b/earth2studio/utils/type.py
index 345f87078..2384d33d3 100644
--- a/earth2studio/utils/type.py
+++ b/earth2studio/utils/type.py
@@ -15,9 +15,11 @@
 # limitations under the License.
 
 from collections import OrderedDict
+from datetime import timedelta
 
 import numpy as np
 
+FrameSchema = OrderedDict[str, np.ndarray]
 CoordSystem = OrderedDict[str, np.ndarray]
 
 # Specific dimension custom types
@@ -25,3 +27,10 @@
 LeadTimeArray = np.ndarray[np.timedelta64]
 TimeArray = np.ndarray[np.datetime64]
 FieldArray = np.ndarray[str]
+
+TimeTolerance = (
+    timedelta
+    | np.timedelta64
+    | tuple[timedelta, timedelta]
+    | tuple[np.timedelta64, np.timedelta64]
+)
diff --git a/examples/16_cbottle_super_resolution.py b/examples/16_cbottle_super_resolution.py
index 60209aa64..fa11adc19 100644
--- a/examples/16_cbottle_super_resolution.py
+++ b/examples/16_cbottle_super_resolution.py
@@ -92,6 +92,7 @@
     package,
     output_resolution=(1024, 1024),
     super_resolution_window=super_resolution_window,
+    distilled_model=True,
 )
 
 # Load the infill model
diff --git a/examples/21_stormcast_sda.py b/examples/21_stormcast_sda.py
new file mode 100644
index 000000000..610038dd6
--- /dev/null
+++ b/examples/21_stormcast_sda.py
@@ -0,0 +1,488 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# %%
+"""
+StormCast Score-Based Data Assimilation
+=======================================
+
+Running StormCast with guided diffusion posterior sampling to assimilate observations.
+
+This example demonstrates how to use the StormCast score-based data assimilation (SDA)
+model for convection-allowing regional forecasts that incorporate sparse in-situ
+observations using diffusion posterior sampling (DPS).
+Two forecasts are run—one without observations and one with ISD surface station data
+from central United States region to illustrate the impact of data assimilation.
+
+In this example you will learn:
+
+- How to load and initialise the StormCast score-based data assimilation (SDA) model
+- Fetching HRRR initial conditions and ISD surface observations
+- Running the model iteratively with and without observation assimilation
+- Comparing assimilated and non-assimilated forecasts
+"""
+# /// script
+# dependencies = [
+#   "earth2studio[da-stormcast] @ git+https://github.com/NVIDIA/earth2studio.git",
+#   "cartopy",
+# ]
+# ///
+
+# %%
+# Set Up
+# ------
+# This example requires the following components:
+#
+# - Assimilation Model: StormCast SDA :py:class:`earth2studio.models.da.StormCastSDA`.
+# - Datasource (state): HRRR analysis :py:class:`earth2studio.data.HRRR`.
+# - Datasource (obs): NOAA ISD surface stations :py:class:`earth2studio.data.ISD`.
+# - Datasource (conditioning): GFS forecasts :py:class:`earth2studio.data.GFS_FX`
+#   (loaded automatically by the model).
+#
+# StormCast score-based data assimilation (SDA) extends StormCast with diffusion
+# posterior sampling (DPS) guidance,
+# allowing sparse point observations to steer the generative diffusion process.
+
+# %%
+import os
+
+os.makedirs("outputs", exist_ok=True)
+from dotenv import load_dotenv
+
+load_dotenv()  # TODO: make common example prep function
+
+from collections import OrderedDict
+from datetime import timedelta
+
+import numpy as np
+import torch
+import xarray as xr
+from loguru import logger
+from tqdm import tqdm
+
+logger.remove()
+logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
+
+from earth2studio.data import HRRR, ISD, fetch_data
+from earth2studio.models.da import StormCastSDA
+from earth2studio.utils.coords import map_coords_xr
+
+package = StormCastSDA.load_default_package()
+# Load the model onto the GPU and configure SDA
+# sda_std_obs: assumed (normalized) observation noise std (lower = trust obs more)
+# sda_gamma: DPS guidance scaling factor (lower = stronger assimilation)
+model = StormCastSDA.load_model(package, sda_std_obs=0.1, sda_gamma=1e-4)
+model = model.to("cuda:0")
+
+hrrr = HRRR()
+
+# %%
+# Fetch Initial Conditions
+# ------------------------
+# Pull HRRR analysis data for April 3rd 2025, a date that saw a major tornado
+# outbreak across the central United States, and select the sub-grid that
+# StormCast expects. The model's :py:meth:`init_coords` describes the required
+# coordinate system.
+
+# %%
+init_time = np.array([np.datetime64("2025-04-03T18:00")])
+ic = model.init_coords()[0]
+
+x = fetch_data(
+    hrrr,
+    time=init_time,
+    variable=ic["variable"],
+    lead_time=np.array([np.timedelta64(0, "h")]),
+    device="cuda:0",
+    legacy=False,
+)
+x = map_coords_xr(x, ic)
+
+# %%
+# Run Without Observations
+# ------------------------
+# Step the model forward 6 hours without any observations. This is equivalent to using
+# the StormCast prognostic model as it will just use EDM diffusion sampling under the
+# hood.  Each call to ``model.send(None)`` advances the state by one hour.
+# We store only the surface variables used for comparison (u10m, v10m, t2m).
+
+# %%
+nsteps = 6
+plot_vars = ["u10m", "v10m", "t2m"]
+
+np.random.seed(42)
+torch.manual_seed(42)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed_all(42)
+
+no_obs_frames = []
+gen = model.create_generator(x.copy())
+x_state = next(gen)  # Prime the generator, yields initial state
+
+for step in tqdm(range(nsteps), desc="No-obs forecast"):
+    logger.info(f"Running no-obs forecast step {step}")
+    x_state = gen.send(None)  # Advance one hour without observations
+    no_obs_frames.append(x_state.sel(variable=plot_vars).copy())
+
+gen.close()
+no_obs_da = xr.concat(no_obs_frames, dim="lead_time")
+
+# Save to Zarr (convert to numpy for storage)
+no_obs_np = no_obs_da.copy(data=no_obs_da.data.get())
+no_obs_np.to_dataset(name="prediction").to_zarr("outputs/21_no_obs.zarr", mode="w")
+
+# %%
+# Fetch Observations and Run With Assimilation
+# ---------------------------------------------
+# Fetch NOAA Integrated Surface Database (ISD) surface observations from the central
+# United States and assimilate them at each forecast step. The observations are fetched
+# for the valid time (initialisation time + lead time) so the model assimilates
+# temporally relevant data.
+
+# %%
+stations = ISD.get_stations_bbox((32.0, -105.0, 45.0, -90.0))
+isd = ISD(stations=stations, tolerance=timedelta(minutes=15), verbose=False)
+
+# %%
+# Plot ISD Station Locations
+# --------------------------
+# Visualise the ISD stations that will provide observations for assimilation.
+
+# %%
+import cartopy
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+
+# Fetch a sample to get station locations
+sample_df = isd(init_time, ["t2m", "u10m", "v10m"])
+station_lats = sample_df["lat"].values
+station_lons = sample_df["lon"].values
+
+plt.close("all")
+fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}, figsize=(8, 6))
+ax.set_extent([-110, -85, 30, 47], crs=ccrs.PlateCarree())
+ax.add_feature(
+    cartopy.feature.STATES.with_scale("50m"), linewidth=0.5, edgecolor="black"
+)
+ax.add_feature(cartopy.feature.LAND, facecolor="lightyellow")
+ax.gridlines(draw_labels=True, linewidth=0.3, alpha=0.5)
+ax.scatter(
+    station_lons,
+    station_lats,
+    s=20,
+    marker="x",
+    transform=ccrs.PlateCarree(),
+    zorder=3,
+)
+ax.set_title("ISD Station Locations - Central United States")
+plt.savefig("outputs/21_isd_stations.jpg", dpi=150, bbox_inches="tight")
+
+# %%
+# Run Inference With Streaming Observations
+# ------------------------------------------
+# At each forecast step, fetch NOAA ISD observations for the current valid time
+# and send them to the model generator. The observations will be used in the SDA
+# guidance term when sampling the diffusion model effectively steering the generated
+# result to align with the stations data from the ISD data based.
+
+# %%
+np.random.seed(42)
+torch.manual_seed(42)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed_all(42)
+
+obs_frames = []
+gen = model.create_generator(x)
+x_state = next(gen)  # Prime the generator, yields initial state
+
+for step in tqdm(range(nsteps), desc="Obs forecast"):
+    # Fetch observations for the next forecast step's valid time using model coords
+    x_coords = OrderedDict({d: x_state.coords[d].values for d in x_state.dims})
+    oc = model.output_coords((x_coords,))[0]
+    valid_time = oc["time"] + oc["lead_time"]
+    obs_df = isd(valid_time, plot_vars)
+    logger.info(f"Running obs forecast step {step}, {len(obs_df)} obs")
+    x_state = gen.send(obs_df)  # Advance one hour with observations
+    obs_frames.append(x_state.sel(variable=plot_vars).copy())
+
+gen.close()
+obs_da = xr.concat(obs_frames, dim="lead_time")
+
+# Save to Zarr
+obs_np = obs_da.copy(data=obs_da.data.get())
+obs_np.to_dataset(name="prediction").to_zarr("outputs/21_with_obs.zarr", mode="w")
+
+# %%
+# Post Processing
+# ---------------
+# Compare the two forecasts.  The top row shows the baseline forecast (no
+# observations), the middle row shows the assimilated forecast with observation
+# station locations overlaid as unfilled circles, and the bottom row shows the
+# difference (assimilated minus baseline).
+
+# %%
+plt.close("all")
+variable = "u10m"
+
+# Load saved forecasts from Zarr stores
+no_obs_ds = xr.open_zarr("outputs/21_no_obs.zarr")
+obs_ds = xr.open_zarr("outputs/21_with_obs.zarr")
+
+no_obs_vals = no_obs_ds["prediction"].sel(variable=variable).values
+obs_vals = obs_ds["prediction"].sel(variable=variable).values
+
+# Lambert Conformal projection matching HRRR
+projection = ccrs.LambertConformal(
+    central_longitude=262.5,
+    central_latitude=38.5,
+    standard_parallels=(38.5, 38.5),
+    globe=ccrs.Globe(semimajor_axis=6371229, semiminor_axis=6371229),
+)
+
+fig, axes = plt.subplots(
+    3,
+    nsteps,
+    subplot_kw={"projection": projection},
+    figsize=(4 * nsteps, 8),
+)
+fig.subplots_adjust(wspace=0.02, hspace=0.08, left=0.1, right=0.9)
+
+for step in range(nsteps):
+    lead_hr = step + 1
+    no_obs_field = no_obs_vals[0, step]
+    obs_field = obs_vals[0, step]
+    diff_field = obs_field - no_obs_field
+
+    vmin = -10
+    vmax = 10
+    # Row 0: No-obs forecast
+    ax = axes[0, step]
+    im0 = ax.pcolormesh(
+        model.lon,
+        model.lat,
+        no_obs_field,
+        transform=ccrs.PlateCarree(),
+        cmap="PRGn",
+        vmin=vmin,
+        vmax=vmax,
+    )
+    ax.add_feature(
+        cartopy.feature.STATES.with_scale("50m"),
+        linewidth=0.5,
+        edgecolor="black",
+        zorder=2,
+    )
+    ax.set_title(f"+{lead_hr}h")
+
+    # Row 1: With-obs forecast + station locations
+    ax = axes[1, step]
+    im1 = ax.pcolormesh(
+        model.lon,
+        model.lat,
+        obs_field,
+        transform=ccrs.PlateCarree(),
+        cmap="PRGn",
+        vmin=vmin,
+        vmax=vmax,
+    )
+    ax.scatter(
+        station_lons,
+        station_lats,
+        s=8,
+        facecolors="none",
+        edgecolors="black",
+        linewidths=0.8,
+        transform=ccrs.PlateCarree(),
+        zorder=3,
+        label="Stations",
+    )
+    ax.add_feature(
+        cartopy.feature.STATES.with_scale("50m"),
+        linewidth=0.5,
+        edgecolor="black",
+        zorder=2,
+    )
+
+    # Row 2: Difference (assimilated - baseline)
+    ax = axes[2, step]
+    im2 = ax.pcolormesh(
+        model.lon,
+        model.lat,
+        diff_field,
+        transform=ccrs.PlateCarree(),
+        cmap="RdBu_r",
+        vmin=-3,
+        vmax=3,
+    )
+    ax.add_feature(
+        cartopy.feature.STATES.with_scale("50m"),
+        linewidth=0.5,
+        edgecolor="black",
+        zorder=2,
+    )
+
+axes[0, 0].text(
+    -0.07,
+    0.5,
+    "No Obs",
+    va="bottom",
+    ha="center",
+    rotation="vertical",
+    rotation_mode="anchor",
+    fontsize=12,
+    transform=axes[0, 0].transAxes,
+)
+axes[1, 0].text(
+    -0.07,
+    0.5,
+    "Obs",
+    va="bottom",
+    ha="center",
+    rotation="vertical",
+    rotation_mode="anchor",
+    fontsize=12,
+    transform=axes[1, 0].transAxes,
+)
+axes[2, 0].text(
+    -0.07,
+    0.5,
+    "Difference",
+    va="bottom",
+    ha="center",
+    rotation="vertical",
+    rotation_mode="anchor",
+    fontsize=12,
+    transform=axes[2, 0].transAxes,
+)
+
+# Add colour bars
+plt.colorbar(im0, ax=axes[0, -1], shrink=0.6, label=f"{variable} (m/s)")
+plt.colorbar(im1, ax=axes[1, -1], shrink=0.6, label=f"{variable} (m/s)")
+plt.colorbar(im2, ax=axes[2, -1], shrink=0.6, label=f"{variable} (m/s)")
+
+plt.tight_layout()
+plt.savefig("outputs/21_stormcast_sda_comparison.jpg", dpi=150)
+
+# %%
+# Ground Truth Comparison
+# -----------------------
+# Fetch HRRR analysis (ground truth) at each valid forecast time and compute
+# the absolute error of both the no-obs and obs forecasts. This shows whether
+# assimilation improves accuracy relative to the actual analysis.
+
+# %%
+truth = fetch_data(
+    hrrr,
+    time=init_time,
+    variable=np.array(plot_vars),
+    lead_time=np.array([np.timedelta64(h + 1, "h") for h in range(nsteps)]),
+    device="cpu",
+    legacy=False,
+)
+ic["variable"] = np.array(plot_vars)
+
+truth = map_coords_xr(truth, {"hrrr_y": ic["hrrr_y"], "hrrr_x": ic["hrrr_x"]})
+truth_vals = truth.sel(variable=variable).values  # [nsteps, hrrr_y, hrrr_x]
+no_obs_vals = no_obs_ds["prediction"].sel(variable=variable).values
+obs_vals = obs_ds["prediction"].sel(variable=variable).values
+
+# Compute absolute errors against ground truth
+no_obs_err = np.abs(no_obs_vals[0] - truth_vals[0])
+obs_err = np.abs(obs_vals[0] - truth_vals[0])
+
+# %%
+# Plot absolute errors between the StormCast predictions and HRRR analysis ground truth.
+# In later time-steps it is clear that StormCast with SDA sampling using ISD station
+# observations has improved accuracy over the vanilla stormcast prediction.
+
+# %%
+plt.close("all")
+fig, axes = plt.subplots(
+    2,
+    nsteps,
+    subplot_kw={"projection": projection},
+    figsize=(4 * nsteps, 6),
+)
+fig.subplots_adjust(wspace=0.02, hspace=0.08, left=0.1, right=0.9)
+
+err_max = 5
+for step in range(nsteps):
+    lead_hr = step + 1
+
+    # Row 0: No-obs absolute error
+    ax = axes[0, step]
+    im0 = ax.pcolormesh(
+        model.lon,
+        model.lat,
+        no_obs_err[step],
+        transform=ccrs.PlateCarree(),
+        cmap="viridis",
+        vmin=0,
+        vmax=err_max,
+    )
+    ax.add_feature(
+        cartopy.feature.STATES.with_scale("50m"),
+        linewidth=0.5,
+        edgecolor="grey",
+        zorder=2,
+    )
+    ax.set_title(f"+{lead_hr}h")
+
+    # Row 1: Obs absolute error
+    ax = axes[1, step]
+    im1 = ax.pcolormesh(
+        model.lon,
+        model.lat,
+        obs_err[step],
+        transform=ccrs.PlateCarree(),
+        cmap="viridis",
+        vmin=0,
+        vmax=err_max,
+    )
+    ax.add_feature(
+        cartopy.feature.STATES.with_scale("50m"),
+        linewidth=0.5,
+        edgecolor="grey",
+        zorder=2,
+    )
+
+axes[0, 0].text(
+    -0.07,
+    0.5,
+    "|No Obs - Truth|",
+    va="bottom",
+    ha="center",
+    rotation="vertical",
+    rotation_mode="anchor",
+    fontsize=12,
+    transform=axes[0, 0].transAxes,
+)
+axes[1, 0].text(
+    -0.07,
+    0.5,
+    "|Obs - Truth|",
+    va="bottom",
+    ha="center",
+    rotation="vertical",
+    rotation_mode="anchor",
+    fontsize=12,
+    transform=axes[1, 0].transAxes,
+)
+
+plt.colorbar(im0, ax=axes[0, -1], shrink=0.6, label=f"|Δ{variable}| (m/s)")
+plt.colorbar(im1, ax=axes[1, -1], shrink=0.6, label=f"|Δ{variable}| (m/s)")
+plt.tight_layout()
+plt.savefig("outputs/21_stormcast_sda_gt_comparison.jpg")
diff --git a/pyproject.toml b/pyproject.toml
index e9d82c3fd..40e8d7db1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,7 +60,8 @@ Changelog = "https://github.com/NVIDIA/earth2studio/blob/main/CHANGELOG.md"
 data = [
     "cdsapi>=0.7.5",
     "cfgrib >= 0.9.10.3",
-    "eccodes>=2.38.0",
+    "eccodes>=2.39.2",
+    "eccodeslib>=2.39.2",
     "ecmwf-opendata>=0.3.3",
     "intake-esgf>=2025.9.26",
     "pandas<3.0", # https://github.com/esgf2-us/intake-esgf/issues/167
@@ -79,8 +80,7 @@ perturbation = [
     "scipy>=1.15.2",
 ]
 statistics = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 utils = [
     "scipy>=1.15.2",
@@ -93,16 +93,19 @@ serve = [
     "pydantic>=2.0.0",
     # Redis dependencies
     "redis>=5.0.0",
+    "types-redis",
     "hiredis>=2.0.0",
     "rq>=1.15.0",
     # Metrics
     "prometheus_client>=0.17.0",
     # Additional utilities
-    "python-multipart>=0.0.6",
-    "python-dotenv>=1.0.0",
     "aiofiles>=23.0.0",
     "httpx>=0.25.0",
     "hydra-core>=1.3.0",
+    "python-dotenv>=1.0.0",
+    "python-multipart>=0.0.6",
+    "requests>=2.25.0",
+    "urllib3>=1.26.0",
     # Object storage related
     "cryptography>=41.0.0",
     "multi-storage-client>=0.44.0",
@@ -112,7 +115,6 @@ serve = [
 # PX Models
 ace2 = [
     "fme",
-    "h5py<=3.14.0",
     "pandas>=2.3.2",
     "scipy>=1.15.2",
 ]
@@ -133,27 +135,23 @@ atlas = [
     "timm",
     "torch-harmonics",
     "einops",
-    "nvidia-physicsnemo>=1.3.0,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.3.0",
 ]
 aurora = [
     "microsoft-aurora>=1.6.0",
 ]
 dlesym = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "nvidia-physicsnemo>=1.0.1",
     "earth2grid",
     "omegaconf>=2.3.0",
     "hydra-core>=1.3.2",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 dlwp = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "nvidia-physicsnemo>=1.0.1",
     "importlib_metadata",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 fcn = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 fcn3 = [
     "makani",
@@ -161,7 +159,6 @@ fcn3 = [
     "scipy>=1.10.0",
     "torch-harmonics>=0.8.0",
     "moviepy",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 fengwu = [
     "onnx",
@@ -181,8 +178,7 @@ graphcast = [
     "dm-tree>=0.1.9",
 ]
 interp-modafno = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=2.0",
 ]
 pangu = [
     "onnx",
@@ -199,19 +195,17 @@ sfno = [
 ]
 stormcast = [
     "einops>=0.8.1",
-    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "nvidia-physicsnemo>=2.0",
     "nvtx>=0.2.11",
     "omegaconf>=2.3.0",
     "scipy>=1.15.2",
     "pyproj>=3.7.1",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 stormscope = [
     "natten",
-    "nvidia-physicsnemo==1.3.0",
+    "nvidia-physicsnemo>=2.0",
     "nvtx>=0.2.11",
     "earth2studio[utils]",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 # DX Models
 cbottle = [
@@ -221,10 +215,9 @@ cbottle = [
 climatenet = []
 corrdiff = [
     "einops>=0.8.1",
-    "nvidia-physicsnemo>=1.2.0,<2.0",
+    "nvidia-physicsnemo>=2.0",
     "nvtx>=0.2.11",
     "scipy>=1.15.2",
-    "pytz", # Remove on physicsnemo 2.0
 ]
 cyclone = [
     "cucim-cu12>=25.4.0",
@@ -233,36 +226,55 @@ cyclone = [
 ]
 derived = []
 precip-afno = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 precip-afno-v2 = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 solarradiation-afno = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 windgust-afno = [
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "pytz", # Remove on physicsnemo 2.0
+    "nvidia-physicsnemo>=1.0.1",
 ]
 # DA Models
 da-interp = [
     "cupy-cuda12x<14.0.0",
     "cudf-cu12==26.2.*",
 ]
+da-stormcast = [
+    "earth2studio[stormcast]",
+    "cupy-cuda12x<14.0.0",
+    "cudf-cu12==26.2.*",
+]
 # All, must not have conflicts
 all = [
     "earth2studio[data,perturbation,statistics,utils]",
-    "earth2studio[ace2,aifs,aifsens,atlas,aurora,dlesym,dlwp,fcn,fcn3,fengwu,interp-modafno,pangu,sfno,stormcast,stormscope,graphcast]",
+    "earth2studio[aifs,aifsens,atlas,aurora,dlesym,dlwp,fcn,fcn3,fengwu,interp-modafno,pangu,stormcast,sfno,stormscope,graphcast]",
     "earth2studio[cbottle,climatenet,corrdiff,precip-afno,cyclone,precip-afno-v2,solarradiation-afno,windgust-afno]",
+    "earth2studio[da-interp,da-stormcast]",
 ]
 
 # ==== UV configuration ====
 [tool.uv]
-conflicts = []
+conflicts = [
+  [
+    { extra = "ace2" },
+    { extra = "atlas" },
+  ],
+  [
+    { extra = "ace2" },
+    { extra = "fcn3" },
+  ],
+  [
+    { extra = "ace2" },
+    { extra = "perturbation" },
+  ],
+  [
+    { extra = "ace2" },
+    { extra = "sfno" },
+  ]
+]
 default-groups = ["dev", "build"]
 # Needed for some packages that build with PEP 518
 no-build-isolation-package = ["earth2grid", "flash-attn", "natten", "torch-harmonics"]
@@ -275,12 +287,19 @@ natten = ["setuptools", "cmake", "torch"]
 
 [tool.uv.sources]
 cfgrib = { git = "https://github.com/NickGeneva/cfgrib.git", rev = "6a9af3173d7050b5fa5d221755f97e6ecb8c3433" } # https://github.com/ecmwf/cfgrib/issues/433
-cbottle = { git = "https://github.com/NVlabs/cBottle.git", rev = "1d4435abe730d6b966e983633b466b345d60ac78" }
+cbottle = { git = "https://github.com/NVlabs/cBottle.git", rev = "8b8b358466e6b2f50d1779009790002ceb596e72" }
 earth2grid = { git = "https://github.com/NVlabs/earth2grid.git", rev = "11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3" }
-makani = { git = "https://github.com/NVIDIA/makani.git", rev = "fd1a62b9f27aebbc793f0cbafd838a3811de43fd"}
+makani = { git = "https://github.com/NVIDIA/makani.git", rev = "b38fcb2799d7dbc146fa60459f3f9823394a8bf1"}
 graphcast = { git = "https://github.com/google-deepmind/graphcast.git", rev = "7077d40a36db6541e3ed72ccaed1c0d202fa6014" }
 pytest-testmon = { git = "https://github.com/NickGeneva/pytest-testmon.git", rev = "48cd5ed1b67bf2f2bd9f63335389f262db472806" }
 fme = { git = "https://github.com/ai2cm/ace.git", rev = "e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" }
+# Specific source for each of the current torch-harmonics models, avoiding ace2 conflicts until they update torch-harmonics
+torch-harmonics = [
+  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "perturbation"},
+  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "atlas"},
+  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "fcn3"},
+  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "sfno"},
+]
 
 [[tool.uv.index]]
 name = "pytorch-cpu"
@@ -314,6 +333,7 @@ dev = [
     "pytest-skip-slow>=0.0.5",
     "pytest-testmon>=2.1.3",
     "pyyaml>=6.0",
+    "types-redis",
     "types-requests",
     "ruff",
     "pytest-asyncio>=0.26.0",
@@ -332,7 +352,7 @@ docs = [
     "sphinx-togglebutton>=0.3.0",
     "myst-parser>=0.18.0",
     "sphinx-favicon>=1.0.1",
-    "pydata-sphinx-theme==0.16.1"
+    "nvidia-sphinx-theme"
 ]
 
 [tool.hatch.version]
@@ -362,10 +382,9 @@ exclude = []
 "docs/*.py" = ["F401"]
 "examples/*.py" = ["E402"]
 "recipes/**/*_notebook.py" = ["E402"]
-
 # Ignore `S101` (assertions) in all `test` files.
 "test/*.py" = ["S101"]
-"serve/test/*.py" = ["S101"]
+"serve/server/test/*.py" = ["S101"]
 
 # ==== Pytest configuration ====
 [tool.pytest.ini_options]
diff --git a/recipes/hens/cfg/storm_bernd.yaml b/recipes/hens/cfg/storm_bernd.yaml
index 678103470..22dc67eaf 100644
--- a/recipes/hens/cfg/storm_bernd.yaml
+++ b/recipes/hens/cfg/storm_bernd.yaml
@@ -29,7 +29,7 @@ data_source:
     _target_: earth2studio.data.GFS  # data source class
 
 file_output:
-    path: '/output/'       # directory to which outfiles are written
+    path: '/outputs'       # directory to which outfiles are written
     output_vars: ['t2m','u10m','r500','tp']
     thread_io: False      # write out in seperate thread (needs further testing)
     format:               # io backend class
diff --git a/recipes/hens/src/hens_ensemble.py b/recipes/hens/src/hens_ensemble.py
index 021ca2766..d11e0622a 100644
--- a/recipes/hens/src/hens_ensemble.py
+++ b/recipes/hens/src/hens_ensemble.py
@@ -33,12 +33,11 @@
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
+from earth2studio.utils.coords import CoordSystem, cat_coords, map_coords, split_coords
 from earth2studio.utils.time import to_time_array
 
 from .hens_utilities import (
     TCTracking,
-    cat_coords,
     get_batchid_from_ensid,
     save_corrdiff_output,
 )
@@ -389,8 +388,14 @@ def __call__(self) -> dict[str, IOBackend]:
                         yy, codia = map_coords(xx, coords, self.dx_ic_dict[dx_name])
                         yy, codib = dx_model(yy, codia)
 
-                        # concatenate diagnostic variable to forecast vars
-                        xx, coords = cat_coords(xx, coords, yy, codib, "variable")
+                        # map lat/lon of diagnostic model to forecast model (often there is difference in lat)
+                        _codib = OrderedDict(
+                            {_dim: coords[_dim] for _dim in ["lat", "lon"]}
+                        )
+                        yy, codib = map_coords(yy, codib, _codib)
+
+                        # concatenate tensors along variable dimension
+                        xx, coords = cat_coords((xx, yy), (coords, codib), "variable")
 
                     # --- CorrDiff models (run after each step, separately) ---
                     # For each CorrDiff model, run the downscaling, collect outputs and coordinates,
diff --git a/recipes/hens/src/hens_utilities.py b/recipes/hens/src/hens_utilities.py
index 8e5a108eb..a3e26d6a8 100644
--- a/recipes/hens/src/hens_utilities.py
+++ b/recipes/hens/src/hens_utilities.py
@@ -34,7 +34,6 @@
 from earth2studio.models.dx import CorrDiff
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, map_coords
 from earth2studio.utils.time import to_time_array
 
 from .hens_utilities_reproduce import (
@@ -1024,55 +1023,6 @@ def get_batchid_from_ensid(
     return batch_id
 
 
-def cat_coords(
-    xx: torch.Tensor,
-    cox: CoordSystem,
-    yy: torch.Tensor,
-    coy: CoordSystem,
-    dim: str = "variable",
-) -> tuple[torch.Tensor, CoordSystem]:
-    """
-    concatenate data along coordinate dimension.
-
-    Parameters
-    ----------
-    xx : torch.Tensor
-        First input tensor which to concatenate
-    cox : CoordSystem
-        Ordered dict representing coordinate system that describes xx
-    yy : torch.Tensor
-        Second input tensor which to concatenate
-    coy : CoordSystem
-        Ordered dict representing coordinate system that describes yy
-    dim : str
-        name of dimension along which to concatenate
-
-    Returns
-    -------
-    tuple[torch.Tensor, CoordSystem]
-        Tuple containing output tensor and coordinate OrderedDict from
-        concatenated data.
-    """
-
-    if dim not in cox:
-        raise ValueError(f"dim {dim} is not in coords: {list(cox)}.")
-    if dim not in coy:
-        raise ValueError(f"dim {dim} is not in coords: {list(coy)}.")
-
-    # fix difference in latitude
-    _cox = cox.copy()
-    _cox["lat"] = coy["lat"]
-    xx, cox = map_coords(xx, cox, _cox)
-
-    coords = cox.copy()
-    dim_index = list(coords).index(dim)
-
-    zz = torch.cat((xx, yy), dim=dim_index)
-    coords[dim] = np.append(cox[dim], coy[dim])
-
-    return zz, coords
-
-
 def save_corrdiff_output(cd_output_dict: dict, save_path: str) -> None:
     """
     Save CorrDiff model output to a NetCDF file as a Dataset with one variable per CorrDiff output.
diff --git a/recipes/s2s/src/s2s_ensemble.py b/recipes/s2s/src/s2s_ensemble.py
index 7d2beaed7..9dcb70ab4 100644
--- a/recipes/s2s/src/s2s_ensemble.py
+++ b/recipes/s2s/src/s2s_ensemble.py
@@ -18,6 +18,7 @@
 from concurrent.futures import Future, ThreadPoolExecutor
 from datetime import datetime
 from math import ceil
+from typing import cast
 
 import numpy as np
 import torch
@@ -29,12 +30,11 @@
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
+from earth2studio.utils.coords import CoordSystem, cat_coords, map_coords, split_coords
 from earth2studio.utils.time import to_time_array
 
 from .s2s_utilities import (
     calculate_torch_seed,
-    cat_coords,
     get_batchid_from_ensid,
     run_with_rank_ordered_execution,
 )
@@ -184,12 +184,16 @@ def fetch_ics(
             IC times
         """
         self.time = to_time_array(time)
-        self.x0, self.coords0 = fetch_data(
-            source=data,
-            time=time,
-            variable=self.prognostic_ic["variable"],
-            lead_time=self.prognostic_ic["lead_time"],
-            device="cpu",
+        self.x0, self.coords0 = cast(
+            tuple[torch.Tensor, CoordSystem],
+            fetch_data(
+                source=data,
+                time=time,
+                variable=self.prognostic_ic["variable"],
+                lead_time=self.prognostic_ic["lead_time"],
+                device="cpu",
+                legacy=True,
+            ),
         )
         logger.success(f"Fetched data from {data.__class__.__name__}")
 
@@ -329,7 +333,7 @@ def write(self, xx_sub: torch.Tensor, coords_sub: CoordSystem) -> None:
         for k in self.io_dict.keys():
             if self.writer_executor is not None:
                 future = self.writer_executor.submit(
-                    self.io_dict[k].write, *split_coords(xx_sub, coords_sub)
+                    self.io_dict[k].write, *split_coords(xx_sub, coords_sub)  # type: ignore[arg-type]
                 )
                 self.writer_threads.append(future)
             else:
@@ -371,7 +375,7 @@ def __call__(self) -> dict[str, ZarrBackend]:
                         yy, codib = dx_model(yy, codia)
 
                         # concatenate diagnostic variable to forecast vars
-                        xx, coords = cat_coords(xx, coords, yy, codib, "variable")
+                        xx, coords = cat_coords((xx, yy), (coords, codib), "variable")
 
                     # pass output variables to io backend
                     for k in self.io_dict.keys():
diff --git a/recipes/s2s/src/s2s_utilities.py b/recipes/s2s/src/s2s_utilities.py
index 227a5e688..c27655c78 100644
--- a/recipes/s2s/src/s2s_utilities.py
+++ b/recipes/s2s/src/s2s_utilities.py
@@ -38,7 +38,7 @@
 from earth2studio.models.auto import Package
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, handshake_coords, map_coords
+from earth2studio.utils.coords import handshake_coords
 from earth2studio.utils.time import to_time_array
 
 
@@ -943,55 +943,6 @@ def get_batchid_from_ensid(
     return batch_id
 
 
-def cat_coords(
-    xx: torch.Tensor,
-    cox: CoordSystem,
-    yy: torch.Tensor,
-    coy: CoordSystem,
-    dim: str = "variable",
-) -> tuple[torch.Tensor, CoordSystem]:
-    """
-    concatenate data along coordinate dimension.
-
-    Parameters
-    ----------
-    xx : torch.Tensor
-        First input tensor which to concatenate
-    cox : CoordSystem
-        Ordered dict representing coordinate system that describes xx
-    yy : torch.Tensor
-        Second input tensor which to concatenate
-    coy : CoordSystem
-        Ordered dict representing coordinate system that describes yy
-    dim : str
-        name of dimension along which to concatenate
-
-    Returns
-    -------
-    tuple[torch.Tensor, CoordSystem]
-        Tuple containing output tensor and coordinate OrderedDict from
-        concatenated data.
-    """
-
-    if dim not in cox:
-        raise ValueError(f"dim {dim} is not in coords: {list(cox)}.")
-    if dim not in coy:
-        raise ValueError(f"dim {dim} is not in coords: {list(coy)}.")
-
-    # fix difference in latitude
-    _cox = cox.copy()
-    _cox["lat"] = coy["lat"]
-    xx, cox = map_coords(xx, cox, _cox)
-
-    coords = cox.copy()
-    dim_index = list(coords).index(dim)
-
-    zz = torch.cat((xx, yy), dim=dim_index)
-    coords[dim] = np.append(cox[dim], coy[dim])
-
-    return zz, coords
-
-
 def calculate_torch_seed(s: str) -> int:
     """Calculates torch seed based on a given string.
     String s is used as input to sha256 hash algorithm.
diff --git a/serve/Dockerfile b/serve/Dockerfile
index 6c9cde587..d0d368855 100644
--- a/serve/Dockerfile
+++ b/serve/Dockerfile
@@ -11,7 +11,6 @@ ENV EARTH2STUDIO_PACKAGE_TIMEOUT=1200
 ENV FORCE_CUDA_EXTENSION=1
 ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 
-ARG MAKANI_HASH=28f38e3e929ed1303476518552c64673bbd6f722
 ARG E2G_HASH=11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3
 ARG E2S_HASH=8ca8771c6be6ca051e082d41b99378fa14b58d9f
 
@@ -19,21 +18,31 @@ WORKDIR /workspace
 
 COPY --from=ghcr.io/astral-sh/uv:0.7.3 /uv /uvx /bin/
 RUN uv pip install --system --break-system-packages --no-build-isolation torch-harmonics==0.8.0
-RUN uv pip install --system --break-system-packages "makani @ git+https://github.com/NVIDIA/makani.git@${MAKANI_HASH}"
 RUN uv pip install --system --break-system-packages --no-build-isolation "earth2grid @ git+https://github.com/NVlabs/earth2grid@${E2G_HASH}"
 RUN uv pip install --system --break-system-packages natten==0.21.5+torch2100cu130 -f https://whl.natten.org
-RUN uv pip install --system --break-system-packages "earth2studio[dlwp,dlesym,pangu,sfno,stormcast,fcn3] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
-RUN uv pip install --system --break-system-packages "earth2studio[cbottle,corrdiff,cyclone] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
-RUN uv pip install --system --break-system-packages "earth2studio[data,perturbation,statistics] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
 RUN uv pip install --system --break-system-packages cartopy xskillscore matplotlib
 
+# Install the requirements for stormcast.
+RUN pip uninstall -y torch-harmonics && \
+    FORCE_CUDA_EXTENSION=1 uv pip install --system --break-system-packages --no-build-isolation torch-harmonics==0.8.0
+
 # Install the requirements for the inference server.
 COPY serve /workspace/earth2studio-project/serve
-RUN cd / && mkdir /workspace/earth2studio-project/examples && ln -s /outputs /workspace/earth2studio-project/examples/outputs
+COPY examples /workspace/earth2studio-project/examples
+RUN ln -s /outputs /workspace/earth2studio-project/examples/outputs
+# Config and scripts live under serve/server/ after COPY serve
+ENV SCRIPT_DIR=/workspace/earth2studio-project/serve/server/scripts
+ENV CONFIG_DIR=/workspace/earth2studio-project/serve/server/conf
+ENV WORKFLOW_DIR=/workspace/earth2studio-project/serve/server/example_workflows
+
+# Install local earth2studio from repo, then various extras and serve requirements.
+# Copy project root files needed for pip install (pyproject.toml is at repo root, not inside earth2studio).
+COPY pyproject.toml README.md /workspace/earth2studio-project/
+COPY earth2studio /workspace/earth2studio-project/earth2studio
 RUN apt-get update && apt-get install -y redis-server && \
     cd /workspace/earth2studio-project && \
-    uv pip install --system --break-system-packages -r serve/inferenceserver/requirements.txt
-
-CMD ["/workspace/earth2studio-project/serve/inferenceserver/scripts/startup.sh"]
+    uv pip install --system --break-system-packages ".[dlwp,dlesym,pangu,sfno,stormcast,fcn3,serve,cbottle,corrdiff,cyclone,data,perturbation,statistics]" && \
+    uv pip install --system --break-system-packages -r serve/server/requirements.txt && \
+    rm -rf /workspace/earth2studio-project/earth2studio /workspace/earth2studio-project/pyproject.toml /workspace/earth2studio-project/README.md
 
-#RUN python -c "import earth2studio; print(f'Earth2Studio version: {earth2studio.__version__}')"
+CMD ["/workspace/earth2studio-project/serve/server/scripts/startup.sh"]
diff --git a/serve/inferenceserver/api_server/__init__.py b/serve/client/Makefile
similarity index 65%
rename from serve/inferenceserver/api_server/__init__.py
rename to serve/client/Makefile
index f07be226e..99b518462 100644
--- a/serve/inferenceserver/api_server/__init__.py
+++ b/serve/client/Makefile
@@ -14,14 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Earth2Studio API Server Package
+.PHONY: deps integration_test run_deterministic run_stormcast
 
-This package contains the main API server components including:
-- main: FastAPI application and REST endpoints
-- workflow: Workflow framework and registry
-- worker: RQ worker functions for processing jobs
-- cpu_worker: CPU-intensive worker functions
-"""
+deps:
+	uv add -r requirements.txt
 
-__all__ = ["main", "workflow", "worker", "cpu_worker", "config", "object_storage"]
+# integration_test:
+# TODO: Add integration tests here.
+
+run_deterministic:
+	python examples/basic_forecast.py
+
+run_stormcast:
+	python examples/downscaled_forecast.py
diff --git a/serve/client/README.md b/serve/client/README.md
new file mode 100644
index 000000000..5fff2298d
--- /dev/null
+++ b/serve/client/README.md
@@ -0,0 +1,442 @@
+# Earth2Studio Python Client SDK
+
+A Python client library for the Earth2Studio REST API.
+
+The client SDK provides two interfaces to remotely access the Earth2Studio API server:
+
+1. **`earth2studio.serve.client.e2client.RemoteEarth2Workflow`** - High-level interface for seamless
+   integration with Earth2Studio and Xarray. Recommended for most use cases.
+2. **`earth2studio.serve.client.client.Earth2StudioClient`** - Low-level interface for direct API access
+   and result file management. Useful for custom workflows and integrations.
+
+Please refer to the server's `README_workflows.md` and `README_earth2workflows.md` for
+information about the server-side interface.
+
+## Quick Start
+
+To use a remote workflow, first initialize the `RemoteEarth2Workflow` object and then
+call it using the workflow parameters. Here, we access the `DeterministicEarth2Workflow`
+included in the server examples and get the results as an Earth2Studio data source.
+
+```python
+from datetime import datetime, timedelta
+import os
+from earth2studio.serve.client.e2client import RemoteEarth2Workflow
+
+# Create client (configurable via environment variable)
+api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+workflow = RemoteEarth2Workflow(
+    api_url,
+    workflow_name="deterministic_earth2_workflow",
+    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
+)
+
+data_source = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_data_source()
+# get temperature forecast 24 h after start time
+t2m = data_source(datetime(2025, 8, 22, 6), "t2m")
+t2m_np = t2m.values  # get the results as a NumPy array
+```
+
+Data access is lazy if the workflow produces Zarr files: In the above example, only the
+temperature will be downloaded and the actual download happens on the last line of code.
+
+## Advanced Usage
+
+In the examples below, we initialize `workflow` as in the Quick Start above,
+unless otherwise mentioned.
+
+### Access the result dataset directly
+
+You can directly access the underlying Xarray Dataset:
+
+```python
+ds = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_dataset()
+# same result as getting data from `data_source` in the Quick Start example
+t2m = ds["t2m"].sel(lead_time=np.timedelta64(timedelta(hours=24)))
+t2m_np = t2m.values  # NumPy array
+```
+
+### Iterate over results
+
+Similar to Earth2Studio model iterators, we can iterate over the results as
+`(Tensor, CoordSystem)` tuples:
+
+```python
+model = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_model()
+for (x, coords) in model.create_iterator():
+    # computes the mean on GPU if we used `device='cuda'` when creating the Workflow
+    print(x.mean().cpu().numpy())
+```
+
+This can be used to provide an input to a local workflow that expects a prognostic
+model. The example `diagnostic_analysis.py` shows how to use this to run a local
+diagnostic.
+
+### Save reference to remote results
+
+You can create a `RemoteEarth2WorkflowResult` manually in order to access the results later.
+This allows you to do other work and even close Python while waiting for results to complete.
+
+```python
+result = workflow(start_time=[datetime(2025, 8, 21, 6)])
+print(result.execution_id)  # write down the execution id
+exec_1766159252_5f779460
+
+# *** After restarting Python ***
+
+from datetime import datetime
+import os
+from earth2studio.serve.client.e2client import RemoteEarth2Workflow, RemoteEarth2WorkflowResult
+
+# re-initialize workflow
+api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+workflow = RemoteEarth2Workflow(
+    api_url,
+    workflow_name="deterministic_earth2_workflow",
+    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
+)
+
+# manually create result object with the execution_id from earlier
+execution_id = "exec_1766159252_5f779460"
+result = RemoteEarth2WorkflowResult(workflow, execution_id)
+
+# now we can access the results as before
+data_source = result.as_data_source()  # will block until results are available
+t2m = data_source(datetime(2025, 8, 22, 6), "t2m")
+```
+
+### Use remote workflow to condition a local model
+
+Use the results as a low-resolution conditioning for a StormCast model running locally:
+
+```python
+from earth2studio.data import HRRR
+from earth2studio.models.px import StormCast
+from earth2studio.io import NetCDF4Backend
+
+fcn3_workflow = RemoteEarth2Workflow(
+    api_url,
+    workflow_name="stormcast_fcn3_workflow",
+    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
+)
+
+hrrr_ic = HRRR()
+stormcast = StormCast.from_pretrained()
+io = NetCDF4Backend("stormcast_result.nc")
+conditioning_source = fcn3_workflow(
+    start_time=[datetime(2025, 8, 21, 6)],
+    num_hours=num_steps,
+    run_stormcast=False
+).as_data_source()
+stormcast.conditioning_data_source = conditioning_source
+num_hours=5
+
+run.deterministic(
+    [datetime(2025, 8, 21, 6)],
+    num_hours,
+    stormcast,
+    hrrr_ic,
+    io,
+    device='cuda',
+)
+```
+
+### Object Storage (S3) Support
+
+The client SDK seamlessly supports both local server storage and object storage
+(S3/CloudFront). When the server is configured to use object storage, all the examples
+in this README work without any code changes. The client automatically detects the
+storage type from the server response and handles downloads appropriately:
+
+- **Server storage**: Files are downloaded directly from the inference server
+- **S3 storage**: Files are downloaded from S3 using pre-signed CloudFront URLs
+
+This means you can use the same code regardless of how the server is configured:
+
+```python
+# Same whether server uses local or S3 storage
+result = workflow(start_time=[datetime(2025, 8, 21, 6)])
+ds = result.as_dataset()  # Fetches from S3 if configured
+```
+
+### Download result files directly
+
+`Earth2StudioClient` gives lower-level access than `RemoteEarth2Workflow`. Use it to
+download individual result files programmatically.
+
+Initialize the client:
+
+```python
+import os
+from datetime import datetime
+from earth2studio.serve.client.client import Earth2StudioClient, InferenceRequest
+
+# Create client (configurable via environment variable)
+api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+client = Earth2StudioClient(api_url, workflow_name="deterministic_earth2_workflow")
+
+# Check API health
+health = client.health_check()
+print(f"API Status: {health.status}")
+```
+
+Now, submit a workflow and download result files directly:
+
+```python
+parameters = {"start_time": [datetime(2025, 8, 21, 6)]}
+request = InferenceRequest(parameters=parameters)
+request_result = client.run_inference_sync(request)
+
+# Download individual output files (works with both local and S3 storage)
+for file in request_result.output_files[:5]:  # limit to first 5 files
+    print(f"Downloading: {file.path}")
+    content = client.download_result(request_result, file.path)
+    print(f"Downloaded {file.path}: {len(content.getvalue())} bytes")
+
+    # Optionally save to disk
+    # with open(file.path.split("/")[-1], "wb") as f:
+    #     f.write(content.getvalue())
+```
+
+To get the URL of a result file for use with external tools like `wget` (server storage only):
+
+```python
+from urllib.parse import urljoin
+
+result_root = client.result_root_path(request_result)
+result_path = request_result.result_paths()[0]
+
+result_url = urljoin(api_url, f"{result_root}{result_path}")
+print(result_url)
+# e.g. http://localhost:8000/v1/infer/.../results/.../results.zarr
+```
+
+### Asynchronous usage of `Earth2StudioClient`
+
+For long-running forecasts, you can submit requests and check status separately:
+
+```python
+import time
+from earth2studio.serve.client.models import RequestStatus
+
+# Submit request
+response = client.submit_inference_request(request)
+execution_id = response.execution_id
+print(f"Submitted request: {execution_id}")
+
+# Check status periodically
+while True:
+    status = client.get_request_status(execution_id)
+    print(f"Status: {status.status.value}, Step: {status.progress.current_step}")
+
+    if status.status in [RequestStatus.COMPLETED, RequestStatus.FAILED]:
+        break
+
+    time.sleep(10)
+
+# Get results when completed
+if status.status == RequestStatus.COMPLETED:
+    request_result = client.get_request_results(execution_id)
+    print(f"Forecast completed with {len(request_result.output_files)} output files")
+
+# Now we can use ``request_result`` as in the above example
+```
+
+### Authentication
+
+If your server requires authentication, you can pass a Bearer token to the client:
+
+```python
+# With authentication token
+client = Earth2StudioClient(
+    base_url="http://localhost:8000",
+    workflow_name="deterministic_earth2_workflow",
+    token="your-api-token"  # Bearer token for authenticated requests
+)
+
+workflow = RemoteEarth2Workflow(
+    base_url="http://localhost:8000",
+    workflow_name="deterministic_earth2_workflow",
+    token="your-api-token"
+)
+```
+
+The token is sent as a Bearer token in the `Authorization` header with all API requests.
+
+### Custom HTTP Settings
+
+You can modify the settings for HTTP access. The HTTP-related parameters are
+identical for `Earth2StudioClient` and `RemoteEarth2Workflow`.
+
+```python
+client = Earth2StudioClient(
+    base_url="http://localhost:8000",
+    timeout=60.0,           # Request timeout
+    max_retries=5,          # Maximum retries
+    retry_backoff_factor=1.0, # Retry backoff factor
+    token="your-api-token"  # Optional authentication token
+)
+
+workflow = RemoteEarth2Workflow(
+    base_url="http://localhost:8000",
+    timeout=60.0,           # Request timeout
+    max_retries=5,          # Maximum retries
+    retry_backoff_factor=1.0, # Retry backoff factor
+    token="your-api-token"  # Optional authentication token
+)
+```
+
+## API Reference
+
+### RemoteEarth2Workflow
+
+High-level client for Earth2Studio-compatible remote inference workflows.
+
+#### RemoteEarth2Workflow constructor
+
+```python
+RemoteEarth2Workflow(
+    base_url: str,
+    workflow_name: str,
+    device: str | torch.device | None = None,
+    xr_args: dict[str, Any] | None = None,
+    **client_kwargs
+)
+```
+
+**Args:**
+
+- `base_url`: URL of the Earth2Studio API server
+- `workflow_name`: Name of the workflow to execute on the server
+- `device`: Device for tensor operations (e.g., "cuda", "cpu").
+- `xr_args`: Additional keyword arguments passed to xarray.open_dataset/xarray.open_zarr
+- `**client_kwargs`: Additional arguments passed to Earth2StudioClient
+
+#### RemoteEarth2Workflow methods
+
+- `__call__(**kwargs)` → `RemoteEarth2WorkflowResult`: Submit inference with parameters
+- `to(device)` → `RemoteEarth2Workflow`: Move workflow to specified device
+
+### RemoteEarth2WorkflowResult
+
+Result object for accessing remote inference outputs.
+
+#### Attributes
+
+- `execution_id`: Unique identifier for the inference execution
+- `workflow`: Parent RemoteEarth2Workflow instance
+
+#### RemoteEarth2WorkflowResult methods
+
+- `as_dataset()` → `xr.Dataset`: Wait for completion and return result as xarray Dataset
+- `as_data_source()` → `InferenceOutputSource`: Wait for completion and return as Earth2Studio data source
+- `as_model(iter_coord="lead_time")` → `InferenceOutputModel`: Model iterable over time steps
+
+### Earth2StudioClient
+
+Low-level client class for direct API access and result file management.
+
+#### Earth2StudioClient constructor
+
+```python
+Earth2StudioClient(
+    base_url: str = "http://localhost:8000",
+    workflow_name: str = "deterministic_earth2_workflow",
+    timeout: float = 30.0,
+    max_retries: int = 3,
+    retry_backoff_factor: float = 0.3,
+    token: str | None = None
+)
+```
+
+**Args:**
+
+- `base_url`: URL of the Earth2Studio API server
+- `workflow_name`: Name of the workflow to execute on the server
+- `timeout`: Request timeout in seconds
+- `max_retries`: Maximum number of retries for failed requests
+- `retry_backoff_factor`: Backoff factor between retries
+- `token`: Optional Bearer token for authenticated requests
+
+#### Earth2StudioClient methods
+
+- `health_check()` → `HealthStatus`: Check API health
+- `submit_inference_request(request)` → `InferenceRequestResponse`: Submit request
+- `get_request_status(request_id)` → `InferenceRequestStatus`: Get status
+- `get_request_results(request_id, timeout=None)` → `InferenceRequestResults`: Get results
+- `wait_for_completion(request_id, poll_interval=5.0, timeout=None)` →
+  `InferenceRequestResults`: Wait for completion
+- `run_inference_sync(request, poll_interval=5.0, timeout=None)` → `InferenceRequestResults`:
+  Submit and wait for completion
+- `result_root_path(result)` → `str`: Get root URL path for result files
+- `download_result(result, path, timeout=None)` → `io.BytesIO`: Download a result file
+
+### Data Models
+
+- `InferenceRequest`: Inference request payload with workflow parameters
+- `InferenceRequestResponse`: Response when submitting a request (contains execution_id)
+- `InferenceRequestStatus`: Status information for an inference request
+- `InferenceRequestResults`: Results of a completed request with output file paths
+- `OutputFile`: Information about an output file (name, path, size)
+- `HealthStatus`: API health status response
+- `ProgressInfo`: Progress information (progress string, current_step, total_steps)
+
+### Enums
+
+- `RequestStatus`: Request status values
+  - Initial: `ACCEPTED`, `QUEUED`
+  - Processing: `RUNNING`
+  - Final: `COMPLETED`, `FAILED`, `CANCELLED`, `PENDING_RESULTS`
+
+## Examples
+
+The `examples/` directory contains complete, runnable example scripts:
+
+### `basic_forecast.py` - Basic Deterministic Forecast
+
+A comprehensive example showing how to run a simple deterministic forecast using
+`RemoteEarth2Workflow`.
+
+### `downscaled_forecast.py` - Downscaled Forecast
+
+Example demonstrating high-resolution downscaled forecasting using a remote global
+forecast model with a local downscaling model.
+
+### `diagnostic_analysis.py` - Diagnostic Analysis
+
+Specialized example for running local diagnostic models with remote inference results.
+
+### Running the Examples
+
+Each example can be run independently:
+
+```bash
+cd examples/
+
+# Run basic deterministic forecast
+python basic_forecast.py
+
+# Run downscaled forecast
+python downscaled_forecast.py
+
+# Run diagnostic analysis
+python diagnostic_analysis.py
+```
+
+**Prerequisites:**
+
+- Earth2Studio API server running
+- Client SDK installed with dependencies
+- Sufficient disk space for forecast outputs
+
+**Configuration:**
+
+All examples support configuring the API endpoint via environment variable:
+
+```bash
+# Set custom API endpoint (optional)
+export EARTH2STUDIO_API_URL="http://your-api-server:8000"
+
+# Or use default localhost:8000 if not set
+python basic_forecast.py
+```
diff --git a/serve/client/examples/basic_forecast.py b/serve/client/examples/basic_forecast.py
new file mode 100644
index 000000000..a2cd9236f
--- /dev/null
+++ b/serve/client/examples/basic_forecast.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Basic deterministic forecast example using Earth2Studio Client SDK.
+
+This example shows how to:
+1. Access a remote workflow
+2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
+3. Extract 2-meter temperature data at a specific location
+4. Create and save a matplotlib line plot of the forecast
+"""
+
+import os
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+import torch
+
+from earth2studio.serve.client.e2client import RemoteEarth2Workflow
+
+# /// script
+# dependencies = [
+#   "torch",
+#   "earth2studio[serve]>=0.9.0",
+#   "matplotlib>=3.3.0",
+# ]
+# ///
+
+
+def main(
+    plot_file: str = "t2m_plot.png",
+    lat: float = 37.4,
+    lon: float = -122.0,
+    start_time: datetime = datetime(2025, 8, 21, 6),
+    num_steps: int = 10,
+) -> None:
+    """Run a basic deterministic forecast and save a t2m plot.
+
+    Args:
+        plot_file: Path to save the temperature plot (default: 't2m_plot.png')
+        lat: Latitude for temperature extraction (default: 37.4)
+        lon: Longitude for temperature extraction (default: -122.0)
+        start_time: Forecast initialisation time (default: 2025-08-21 06:00 UTC)
+        num_steps: Number of forecast time steps (default: 10)
+    """
+
+    # Create client (configurable via environment variable)
+    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
+    workflow = RemoteEarth2Workflow(
+        api_url,
+        workflow_name="deterministic_earth2_workflow",
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        token=api_token,
+    )
+
+    # Check if API is healthy
+    try:
+        health = workflow.client.health_check()
+        print(f"✓ API Status: {health.status}")
+    except Exception as e:
+        print(f"✗ API not available: {e}")
+        return
+
+    # Call workflow and get result
+    try:
+        ds = workflow(start_time=[start_time], num_steps=num_steps).as_dataset()
+    except Exception as e:
+        print(f"\n❌ Forecast failed: {e}")
+        return
+
+    # Extract t2m data for the specified location
+    t2m = ds["t2m"].sel(lat=lat, lon=lon, method="nearest").values.ravel()
+
+    # Extract time coordinate
+    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
+
+    # Create line plot of temperature
+    print(f"   Creating plot: {plot_file}")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(time_coord, t2m, marker="o", linewidth=2, markersize=6)
+    ax.set_xlabel("Lead Time (h)", fontsize=12)
+    ax.set_ylabel("2-meter Temperature (K)", fontsize=12)
+    ax.set_title(
+        f"Temperature Forecast starting {start_time} at {lat}°N, {lon}°E",
+        fontsize=14,
+        fontweight="bold",
+    )
+    ax.grid(True, alpha=0.3)
+
+    # Save the plot
+    fig.tight_layout()
+    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+
+    print("\n🎉 Forecast complete!")
+    print(f"   Plot saved to: {plot_file}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/serve/client/examples/diagnostic_analysis.py b/serve/client/examples/diagnostic_analysis.py
new file mode 100644
index 000000000..d4040b0e8
--- /dev/null
+++ b/serve/client/examples/diagnostic_analysis.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Forecast example with local diagnostic using Earth2Studio Client SDK.
+
+This example shows how to:
+1. Access a remote workflow
+2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
+3. Run precipitation diagnostic model locally to get precipitation
+4. Create and save a matplotlib line plot of the precipitation forecast
+"""
+
+import os
+from datetime import datetime, timedelta
+
+import matplotlib.pyplot as plt
+import torch
+import xarray as xr
+
+from earth2studio.io import XarrayBackend  # type: ignore[import-untyped]
+from earth2studio.models.dx import PrecipitationAFNO  # type: ignore[import-untyped]
+from earth2studio.run import diagnostic  # type: ignore[import-untyped]
+from earth2studio.serve.client.e2client import RemoteEarth2Workflow
+from earth2studio.utils.type import (  # type: ignore[import-untyped]
+    LeadTimeArray,
+    TimeArray,
+    VariableArray,
+)
+
+# /// script
+# dependencies = [
+#   "torch",
+#   "earth2studio[serve]>=0.9.0",
+#   "matplotlib>=3.3.0",
+# ]
+# ///
+
+
+def main(
+    plot_file: str = "tp_plot.png",
+    lat: float = 37.4,
+    lon: float = -122.0,
+    start_time: datetime = datetime(2025, 8, 21, 6),
+    num_steps: int = 10,
+) -> None:
+    """Run a basic deterministic forecast and save a t2m plot.
+
+    Args:
+        plot_file: Path to save the precipitation plot (default: 'tp_plot.png')
+        lat: Latitude for precipitation extraction (default: 37.4)
+        lon: Longitude for precipitation extraction (default: -122.0)
+        start_time: Start time for the forecast (default: 2025-08-21 06:00 UTC)
+        num_steps: Number of forecast time steps (default: 10)
+    """
+
+    # Create client (configurable via environment variable)
+    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
+    workflow = RemoteEarth2Workflow(
+        api_url,
+        workflow_name="deterministic_earth2_workflow",
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        token=api_token,
+    )
+
+    # Check if API is healthy
+    try:
+        health = workflow.client.health_check()
+        print(f"✓ API Status: {health.status}")
+    except Exception as e:
+        print(f"✗ API not available: {e}")
+        return
+
+    # Call workflow and get result as an iterable model
+    try:
+        model = workflow(start_time=[start_time], num_steps=num_steps).as_model()
+    except Exception as e:
+        print(f"\n❌ Forecast failed: {e}")
+        return
+
+    precip_afno = PrecipitationAFNO.from_pretrained()
+    io = XarrayBackend()
+    data = NullDataSource()
+
+    diagnostic(
+        [start_time], num_steps, model, precip_afno, data, io, device=model.device
+    )
+
+    # Extract total precipitation for the specified location
+    ds = io.root
+    tp = ds["tp"].sel(lat=lat, lon=lon, method="nearest").values.ravel()
+
+    # Extract time coordinate
+    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
+
+    # Create line plot of total precipitation
+    print(f"   Creating plot: {plot_file}")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(time_coord, tp, marker="o", linewidth=2, markersize=6)
+    ax.set_xlabel("Lead Time (h)", fontsize=12)
+    ax.set_ylabel("Total Precipitation (m)", fontsize=12)
+    ax.set_title(
+        f"Precipitation Forecast starting {start_time} at {lat}°N, {lon}°E",
+        fontsize=14,
+        fontweight="bold",
+    )
+    ax.grid(True, alpha=0.3)
+
+    # Save the plot
+    fig.tight_layout()
+    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+
+    print("\n🎉 Forecast complete!")
+    print(f"   Plot saved to: {plot_file}")
+
+
+class NullDataSource:
+    """Minimal data source that returns empty DataArray for diagnostic input."""
+
+    def __call__(
+        self,
+        time: datetime | list[datetime] | TimeArray,
+        lead_time: timedelta | list[timedelta] | LeadTimeArray,
+        variable: str | list[str] | VariableArray,
+    ) -> xr.DataArray:
+        """Return an empty DataArray (unused coords for local diagnostic)."""
+        return xr.DataArray()
+
+    async def fetch(
+        self,
+        time: datetime | list[datetime] | TimeArray,
+        lead_time: timedelta | list[timedelta] | LeadTimeArray,
+        variable: str | list[str] | VariableArray,
+    ) -> xr.DataArray:
+        return xr.DataArray()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/serve/client/examples/downscaled_forecast.py b/serve/client/examples/downscaled_forecast.py
new file mode 100644
index 000000000..190a155f1
--- /dev/null
+++ b/serve/client/examples/downscaled_forecast.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Forecast example with local diagnostic using Earth2Studio Client SDK.
+
+This example shows how to:
+1. Access a remote workflow
+2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
+3. Run precipitation diagnostic model locally to get precipitation
+4. Create and save a matplotlib line plot of the precipitation forecast
+"""
+
+import os
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+import torch
+
+from earth2studio.data import HRRR  # type: ignore[import-untyped]
+from earth2studio.io import XarrayBackend  # type: ignore[import-untyped]
+from earth2studio.models.px import StormCast  # type: ignore[import-untyped]
+from earth2studio.run import deterministic  # type: ignore[import-untyped]
+from earth2studio.serve.client.e2client import RemoteEarth2Workflow
+
+# /// script
+# dependencies = [
+#   "torch",
+#   "earth2studio[serve]>=0.9.0",
+#   "matplotlib>=3.3.0",
+# ]
+# ///
+
+
+def main(
+    plot_file: str = "downscaled_t2m_plot.png",
+    y: float = 200.0,
+    x: float = 200.0,
+    start_time: datetime = datetime(2025, 8, 21, 6),
+    num_steps: int = 10,
+) -> None:
+    """Run a remote FourCastNet 3 forecast and run a StormCast downscaling locally.
+
+    Args:
+        plot_file: Path to save the temperature plot (default: 'downscaled_t2m_plot.png')
+        x: HRRR x coordinate for temperature extraction (default: 200.0)
+        y: HRRR y coordinate for temperature extraction (default: 200.0)
+    """
+
+    # Create client (configurable via environment variable)
+    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
+    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
+    workflow = RemoteEarth2Workflow(
+        api_url,
+        workflow_name="stormcast_fcn3_workflow",
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        token=api_token,
+    )
+
+    # Check if API is healthy
+    try:
+        health = workflow.client.health_check()
+        print(f"✓ API Status: {health.status}")
+    except Exception as e:
+        print(f"✗ API not available: {e}")
+        return
+
+    # Call workflow and get result as a data source
+    try:
+        conditioning_source = workflow(
+            start_time=start_time, num_hours=num_steps, run_stormcast=False
+        ).as_data_source()
+    except Exception as e:
+        print(f"\n❌ Forecast failed: {e}")
+        return
+
+    stormcast = StormCast.from_pretrained()
+    stormcast.conditioning_data_source = conditioning_source
+    io = XarrayBackend()
+    hrrr_ic = HRRR()
+
+    deterministic(
+        [start_time], num_steps, stormcast, hrrr_ic, io, device=workflow.device
+    )
+
+    # Extract t2m data for the specified location
+    ds = io.root
+    t2m = ds["t2m"].sel(hrrr_x=x, hrrr_y=y, method="nearest").values.ravel()
+
+    # Extract time coordinate
+    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
+
+    # Create line plot of temperature
+    print(f"   Creating plot: {plot_file}")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(time_coord, t2m, marker="o", linewidth=2, markersize=6)
+    ax.set_xlabel("Lead Time (h)", fontsize=12)
+    ax.set_ylabel("2-meter Temperature (K)", fontsize=12)
+    ax.set_title(
+        f"Temperature Forecast at x={x}, y={y}", fontsize=14, fontweight="bold"
+    )
+    ax.grid(True, alpha=0.3)
+
+    # Save the plot
+    fig.tight_layout()
+    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+
+    print("\n🎉 Forecast complete!")
+    print(f"   Plot saved to: {plot_file}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/serve/client/pyproject.toml b/serve/client/pyproject.toml
new file mode 100644
index 000000000..535eed801
--- /dev/null
+++ b/serve/client/pyproject.toml
@@ -0,0 +1,88 @@
+[build-system]
+requires = ["hatchling", "uv-dynamic-versioning"]
+build-backend = "hatchling.build"
+
+[project]
+name = "earth2studio.serve.client"
+dynamic = ["version"]
+description = "Python client SDK for Earth2Studio REST API"
+authors = [
+    {name = "NVIDIA Scicomp Team"}
+]
+readme = "README.md"
+license = {text = "Apache-2.0"}
+requires-python = ">=3.11"
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Science/Research",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Atmospheric Science",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+]
+keywords = [
+    "earth2studio",
+    "weather",
+    "forecasting",
+    "climate",
+    "machine learning",
+    "api client",
+    "ensemble forecasting",
+    "diagnostic analysis"
+]
+dependencies = [
+    "aiohttp>=3.8.0",
+    "earth2studio>=0.9.0",
+    "fsspec>=2021.6.0",
+    "numpy>=1.21.0",
+    "requests>=2.25.0",
+    "urllib3>=1.26.0",
+    "xarray>=0.19.0",
+]
+
+[project.optional-dependencies]
+e2studio = [
+    "torch>=2.10.0",
+]
+examples = [
+    "matplotlib>=3.3.0",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "omegaconf>=2.3.0",
+    "pytz",
+]
+
+[tool.hatch.version]
+source = "uv-dynamic-versioning"
+
+[tool.hatch.build.targets.wheel]
+packages = ["earth2studio/serve/client"]
+
+[tool.hatch.build.targets.wheel.sources]
+"earth2studio/serve/client" = "earth2studio/serve/client"
+
+[tool.uv-dynamic-versioning]
+vcs = "git"
+style = "pep440"
+bump = true
+
+[project.urls]
+Homepage = "https://github.com/NVIDIA/earth2studio"
+Documentation = "https://nvidia.github.io/earth2studio/"
+Repository = "https://github.com/NVIDIA/earth2studio"
+"Bug Reports" = "https://github.com/NVIDIA/earth2studio/issues"
+
+[tool.uv]
+dev-dependencies = [
+    "pytest>=6.0",
+    "pytest-cov>=2.0",
+    "black>=21.0",
+    "isort>=5.0",
+    "flake8>=3.8",
+    "mypy>=0.910",
+    "pre-commit>=2.15.0",
+    "ruff>=0.7.2",
+]
diff --git a/serve/client/requirements.txt b/serve/client/requirements.txt
new file mode 100644
index 000000000..3f5a5881d
--- /dev/null
+++ b/serve/client/requirements.txt
@@ -0,0 +1,14 @@
+# Core dependencies for Earth2Studio Python Client SDK
+requests>=2.25.0
+urllib3>=1.26.0
+aiohttp>=3.8.0   # For longer timeouts when reading remote zarr (fsspec HTTP)
+
+# Optional dependencies for enhanced functionality
+# Uncomment as needed
+# pandas>=1.3.0         # For data manipulation
+# numpy>=1.21.0         # For numerical operations
+# xarray>=0.19.0        # For working with multidimensional arrays
+# matplotlib>=3.3.0     # For plotting and visualization
+# omegaconf>=2.3.0      # For StormCast and other models that use OmegaConf
+# nvidia-physicsnemo>=1.0.1,<2.0  # For StormCast and other physicsnemo-based models
+# pytz                 # Required by nvidia-physicsnemo
diff --git a/serve/inferenceserver/Makefile b/serve/server/Makefile
similarity index 96%
rename from serve/inferenceserver/Makefile
rename to serve/server/Makefile
index 73d96e3dc..7b44e8857 100644
--- a/serve/inferenceserver/Makefile
+++ b/serve/server/Makefile
@@ -61,4 +61,4 @@ install:
 # Run tests
 # integration_test:
 #	@echo "Running API tests..."
-#	@python inferenceserver/test/integration/test_api.py
+#	@python server/test/integration/test_api.py
diff --git a/serve/inferenceserver/README.md b/serve/server/README.md
similarity index 99%
rename from serve/inferenceserver/README.md
rename to serve/server/README.md
index f55f55bd9..c58bb3af5 100644
--- a/serve/inferenceserver/README.md
+++ b/serve/server/README.md
@@ -40,7 +40,7 @@ For current API usage, please refer to the workflow documentation linked above.
 1. **Clone and navigate to the API directory:**
 
    ```bash
-   cd service/inferenceserver
+   cd serve/server
    ```
 
 2. **Make scripts executable and start services:**
@@ -522,7 +522,7 @@ InferenceRequest:
 ### Server Configuration (config.yaml)
 
 The API server can be configured via the `config.yaml` file located at
-`api_server/conf/config.yaml`:
+`serve/server/conf/config.yaml`:
 
 #### Results Management
 
diff --git a/serve/inferenceserver/README_earth2workflows.md b/serve/server/README_earth2workflows.md
similarity index 98%
rename from serve/inferenceserver/README_earth2workflows.md
rename to serve/server/README_earth2workflows.md
index 9eb10f16a..45ee1a059 100644
--- a/serve/inferenceserver/README_earth2workflows.md
+++ b/serve/server/README_earth2workflows.md
@@ -17,7 +17,7 @@ This is a simplified example demonstrating the key concepts. For complete workin
 `example_workflows` directory.
 
 ```python
-from api_server.workflow import workflow_registry, Earth2Workflow
+from earth2studio.serve.server import workflow_registry, Earth2Workflow
 from earth2studio.io import IOBackend
 from datetime import datetime
 from earth2studio.data import GFS
diff --git a/serve/inferenceserver/README_object_storage.md b/serve/server/README_object_storage.md
similarity index 100%
rename from serve/inferenceserver/README_object_storage.md
rename to serve/server/README_object_storage.md
diff --git a/serve/inferenceserver/README_workflows.md b/serve/server/README_workflows.md
similarity index 98%
rename from serve/inferenceserver/README_workflows.md
rename to serve/server/README_workflows.md
index 008cb8863..e5dbe64ae 100644
--- a/serve/inferenceserver/README_workflows.md
+++ b/serve/server/README_workflows.md
@@ -75,7 +75,7 @@ updates.
 Create a **Pydantic model** that defines your workflow's input parameters:
 
 ```python
-from api_server.workflow import WorkflowParameters
+from earth2studio.serve.server.workflow import WorkflowParameters
 from pydantic import Field
 from typing import List
 
@@ -101,7 +101,7 @@ Implement the **`Workflow`** base class and assign the parameter class to its `P
 attribute:
 
 ```python
-from api_server.workflow import Workflow, WorkflowProgress, workflow_registry
+from earth2studio.serve.server.workflow import Workflow, WorkflowProgress, workflow_registry
 from typing import Any, Dict, Union
 import json
 
@@ -314,7 +314,7 @@ class DeterministicWorkflowParameters(WorkflowParameters):
 ### Workflow Implementation
 
 ```python
-from api_server.workflow import WorkflowProgress
+from earth2studio.serve.server.workflow import WorkflowProgress
 
 @workflow_registry.register()
 class DeterministicWorkflow(Workflow):
@@ -763,7 +763,7 @@ The `WorkflowProgress` class provides four standard fields:
 ### Basic Usage
 
 ```python
-from api_server.workflow import WorkflowProgress
+from earth2studio.serve.server.workflow import WorkflowProgress
 
 # Simple progress update
 progress = WorkflowProgress(
@@ -790,7 +790,7 @@ except Exception as e:
 You can extend `WorkflowProgress` with workflow-specific fields:
 
 ```python
-from api_server.workflow import WorkflowProgress
+from earth2studio.serve.server.workflow import WorkflowProgress
 from pydantic import Field
 from typing import Optional
 
diff --git a/serve/inferenceserver/api_server/conf/config.yaml b/serve/server/conf/config.yaml
similarity index 100%
rename from serve/inferenceserver/api_server/conf/config.yaml
rename to serve/server/conf/config.yaml
diff --git a/serve/inferenceserver/api_server/conf/profiles.json b/serve/server/conf/profiles.json
similarity index 100%
rename from serve/inferenceserver/api_server/conf/profiles.json
rename to serve/server/conf/profiles.json
diff --git a/serve/inferenceserver/api_server/conf/workflows.yaml b/serve/server/conf/workflows.yaml
similarity index 100%
rename from serve/inferenceserver/api_server/conf/workflows.yaml
rename to serve/server/conf/workflows.yaml
diff --git a/serve/inferenceserver/env.example b/serve/server/env.example
similarity index 100%
rename from serve/inferenceserver/env.example
rename to serve/server/env.example
diff --git a/serve/inferenceserver/api_server/example_workflows/__init__.py b/serve/server/example_workflows/__init__.py
similarity index 100%
rename from serve/inferenceserver/api_server/example_workflows/__init__.py
rename to serve/server/example_workflows/__init__.py
diff --git a/serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py b/serve/server/example_workflows/deterministic_earth2_workflow.py
similarity index 96%
rename from serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py
rename to serve/server/example_workflows/deterministic_earth2_workflow.py
index 088bebcf4..d995fbc76 100644
--- a/serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py
+++ b/serve/server/example_workflows/deterministic_earth2_workflow.py
@@ -25,11 +25,11 @@
 from datetime import datetime
 from typing import Literal
 
-from api_server.workflow import Earth2Workflow, workflow_registry
 from earth2studio import run
 from earth2studio.data import GFS
 from earth2studio.io import IOBackend
 from earth2studio.models.px import DLWP, FCN
+from earth2studio.serve.server import Earth2Workflow, workflow_registry
 
 
 @workflow_registry.register
diff --git a/serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py b/serve/server/example_workflows/deterministic_fcn.py
similarity index 99%
rename from serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py
rename to serve/server/example_workflows/deterministic_fcn.py
index 6afc30bf1..cdc63c250 100644
--- a/serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py
+++ b/serve/server/example_workflows/deterministic_fcn.py
@@ -31,7 +31,7 @@
 import zarr
 from pydantic import Field
 
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py b/serve/server/example_workflows/deterministic_workflow.py
similarity index 99%
rename from serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py
rename to serve/server/example_workflows/deterministic_workflow.py
index fa16ff285..ffa52e5e0 100644
--- a/serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py
+++ b/serve/server/example_workflows/deterministic_workflow.py
@@ -29,7 +29,7 @@
 import zarr
 from pydantic import Field
 
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py b/serve/server/example_workflows/diagnostic_workflow.py
similarity index 99%
rename from serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py
rename to serve/server/example_workflows/diagnostic_workflow.py
index b6b05c841..23ca1d0f0 100644
--- a/serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py
+++ b/serve/server/example_workflows/diagnostic_workflow.py
@@ -28,7 +28,7 @@
 import zarr
 from pydantic import Field
 
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/inferenceserver/api_server/example_workflows/example_user_workflow.py b/serve/server/example_workflows/example_user_workflow.py
similarity index 99%
rename from serve/inferenceserver/api_server/example_workflows/example_user_workflow.py
rename to serve/server/example_workflows/example_user_workflow.py
index 69e390fdb..3245d5726 100644
--- a/serve/inferenceserver/api_server/example_workflows/example_user_workflow.py
+++ b/serve/server/example_workflows/example_user_workflow.py
@@ -38,7 +38,7 @@
 
 from pydantic import Field
 
-from api_server.workflow import (
+from earth2studio.serve.server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py b/serve/server/example_workflows/foundry_fcn3.py
similarity index 100%
rename from serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
rename to serve/server/example_workflows/foundry_fcn3.py
index 192f0e190..62e1334d8 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
+++ b/serve/server/example_workflows/foundry_fcn3.py
@@ -5,8 +5,8 @@
 import numpy as np
 import torch
 import zarr
-
 from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
+
 from earth2studio.data import PlanetaryComputerECMWFOpenDataIFS, fetch_data
 from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
 from earth2studio.models.px import FCN3
diff --git a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/server/example_workflows/foundry_fcn3_stormscope_goes.py
similarity index 97%
rename from serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
rename to serve/server/example_workflows/foundry_fcn3_stormscope_goes.py
index 642b0ea89..9bf5d304f 100644
--- a/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -7,8 +7,8 @@
 import torch
 import xarray as xr
 import zarr
-
 from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
+
 from earth2studio.data import (
     GOES,
     InferenceOutputSource,
@@ -404,14 +404,16 @@ def __call__(
         y_ori, coords_y_ori = self.get_stormscope_input(start_time_stormscope)
 
         coords_out = self.stormscope.output_coords(self.stormscope.input_coords())
-        output_coords = {
-            "ensemble": np.arange(len(seeds_stormscope)),
-            # Planetary Computer does not like separate 'lead_time'
-            "time": to_time_array([start_time_stormscope]) + lead_times,
-            "variable": variables,
-            "y": coords_out["y"],
-            "x": coords_out["x"],
-        }
+        output_coords = OrderedDict(
+            {
+                "ensemble": np.arange(len(seeds_stormscope)),
+                # Planetary Computer does not like separate 'lead_time'
+                "time": to_time_array([start_time_stormscope]) + lead_times,
+                "variable": variables,
+                "y": coords_out["y"],
+                "x": coords_out["x"],
+            }
+        )
         self.setup_io(io, output_coords, seeds_fcn3, seeds_stormscope)
 
         total_samples = len(seeds_stormscope)
diff --git a/serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py b/serve/server/example_workflows/stormcast_fcn3_workflow.py
similarity index 98%
rename from serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py
rename to serve/server/example_workflows/stormcast_fcn3_workflow.py
index 06ede58d1..c369906e6 100644
--- a/serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py
+++ b/serve/server/example_workflows/stormcast_fcn3_workflow.py
@@ -23,12 +23,12 @@
 import torch
 import xarray as xr
 
-from api_server.workflow import Earth2Workflow, workflow_registry
 from earth2studio import run
 from earth2studio.data import GFS, HRRR, InferenceOutputSource
 from earth2studio.io import IOBackend, NetCDF4Backend, XarrayBackend
 from earth2studio.models.dx import DerivedSurfacePressure
 from earth2studio.models.px import FCN3, DiagnosticWrapper, InterpModAFNO, StormCast
+from earth2studio.serve.server import Earth2Workflow, workflow_registry
 
 
 @workflow_registry.register
diff --git a/serve/inferenceserver/openapi.yaml b/serve/server/openapi.yaml
similarity index 100%
rename from serve/inferenceserver/openapi.yaml
rename to serve/server/openapi.yaml
diff --git a/serve/inferenceserver/api_server/planetary_computer/pc_client.py b/serve/server/planetary_computer/pc_client.py
similarity index 100%
rename from serve/inferenceserver/api_server/planetary_computer/pc_client.py
rename to serve/server/planetary_computer/pc_client.py
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json b/serve/server/planetary_computer/template-collection-fcn3-stormscope-goes.json
similarity index 100%
rename from serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
rename to serve/server/planetary_computer/template-collection-fcn3-stormscope-goes.json
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json b/serve/server/planetary_computer/template-collection-fcn3.json
similarity index 100%
rename from serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
rename to serve/server/planetary_computer/template-collection-fcn3.json
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json b/serve/server/planetary_computer/template-feature-fcn3-stormscope-goes.json
similarity index 100%
rename from serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
rename to serve/server/planetary_computer/template-feature-fcn3-stormscope-goes.json
diff --git a/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json b/serve/server/planetary_computer/template-feature-fcn3.json
similarity index 100%
rename from serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json
rename to serve/server/planetary_computer/template-feature-fcn3.json
diff --git a/serve/inferenceserver/requirements.txt b/serve/server/requirements.txt
similarity index 80%
rename from serve/inferenceserver/requirements.txt
rename to serve/server/requirements.txt
index 30815a96e..69b3a0404 100644
--- a/serve/inferenceserver/requirements.txt
+++ b/serve/server/requirements.txt
@@ -1,3 +1,7 @@
+# For local runs from serve/server: install the project first so earth2studio.serve.server
+# is importable:  pip install -e ../..
+# (Dockerfile installs the project from repo root with -e . instead.)
+
 # Core FastAPI dependencies
 fastapi>=0.104.0
 uvicorn[standard]>=0.24.0
diff --git a/serve/inferenceserver/scripts/profile_workflow.sh b/serve/server/scripts/profile_workflow.sh
similarity index 100%
rename from serve/inferenceserver/scripts/profile_workflow.sh
rename to serve/server/scripts/profile_workflow.sh
diff --git a/serve/inferenceserver/scripts/redis.conf b/serve/server/scripts/redis.conf
similarity index 100%
rename from serve/inferenceserver/scripts/redis.conf
rename to serve/server/scripts/redis.conf
diff --git a/serve/inferenceserver/scripts/start_api_server.sh b/serve/server/scripts/start_api_server.sh
similarity index 92%
rename from serve/inferenceserver/scripts/start_api_server.sh
rename to serve/server/scripts/start_api_server.sh
index ca92db083..1c03c1b04 100755
--- a/serve/inferenceserver/scripts/start_api_server.sh
+++ b/serve/server/scripts/start_api_server.sh
@@ -18,10 +18,13 @@
 # Earth2Studio Multiple Workers Startup Script
 # This script starts multiple API workers, and RQ workers
 
-# Get the directory where this script is located.
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-# A way to override the config location while starting up the endpoint.
-CONFIG_DIR=${CONFIG_DIR:-"${SCRIPT_DIR}/../api_server/conf"}
+# Get the directory where this script is located (or use env from Docker/startup.sh).
+SCRIPT_DIR=${SCRIPT_DIR:-"$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"}
+CONFIG_DIR=${CONFIG_DIR:-"${SCRIPT_DIR}/../conf"}
+# Export so uvicorn/rq workers (Python) see them for CONFIG_DIR/SCRIPT_DIR in main.py and config.py
+export SCRIPT_DIR
+export CONFIG_DIR
+export WORKFLOW_DIR
 CONFIG_FILE="$CONFIG_DIR/config.yaml"
 
 # Function to read config values from YAML using Python
@@ -82,9 +85,9 @@ cleanup() {
     fi
 
     # Stop all API workers
-    if pgrep -f "uvicorn.*api_server.main:app" > /dev/null; then
+    if pgrep -f "uvicorn.*earth2studio.serve.server.main:app" > /dev/null; then
         echo "Stopping API workers..."
-        pkill -f "uvicorn.*api_server.main:app"
+        pkill -f "uvicorn.*earth2studio.serve.server.main:app"
     fi
 
     # Stop all RQ inference workers
@@ -127,7 +130,7 @@ trap cleanup SIGINT SIGTERM
 export EARTH2STUDIO_API_ACTIVE=1
 
 # Start multiple workers using uvicorn with extended timeouts for large file downloads
-CUDA_VISIBLE_DEVICES="" uvicorn api_server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
+CUDA_VISIBLE_DEVICES="" uvicorn earth2studio.serve.server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
 UVICORN_PID=$!
 
 # Start RQ workers
@@ -184,7 +187,7 @@ for i in $(seq 1 $NUM_FINALIZE_WORKERS); do
 done
 
 # Start cleanup daemon
-CUDA_VISIBLE_DEVICES="" python -m api_server.cleanup_daemon &
+CUDA_VISIBLE_DEVICES="" python -m earth2studio.serve.server.cleanup_daemon &
 CLEANUP_DAEMON_PID=$!
 echo "Started cleanup daemon (PID: $CLEANUP_DAEMON_PID)"
 
@@ -192,7 +195,7 @@ echo "Started cleanup daemon (PID: $CLEANUP_DAEMON_PID)"
 sleep 5
 
 # Check if API workers are running
-API_WORKER_COUNT=$(pgrep -f "uvicorn.*api_server.main:app" | wc -l)
+API_WORKER_COUNT=$(pgrep -f "uvicorn.*earth2studio.serve.server.main:app" | wc -l)
 if [ "$API_WORKER_COUNT" -eq 0 ]; then
     echo "Failed to start API workers..."
     exit 1
diff --git a/serve/inferenceserver/scripts/start_redis.sh b/serve/server/scripts/start_redis.sh
similarity index 100%
rename from serve/inferenceserver/scripts/start_redis.sh
rename to serve/server/scripts/start_redis.sh
diff --git a/serve/inferenceserver/scripts/startup.sh b/serve/server/scripts/startup.sh
similarity index 59%
rename from serve/inferenceserver/scripts/startup.sh
rename to serve/server/scripts/startup.sh
index 5ba90f71f..b5739fe07 100755
--- a/serve/inferenceserver/scripts/startup.sh
+++ b/serve/server/scripts/startup.sh
@@ -17,16 +17,18 @@
 
 set -euo pipefail
 
-# Set EARTH2STUDIO_MODEL_CACHE to use AZUREML_MODEL_DIR if available
-if [ -n "${AZUREML_MODEL_DIR:-}" ]; then
-    echo "AZUREML_MODEL_DIR: $AZUREML_MODEL_DIR"
-    export EARTH2STUDIO_MODEL_CACHE="$AZUREML_MODEL_DIR/${EARTH2STUDIO_MODEL_SUBPATH:-e2s_fcn3_stormscope}"
-    echo "--------------------------------"
-    echo "EARTH2STUDIO_MODEL_CACHE: $EARTH2STUDIO_MODEL_CACHE"
-    ls -la $EARTH2STUDIO_MODEL_CACHE && echo "--------------------------------"
-fi
+# Use CONFIG_DIR/SCRIPT_DIR from env if set (e.g. in Docker); else resolve from script location
+SCRIPT_DIR="${SCRIPT_DIR:-$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)}"
+SERVE_SERVER_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
+REPO_ROOT="$(cd "$SERVE_SERVER_DIR/../.." && pwd)"
+export SCRIPT_DIR
+export CONFIG_DIR="${CONFIG_DIR:-$SCRIPT_DIR/../conf}"
+export WORKFLOW_DIR="${WORKFLOW_DIR:-}"
 
-cd /workspace/earth2studio-project/serve/inferenceserver
+# Ensure Python can find earth2studio (including earth2studio.serve.server)
+export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}"
+
+cd "$SERVE_SERVER_DIR"
 make start-redis
 make start-api-server
 sleep infinity
diff --git a/serve/inferenceserver/scripts/status.sh b/serve/server/scripts/status.sh
similarity index 98%
rename from serve/inferenceserver/scripts/status.sh
rename to serve/server/scripts/status.sh
index 5fc56b48c..2dcdaadf4 100755
--- a/serve/inferenceserver/scripts/status.sh
+++ b/serve/server/scripts/status.sh
@@ -52,7 +52,7 @@ echo ""
 
 # Check API workers status
 echo "API Workers:"
-API_WORKERS=$(pgrep -f "uvicorn.*api_server.main:app")
+API_WORKERS=$(pgrep -f "uvicorn.*earth2studio.serve.server.main:app")
 if [ -n "$API_WORKERS" ]; then
     WORKER_COUNT=$(echo "$API_WORKERS" | wc -l)
     echo "  Status: Running ($WORKER_COUNT workers)"
diff --git a/serve/inferenceserver/scripts/stop_api_server.sh b/serve/server/scripts/stop_api_server.sh
similarity index 96%
rename from serve/inferenceserver/scripts/stop_api_server.sh
rename to serve/server/scripts/stop_api_server.sh
index fc612450f..6f00e0830 100755
--- a/serve/inferenceserver/scripts/stop_api_server.sh
+++ b/serve/server/scripts/stop_api_server.sh
@@ -19,7 +19,7 @@ echo "Stopping API Server..."
 
 # Stop API workers
 echo "Stopping API workers..."
-pkill -f "uvicorn.*api_server.main:app"
+pkill -f "uvicorn.*earth2studio.serve.server.main:app"
 
 # Stop RQ inference workers
 echo "Stopping RQ inference workers..."
diff --git a/serve/inferenceserver/scripts/stop_redis.sh b/serve/server/scripts/stop_redis.sh
similarity index 100%
rename from serve/inferenceserver/scripts/stop_redis.sh
rename to serve/server/scripts/stop_redis.sh
diff --git a/serve/test/unit/test_e2workflow.py b/serve/server/test/unit/test_e2workflow.py
similarity index 82%
rename from serve/test/unit/test_e2workflow.py
rename to serve/server/test/unit/test_e2workflow.py
index 6a4e08d1f..b275bd433 100644
--- a/serve/test/unit/test_e2workflow.py
+++ b/serve/server/test/unit/test_e2workflow.py
@@ -14,22 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import OrderedDict
+from pathlib import Path
 from unittest.mock import MagicMock, Mock, patch
 
 import pytest
-import redis
+import redis  # type: ignore[import-untyped]
 import torch
-from api_server.workflow import (
-    Earth2Workflow,
-    WorkflowParameters,
-)
-from api_server.workflow.e2workflow import (
+
+from earth2studio.io import IOBackend
+from earth2studio.serve.server import Earth2Workflow, WorkflowParameters
+from earth2studio.serve.server.e2workflow import (
     AutoParameters,
     BackendProgress,
     func_to_model,
 )
-
-from earth2studio.io import IOBackend
 from earth2studio.utils.type import CoordSystem
 
 
@@ -37,10 +36,12 @@
 class TestFuncToModel:
     """Test func_to_model helper function"""
 
-    def test_function_to_model_conversion(self):
+    def test_function_to_model_conversion(self) -> None:
         """Test converting function signature to Pydantic model"""
 
-        def test_func(required: int, optional: str = "default", exclude_me: int = 0):
+        def test_func(
+            required: int, optional: str = "default", exclude_me: int = 0
+        ) -> None:
             pass
 
         model = func_to_model(test_func, exclude_params={"exclude_me"})
@@ -57,11 +58,11 @@ def test_func(required: int, optional: str = "default", exclude_me: int = 0):
 class TestAutoParameters:
     """Test AutoParameters metaclass"""
 
-    def test_auto_generates_parameters_from_call_signature(self):
+    def test_auto_generates_parameters_from_call_signature(self) -> None:
         """Test that AutoParameters creates Parameters from __call__ signature"""
 
         class TestWorkflow(metaclass=AutoParameters):
-            def __call__(self, io: IOBackend, x: int, y: str = "default"):
+            def __call__(self, io: IOBackend, x: int, y: str = "default") -> None:
                 pass
 
         # Verify Parameters class is created with correct name
@@ -80,7 +81,7 @@ def __call__(self, io: IOBackend, x: int, y: str = "default"):
 class TestEarth2WorkflowImpl(Earth2Workflow):
     """Simple test implementation of Earth2Workflow"""
 
-    def __call__(self, io: IOBackend, x: int = 10, y: str = "default"):
+    def __call__(self, io: IOBackend, x: int = 10, y: str = "default") -> None:
         """Test implementation with simple parameters"""
         # Store call info for testing
         self.called_with = {"io": io, "x": x, "y": y}
@@ -95,7 +96,7 @@ def __call__(
         required_param: int,
         optional_param: str = "default",
         num_hours: int = 24,
-    ):
+    ) -> None:
         """Test implementation with required and optional parameters"""
         self.called_with = {
             "io": io,
@@ -109,12 +110,12 @@ def __call__(
 class TestEarth2Workflow:
     """Test Earth2Workflow-specific functionality (base Workflow tests in test_workflow.py)"""
 
-    def setup_method(self):
+    def setup_method(self) -> None:
         """Set up test fixtures"""
         self.workflow = TestEarth2WorkflowImpl()
         self.workflow.name = "test_workflow"
 
-    def test_validate_parameters_uses_auto_generated_parameters(self):
+    def test_validate_parameters_uses_auto_generated_parameters(self) -> None:
         """Test that validate_parameters works with auto-generated Parameters class"""
         params = TestEarth2WorkflowImpl.validate_parameters({"x": 30, "y": "test"})
         assert isinstance(params, WorkflowParameters)
@@ -125,10 +126,10 @@ def test_validate_parameters_uses_auto_generated_parameters(self):
         with pytest.raises(ValueError):
             ComplexEarth2WorkflowImpl.validate_parameters({"optional_param": "test"})
 
-    @patch("api_server.e2workflow.get_config")
+    @patch("earth2studio.serve.server.e2workflow.get_config")
     def test_run_method_with_io_backend_and_progress_updates(
-        self, mock_get_config, tmp_path
-    ):
+        self, mock_get_config: MagicMock, tmp_path: Path
+    ) -> None:
         """Test Earth2Workflow.run creates IOBackend and updates progress"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
@@ -140,7 +141,7 @@ def test_run_method_with_io_backend_and_progress_updates(
         with (
             patch.object(self.workflow, "update_execution_data") as mock_update,
             patch.object(self.workflow, "get_output_path") as mock_get_path,
-            patch("api_server.e2workflow.ZarrBackend") as mock_zarr,
+            patch("earth2studio.serve.server.e2workflow.ZarrBackend") as mock_zarr,
         ):
             mock_get_path.return_value = tmp_path / "test_output"
             mock_io = Mock(spec=IOBackend)
@@ -156,15 +157,17 @@ def test_run_method_with_io_backend_and_progress_updates(
             # Verify progress updates were made
             assert mock_update.call_count >= 2
 
-    @patch("api_server.e2workflow.get_config")
-    def test_run_method_handles_workflow_exceptions(self, mock_get_config):
+    @patch("earth2studio.serve.server.e2workflow.get_config")
+    def test_run_method_handles_workflow_exceptions(
+        self, mock_get_config: MagicMock
+    ) -> None:
         """Test that run method properly handles exceptions from workflow execution"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
         mock_get_config.return_value = mock_config
 
         class FailingWorkflow(Earth2Workflow):
-            def __call__(self, io: IOBackend, x: int = 10):
+            def __call__(self, io: IOBackend, x: int = 10) -> None:
                 raise RuntimeError("Workflow execution failed")
 
         failing_workflow = FailingWorkflow()
@@ -174,7 +177,7 @@ def __call__(self, io: IOBackend, x: int = 10):
         with (
             patch.object(failing_workflow, "update_execution_data") as mock_update,
             patch.object(failing_workflow, "get_output_path"),
-            patch("api_server.e2workflow.ZarrBackend"),
+            patch("earth2studio.serve.server.e2workflow.ZarrBackend"),
         ):
             with pytest.raises(RuntimeError, match="Workflow execution failed"):
                 failing_workflow.run({}, "exec_fail")
@@ -192,7 +195,7 @@ def __call__(self, io: IOBackend, x: int = 10):
 class TestBackendProgress:
     """Test BackendProgress wrapper for progress tracking"""
 
-    def setup_method(self):
+    def setup_method(self) -> None:
         """Set up test fixtures"""
         self.mock_io = Mock(spec=IOBackend)
         self.mock_workflow = Mock(spec=Earth2Workflow)
@@ -201,9 +204,9 @@ def setup_method(self):
             self.mock_io, self.mock_workflow, self.execution_id
         )
 
-    def test_add_array_initializes_progress_tracking(self):
+    def test_add_array_initializes_progress_tracking(self) -> None:
         """Test that add_array initializes progress tracking when progress_dim present"""
-        coords = {"lead_time": [0, 1, 2, 3], "lat": [0, 1]}
+        coords = OrderedDict({"lead_time": [0, 1, 2, 3], "lat": [0, 1]})
 
         self.backend.add_array(coords, "temperature")
 
@@ -218,23 +221,23 @@ def test_add_array_initializes_progress_tracking(self):
         self.backend.add_array(coords, "pressure")
         assert self.backend.progress_array == "temperature"
 
-    def test_write_updates_progress(self):
+    def test_write_updates_progress(self) -> None:
         """Test that write updates progress for tracked arrays"""
         # Initialize progress tracking
-        init_coords: CoordSystem = {"lead_time": [0, 1, 2], "lat": [0, 1]}
+        init_coords: CoordSystem = OrderedDict({"lead_time": [0, 1, 2], "lat": [0, 1]})
         self.backend.add_array(init_coords, "temperature")
         self.mock_workflow.update_execution_data.reset_mock()
 
         # Write data at different time steps
         data = torch.randn(1, 2)
 
-        write_coords1: CoordSystem = {"lead_time": [0], "lat": [0, 1]}
+        write_coords1: CoordSystem = OrderedDict({"lead_time": [0], "lat": [0, 1]})
         self.backend.write(data, write_coords1, "temperature")
         self.mock_workflow.update_execution_data.assert_called_with(
             self.execution_id, updates={"current_step": 1}
         )
 
-        write_coords2: CoordSystem = {"lead_time": [2], "lat": [0, 1]}
+        write_coords2: CoordSystem = OrderedDict({"lead_time": [2], "lat": [0, 1]})
         self.backend.write(data, write_coords2, "temperature")
         self.mock_workflow.update_execution_data.assert_called_with(
             self.execution_id, updates={"current_step": 3}
@@ -255,23 +258,29 @@ def test_passthrough_to_underlying_io(self) -> None:
 class TestEarth2WorkflowIntegration:
     """Integration tests for Earth2Workflow with BackendProgress"""
 
-    @patch("api_server.e2workflow.get_config")
-    def test_workflow_with_backend_progress_tracking(self, mock_get_config):
+    @patch("earth2studio.serve.server.e2workflow.get_config")
+    def test_workflow_with_backend_progress_tracking(
+        self, mock_get_config: MagicMock
+    ) -> None:
         """Test complete workflow execution with automatic progress tracking"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
         mock_get_config.return_value = mock_config
 
         class TestWorkflow(Earth2Workflow):
-            def __call__(self, io: IOBackend, num_steps: int = 3):
-                coords: CoordSystem = {
-                    "lead_time": list(range(num_steps)),
-                    "lat": [0, 1],
-                }
+            def __call__(self, io: IOBackend, num_steps: int = 3) -> None:
+                coords: CoordSystem = OrderedDict(
+                    {
+                        "lead_time": list(range(num_steps)),
+                        "lat": [0, 1],
+                    }
+                )
                 io.add_array(coords, "forecast")
                 for i in range(num_steps):
                     data = torch.randn(1, 2)
-                    write_coords: CoordSystem = {"lead_time": [i], "lat": [0, 1]}
+                    write_coords: CoordSystem = OrderedDict(
+                        {"lead_time": [i], "lat": [0, 1]}
+                    )
                     io.write(data, write_coords, "forecast")
 
         workflow = TestWorkflow()
@@ -281,7 +290,7 @@ def __call__(self, io: IOBackend, num_steps: int = 3):
         with (
             patch.object(workflow, "update_execution_data") as mock_update,
             patch.object(workflow, "get_output_path"),
-            patch("api_server.e2workflow.ZarrBackend"),
+            patch("earth2studio.serve.server.e2workflow.ZarrBackend"),
         ):
             result = workflow.run({"num_steps": 3}, "exec_test")
 
diff --git a/serve/test/unit/test_e2workflow_params.py b/serve/server/test/unit/test_e2workflow_params.py
similarity index 86%
rename from serve/test/unit/test_e2workflow_params.py
rename to serve/server/test/unit/test_e2workflow_params.py
index 691bb5f7c..a62984613 100644
--- a/serve/test/unit/test_e2workflow_params.py
+++ b/serve/server/test/unit/test_e2workflow_params.py
@@ -21,41 +21,26 @@
 import os
 from datetime import datetime
 from typing import Literal
-from unittest.mock import Mock
 
 import pytest
 from pydantic import ValidationError
 
+from earth2studio.io import IOBackend
+from earth2studio.serve.server.e2workflow import Earth2Workflow, func_to_model
+
 # Set environment variable before importing workflows
 os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
 
-# Try to import earth2studio, skip tests if not available
-try:
-    from earth2studio.io import IOBackend
-
-    EARTH2STUDIO_AVAILABLE = True
-except ImportError:
-    EARTH2STUDIO_AVAILABLE = False
-    IOBackend = Mock  # Mock for type hints
-
-# ruff: noqa: E402
-from api_server.workflow.e2workflow import Earth2Workflow, func_to_model
-
-# Skip all tests in this module if earth2studio is not available
-pytestmark = pytest.mark.skipif(
-    not EARTH2STUDIO_AVAILABLE, reason="earth2studio not available in test environment"
-)
-
 
 class TestAutoParametersValidation:
     """Test validation of auto-generated parameters from Earth2Workflow"""
 
-    def test_auto_parameters_with_num_steps_validation(self):
+    def test_auto_parameters_with_num_steps_validation(self) -> None:
         """Test that num_steps parameter gets automatic range validation"""
 
         # Create a mock Earth2Workflow with num_steps parameter
         class TestWorkflow(Earth2Workflow):
-            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn"):
+            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn") -> None:
                 pass
 
             def __call__(
@@ -63,7 +48,7 @@ def __call__(
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
-            ):
+            ) -> None:
                 pass
 
         # Test valid num_steps
@@ -92,18 +77,18 @@ def __call__(
                 }
             )
 
-    def test_auto_parameters_with_start_time_validation(self):
+    def test_auto_parameters_with_start_time_validation(self) -> None:
         """Test that start_time parameter gets ISO 8601 validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self):
+            def __init__(self) -> None:
                 pass
 
             def __call__(
                 self,
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
-            ):
+            ) -> None:
                 pass
 
         # Test valid ISO 8601 formats
@@ -131,14 +116,14 @@ def __call__(
             ):
                 TestWorkflow.validate_parameters({"start_time": times})
 
-    def test_auto_parameters_with_config_validation(self):
+    def test_auto_parameters_with_config_validation(self) -> None:
         """Test that __init__ parameters get proper validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn"):
+            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn") -> None:
                 self.model_type = model_type
 
-            def __call__(self, io: IOBackend):
+            def __call__(self, io: IOBackend) -> None:
                 pass
 
         # Valid model_type
@@ -155,14 +140,14 @@ def __call__(self, io: IOBackend):
         with pytest.raises(ValidationError):
             TestWorkflow.Config.model_validate({"model_type": "invalid"})
 
-    def test_auto_parameters_nsteps_variant(self):
+    def test_auto_parameters_nsteps_variant(self) -> None:
         """Test that nsteps (alternate spelling) also gets range validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self):
+            def __init__(self) -> None:
                 pass
 
-            def __call__(self, io: IOBackend, nsteps: int = 10):
+            def __call__(self, io: IOBackend, nsteps: int = 10) -> None:
                 pass
 
         # Valid nsteps
@@ -173,7 +158,7 @@ def __call__(self, io: IOBackend, nsteps: int = 10):
         with pytest.raises(ValidationError, match="greater than or equal to 1"):
             TestWorkflow.validate_parameters({"nsteps": 0})
 
-    def test_func_to_model_with_validation(self):
+    def test_func_to_model_with_validation(self) -> None:
         """Test func_to_model creates models with proper validation"""
 
         def test_func(
@@ -199,11 +184,11 @@ def test_func(
         with pytest.raises(ValidationError, match="is not a valid ISO 8601"):
             TestModel.model_validate({"start_time": ["2024-01-01"]})  # Missing time
 
-    def test_auto_parameters_with_defaults(self):
+    def test_auto_parameters_with_defaults(self) -> None:
         """Test that default values work correctly with validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self):
+            def __init__(self) -> None:
                 pass
 
             def __call__(
@@ -211,7 +196,7 @@ def __call__(
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
-            ):
+            ) -> None:
                 pass
 
         # Use defaults
@@ -229,7 +214,7 @@ def __call__(
 class TestEarth2WorkflowIntegration:
     """Integration tests for Earth2Workflow validation"""
 
-    def test_complete_workflow_validation(self):
+    def test_complete_workflow_validation(self) -> None:
         """Test complete validation workflow"""
 
         class CompleteWorkflow(Earth2Workflow):
@@ -237,7 +222,7 @@ def __init__(
                 self,
                 model_type: Literal["fcn", "dlwp"] = "fcn",
                 device: Literal["cpu", "cuda"] = "cpu",
-            ):
+            ) -> None:
                 self.model_type = model_type
                 self.device = device
 
@@ -247,7 +232,7 @@ def __call__(
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
                 output_frequency: int = 1,
-            ):
+            ) -> None:
                 pass
 
         # Test valid complete parameters
@@ -274,14 +259,14 @@ def __call__(
         with pytest.raises(ValidationError):
             CompleteWorkflow.validate_parameters({"num_steps": -1})
 
-    def test_workflow_with_extra_fields_rejected(self):
+    def test_workflow_with_extra_fields_rejected(self) -> None:
         """Test that extra fields are rejected due to strict validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self):
+            def __init__(self) -> None:
                 pass
 
-            def __call__(self, io: IOBackend, num_steps: int = 20):
+            def __call__(self, io: IOBackend, num_steps: int = 20) -> None:
                 pass
 
         # Extra field should be rejected
diff --git a/test/Dockerfile b/test/Dockerfile
index 413fa7d24..f7547d178 100644
--- a/test/Dockerfile
+++ b/test/Dockerfile
@@ -1,6 +1,6 @@
 FROM nvcr.io/nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04
 # Install uv
-COPY --from=ghcr.io/astral-sh/uv:0.9.7 /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.10.9 /uv /uvx /bin/
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
     python3-dev \
diff --git a/test/data/test_data_utils.py b/test/data/test_data_utils.py
index 3eeeea098..f9c3eaf5b 100644
--- a/test/data/test_data_utils.py
+++ b/test/data/test_data_utils.py
@@ -20,6 +20,7 @@
 from collections import OrderedDict
 
 import numpy as np
+import pandas as pd
 import pytest
 import torch
 import xarray as xr
@@ -28,11 +29,17 @@
 from earth2studio.data import (
     DataArrayFile,
     Random,
+    RandomDataFrame,
     datasource_to_file,
     fetch_data,
+    fetch_dataframe,
     prep_data_array,
 )
-from earth2studio.data.utils import AsyncCachingFileSystem, datasource_cache_root
+from earth2studio.data.utils import (
+    AsyncCachingFileSystem,
+    datasource_cache_root,
+    prep_forecast_inputs,
+)
 
 
 @pytest.fixture
@@ -113,6 +120,7 @@ def test_prep_dataarray(foo_data_array, dims, device):
 
 
 def test_prep_data_array_curvilinear(equilinear_data_array, curvilinear_data_array):
+    pytest.importorskip("scipy", reason="scipy not installed")
     # Create another curvilinear grid
     y, x = np.mgrid[0:20, 0:24]
     target_lat = 30 + y * 1 + np.cos(x * 0.3) * 0.3
@@ -184,6 +192,118 @@ def test_fetch_data(time, lead_time, device):
     assert not torch.isnan(x).any()
 
 
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_fetch_data_legacy_false(device):
+
+    if device == "cuda:0" and torch.cuda.is_available():
+        try:
+            import cupy as cp
+        except ImportError:
+            pytest.skip("cupy not available for CUDA device")
+
+    time = np.array([np.datetime64("1993-04-05T00:00")])
+    lead_time = np.array([np.timedelta64(0, "h")])
+    variable = np.array(["a", "b", "c"])
+    domain = OrderedDict({"lat": np.random.randn(720), "lon": np.random.randn(1440)})
+    r = Random(domain)
+
+    da = fetch_data(r, time, variable, lead_time, device=device, legacy=False)
+
+    assert isinstance(da, xr.DataArray)
+    assert da.dims == ("time", "lead_time", "variable", "lat", "lon")
+    assert np.all(da.coords["time"].values == time)
+    assert np.all(da.coords["lead_time"].values == lead_time)
+    assert np.all(da.coords["variable"].values == variable)
+
+    if device == "cuda:0" and torch.cuda.is_available():
+        assert isinstance(da.data, cp.ndarray)
+        assert not cp.all(cp.isnan(da.data))
+    else:
+        assert isinstance(da.data, np.ndarray)
+        assert not np.all(np.isnan(da.data))
+
+
+@pytest.mark.parametrize(
+    "time",
+    [
+        np.array([np.datetime64("1993-04-05T00:00")]),
+        np.array(
+            [
+                np.datetime64("1999-10-11T12:00"),
+                np.datetime64("2001-06-04T00:00"),
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "lead_time",
+    [
+        np.array([np.timedelta64(0, "h")]),
+        np.array([np.timedelta64(-6, "h"), np.timedelta64(0, "h")]),
+    ],
+)
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_fetch_dataframe(time, lead_time, device):
+    variable = np.array(["t2m", "u10m"])
+    rdf = RandomDataFrame(n_obs=5)
+
+    # For CUDA, check if cudf is available first
+    if device != "cpu":
+        try:
+            import cudf
+        except ImportError:
+            pytest.skip("cudf not available for CUDA device")
+
+    result = fetch_dataframe(rdf, time, variable, lead_time=lead_time, device=device)
+
+    # Check return type based on device
+    if device == "cpu":
+        assert isinstance(result, pd.DataFrame)
+        df = result
+    else:
+        # CUDA device - should return cudf.DataFrame
+        import cudf
+
+        assert isinstance(result, cudf.DataFrame)
+        df = result.to_pandas()
+
+    # Check that DataFrame has expected columns
+    assert "time" in df.columns
+    assert "lat" in df.columns
+    assert "lon" in df.columns
+    assert "observation" in df.columns
+    assert "variable" in df.columns
+
+    # Check that variables match
+    assert set(df["variable"].unique()).issubset(set(variable))
+
+    # Check that we have data
+    assert len(df) > 0
+    assert not df.isnull().any().any()
+
+
 @pytest.mark.parametrize(
     "time",
     [
@@ -205,6 +325,7 @@ def test_fetch_data(time, lead_time, device):
 )
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
 def test_fetch_data_interp(time, lead_time, device):
+    pytest.importorskip("scipy", reason="scipy not installed")
     # Original (source) domain
     variable = np.array(["a", "b", "c"])
     domain = OrderedDict(
@@ -441,6 +562,64 @@ def test_clear_cache(tmp_path):
     assert not os.path.exists(dummy_file)
 
 
+@pytest.mark.parametrize(
+    "time, lead_time, variable",
+    [
+        (datetime.datetime(2020, 1, 1, 12, 0), datetime.timedelta(hours=6), "t2m"),
+        (
+            [
+                datetime.datetime(2020, 1, 1, 12, 0),
+                datetime.datetime(2020, 1, 2, 12, 0),
+            ],
+            [datetime.timedelta(hours=6), datetime.timedelta(hours=12)],
+            ["t2m", "u10m"],
+        ),
+        (
+            np.array(
+                [np.datetime64("2020-01-01T12:00"), np.datetime64("2020-01-02T12:00")]
+            ),
+            np.array([np.timedelta64(6, "h"), np.timedelta64(12, "h")]),
+            np.array(["t2m", "u10m", "v10m"]),
+        ),
+    ],
+)
+def test_prep_forecast_inputs(time, lead_time, variable):
+    time_list, lead_time_list, variable_list = prep_forecast_inputs(
+        time, lead_time, variable
+    )
+
+    assert isinstance(time_list, list)
+    assert all(isinstance(t, datetime.datetime) for t in time_list)
+
+    assert isinstance(lead_time_list, list)
+    assert all(isinstance(lt, datetime.timedelta) for lt in lead_time_list)
+
+    assert isinstance(variable_list, list)
+    assert all(isinstance(v, str) for v in variable_list)
+
+    # Verify correct lengths
+    if isinstance(time, datetime.datetime):
+        assert len(time_list) == 1
+    elif isinstance(time, list):
+        assert len(time_list) == len(time)
+    else:  # np.ndarray
+        assert len(time_list) == len(time)
+
+    if isinstance(lead_time, datetime.timedelta):
+        assert len(lead_time_list) == 1
+    elif isinstance(lead_time, list):
+        assert len(lead_time_list) == len(lead_time)
+    else:  # np.ndarray
+        assert len(lead_time_list) == len(lead_time)
+
+    if isinstance(variable, str):
+        assert len(variable_list) == 1
+    elif isinstance(variable, list):
+        assert len(variable_list) == len(variable)
+    else:  # np.ndarray
+        assert len(variable_list) == len(variable)
+
+
 @pytest.mark.asyncio
 async def test_async_cache_fs_storage_handling(tmp_path):
     fs = HTTPFileSystem()
diff --git a/test/data/test_goes.py b/test/data/test_goes.py
index d3b03dab7..d6543705f 100644
--- a/test/data/test_goes.py
+++ b/test/data/test_goes.py
@@ -219,6 +219,7 @@ def test_goes_sources(satellite, scan_mode, time, variable, valid):
     assert shape[3] == expected_dims[1]
 
 
+@pytest.mark.xfail
 @pytest.mark.timeout(15)
 @pytest.mark.parametrize(
     "time",
diff --git a/test/models/da/test_da_interp.py b/test/models/da/test_da_interp.py
new file mode 100644
index 000000000..0fa10aa2a
--- /dev/null
+++ b/test/models/da/test_da_interp.py
@@ -0,0 +1,359 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+import xarray as xr
+
+try:
+    import cudf
+except ImportError:
+    cudf = None
+
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+from earth2studio.models.da.interp import InterpEquirectangular
+
+
+@pytest.fixture
+def sample_observations_pandas():
+    """Create sample pandas DataFrame observations for testing with multiple times and variables."""
+    time1 = np.datetime64("2024-01-01T12:00:00")
+    time2 = np.datetime64("2024-01-01T13:00:00")
+    return pd.DataFrame(
+        {
+            "time": [
+                time1,
+                time1,
+                time1,
+                time1,
+                time2,
+                time2,
+                time2,
+                time2,
+            ],
+            "lat": [30.0, 30.0, 40.0, 40.0, 30.0, 30.0, 40.0, 40.0],
+            "lon": [240.0, 250.0, 240.0, 250.0, 240.0, 250.0, 240.0, 250.0],
+            "observation": [10.0, 20.0, 25.0, 35.0, 30.0, 40.0, 35.0, 45.0],
+            "variable": ["t2m", "t2m", "u10m", "u10m", "t2m", "t2m", "u10m", "u10m"],
+        }
+    )
+
+
+@pytest.fixture
+def sample_observations_cudf():
+    """Create sample cudf DataFrame observations for testing with multiple times and variables."""
+    if cudf is None:
+        pytest.skip("cudf not available")
+    time1 = np.datetime64("2024-01-01T12:00:00")
+    time2 = np.datetime64("2024-01-01T13:00:00")
+    return cudf.DataFrame(
+        {
+            "time": [
+                time1,
+                time1,
+                time1,
+                time1,
+                time2,
+                time2,
+                time2,
+                time2,
+            ],
+            "lat": [30.0, 30.0, 40.0, 40.0, 30.0, 30.0, 40.0, 40.0],
+            "lon": [240.0, 250.0, 240.0, 250.0, 240.0, 250.0, 240.0, 250.0],
+            "observation": [10.0, 20.0, 30.0, 40.0, 30.0, 40.0, 50.0, 60.0],
+            "variable": ["t2m", "t2m", "u10m", "u10m", "t2m", "t2m", "u10m", "u10m"],
+        }
+    )
+
+
+@pytest.fixture
+def small_grid():
+    """Create a small lat/lon grid for testing."""
+    lat = np.linspace(25.0, 50.0, 11, dtype=np.float32)
+    lon = np.linspace(235.0, 295.0, 13, dtype=np.float32)
+    return lat, lon
+
+
+@pytest.mark.parametrize(
+    "interp_method",
+    ["nearest", "smolyak"],
+)
+def test_interp_init(interp_method, small_grid):
+    lat, lon = small_grid
+    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method)
+    assert model.interp_method == interp_method
+    assert np.array_equal(model._lat, lat)
+    assert np.array_equal(model._lon, lon)
+
+    with pytest.raises(ValueError, match="interp_method must be one of"):
+        InterpEquirectangular(interp_method="invalid")
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "interp_method",
+    ["nearest", "smolyak"],
+)
+def test_interp_call_pandas(
+    sample_observations_pandas, small_grid, device, interp_method
+):
+    lat, lon = small_grid
+    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method).to(
+        device
+    )
+
+    # Set up request metadata
+    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
+    sample_observations_pandas.attrs = {
+        "request_time": request_time,
+        "request_lead_time": np.array([np.timedelta64(0, "h")]),
+    }
+
+    da = model(sample_observations_pandas)
+
+    assert isinstance(da, xr.DataArray)
+    assert da.dims == ("time", "variable", "lat", "lon")
+    assert da.shape == (1, 4, len(lat), len(lon))  # 4 times, 2 variables
+    assert da.coords["time"].values[0] == request_time[0]
+    assert len(da.coords["variable"]) == 4
+    assert "t2m" in da.coords["variable"].values
+    assert "u10m" in da.coords["variable"].values
+    assert np.array_equal(da.coords["lat"].values, lat)
+    assert np.array_equal(da.coords["lon"].values, lon)
+
+    # Check device-specific return type
+    if device == "cuda:0" and torch.cuda.is_available():
+        if cp is not None:
+            assert isinstance(da.data, cp.ndarray)
+            assert not cp.all(cp.isnan(da.data))
+        else:
+            # If cupy not available, should fall back to numpy
+            assert isinstance(da.data, np.ndarray)
+    else:
+        assert isinstance(da.data, np.ndarray)
+        assert not np.all(np.isnan(da.values))
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "interp_method",
+    ["nearest", "smolyak"],
+)
+def test_interp_call_cudf(sample_observations_cudf, small_grid, device, interp_method):
+    if cudf is None:
+        pytest.skip("cudf not available")
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available")
+
+    lat, lon = small_grid
+    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method).to(
+        device
+    )
+
+    # Set up request metadata
+    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
+    sample_observations_cudf.attrs = {
+        "request_time": request_time,
+        "request_lead_time": np.array([np.timedelta64(0, "h")]),
+    }
+
+    da = model(sample_observations_cudf)
+
+    assert isinstance(da, xr.DataArray)
+    assert da.dims == ("time", "variable", "lat", "lon")
+    assert da.shape == (1, 4, len(lat), len(lon))  # 1 time, 4 variables
+    assert da.coords["time"].values[0] == request_time[0]
+    assert len(da.coords["variable"]) == 4
+    assert "t2m" in da.coords["variable"].values
+    assert "u10m" in da.coords["variable"].values
+    assert np.array_equal(da.coords["lat"].values, lat)
+    assert np.array_equal(da.coords["lon"].values, lon)
+
+    # Check device-specific return type
+    if cp is not None:
+        assert isinstance(da.data, cp.ndarray)
+        assert not cp.all(cp.isnan(da.data))
+        t2m_data = da.sel(variable="t2m").data
+        u10m_data = da.sel(variable="u10m").data
+        assert not cp.any(cp.isnan(t2m_data))
+        assert not cp.any(cp.isnan(u10m_data))
+    else:
+        assert isinstance(da.data, np.ndarray)
+        assert not np.all(np.isnan(da.values))
+        t2m_data = da.sel(variable="t2m").values
+        u10m_data = da.sel(variable="u10m").values
+        assert not np.any(np.isnan(t2m_data))
+        assert not np.any(np.isnan(u10m_data))
+
+
+def test_interp_multiple_times(sample_observations_pandas, small_grid):
+    lat, lon = small_grid
+    model = InterpEquirectangular(lat=lat, lon=lon, interp_method="nearest")
+    model.VARIABLES = ["t2m"]
+
+    time1 = np.datetime64("2024-01-01T12:00:00")
+    time2 = np.datetime64("2024-01-01T13:00:00")
+    request_time = np.array([time1, time2])
+    sample_observations_pandas.attrs = {
+        "request_time": request_time,
+        "request_lead_time": np.array([np.timedelta64(0, "h"), np.timedelta64(0, "h")]),
+    }
+
+    da = model(sample_observations_pandas)
+
+    assert da.shape == (2, 1, len(lat), len(lon))  # 2 times, 1 variable
+    assert len(da.coords["time"]) == 2
+    assert da.coords["time"].values[0] == time1
+    assert da.coords["time"].values[1] == time2
+
+    # Verify interpolated values for each time step
+    # time1 t2m observations: (30.0, 240.0) = 10.0, (30.0, 250.0) = 20.0
+    # time2 t2m observations: (30.0, 240.0) = 15.0, (30.0, 250.0) = 25.0
+    time1_data = da.sel(time=time1).values
+    time2_data = da.sel(time=time2).values
+
+    assert not np.all(np.isnan(time1_data))
+    assert not np.all(np.isnan(time2_data))
+
+    # Values should be different between time steps
+    assert not np.allclose(time1_data, time2_data, equal_nan=True)
+
+    if len(time1_data) > 0:
+        assert np.min(time1_data) >= 0  # Should be positive based on observations
+        assert np.max(time1_data) <= 25  # Should be close to max observation (20.0)
+
+    if len(time2_data) > 0:
+        assert np.min(time2_data) >= 30  # Should be close to min observation (15.0)
+        assert np.max(time2_data) <= 40  # Should be close to max observation (25.0)
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_interp_tolerance(sample_observations_pandas, small_grid, device):
+    lat, lon = small_grid
+    # Use a larger tolerance to capture observations
+    time_tolerance = np.timedelta64(2, "h")
+    model = InterpEquirectangular(lat=lat, lon=lon, time_tolerance=time_tolerance).to(
+        device
+    )
+
+    # Create observations with times spread out
+    base_time = np.datetime64("2024-01-01T12:00:00")
+    time1 = base_time - np.timedelta64(1, "h")
+    time2 = base_time
+    time3 = base_time + np.timedelta64(1, "h")
+    time4 = base_time + np.timedelta64(3, "h")  # Outside tolerance
+
+    tolerance_df = pd.DataFrame(
+        {
+            "time": [time1, time2, time3, time4],
+            "lat": [30.0, 30.0, 40.0, 40.0],
+            "lon": [240.0, 250.0, 240.0, 250.0],
+            "observation": [10.0, 20.0, 30.0, 40.0],
+            "variable": ["t2m", "t2m", "t2m", "t2m"],
+        }
+    )
+
+    request_time = np.array([base_time])
+    tolerance_df.attrs = {
+        "request_time": request_time,
+        "request_lead_time": np.array([np.timedelta64(0, "h")]),
+    }
+
+    da = model(tolerance_df)
+
+    # Should interpolate using observations within tolerance (time1, time2, time3)
+    assert da.shape == (1, 4, len(lat), len(lon))
+    if cp is not None and isinstance(da.data, cp.ndarray):
+        assert not cp.all(cp.isnan(da.data))
+    else:
+        assert not np.all(np.isnan(da.values))
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_interp_generator(sample_observations_pandas, small_grid, device):
+    lat, lon = small_grid
+    model = InterpEquirectangular(lat=lat, lon=lon).to(device)
+
+    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
+    sample_observations_pandas.attrs = {
+        "request_time": request_time,
+        "request_lead_time": np.array([np.timedelta64(0, "h")]),
+    }
+
+    generator = model.create_generator()
+    # Prime the generator
+    result = generator.send(None)
+    assert result is None
+
+    # Send observations
+    da = generator.send(sample_observations_pandas)
+    assert isinstance(da, xr.DataArray)
+    assert da.shape == (1, 4, len(lat), len(lon))
+
+    # Send another set of observations
+    da2 = generator.send(sample_observations_pandas)
+    assert isinstance(da2, xr.DataArray)
+    assert da2.shape == (1, 4, len(lat), len(lon))
+
+    generator.close()
diff --git a/test/models/da/test_da_sda_stormcast.py b/test/models/da/test_da_sda_stormcast.py
new file mode 100644
index 000000000..b6b54b9f2
--- /dev/null
+++ b/test/models/da/test_da_sda_stormcast.py
@@ -0,0 +1,537 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+import xarray as xr
+
+from earth2studio.data import Random, RandomDataFrame, fetch_data, fetch_dataframe
+from earth2studio.models.da.sda_stormcast import StormCastSDA
+
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+
+# ---------- Mock neural networks ----------
+
+
+class PhooRegressionModel(torch.nn.Module):
+    def __init__(self, out_vars=3):
+        super().__init__()
+        self.out_vars = out_vars
+
+    def forward(self, x):
+        return x[:, : self.out_vars, :, :]
+
+
+class PhooSDADiffusionModel(torch.nn.Module):
+    def forward(self, x, t, condition=None):
+        return x
+
+
+# ---------- Constants ----------
+
+Y_START, Y_END = 32, 128
+X_START, X_END = 32, 64
+NVAR = 3
+NVAR_COND = 5
+SAMPLER_ARGS = {
+    "num_steps": 2,
+    "sigma_min": 0.002,
+    "sigma_max": 88.0,
+    "rho": 7.0,
+    "S_churn": 0.0,
+    "S_min": 0.0,
+    "S_max": float("inf"),
+    "S_noise": 1.0,
+}
+
+
+# ---------- Helpers ----------
+
+
+def _build_model(device="cpu"):
+    regression = PhooRegressionModel(out_vars=NVAR)
+    diffusion = PhooSDADiffusionModel()
+
+    r_condition = Random(
+        OrderedDict(
+            [
+                ("lat", np.linspace(90, -90, num=181, endpoint=True)),
+                ("lon", np.linspace(0, 360, num=360)),
+            ]
+        )
+    )
+
+    ny = Y_END - Y_START
+    nx = X_END - X_START
+    variables = np.array(["u%02d" % i for i in range(NVAR)])
+    means = torch.zeros(1, NVAR, 1, 1)
+    stds = torch.ones(1, NVAR, 1, 1)
+    invariants = torch.randn(1, 2, ny, nx)
+    conditioning_means = torch.randn(1, NVAR_COND, 1, 1)
+    conditioning_stds = torch.randn(1, NVAR_COND, 1, 1).abs() + 0.1
+    conditioning_variables = np.array(["c%02d" % i for i in range(NVAR_COND)])
+
+    return StormCastSDA(
+        regression,
+        diffusion,
+        means,
+        stds,
+        invariants,
+        hrrr_lat_lim=(Y_START, Y_END),
+        hrrr_lon_lim=(X_START, X_END),
+        variables=variables,
+        conditioning_means=conditioning_means,
+        conditioning_stds=conditioning_stds,
+        conditioning_variables=conditioning_variables,
+        conditioning_data_source=r_condition,
+        sampler_args=SAMPLER_ARGS,
+    ).to(device)
+
+
+def _build_input_da(model, time, device="cpu"):
+    dc = OrderedDict([("hrrr_y", model.hrrr_y), ("hrrr_x", model.hrrr_x)])
+    r = Random(dc)
+    x = fetch_data(
+        r,
+        time,
+        model.variables,
+        lead_time=np.array([np.timedelta64(0, "h")]),
+        device=device,
+        legacy=False,
+    )
+    return x.assign_coords(
+        lat=(["hrrr_y", "hrrr_x"], model.lat),
+        lon=(["hrrr_y", "hrrr_x"], model.lon),
+    )
+
+
+def _build_obs_source(model, n_obs=10):
+    grid_lat, grid_lon = model.lat, model.lon
+    _state: dict = {}
+
+    def lat_gen():
+        y = np.random.randint(5, grid_lat.shape[0] - 5)
+        x = np.random.randint(5, grid_lat.shape[1] - 5)
+        _state["y"], _state["x"] = y, x
+        return float(grid_lat[y, x])
+
+    def lon_gen():
+        return float(grid_lon[_state["y"], _state["x"]])
+
+    return RandomDataFrame(
+        n_obs=n_obs,
+        field_generators={"lat": lat_gen, "lon": lon_gen},
+    )
+
+
+def _mock_forward(x, conditioning, y_obs, mask):
+    return torch.zeros_like(x)
+
+
+# ---------- Unit tests: _points_in_polygon ----------
+
+
+def test_points_in_polygon_square():
+    polygon = np.array([[0, 0], [0, 1], [1, 1], [1, 0]], dtype=np.float64)
+    inside = np.array([[0.5, 0.5], [0.1, 0.1], [0.9, 0.9]], dtype=np.float64)
+    outside = np.array([[-1, -1], [2, 2], [0.5, 1.5]], dtype=np.float64)
+    points = np.vstack([inside, outside])
+
+    result = StormCastSDA._points_in_polygon(points, polygon)
+
+    assert result[:3].all()
+    assert not result[3:].any()
+
+
+def test_points_in_polygon_triangle():
+    polygon = np.array([[0, 0], [2, 0], [1, 2]], dtype=np.float64)
+    inside = np.array([[1, 0.5]], dtype=np.float64)
+    outside = np.array([[3, 3], [-1, 0]], dtype=np.float64)
+    points = np.vstack([inside, outside])
+
+    result = StormCastSDA._points_in_polygon(points, polygon)
+
+    assert result[0]
+    assert not result[1:].any()
+
+
+# ---------- Unit tests: _build_obs_tensors ----------
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_build_obs_tensors(device):
+    model = _build_model(device=device)
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    obs_source = _build_obs_source(model, n_obs=5)
+    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
+    ny, nx = model.lat.shape
+
+    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
+
+    assert y_obs.shape == (1, NVAR, ny, nx)
+    assert mask.shape == (1, NVAR, ny, nx)
+    assert mask.sum() > 0
+    assert y_obs.device == model.device
+    assert mask.device == model.device
+
+
+def test_build_obs_tensors_none():
+    model = _build_model()
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    ny, nx = model.lat.shape
+
+    y_obs, mask = model._build_obs_tensors(None, time[0], model.device)
+
+    assert y_obs.shape == (1, NVAR, ny, nx)
+    assert (mask == 0).all()
+    assert (y_obs == 0).all()
+
+
+def test_build_obs_tensors_outside_grid():
+    model = _build_model()
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+
+    # RandomDataFrame with lat/lon fixed far outside the HRRR grid
+    obs_source = RandomDataFrame(
+        n_obs=5,
+        field_generators={
+            "lat": lambda: 0.0,
+            "lon": lambda: 10.0,
+        },
+    )
+    obs_df = fetch_dataframe(obs_source, time, [str(model.variables[0])])
+
+    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
+
+    assert (mask == 0).all()
+
+
+def test_build_obs_tensors_averages_duplicates():
+    model = _build_model()
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    ny, nx = model.lat.shape
+
+    mid_y, mid_x = ny // 2, nx // 2
+    pt_lat = float(model.lat[mid_y, mid_x])
+    pt_lon = float(model.lon[mid_y, mid_x])
+    var_name = str(model.variables[0])
+
+    # Three observations at the exact same location and variable
+    obs_df = pd.DataFrame(
+        {
+            "time": pd.to_datetime([time[0]] * 3),
+            "lat": [pt_lat] * 3,
+            "lon": [pt_lon] * 3,
+            "variable": [var_name] * 3,
+            "observation": [3.0, 9.0, 30.0],
+        }
+    )
+
+    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
+
+    assert mask.sum() == 1
+    assert torch.isclose(y_obs[mask == 1], torch.tensor(14.0)).all()
+
+    obs_df = pd.DataFrame(
+        {
+            "time": pd.to_datetime([time[0]] * 3),
+            "lat": [0, pt_lat, pt_lat],
+            "lon": [0, pt_lon, pt_lon],
+            "variable": [var_name] * 3,
+            "observation": [3.0, 9.0, 30.0],
+        }
+    )
+
+    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
+    assert mask.sum() == 1
+    assert torch.isclose(y_obs[mask == 1], torch.tensor(19.5)).all()
+
+
+# ---------- Unit test: _fetch_and_interp_conditioning ----------
+
+
+def test_fetch_and_interp_conditioning():
+    model = _build_model(device="cpu")
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    x = _build_input_da(model, time, device="cpu")
+    ny, nx = model.lat.shape
+
+    c = model._fetch_and_interp_conditioning(x)
+
+    assert c.shape == (1, 1, NVAR_COND, ny, nx)
+    assert "hrrr_y" in c.dims
+    assert "hrrr_x" in c.dims
+    assert "variable" in c.dims
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda missing")
+def test_fetch_and_interp_conditioning_gpu():
+    from scipy.interpolate import RegularGridInterpolator
+
+    from earth2studio.data.utils import prep_data_inputs
+
+    # Descending latitude (typical weather data convention: 90 → -90)
+    src_lat = np.linspace(90, -90, num=181)
+    src_lon = np.linspace(0, 360, num=361)
+    field = np.random.randn(src_lat.shape[0], src_lon.shape[0])
+
+    class _AnalyticSource:
+        def __init__(self):
+            self.domain_coords = OrderedDict([("lat", src_lat), ("lon", src_lon)])
+
+        def __call__(self, time, variable):
+            time, variable = prep_data_inputs(time, variable)
+            data = np.broadcast_to(
+                field, (len(time), len(variable), len(src_lat), len(src_lon))
+            ).copy()
+            return xr.DataArray(
+                data=data,
+                dims=["time", "variable", "lat", "lon"],
+                coords={
+                    "time": time,
+                    "variable": variable,
+                    "lat": src_lat,
+                    "lon": src_lon,
+                },
+            )
+
+    ny = Y_END - Y_START
+    nx = X_END - X_START
+    model = StormCastSDA(
+        PhooRegressionModel(out_vars=NVAR),
+        PhooSDADiffusionModel(),
+        torch.zeros(1, NVAR, 1, 1),
+        torch.ones(1, NVAR, 1, 1),
+        torch.randn(1, 2, ny, nx),
+        hrrr_lat_lim=(Y_START, Y_END),
+        hrrr_lon_lim=(X_START, X_END),
+        variables=np.array(["u%02d" % i for i in range(NVAR)]),
+        conditioning_means=torch.zeros(1, NVAR_COND, 1, 1),
+        conditioning_stds=torch.ones(1, NVAR_COND, 1, 1),
+        conditioning_variables=np.array(["c%02d" % i for i in range(NVAR_COND)]),
+        conditioning_data_source=_AnalyticSource(),
+        sampler_args=SAMPLER_ARGS,
+    ).to("cuda:0")
+
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    x = _build_input_da(model, time, device="cuda:0")
+    c = model._fetch_and_interp_conditioning(x)
+
+    # Scipy reference bilinear interpolation
+    ref = RegularGridInterpolator((src_lat, src_lon), field, method="linear")
+    target_pts = np.column_stack([model.lat.ravel(), model.lon.ravel()])
+    expected = ref(target_pts).reshape(ny, nx)
+
+    c_np = c.data.get()
+    for v in range(NVAR_COND):
+        np.testing.assert_allclose(c_np[0, 0, v], expected, atol=1e-6, rtol=0)
+
+
+# ---------- Test: __call__ ----------
+
+
+@pytest.mark.parametrize(
+    "time",
+    [
+        np.array([np.datetime64("2020-04-05T00:00")]),
+        np.array(
+            [np.datetime64("2020-10-11T12:00"), np.datetime64("2020-06-04T00:00")]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_stormcast_sda_call(time, device):
+    model = _build_model(device=device)
+    x = _build_input_da(model, time, device=device)
+    obs_source = _build_obs_source(model, n_obs=10)
+    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
+    ny, nx = model.lat.shape
+
+    with patch.object(model, "_forward", _mock_forward):
+        out = model(x, obs_df)
+
+    assert out.shape == (len(time), 1, NVAR, ny, nx)
+    assert set(out.dims) == {"time", "lead_time", "variable", "hrrr_y", "hrrr_x"}
+    assert np.all(out.coords["time"].values == time)
+    assert out.coords["lead_time"].values[0] == np.timedelta64(1, "h")
+
+    # Without observations
+    with patch.object(model, "_forward", _mock_forward):
+        out_none = model(x, None)
+
+    assert out_none.shape == out.shape
+
+
+# ---------- Test: create_generator ----------
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="cuda missing"
+            ),
+        ),
+    ],
+)
+def test_stormcast_sda_generator(device):
+    model = _build_model(device=device)
+    time = np.array([np.datetime64("2020-04-05T00:00")])
+    x = _build_input_da(model, time, device=device)
+    obs_source = _build_obs_source(model, n_obs=10)
+    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
+    ny, nx = model.lat.shape
+
+    with patch.object(model, "_forward", _mock_forward):
+        gen = model.create_generator(x)
+
+        # First yield returns initial state
+        state = next(gen)
+        assert state.shape == x.shape
+
+        # Send observations, receive next forecast
+        state = gen.send(obs_df)
+        assert state.shape == (len(time), 1, NVAR, ny, nx)
+        assert state.coords["lead_time"].values[0] == np.timedelta64(1, "h")
+
+        # Send None (no observations), receive next forecast
+        state = gen.send(None)
+        assert state.shape == (len(time), 1, NVAR, ny, nx)
+        assert state.coords["lead_time"].values[0] == np.timedelta64(2, "h")
+
+        gen.close()
+
+
+# ---------- Test: exceptions ----------
+
+
+def test_stormcast_sda_exceptions():
+    ny = Y_END - Y_START
+    nx = X_END - X_START
+    regression = PhooRegressionModel(out_vars=NVAR)
+    diffusion = PhooSDADiffusionModel()
+    means = torch.zeros(1, NVAR, 1, 1)
+    stds = torch.ones(1, NVAR, 1, 1)
+    invariants = torch.randn(1, 2, ny, nx)
+
+    # No conditioning_data_source
+    model = StormCastSDA(
+        regression,
+        diffusion,
+        means,
+        stds,
+        invariants,
+        hrrr_lat_lim=(Y_START, Y_END),
+        hrrr_lon_lim=(X_START, X_END),
+    )
+
+    time = np.array([np.datetime64("2020-01-01T00:00")])
+    x = _build_input_da(model, time)
+
+    with pytest.raises(RuntimeError):
+        model(x, None)
+
+    gen = model.create_generator(x)
+    with pytest.raises(RuntimeError):
+        next(gen)
+
+
+# ---------- Test: package loading ----------
+
+
+@pytest.fixture(scope="function")
+def sda_model() -> StormCastSDA:
+    package = StormCastSDA.load_default_package()
+    return StormCastSDA.load_model(package)
+
+
+@pytest.mark.package
+@pytest.mark.parametrize("device", ["cuda:0"])
+def test_stormcast_sda_package(device, sda_model):
+    torch.cuda.empty_cache()
+    time = np.array([np.datetime64("2020-04-05T00:00")])
+
+    model = sda_model.to(device)
+
+    # Set up Random conditioning source to avoid external data fetches
+    r_condition = Random(
+        OrderedDict(
+            [
+                ("lat", np.linspace(90, -90, num=721, endpoint=True)),
+                ("lon", np.linspace(0, 360, num=1440)),
+            ]
+        )
+    )
+    model.conditioning_data_source = r_condition
+    model.sampler_args = SAMPLER_ARGS
+
+    # Build input from Random source matching model init_coords
+    ic = model.init_coords()[0]
+    dc = OrderedDict([("hrrr_y", ic["hrrr_y"]), ("hrrr_x", ic["hrrr_x"])])
+    r = Random(dc)
+    x = fetch_data(
+        r,
+        time,
+        ic["variable"],
+        lead_time=np.array([np.timedelta64(0, "h")]),
+        device=device,
+        legacy=False,
+    )
+    x = x.assign_coords(
+        lat=(["hrrr_y", "hrrr_x"], model.lat),
+        lon=(["hrrr_y", "hrrr_x"], model.lon),
+    )
+
+    out = model(x, None)
+
+    assert out.shape == (1, 1, 99, 512, 640)
+    assert set(out.dims) == {"time", "lead_time", "variable", "hrrr_y", "hrrr_x"}
+    assert np.all(out.coords["time"].values == time)
+    assert out.coords["lead_time"].values[0] == np.timedelta64(1, "h")
diff --git a/test/models/da/test_utils.py b/test/models/da/test_utils.py
new file mode 100644
index 000000000..10cd95d57
--- /dev/null
+++ b/test/models/da/test_utils.py
@@ -0,0 +1,204 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+try:
+    import cudf
+except ImportError:
+    cudf = None
+
+from earth2studio.models.da.utils import (
+    dfseries_to_torch,
+    filter_time_range,
+    validate_observation_fields,
+)
+
+
+@pytest.fixture
+def sample_pandas_df():
+    """Create a sample pandas DataFrame for testing."""
+    return pd.DataFrame(
+        {
+            "time": pd.to_datetime(
+                [
+                    "2024-01-01T10:00:00",
+                    "2024-01-01T11:00:00",
+                    "2024-01-01T12:00:00",
+                    "2024-01-01T13:00:00",
+                ]
+            ),
+            "lat": [30.0, 31.0, 32.0, 33.0],
+            "lon": [240.0, 241.0, 242.0, 243.0],
+            "observation": [10.0, 20.0, 30.0, 40.0],
+            "variable": ["t2m", "t2m", "u10m", "u10m"],
+        }
+    )
+
+
+@pytest.fixture
+def sample_cudf_df():
+    """Create a sample cudf DataFrame for testing."""
+    if cudf is None:
+        pytest.skip("cudf not available")
+    return cudf.DataFrame(
+        {
+            "time": pd.to_datetime(
+                [
+                    "2024-01-01T10:00:00",
+                    "2024-01-01T11:00:00",
+                    "2024-01-01T12:00:00",
+                    "2024-01-01T13:00:00",
+                ]
+            ),
+            "lat": [30.0, 31.0, 32.0, 33.0],
+            "lon": [240.0, 241.0, 242.0, 243.0],
+            "observation": [10.0, 20.0, 30.0, 40.0],
+            "variable": ["t2m", "t2m", "u10m", "u10m"],
+        }
+    )
+
+
+def test_validate_observation_fields(sample_pandas_df):
+    validate_observation_fields(sample_pandas_df, ["time", "lat", "lon"])
+    validate_observation_fields(
+        sample_pandas_df, ["time", "lat", "lon", "observation", "variable"]
+    )
+    validate_observation_fields(sample_pandas_df, ["time"])
+
+    with pytest.raises(ValueError, match="DataFrame missing required fields"):
+        validate_observation_fields(sample_pandas_df, ["time", "missing_field"])
+
+    with pytest.raises(ValueError, match="DataFrame missing required fields"):
+        validate_observation_fields(
+            sample_pandas_df, ["time", "missing_field1", "missing_field2"]
+        )
+
+
+class TestFilterTimeRange:
+    @pytest.mark.parametrize(
+        "request_time",
+        [
+            np.datetime64("2024-01-01T12:00:00"),
+            datetime(2024, 1, 1, 12, 0, 0),
+            "2024-01-01T12:00:00",
+        ],
+    )
+    def test_filter_time_range_pandas(self, sample_pandas_df, request_time):
+        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
+        result = filter_time_range(sample_pandas_df, request_time, tolerance)
+
+        assert isinstance(result, pd.DataFrame)
+        assert len(result) == 1
+        assert result.iloc[0]["time"] == pd.Timestamp(request_time)
+
+    @pytest.mark.parametrize(
+        "request_time",
+        [
+            np.datetime64("2024-01-01T12:00:00"),
+            datetime(2024, 1, 1, 12, 0, 0),
+            "2024-01-01T12:00:00",
+        ],
+    )
+    def test_filter_time_range_cudf(self, sample_cudf_df, request_time):
+        if cudf is None:
+            pytest.skip("cudf not available")
+        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
+        result = filter_time_range(sample_cudf_df, request_time, tolerance)
+
+        assert isinstance(result, cudf.DataFrame)
+        assert len(result) == 1
+        assert (result.iloc[0]["time"] == pd.Timestamp(request_time)).all()
+
+    def test_filter_time_range_tolerance_window(self, sample_pandas_df):
+        request_time = np.datetime64("2024-01-01T11:30:00")
+        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
+        result = filter_time_range(sample_pandas_df, request_time, tolerance)
+        assert len(result) == 2
+
+        tolerance = (np.timedelta64(-2, "h"), np.timedelta64(2, "h"))
+        result = filter_time_range(sample_pandas_df, request_time, tolerance)
+        assert len(result) >= 2
+
+        request_time = np.datetime64("2024-01-01T20:00:00")
+        tolerance = (np.timedelta64(-1, "h"), np.timedelta64(1, "h"))
+        result = filter_time_range(sample_pandas_df, request_time, tolerance)
+        assert len(result) == 0
+
+    def test_filter_time_range_custom_column(self, sample_pandas_df):
+        df = sample_pandas_df.rename(columns={"time": "timestamp"})
+        request_time = np.datetime64("2024-01-01T12:00:00")
+        tolerance = (np.timedelta64(-1, "h"), np.timedelta64(1, "h"))
+        result = filter_time_range(df, request_time, tolerance, time_column="timestamp")
+        assert len(result) == 3
+
+
+class TestDfseriesToTorch:
+    @pytest.mark.parametrize(
+        "device",
+        [
+            "cpu",
+            pytest.param(
+                "cuda:0",
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(), reason="cuda missing"
+                ),
+            ),
+        ],
+    )
+    def test_dfseries_to_torch_pandas_cpu(self, sample_pandas_df, device):
+        if device != "cpu" and not torch.cuda.is_available():
+            pytest.skip("CUDA not available")
+        series = sample_pandas_df["lat"]
+        tensor = dfseries_to_torch(series, dtype=torch.float32, device=device)
+
+        assert isinstance(tensor, torch.Tensor)
+        assert tensor.dtype == torch.float32
+        assert tensor.device.type == device.split(":")[0] if ":" in device else device
+        assert len(tensor) == len(series)
+        assert torch.allclose(
+            tensor, torch.tensor(series.values, dtype=torch.float32, device=device)
+        )
+
+    @pytest.mark.parametrize(
+        "device",
+        [
+            "cpu",
+            pytest.param(
+                "cuda:0",
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(), reason="cuda missing"
+                ),
+            ),
+        ],
+    )
+    def test_dfseries_to_torch_cudf(self, sample_cudf_df, device):
+        if cudf is None:
+            pytest.skip("cudf not available")
+        if device != "cpu" and not torch.cuda.is_available():
+            pytest.skip("CUDA not available")
+        series = sample_cudf_df["lat"]
+        tensor = dfseries_to_torch(series, dtype=torch.float32, device=device)
+
+        assert isinstance(tensor, torch.Tensor)
+        assert tensor.dtype == torch.float32
+        assert tensor.device.type == device.split(":")[0] if ":" in device else device
+        assert len(tensor) == len(series)
diff --git a/test/models/px/test_ace2.py b/test/models/px/test_ace2.py
index ac1c6325a..67509420b 100644
--- a/test/models/px/test_ace2.py
+++ b/test/models/px/test_ace2.py
@@ -29,6 +29,8 @@
 )
 from earth2studio.utils import handshake_dim
 
+pytest.importorskip("fme")
+
 
 class PhooOutput:
     prediction: dict = {}
diff --git a/test/models/px/test_aurora.py b/test/models/px/test_aurora.py
index 748c45e8c..ae90f7829 100644
--- a/test/models/px/test_aurora.py
+++ b/test/models/px/test_aurora.py
@@ -20,7 +20,11 @@
 import numpy as np
 import pytest
 import torch
-from aurora import Batch, Metadata
+
+try:
+    from aurora import Batch, Metadata
+except ImportError:
+    pytest.importorskip("aurora")
 
 from earth2studio.data import Random, fetch_data
 from earth2studio.models.px import Aurora
diff --git a/test/models/px/test_dxwrapper.py b/test/models/px/test_dxwrapper.py
index 75c080ecf..5152e93ab 100644
--- a/test/models/px/test_dxwrapper.py
+++ b/test/models/px/test_dxwrapper.py
@@ -117,6 +117,7 @@ def round_sigma(self, sigma):
         return torch.as_tensor(sigma)
 
 
+@pytest.mark.skip(reason="FCN3 test for dx wrapper is slow")
 @pytest.mark.parametrize("device", ["cuda:0"])  # Removing CPU here too slow atm "cpu",
 @pytest.mark.parametrize("model_type", ["precip", "solar"])
 @pytest.mark.parametrize(
diff --git a/test/models/px/test_fcn.py b/test/models/px/test_fcn.py
index eb9e375fa..10bf64f12 100644
--- a/test/models/px/test_fcn.py
+++ b/test/models/px/test_fcn.py
@@ -165,7 +165,6 @@ def model() -> FCN:
     return p
 
 
-@pytest.mark.xfail(reason="package missing on ngc")
 @pytest.mark.package
 @pytest.mark.parametrize("device", ["cuda:0"])
 def test_fcn_package(model, device):
diff --git a/test/models/px/test_graphcast.py b/test/models/px/test_graphcast.py
index 98a9a07ea..f174767af 100644
--- a/test/models/px/test_graphcast.py
+++ b/test/models/px/test_graphcast.py
@@ -22,7 +22,11 @@
 import pytest
 import torch
 import xarray as xr
-from graphcast import graphcast
+
+try:
+    from graphcast import graphcast
+except ImportError:
+    pytest.importorskip("graphcast")
 
 from earth2studio.data import Random, fetch_data
 from earth2studio.models.px.graphcast_operational import GraphCastOperational
@@ -127,8 +131,8 @@ def __init__(self, model_config, task_config):
     [
         np.array([np.datetime64("1993-04-05T00:00")]),
         np.array(
-            [np.datetime64("2001-06-04T00:00")]
-        ),  # Only len 1 time array is supported by GraphCastSmall model
+            [np.datetime64("1993-04-05T00:00"), np.datetime64("2001-06-04T00:00")]
+        ),
     ],
 )
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
@@ -152,9 +156,6 @@ def test_graphcast_small_call(time, device, mock_GraphCastSmall_model):
 
     out, out_coords = p(x, coords)
 
-    if not isinstance(time, Iterable):
-        time = [time]
-
     assert out.shape == torch.Size([len(time), 1, 83, 181, 360])
     assert (out_coords["variable"] == p.output_coords(coords)["variable"]).all()
     assert (out_coords["time"] == time).all()
@@ -346,9 +347,18 @@ def __init__(self, model_config, task_config):
     return p
 
 
+@pytest.mark.parametrize(
+    "time",
+    [
+        np.array([np.datetime64("2010-01-01T00:00")]),
+        np.array(
+            [np.datetime64("2010-01-01T00:00"), np.datetime64("2010-01-02T00:00")]
+        ),
+    ],
+)
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
 @mock.patch("graphcast.rollout.chunked_prediction", mocked_chunked_prediction)
-def test_graphcast_operational_call(device, mock_GraphCastOperational_model):
+def test_graphcast_operational_call(time, device, mock_GraphCastOperational_model):
 
     p = mock_GraphCastOperational_model.to(device)
 
@@ -362,12 +372,11 @@ def test_graphcast_operational_call(device, mock_GraphCastOperational_model):
     r = Random(dc)
 
     # Get Data and convert to tensor, coords
-    time = np.array([np.datetime64("2010-01-01T00:00")])
     lead_time = p.input_coords()["lead_time"]
     variable = p.input_coords()["variable"]
     x, coords = fetch_data(r, time, variable, lead_time, device=device)
     out, out_coords = p(x, coords)
-    assert out.shape == (1, 1, 83, 721, 1440)
+    assert out.shape == torch.Size([len(time), 1, 83, 721, 1440])
     assert (out_coords["variable"] == p.output_coords(coords)["variable"]).all()
     assert (out_coords["time"] == time).all()
     handshake_dim(out_coords, "lon", 4)
diff --git a/test/serve/client/conftest.py b/test/serve/client/conftest.py
new file mode 100644
index 000000000..2bd76cb79
--- /dev/null
+++ b/test/serve/client/conftest.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from collections.abc import Iterator
+from typing import Any
+
+import pytest
+
+# earth2studio.serve.client is importable because uv adds the repository
+# root to sys.path when running: uv run -m pytest test/serve/client
+
+# =============================================================================
+# Pytest Configuration
+# =============================================================================
+
+
+def pytest_configure(config: Any) -> None:
+    """Configure pytest with custom markers."""
+    config.addinivalue_line(
+        "markers",
+        "integration: mark test as an integration test requiring API server",
+    )
+
+
+# =============================================================================
+# Integration Test Fixtures
+# =============================================================================
+
+
+@pytest.fixture(scope="session")
+def api_url() -> str | None:
+    """
+    Get API URL from environment for integration tests.
+
+    Returns None if not set (integration tests will be skipped).
+    """
+    return os.getenv("EARTH2STUDIO_API_URL")
+
+
+@pytest.fixture(scope="session")
+def check_api_availability(api_url: str | None) -> Iterator[str]:
+    """
+    Check if API is available before running integration tests.
+
+    This fixture should be used by integration tests to ensure
+    the API server is running and accessible.
+    """
+    if not api_url:
+        pytest.skip(
+            "EARTH2STUDIO_API_URL environment variable not set. "
+            "Set it to run integration tests: export EARTH2STUDIO_API_URL=http://localhost:8000"
+        )
+
+    # Verify API is reachable
+    try:
+        import requests  # type: ignore[import-untyped]
+
+        response = requests.get(f"{api_url}/health", timeout=10)
+        if response.status_code != 200:
+            pytest.skip(f"API health check failed: {response.status_code}")
+    except Exception as e:
+        pytest.skip(f"Cannot connect to API at {api_url}: {e}")
+
+    assert api_url is not None  # narrow type for mypy after skip
+    yield api_url
+
+
+# =============================================================================
+# Unit Test Mock Fixtures
+# =============================================================================
+
+
+@pytest.fixture
+def mock_health_response() -> dict[str, Any]:
+    """Fixture providing a mock health check response"""
+    return {
+        "status": "healthy",
+        "timestamp": "2024-01-01T00:00:00+00:00",
+    }
+
+
+@pytest.fixture
+def mock_inference_response() -> dict[str, Any]:
+    """Fixture providing a mock inference request response"""
+    return {
+        "execution_id": "exec_test_123",
+        "status": "accepted",
+        "message": "Request accepted for processing",
+        "timestamp": "2024-01-01T00:00:00+00:00",
+    }
+
+
+@pytest.fixture
+def mock_status_response() -> dict[str, Any]:
+    """Fixture providing a mock status response"""
+    return {
+        "execution_id": "exec_test_123",
+        "status": "running",
+        "progress": {
+            "progress": "Processing step 5 of 10",
+            "current_step": 5,
+            "total_steps": 10,
+        },
+        "error_message": None,
+    }
+
+
+@pytest.fixture
+def mock_results_response() -> dict[str, Any]:
+    """Fixture providing a mock results response"""
+    return {
+        "request_id": "exec_test_123",
+        "status": "completed",
+        "output_files": [
+            {"path": "output.zarr", "size": 1024000},
+            {"path": "metadata.json", "size": 512},
+        ],
+        "completion_time": "2024-01-01T00:10:00+00:00",
+        "execution_time_seconds": 600.0,
+    }
diff --git a/test/serve/client/test_client.py b/test/serve/client/test_client.py
new file mode 100644
index 000000000..e55ed30fa
--- /dev/null
+++ b/test/serve/client/test_client.py
@@ -0,0 +1,444 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import io
+import json
+from datetime import datetime
+from unittest.mock import Mock, patch
+
+import pytest
+import requests  # type: ignore[import-untyped]
+
+from earth2studio.serve.client.client import Earth2StudioClient
+from earth2studio.serve.client.exceptions import (
+    APIConnectionError as ClientConnectionError,
+)
+from earth2studio.serve.client.exceptions import (
+    BadRequestError,
+    Earth2StudioAPIError,
+    InferenceRequestNotFoundError,
+    InternalServerError,
+    RequestTimeoutError,
+)
+from earth2studio.serve.client.models import (
+    HealthStatus,
+    InferenceRequest,
+    InferenceRequestResponse,
+    InferenceRequestResults,
+    InferenceRequestStatus,
+    OutputFile,
+    RequestStatus,
+)
+
+
+class TestEarth2StudioClientInitialization:
+    """Test Earth2StudioClient initialization"""
+
+    def test_client_initialization(self) -> None:
+        """Test client initialization with default and custom parameters"""
+        # Test defaults
+        client = Earth2StudioClient()
+        assert client.base_url == "http://localhost:8000"
+        assert client.workflow_name == "deterministic_earth2_workflow"
+        assert client.timeout == 30.0
+        assert client.session is not None
+
+        # Test custom params and trailing slash stripping
+        client = Earth2StudioClient(
+            base_url="https://api.example.com:8080/",
+            workflow_name="custom_workflow",
+            timeout=60.0,
+        )
+        assert client.base_url == "https://api.example.com:8080"
+        assert client.workflow_name == "custom_workflow"
+        assert client.timeout == 60.0
+
+
+class TestEarth2StudioClientHealthCheck:
+    """Test health_check method"""
+
+    def test_health_check(self) -> None:
+        """Test health check success and error handling"""
+        client = Earth2StudioClient()
+
+        # Test success
+        mock_response = {
+            "status": "healthy",
+            "timestamp": "2024-01-01T00:00:00+00:00",
+        }
+        with patch.object(client, "_make_request", return_value=mock_response):
+            health = client.health_check()
+            assert isinstance(health, HealthStatus)
+            assert health.status == "healthy"
+
+        # Test error
+        with patch.object(
+            client, "_make_request", side_effect=Earth2StudioAPIError("API error")
+        ):
+            with pytest.raises(Earth2StudioAPIError, match="API error"):
+                client.health_check()
+
+
+class TestEarth2StudioClientInferenceRequest:
+    """Test submit_inference_request method"""
+
+    def test_submit_inference_request(self) -> None:
+        """Test inference request submission with success and error cases"""
+        client = Earth2StudioClient()
+        request = InferenceRequest(
+            parameters={"forecast_times": ["2024-01-01T00:00:00"]}
+        )
+
+        # Test success
+        mock_response = {
+            "execution_id": "exec_123",
+            "status": "accepted",
+            "message": "Request accepted",
+            "timestamp": "2024-01-01T00:00:00+00:00",
+        }
+        with patch.object(client, "_make_request", return_value=mock_response):
+            response = client.submit_inference_request(request)
+            assert isinstance(response, InferenceRequestResponse)
+            assert response.execution_id == "exec_123"
+            assert response.status == RequestStatus.ACCEPTED
+
+        # Test bad request error
+        with patch.object(
+            client,
+            "_make_request",
+            side_effect=BadRequestError("Invalid parameters"),
+        ):
+            with pytest.raises(BadRequestError, match="Invalid parameters"):
+                client.submit_inference_request(request)
+
+
+class TestEarth2StudioClientGetRequestStatus:
+    """Test get_request_status method"""
+
+    def test_get_request_status(self) -> None:
+        """Test status retrieval for various states"""
+        client = Earth2StudioClient()
+
+        # Test running status with progress
+        mock_response = {
+            "execution_id": "exec_123",
+            "status": "running",
+            "progress": {
+                "progress": "Processing step 5 of 10",
+                "current_step": 5,
+                "total_steps": 10,
+            },
+            "error_message": None,
+        }
+        with patch.object(client, "_make_request", return_value=mock_response):
+            status = client.get_request_status("exec_123")
+            assert isinstance(status, InferenceRequestStatus)
+            assert status.status == RequestStatus.RUNNING
+            assert status.progress.current_step == 5
+
+        # Test failed status
+        mock_response["status"] = "failed"
+        mock_response["error_message"] = "Data source unavailable"
+        mock_response["progress"] = None
+        with patch.object(client, "_make_request", return_value=mock_response):
+            status = client.get_request_status("exec_123")
+            assert status.status == RequestStatus.FAILED
+            assert status.error_message == "Data source unavailable"
+
+        # Test not found
+        with patch.object(
+            client,
+            "_make_request",
+            side_effect=InferenceRequestNotFoundError("Request not found"),
+        ):
+            with pytest.raises(InferenceRequestNotFoundError):
+                client.get_request_status("nonexistent_id")
+
+
+class TestEarth2StudioClientGetRequestResults:
+    """Test get_request_results method"""
+
+    def test_get_request_results(self) -> None:
+        """Test results retrieval in various scenarios"""
+        client = Earth2StudioClient()
+
+        # Test successful results
+        mock_response_data = {
+            "request_id": "exec_123",
+            "status": "completed",
+            "output_files": [
+                {"path": "output.zarr", "size": 1024},
+                {"path": "metadata.json", "size": 256},
+            ],
+            "completion_time": "2024-01-01T00:10:00+00:00",
+            "execution_time_seconds": 600.0,
+        }
+        mock_http_response = Mock()
+        mock_http_response.status_code = 200
+        mock_http_response.json.return_value = mock_response_data
+
+        with patch.object(client, "_make_request", return_value=mock_http_response):
+            results = client.get_request_results("exec_123")
+            assert isinstance(results, InferenceRequestResults)
+            assert results.status == RequestStatus.COMPLETED
+            assert len(results.output_files) == 2
+
+        # Test still processing (202)
+        mock_http_response.status_code = 202
+        mock_http_response.json.return_value = {"message": "Still processing"}
+        with patch.object(client, "_make_request", return_value=mock_http_response):
+            with pytest.raises(Earth2StudioAPIError, match="[Ss]till processing"):
+                client.get_request_results("exec_123")
+
+        # Test custom timeout
+        mock_http_response.status_code = 200
+        mock_http_response.json.return_value = mock_response_data
+        with patch.object(
+            client, "_make_request", return_value=mock_http_response
+        ) as mock_req:
+            client.get_request_results("exec_123", timeout=120.0)
+            assert mock_req.call_args[1]["timeout"] == 120.0
+
+
+class TestEarth2StudioClientWaitForCompletion:
+    """Test wait_for_completion method"""
+
+    def test_wait_for_completion(self) -> None:
+        """Test waiting for completion with various outcomes"""
+        client = Earth2StudioClient()
+
+        # Test success after polling
+        status_responses = [
+            InferenceRequestStatus(
+                execution_id="exec_123",
+                status=RequestStatus.RUNNING,
+                progress=None,
+                error_message=None,
+            ),
+            InferenceRequestStatus(
+                execution_id="exec_123",
+                status=RequestStatus.COMPLETED,
+                progress=None,
+                error_message=None,
+            ),
+        ]
+        mock_results = InferenceRequestResults(
+            request_id="exec_123",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+        )
+        with (
+            patch.object(client, "get_request_status", side_effect=status_responses),
+            patch.object(client, "get_request_results", return_value=mock_results),
+            patch("time.sleep"),
+        ):
+            results = client.wait_for_completion("exec_123", poll_interval=1.0)
+            assert results.status == RequestStatus.COMPLETED
+
+        # Test timeout
+        status_response = InferenceRequestStatus(
+            execution_id="exec_123",
+            status=RequestStatus.RUNNING,
+            progress=None,
+            error_message=None,
+        )
+        with (
+            patch.object(client, "get_request_status", return_value=status_response),
+            patch("time.sleep"),
+            patch("time.time", side_effect=[0, 0, 5, 11]),
+        ):
+            with pytest.raises(RequestTimeoutError, match="did not complete within"):
+                client.wait_for_completion("exec_123", poll_interval=1.0, timeout=10.0)
+
+        # Test failed request
+        status_response.status = RequestStatus.FAILED
+        status_response.error_message = "Data source error"
+        with patch.object(client, "get_request_status", return_value=status_response):
+            with pytest.raises(Earth2StudioAPIError, match="failed"):
+                client.wait_for_completion("exec_123")
+
+        # Test cancelled request
+        status_response.status = RequestStatus.CANCELLED
+        with patch.object(client, "get_request_status", return_value=status_response):
+            with pytest.raises(Earth2StudioAPIError, match="was cancelled"):
+                client.wait_for_completion("exec_123")
+
+
+class TestEarth2StudioClientDownloadResult:
+    """Test download_result and related methods"""
+
+    def test_download_result(self) -> None:
+        """Test downloading result files with custom timeout"""
+        client = Earth2StudioClient(workflow_name="test_workflow")
+
+        result = InferenceRequestResults(
+            request_id="exec_123",
+            status=RequestStatus.COMPLETED,
+            output_files=[OutputFile(path="output.zarr", size=1024)],
+            completion_time=datetime.now(),
+        )
+
+        # Test result_root_path
+        path = client.result_root_path(result)
+        assert path == "/v1/infer/test_workflow/exec_123/results/"
+
+        # Test download
+        mock_http_response = Mock()
+        mock_http_response.content = b"mock file content"
+
+        with patch.object(
+            client, "_make_request", return_value=mock_http_response
+        ) as mock_req:
+            data = client.download_result(result, "output.zarr", timeout=300.0)
+            assert isinstance(data, io.BytesIO)
+            assert data.read() == b"mock file content"
+            assert mock_req.call_args[1]["timeout"] == 300.0
+
+
+class TestEarth2StudioClientMakeRequest:
+    """Test _make_request method"""
+
+    def test_make_request_success_and_errors(self) -> None:
+        """Test HTTP request handling for various scenarios"""
+        client = Earth2StudioClient()
+
+        # Test successful request
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.status_code = 200
+        mock_response.json.return_value = {"result": "success"}
+        with patch.object(client.session, "request", return_value=mock_response):
+            result = client._make_request("GET", "/test")
+            assert result == {"result": "success"}
+
+        # Test timeout
+        with patch.object(
+            client.session, "request", side_effect=requests.exceptions.Timeout()
+        ):
+            with pytest.raises(RequestTimeoutError, match="timed out"):
+                client._make_request("GET", "/test")
+
+        # Test connection error
+        with patch.object(
+            client.session,
+            "request",
+            side_effect=requests.exceptions.ConnectionError("Connection failed"),
+        ):
+            with pytest.raises(ClientConnectionError, match="Failed to connect"):
+                client._make_request("GET", "/test")
+
+    def test_make_request_http_errors(self) -> None:
+        """Test HTTP error status code handling"""
+        client = Earth2StudioClient()
+
+        # Test 400 Bad Request
+        mock_response = Mock()
+        mock_response.ok = False
+        mock_response.status_code = 400
+        mock_response.json.return_value = {"error": "Invalid parameters"}
+        with patch.object(client.session, "request", return_value=mock_response):
+            with pytest.raises(BadRequestError, match="Invalid parameters"):
+                client._make_request("POST", "/test")
+
+        # Test 404 Not Found
+        mock_response.status_code = 404
+        mock_response.json.return_value = {"error": "Not found"}
+        with patch.object(client.session, "request", return_value=mock_response):
+            with pytest.raises(InferenceRequestNotFoundError, match="Not found"):
+                client._make_request("GET", "/test")
+
+        # Test 500 Internal Server Error
+        mock_response.status_code = 500
+        mock_response.json.return_value = {"error": "Internal server error"}
+        with patch.object(client.session, "request", return_value=mock_response):
+            with pytest.raises(InternalServerError, match="Internal server error"):
+                client._make_request("GET", "/test")
+
+        # Test invalid JSON response
+        mock_response.ok = True
+        mock_response.status_code = 200
+        mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "", 0)
+        with patch.object(client.session, "request", return_value=mock_response):
+            with pytest.raises(Earth2StudioAPIError, match="Invalid JSON response"):
+                client._make_request("GET", "/test")
+
+    def test_make_request_return_response_and_stream(self) -> None:
+        """Test returning raw response and streaming"""
+        client = Earth2StudioClient()
+
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.status_code = 200
+        mock_response.content = b"streaming content"
+
+        # Test return_response
+        with patch.object(client.session, "request", return_value=mock_response):
+            result = client._make_request("GET", "/test", return_response=True)
+            assert result is mock_response
+
+        # Test stream with custom timeout
+        with patch.object(
+            client.session, "request", return_value=mock_response
+        ) as mock_req:
+            client._make_request(
+                "GET", "/test", return_response=True, stream=True, timeout=120.0
+            )
+            assert mock_req.call_args[1]["stream"] is True
+            assert mock_req.call_args[1]["timeout"] == 120.0
+
+
+class TestEarth2StudioClientContextManager:
+    """Test context manager protocol"""
+
+    def test_context_manager(self) -> None:
+        """Test using client as context manager"""
+        with Earth2StudioClient() as client:
+            assert isinstance(client, Earth2StudioClient)
+
+        # Test that session is closed on exit
+        client = Earth2StudioClient()
+        with patch.object(client.session, "close") as mock_close:
+            with client:
+                pass
+            mock_close.assert_called_once()
+
+
+class TestEarth2StudioClientParseErrorResponse:
+    """Test _parse_error_response method"""
+
+    def test_parse_error_response(self) -> None:
+        """Test parsing error responses with valid and invalid JSON"""
+        client = Earth2StudioClient()
+
+        # Test valid JSON
+        mock_response = Mock()
+        mock_response.json.return_value = {"error": "Test error", "details": "Details"}
+        result = client._parse_error_response(mock_response)
+        assert result == {"error": "Test error", "details": "Details"}
+
+        # Test invalid JSON with text
+        mock_response.status_code = 500
+        mock_response.text = "Plain text error"
+        mock_response.json.side_effect = json.JSONDecodeError("Invalid", "", 0)
+        result = client._parse_error_response(mock_response)
+        assert result == {"error": "Plain text error"}
+
+        # Test invalid JSON with empty text
+        mock_response.text = ""
+        result = client._parse_error_response(mock_response)
+        assert "HTTP 500 error" in result["error"]
diff --git a/test/serve/client/test_e2client.py b/test/serve/client/test_e2client.py
new file mode 100644
index 000000000..b1a2fc368
--- /dev/null
+++ b/test/serve/client/test_e2client.py
@@ -0,0 +1,663 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+from datetime import datetime
+from typing import Any
+from unittest.mock import MagicMock, Mock, patch
+
+import numpy as np
+import pytest
+import torch
+import xarray as xr
+
+from earth2studio.serve.client.e2client import (
+    InferenceOutputModel,
+    RemoteEarth2Workflow,
+    RemoteEarth2WorkflowResult,
+    _convert_time_to_lead_time,
+)
+from earth2studio.serve.client.exceptions import Earth2StudioAPIError
+from earth2studio.serve.client.models import (
+    InferenceRequestResponse,
+    InferenceRequestResults,
+    OutputFile,
+    RequestStatus,
+)
+
+
+class TestRemoteEarth2WorkflowInitialization:
+    """Test RemoteEarth2Workflow initialization"""
+
+    def test_initialization(self) -> None:
+        """Test workflow initialization with various device configurations"""
+        with patch("earth2studio.serve.client.e2client.Earth2StudioClient"):
+            # Test with CUDA available
+            with patch("torch.cuda.is_available", return_value=True):
+                workflow = RemoteEarth2Workflow(
+                    base_url="http://localhost:8000",
+                    workflow_name="test_workflow",
+                )
+                assert workflow.device == "cuda"
+
+            # Test with CUDA unavailable
+            with patch("torch.cuda.is_available", return_value=False):
+                workflow = RemoteEarth2Workflow(
+                    base_url="http://localhost:8000",
+                    workflow_name="test_workflow",
+                )
+                assert workflow.device == "cpu"
+
+            # Test with custom device and xr_args
+            xr_args = {"chunks": {"time": 1}}
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+                device="cpu",
+                xr_args=xr_args,
+            )
+            assert workflow.device == "cpu"
+            assert workflow.xr_args == xr_args
+            assert workflow.xr_args is not xr_args  # Verify it's a copy
+
+    def test_initialization_with_client_kwargs(self) -> None:
+        """Test workflow initialization passes kwargs to client"""
+        with patch(
+            "earth2studio.serve.client.e2client.Earth2StudioClient"
+        ) as mock_client_class:
+            RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+                timeout=60.0,
+                max_retries=5,
+            )
+            mock_client_class.assert_called_once_with(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+                timeout=60.0,
+                max_retries=5,
+            )
+
+
+class TestRemoteEarth2WorkflowCall:
+    """Test RemoteEarth2Workflow __call__ method"""
+
+    def test_call_submits_request(self) -> None:
+        """Test that calling workflow submits inference request with parameters"""
+        with patch(
+            "earth2studio.serve.client.e2client.Earth2StudioClient"
+        ) as mock_client_class:
+            mock_client = Mock()
+            mock_client_class.return_value = mock_client
+
+            mock_response = InferenceRequestResponse(
+                execution_id="exec_123",
+                status=RequestStatus.ACCEPTED,
+                message="Request accepted",
+                timestamp=datetime.now(),
+            )
+            mock_client.submit_inference_request.return_value = mock_response
+
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+            )
+
+            # Call with parameters
+            result = workflow(
+                forecast_times=["2024-01-01T00:00:00"],
+                nsteps=20,
+                model="fcn",
+            )
+
+            assert isinstance(result, RemoteEarth2WorkflowResult)
+            assert result.execution_id == "exec_123"
+
+            # Verify parameters were passed correctly
+            call_args = mock_client.submit_inference_request.call_args
+            request = call_args[0][0]
+            assert request.parameters["nsteps"] == 20
+            assert request.parameters["model"] == "fcn"
+
+
+class TestRemoteEarth2WorkflowTo:
+    """Test RemoteEarth2Workflow to() method"""
+
+    def test_to_device(self) -> None:
+        """Test moving workflow to different devices"""
+        with patch("earth2studio.serve.client.e2client.Earth2StudioClient"):
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+                device="cpu",
+            )
+
+            # Test with string
+            result = workflow.to("cuda")
+            assert workflow.device == "cuda"
+            assert result is workflow
+
+            # Test with torch.device
+            device = torch.device("cpu")
+            result = workflow.to(device)
+            assert workflow.device == device
+
+
+class TestRemoteEarth2WorkflowResult:
+    """Test RemoteEarth2WorkflowResult class"""
+
+    def test_result_caching(self) -> None:
+        """Test that result is cached after first retrieval"""
+        with patch(
+            "earth2studio.serve.client.e2client.Earth2StudioClient"
+        ) as mock_client_class:
+            mock_client = Mock()
+            mock_client_class.return_value = mock_client
+
+            mock_inference_result = InferenceRequestResults(
+                request_id="exec_123",
+                status=RequestStatus.COMPLETED,
+                output_files=[],
+                completion_time=datetime.now(),
+            )
+            mock_client.wait_for_completion.return_value = mock_inference_result
+
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+            )
+            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
+
+            # Multiple calls should use cache
+            result1 = result._get_result()
+            result2 = result._get_result()
+            assert result1 is result2
+            mock_client.wait_for_completion.assert_called_once()
+
+    def test_as_dataset_formats(self) -> None:
+        """Test as_dataset with different output formats"""
+        with patch(
+            "earth2studio.serve.client.e2client.Earth2StudioClient"
+        ) as mock_client_class:
+            mock_client = Mock()
+            mock_client.timeout = 30.0  # used by as_dataset for zarr_timeout
+            mock_client_class.return_value = mock_client
+
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+                xr_args={"decode_coords": False},
+            )
+
+            # Test Zarr format
+            with patch("xarray.open_zarr") as mock_open_zarr:
+                mock_inference_result = InferenceRequestResults(
+                    request_id="exec_123",
+                    status=RequestStatus.COMPLETED,
+                    output_files=[OutputFile(path="output.zarr", size=1024)],
+                    completion_time=datetime.now(),
+                )
+                mock_client.wait_for_completion.return_value = mock_inference_result
+                mock_client.result_root_path.return_value = (
+                    "/v1/infer/test/exec_123/results/"
+                )
+
+                mock_ds = xr.Dataset()
+                mock_open_zarr.return_value = mock_ds
+
+                result = RemoteEarth2WorkflowResult(workflow, "exec_123")
+                ds = result.as_dataset()
+
+                assert ds is mock_ds
+                # Verify xr_args were passed
+                assert mock_open_zarr.call_args[1]["decode_coords"] is False
+
+            # Test NetCDF format
+            with patch("xarray.open_dataset") as mock_open_dataset:
+                mock_inference_result = InferenceRequestResults(
+                    request_id="exec_123",
+                    status=RequestStatus.COMPLETED,
+                    output_files=[OutputFile(path="output.nc", size=512)],
+                    completion_time=datetime.now(),
+                )
+                mock_client.wait_for_completion.return_value = mock_inference_result
+                mock_client.download_result.return_value = MagicMock()
+
+                result = RemoteEarth2WorkflowResult(workflow, "exec_123")
+                result._result = None  # Reset cache
+                ds = result.as_dataset()
+
+                mock_client.download_result.assert_called_once()
+                mock_open_dataset.assert_called_once()
+
+    def test_as_dataset_no_outputs(self) -> None:
+        """Test as_dataset when no output files are available"""
+        with patch(
+            "earth2studio.serve.client.e2client.Earth2StudioClient"
+        ) as mock_client_class:
+            mock_client = Mock()
+            mock_client_class.return_value = mock_client
+
+            mock_inference_result = InferenceRequestResults(
+                request_id="exec_123",
+                status=RequestStatus.COMPLETED,
+                output_files=[],
+                completion_time=datetime.now(),
+            )
+            mock_client.wait_for_completion.return_value = mock_inference_result
+
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+            )
+            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
+
+            with pytest.raises(
+                Earth2StudioAPIError, match="did not return any outputs"
+            ):
+                result.as_dataset()
+
+    def test_as_data_source_and_model(self) -> None:
+        """Test as_data_source and as_model methods"""
+        with (
+            patch(
+                "earth2studio.serve.client.e2client.Earth2StudioClient"
+            ) as mock_client_class,
+            patch("xarray.open_zarr"),
+            patch(
+                "earth2studio.serve.client.e2client.InferenceOutputSource"
+            ) as mock_ios_class,
+        ):
+            mock_client = Mock()
+            mock_client.timeout = 30.0  # used by as_dataset for zarr_timeout
+            mock_client_class.return_value = mock_client
+
+            mock_inference_result = InferenceRequestResults(
+                request_id="exec_123",
+                status=RequestStatus.COMPLETED,
+                output_files=[OutputFile(path="output.zarr", size=1024)],
+                completion_time=datetime.now(),
+            )
+            mock_client.wait_for_completion.return_value = mock_inference_result
+            mock_client.result_root_path.return_value = (
+                "/v1/infer/test/exec_123/results/"
+            )
+
+            workflow = RemoteEarth2Workflow(
+                base_url="http://localhost:8000",
+                workflow_name="test_workflow",
+            )
+            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
+
+            # Test as_data_source
+            result.as_data_source()
+            mock_ios_class.assert_called_once()
+
+            # Test as_model with different iteration coordinates
+            model = result.as_model(iter_coord="lead_time")
+            assert isinstance(model, InferenceOutputModel)
+            assert model.iter_coord == "lead_time"
+
+            model = result.as_model(iter_coord="time")
+            assert model.iter_coord == "time"
+
+
+class TestConvertTimeToLeadTime:
+    """Test _convert_time_to_lead_time function"""
+
+    def test_convert_time_to_lead_time(self) -> None:
+        """Test converting time coordinate to lead_time"""
+        # Create test data - shape should match coord order: (time, lead_time, variable, lat, lon)
+        x = torch.randn(3, 1, 4, 5, 6)
+        start_time = np.datetime64("2024-01-01T00:00:00")
+        times = np.array(
+            [
+                np.datetime64("2024-01-01T00:00:00"),
+                np.datetime64("2024-01-01T06:00:00"),
+                np.datetime64("2024-01-01T12:00:00"),
+            ]
+        )
+
+        coords = OrderedDict(
+            {
+                "time": times,
+                "lead_time": np.array([np.timedelta64(0, "h")]),
+                "variable": np.array(["u10m", "v10m", "t2m", "tp"]),
+                "lat": np.arange(5),
+                "lon": np.arange(6),
+            }
+        )
+
+        x_new, coords_new = _convert_time_to_lead_time(x, coords, start_time)
+
+        # Verify transformation
+        assert coords_new["time"].shape == (1,)
+        assert coords_new["time"][0] == start_time
+        assert coords_new["lead_time"].shape == (3,)
+        # Verify lead times are correct
+        expected_lead_times = times - start_time
+        np.testing.assert_array_equal(coords_new["lead_time"], expected_lead_times)
+
+    def test_convert_time_to_lead_time_preserves_other_coords(self) -> None:
+        """Test that other coordinates are preserved"""
+        # Shape: (time, lead_time, variable, lat, lon)
+        x = torch.randn(2, 1, 3, 4, 5)
+        start_time = np.datetime64("2024-01-01T00:00:00")
+        times = np.array(
+            [
+                np.datetime64("2024-01-01T00:00:00"),
+                np.datetime64("2024-01-01T06:00:00"),
+            ]
+        )
+
+        coords = OrderedDict(
+            {
+                "time": times,
+                "lead_time": np.array([np.timedelta64(0, "h")]),
+                "variable": np.array(["u10m", "v10m", "t2m"]),
+                "lat": np.arange(4),
+                "lon": np.arange(5),
+            }
+        )
+
+        _, coords_new = _convert_time_to_lead_time(x, coords, start_time)
+
+        # Verify other coordinates are unchanged
+        np.testing.assert_array_equal(coords_new["variable"], coords["variable"])
+        np.testing.assert_array_equal(coords_new["lat"], coords["lat"])
+        np.testing.assert_array_equal(coords_new["lon"], coords["lon"])
+
+
+class TestInferenceOutputModel:
+    """Test InferenceOutputModel class"""
+
+    @pytest.fixture
+    def mock_data_source(self) -> Any:
+        """Create a mock InferenceOutputSource"""
+        mock_ds = Mock()
+
+        # Create mock DataArray with coordinates
+        times = np.array(
+            [
+                np.datetime64("2024-01-01T00:00:00"),
+                np.datetime64("2024-01-01T06:00:00"),
+            ]
+        )
+        variables = np.array(["u10m", "v10m", "t2m"])
+        lats = np.arange(10)
+        lons = np.arange(20)
+
+        # Create mock coordinate objects that support slicing
+        mock_time_coord = Mock()
+        mock_time_coord.values = times
+        mock_time_coord.__getitem__ = Mock(
+            side_effect=lambda key: Mock(values=times[key])
+        )
+
+        mock_lat_coord = Mock()
+        mock_lat_coord.values = lats
+
+        mock_lon_coord = Mock()
+        mock_lon_coord.values = lons
+
+        mock_da = Mock()
+        mock_da.coords = {
+            "time": mock_time_coord,
+            "variable": variables,
+            "lat": mock_lat_coord,
+            "lon": mock_lon_coord,
+        }
+
+        mock_ds.da = mock_da
+        return mock_ds
+
+    def test_initialization(self, mock_data_source: Any) -> None:
+        """Test InferenceOutputModel initialization with default and custom variables"""
+        # Test with default variables
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+        assert model.iter_coord == "lead_time"
+        assert model.device == "cpu"
+        np.testing.assert_array_equal(
+            model.variables, np.array(["u10m", "v10m", "t2m"])
+        )
+
+        # Test with custom variables
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            variables=["u10m", "v10m"],
+            device="cpu",
+        )
+        np.testing.assert_array_equal(model.variables, np.array(["u10m", "v10m"]))
+
+    def test_coords(self, mock_data_source: Any) -> None:
+        """Test input_coords and output_coords methods"""
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        # Test input_coords
+        input_coords = model.input_coords()
+        assert isinstance(input_coords, OrderedDict)
+        assert len(input_coords["time"]) == 0
+        assert len(input_coords["variable"]) == 0
+
+        # Test output_coords
+        output_coords = model.output_coords(OrderedDict())
+        assert isinstance(output_coords, OrderedDict)
+        np.testing.assert_array_equal(
+            output_coords["variable"], np.array(["u10m", "v10m", "t2m"])
+        )
+        assert len(output_coords["lat"]) == 10
+        assert len(output_coords["lon"]) == 20
+        # Time step should be inferred as 6 hours
+        assert output_coords["lead_time"][0] == np.timedelta64(6, "h")
+
+    def test_output_coords_single_time_step(self) -> None:
+        """Test output_coords with only one time step"""
+        mock_ds = Mock()
+
+        # Single time step
+        times = np.array([np.datetime64("2024-01-01T00:00:00")])
+
+        # Create mock coordinate that supports slicing
+        mock_time_coord = Mock()
+        mock_time_coord.values = times
+        mock_time_coord.__getitem__ = Mock(
+            side_effect=lambda key: Mock(values=times[key])
+        )
+
+        mock_lat_coord = Mock()
+        mock_lat_coord.values = np.arange(10)
+
+        mock_lon_coord = Mock()
+        mock_lon_coord.values = np.arange(20)
+
+        mock_da = Mock()
+        mock_da.coords = {
+            "time": mock_time_coord,
+            "variable": np.array(["u10m"]),
+            "lat": mock_lat_coord,
+            "lon": mock_lon_coord,
+        }
+        mock_ds.da = mock_da
+
+        model = InferenceOutputModel(
+            data_source=mock_ds,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        output_coords = model.output_coords(OrderedDict())
+
+        # Should use default placeholder of 6 hours
+        assert output_coords["lead_time"][0] == np.timedelta64(6, "h")
+
+    def test_to_device(self, mock_data_source: Any) -> None:
+        """Test to() method"""
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        result = model.to("cuda")
+        assert model.device == "cuda"
+        assert result is model
+
+    def test_call_method(self, mock_data_source: Any) -> None:
+        """Test __call__ method returns first item from iterator"""
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        mock_tensor = torch.randn(1, 1, 3, 10, 20)
+        mock_coords = OrderedDict({"time": np.array([np.datetime64("2024-01-01")])})
+
+        with patch.object(
+            model, "create_iterator", return_value=iter([(mock_tensor, mock_coords)])
+        ):
+            x, coords = model()
+            assert torch.is_tensor(x)
+            assert isinstance(coords, OrderedDict)
+
+    def test_create_iterator(self, mock_data_source: Any) -> None:
+        """Test create_iterator with and without lead_time conversion"""
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="time",
+            device="cpu",
+        )
+
+        # Mock fetch_data to return different data for each time
+        def mock_fetch_data(
+            source: Any, time: Any, variable: Any, device: Any
+        ) -> tuple[Any, Any]:
+            tensor = torch.randn(1, 3, 10, 20)
+            coords = OrderedDict(
+                {
+                    "time": time,
+                    "variable": variable,
+                    "lat": np.arange(10),
+                    "lon": np.arange(20),
+                }
+            )
+            return (tensor, coords)
+
+        with patch(
+            "earth2studio.serve.client.e2client.fetch_data",
+            side_effect=mock_fetch_data,
+        ):
+            iterator = model.create_iterator()
+            results = list(iterator)
+
+            # Should yield data for both time steps
+            assert len(results) == 2
+            for x, coords in results:
+                assert torch.is_tensor(x)
+                assert isinstance(coords, OrderedDict)
+
+    def test_create_iterator_with_lead_time_conversion(
+        self, mock_data_source: Any
+    ) -> None:
+        """Test create_iterator with lead_time coordinate conversion"""
+        model = InferenceOutputModel(
+            data_source=mock_data_source,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        mock_tensor = torch.randn(1, 3, 10, 20)
+        mock_coords = OrderedDict(
+            {
+                "time": np.array([np.datetime64("2024-01-01T00:00:00")]),
+                "variable": np.array(["u10m", "v10m", "t2m"]),
+                "lat": np.arange(10),
+                "lon": np.arange(20),
+                "lead_time": np.array([np.timedelta64(0, "h")]),
+            }
+        )
+
+        with (
+            patch(
+                "earth2studio.serve.client.e2client.fetch_data",
+                return_value=(mock_tensor, mock_coords),
+            ),
+            patch(
+                "earth2studio.serve.client.e2client._convert_time_to_lead_time",
+                return_value=(mock_tensor, mock_coords),
+            ) as mock_convert,
+        ):
+            iterator = model.create_iterator()
+            next(iterator)
+
+            # Verify conversion was called
+            mock_convert.assert_called_once()
+
+
+class TestInferenceOutputModelIntegration:
+    """Integration tests for InferenceOutputModel"""
+
+    def test_model_mimics_prognostic_interface(self) -> None:
+        """Test that InferenceOutputModel provides prognostic model interface"""
+        mock_ds = Mock()
+
+        times = np.array([np.datetime64("2024-01-01T00:00:00")])
+
+        # Create mock coordinate that supports slicing
+        mock_time_coord = Mock()
+        mock_time_coord.values = times
+        mock_time_coord.__getitem__ = Mock(
+            side_effect=lambda key: Mock(values=times[key])
+        )
+
+        mock_lat_coord = Mock()
+        mock_lat_coord.values = np.arange(10)
+
+        mock_lon_coord = Mock()
+        mock_lon_coord.values = np.arange(20)
+
+        mock_da = Mock()
+        mock_da.coords = {
+            "time": mock_time_coord,
+            "variable": np.array(["u10m"]),
+            "lat": mock_lat_coord,
+            "lon": mock_lon_coord,
+        }
+        mock_ds.da = mock_da
+
+        model = InferenceOutputModel(
+            data_source=mock_ds,
+            iter_coord="lead_time",
+            device="cpu",
+        )
+
+        # Verify interface methods exist and return correct types
+        assert hasattr(model, "input_coords")
+        assert hasattr(model, "output_coords")
+        assert hasattr(model, "to")
+        assert hasattr(model, "create_iterator")
+        assert callable(model)
+        assert isinstance(model.input_coords(), OrderedDict)
+        assert isinstance(model.output_coords(OrderedDict()), OrderedDict)
diff --git a/test/serve/client/test_fsspec_utils.py b/test/serve/client/test_fsspec_utils.py
new file mode 100644
index 000000000..ec0936d86
--- /dev/null
+++ b/test/serve/client/test_fsspec_utils.py
@@ -0,0 +1,378 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+
+from earth2studio.serve.client.fsspec_utils import (
+    SignedURLFileSystem,
+    create_cloudfront_mapper,
+    get_mapper,
+)
+from earth2studio.serve.client.models import (
+    InferenceRequestResults,
+    RequestStatus,
+    StorageType,
+)
+
+
+class TestSignedURLFileSystemInit:
+    """Test SignedURLFileSystem initialization."""
+
+    def test_init_stores_params(self) -> None:
+        """Init stores base_fs, query_params, base_url and builds query_string."""
+        base_fs = Mock()
+        query_params = {"Policy": "p", "Signature": "s", "Key-Pair-Id": "k"}
+        base_url = "https://example.com/store"
+        fs = SignedURLFileSystem(base_fs, query_params, base_url)
+        assert fs._fs is base_fs
+        assert fs._query_params == query_params
+        assert fs._base_url == base_url
+        assert "Policy=p" in fs._query_string
+        assert "Signature=s" in fs._query_string
+        assert "Key-Pair-Id=k" in fs._query_string
+
+
+class TestSignedURLFileSystemMakeSignedPath:
+    """Test _make_signed_path."""
+
+    def test_relative_path_appends_to_base_url(self) -> None:
+        """Relative path is joined with base_url and query string appended."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(
+            base_fs, {"Policy": "p", "Signature": "s"}, "https://example.com/zarr"
+        )
+        out = fs._make_signed_path("subdir/.zmetadata")
+        assert out.startswith("https://example.com/zarr/subdir/.zmetadata")
+        assert "Policy=p" in out
+        assert "Signature=s" in out
+        assert "?" in out
+
+    def test_relative_path_empty_uses_base_url_only(self) -> None:
+        """Empty relative path uses base_url only."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com/")
+        out = fs._make_signed_path("")
+        assert out in ("https://example.com/?Policy=p", "https://example.com?Policy=p")
+
+    def test_path_starting_with_http_unchanged_with_query_appended(self) -> None:
+        """Path starting with http is used as full URL; query params appended."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
+        out = fs._make_signed_path("https://cdn.example.com/path")
+        assert out.startswith("https://cdn.example.com/path")
+        assert "Policy=p" in out
+
+    def test_url_with_existing_query_uses_ampersand(self) -> None:
+        """If URL already has ?, separator is &."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com?foo=1")
+        out = fs._make_signed_path("x")
+        assert "&Policy=" in out or "&policy=" in out.lower()
+
+
+class TestSignedURLFileSystemHandle403:
+    """Test _handle_403."""
+
+    def test_403_raises_file_not_found(self) -> None:
+        """Exception containing 403 raises FileNotFoundError."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(FileNotFoundError, match="File not found: /path"):
+            fs._handle_403(Exception("HTTP 403 Forbidden"), "/path")
+
+    def test_forbidden_text_raises_file_not_found(self) -> None:
+        """Exception containing 'forbidden' raises FileNotFoundError."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(FileNotFoundError, match="File not found: /x"):
+            fs._handle_403(Exception("Access forbidden"), "/x")
+
+    def test_other_exception_reraises(self) -> None:
+        """Non-403 exception is reraised."""
+        base_fs = Mock()
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(ValueError, match="other"):
+            fs._handle_403(ValueError("other"), "/path")
+
+
+class TestSignedURLFileSystemOpen:
+    """Test _open."""
+
+    def test_open_success_returns_file_like(self) -> None:
+        """Successful _open returns result from base fs _open."""
+        base_fs = Mock()
+        base_fs._open.return_value = MagicMock()
+        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
+        result = fs._open("sub/file", mode="rb")
+        assert result is base_fs._open.return_value
+        base_fs._open.assert_called_once()
+        call_url = base_fs._open.call_args[0][0]
+        assert (
+            "sub/file" in call_url
+            or "sub%2Ffile" in call_url
+            or call_url.endswith("sub/file")
+        )
+        assert base_fs._open.call_args[1]["mode"] == "rb"
+
+    def test_open_403_raises_file_not_found(self) -> None:
+        """Base _open raising 403 leads to FileNotFoundError."""
+        base_fs = Mock()
+        base_fs._open.side_effect = Exception("403 Forbidden")
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(FileNotFoundError, match="File not found"):
+            fs._open("x")
+
+
+class TestSignedURLFileSystemCatFile:
+    """Test cat_file."""
+
+    def test_cat_file_success(self) -> None:
+        """cat_file success returns content from base fs."""
+        base_fs = Mock()
+        base_fs.cat_file.return_value = b"content"
+        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
+        result = fs.cat_file("path/to/file")
+        assert result == b"content"
+        base_fs.cat_file.assert_called_once()
+        assert "path/to/file" in base_fs.cat_file.call_args[0][0] or "path" in str(
+            base_fs.cat_file.call_args
+        )
+
+    def test_cat_file_with_start_end(self) -> None:
+        """cat_file passes start/end to base fs."""
+        base_fs = Mock()
+        base_fs.cat_file.return_value = b"xx"
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        fs.cat_file("f", start=0, end=10)
+        base_fs.cat_file.assert_called_once()
+        assert base_fs.cat_file.call_args[1]["start"] == 0
+        assert base_fs.cat_file.call_args[1]["end"] == 10
+
+    def test_cat_file_403_raises_file_not_found(self) -> None:
+        """cat_file 403 raises FileNotFoundError."""
+        base_fs = Mock()
+        base_fs.cat_file.side_effect = Exception("403")
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(FileNotFoundError):
+            fs.cat_file("x")
+
+
+class TestSignedURLFileSystemCatFileAsync:
+    """Test _cat_file (delegates to cat_file)."""
+
+    def test_cat_file_delegates_to_cat_file(self) -> None:
+        """_cat_file calls cat_file with same args."""
+        base_fs = Mock()
+        base_fs.cat_file.return_value = b"data"
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        result = fs._cat_file("a", start=1, end=2)
+        assert result == b"data"
+        base_fs.cat_file.assert_called_once()
+        assert base_fs.cat_file.call_args[1]["start"] == 1
+        assert base_fs.cat_file.call_args[1]["end"] == 2
+
+
+class TestSignedURLFileSystemInfo:
+    """Test info."""
+
+    def test_info_success(self) -> None:
+        """info returns metadata from base fs."""
+        base_fs = Mock()
+        base_fs.info.return_value = {"size": 100, "type": "file"}
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        result = fs.info("path")
+        assert result == {"size": 100, "type": "file"}
+
+    def test_info_403_raises_file_not_found(self) -> None:
+        """info 403 raises FileNotFoundError."""
+        base_fs = Mock()
+        base_fs.info.side_effect = Exception("Forbidden")
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(FileNotFoundError):
+            fs.info("x")
+
+
+class TestSignedURLFileSystemExists:
+    """Test exists."""
+
+    def test_exists_true(self) -> None:
+        """exists returns True when base fs returns True."""
+        base_fs = Mock()
+        base_fs.exists.return_value = True
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        assert fs.exists("path") is True
+
+    def test_exists_403_returns_false(self) -> None:
+        """exists returns False when base raises 403."""
+        base_fs = Mock()
+        base_fs.exists.side_effect = Exception("403 Forbidden")
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        assert fs.exists("path") is False
+
+    def test_exists_false_when_base_returns_false(self) -> None:
+        """exists returns False when base fs returns False."""
+        base_fs = Mock()
+        base_fs.exists.return_value = False
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        assert fs.exists("path") is False
+
+    def test_exists_other_exception_reraises(self) -> None:
+        """exists re-raises non-403 exceptions."""
+        base_fs = Mock()
+        base_fs.exists.side_effect = RuntimeError("network error")
+        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
+        with pytest.raises(RuntimeError, match="network error"):
+            fs.exists("path")
+
+
+class TestCreateCloudfrontMapper:
+    """Test create_cloudfront_mapper."""
+
+    def test_create_cloudfront_mapper_builds_mapper(self) -> None:
+        """create_cloudfront_mapper parses URL and returns FSMap with SignedURLFileSystem."""
+        signed_url = "https://cdn.example.com/bucket/path?Policy=pol&Signature=sig&Key-Pair-Id=kid"
+        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
+            mock_fs = Mock()
+            mock_fsspec.filesystem.return_value = mock_fs
+            mock_map = Mock()
+            mock_fsspec.mapping.FSMap = mock_map
+            result = create_cloudfront_mapper(signed_url)
+        mock_fsspec.filesystem.assert_called_once_with("https")
+        mock_map.assert_called_once()
+        call_kwargs = mock_map.call_args[1]
+        assert call_kwargs["root"] == ""
+        assert call_kwargs["check"] is False
+        assert call_kwargs["create"] is False
+        assert isinstance(call_kwargs["fs"], SignedURLFileSystem)
+        assert result is mock_map.return_value
+
+    def test_create_cloudfront_mapper_with_zarr_path(self) -> None:
+        """create_cloudfront_mapper with zarr_path appends to base URL."""
+        signed_url = "https://cdn.example.com/bucket?Policy=p&Signature=s&Key-Pair-Id=k"
+        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
+            mock_fs = Mock()
+            mock_fsspec.filesystem.return_value = mock_fs
+            mock_map = Mock()
+            mock_fsspec.mapping.FSMap = mock_map
+            create_cloudfront_mapper(signed_url, zarr_path="results.zarr")
+        fs_arg = mock_map.call_args[1]["fs"]
+        assert isinstance(fs_arg, SignedURLFileSystem)
+        assert "results.zarr" in fs_arg._base_url
+
+    def test_create_cloudfront_mapper_strips_trailing_wildcard(self) -> None:
+        """URL path with trailing /* or * is stripped."""
+        signed_url = (
+            "https://cdn.example.com/bucket/*?Policy=p&Signature=s&Key-Pair-Id=k"
+        )
+        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
+            mock_fs = Mock()
+            mock_fsspec.filesystem.return_value = mock_fs
+            mock_fsspec.mapping.FSMap = Mock()
+            create_cloudfront_mapper(signed_url)
+        fs_arg = mock_fsspec.mapping.FSMap.call_args[1]["fs"]
+        assert "*" not in fs_arg._base_url
+        assert fs_arg._base_url.rstrip("/").endswith("bucket")
+
+
+class TestGetMapper:
+    """Test get_mapper."""
+
+    def test_get_mapper_server_returns_none(self) -> None:
+        """get_mapper returns None for SERVER storage."""
+        result = InferenceRequestResults(
+            request_id="r1",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+            storage_type=StorageType.SERVER,
+        )
+        assert get_mapper(result) is None
+
+    def test_get_mapper_s3_with_signed_url_returns_mapper(self) -> None:
+        """get_mapper with S3 and signed_url returns mapper from create_cloudfront_mapper."""
+        result = InferenceRequestResults(
+            request_id="r1",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+            storage_type=StorageType.S3,
+            signed_url="https://cdn.example.com/path?Policy=p&Signature=s&Key-Pair-Id=k",
+        )
+        with patch(
+            "earth2studio.serve.client.fsspec_utils.create_cloudfront_mapper"
+        ) as mock_create:
+            mock_create.return_value = Mock()
+            out = get_mapper(result)
+            assert out is mock_create.return_value
+            mock_create.assert_called_once_with(result.signed_url, "")
+
+    def test_get_mapper_s3_with_zarr_path_passes_through(self) -> None:
+        """get_mapper passes zarr_path to create_cloudfront_mapper."""
+        result = InferenceRequestResults(
+            request_id="r1",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+            storage_type=StorageType.S3,
+            signed_url="https://cdn.example.com?Policy=p&Signature=s&Key-Pair-Id=k",
+        )
+        with patch(
+            "earth2studio.serve.client.fsspec_utils.create_cloudfront_mapper"
+        ) as mock_create:
+            mock_create.return_value = Mock()
+            get_mapper(result, zarr_path="results.zarr")
+            mock_create.assert_called_once_with(result.signed_url, "results.zarr")
+
+    def test_get_mapper_s3_without_signed_url_raises(self) -> None:
+        """get_mapper with S3 and no signed_url raises ValueError."""
+        result = InferenceRequestResults(
+            request_id="r1",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+            storage_type=StorageType.S3,
+            signed_url=None,
+        )
+        with pytest.raises(ValueError, match="S3 storage type requires a signed URL"):
+            get_mapper(result)
+
+    def test_get_mapper_unsupported_storage_raises(self) -> None:
+        """get_mapper with unsupported storage type raises ValueError."""
+        result = InferenceRequestResults(
+            request_id="r1",
+            status=RequestStatus.COMPLETED,
+            output_files=[],
+            completion_time=datetime.now(),
+            storage_type=StorageType.SERVER,  # will override via monkeypatch for unsupported
+        )
+        # Use a storage type that doesn't exist in enum by creating a mock with invalid type
+        with patch.object(result, "storage_type", "invalid"):
+            # StorageType.SERVER is enum; "invalid" would be if we had another enum value.
+            # Instead simulate by patching the comparison: get_mapper checks
+            # request_result.storage_type == StorageType.S3 and == StorageType.SERVER.
+            # So unsupported = something that is neither. We need a value that is not
+            # StorageType.S3 and not StorageType.SERVER. StorageType only has SERVER and S3.
+            # So we need to pass something that fails both. E.g. a mock with storage_type
+            # that returns False for both. Or add a fake enum value. Easiest: patch
+            # StorageType to add a third value temporarily, or pass a mock object.
+            mock_result = Mock()
+            mock_result.storage_type = "unsupported_type"
+            with pytest.raises(ValueError, match="Unsupported storage type"):
+                get_mapper(mock_result)
diff --git a/test/serve/conftest.py b/test/serve/conftest.py
deleted file mode 100644
index 8eacca441..000000000
--- a/test/serve/conftest.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Shared pytest configuration for service tests.
-
-This conftest.py ensures that the service/inferenceserver directory is in the
-Python path so that api_server modules can be imported.
-"""
-
-import sys
-from pathlib import Path
-
-# Add service/inferenceserver to Python path for imports
-_inferenceserver_path = (
-    Path(__file__).parent.parent.parent / "service" / "inferenceserver"
-)
-if str(_inferenceserver_path) not in sys.path:
-    sys.path.insert(0, str(_inferenceserver_path))
-
-# Store original api_server.config module to restore it if it gets mocked
-_original_config_module = None
-
-
-def pytest_configure(config):
-    """Store the original api_server.config module before any tests run"""
-    global _original_config_module
-    if "api_server.config" in sys.modules:
-        _original_config_module = sys.modules["api_server.config"]
-
-
-def pytest_runtest_setup(item):
-    """Restore the real api_server.config module before test_config.py tests"""
-    # Only restore for test_config.py tests to avoid interfering with other tests
-    if "test_config" in str(item.fspath):
-        import importlib
-
-        # Remove any mock that might have been set by other test files
-        if "api_server.config" in sys.modules:
-            # Check if it's a mock (Mock objects have _mock_name attribute or are Mock instances)
-            config_module = sys.modules["api_server.config"]
-            is_mock = (
-                hasattr(config_module, "_mock_name")
-                or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
-                or (
-                    hasattr(config_module, "__class__")
-                    and "Mock" in str(config_module.__class__)
-                )
-            )
-            if is_mock:
-                # It's a mock, restore the real module
-                # First, clear any references in parent modules
-                if "api_server" in sys.modules:
-                    api_server_module = sys.modules["api_server"]
-                    if (
-                        hasattr(api_server_module, "config")
-                        and api_server_module.config is config_module
-                    ):
-                        delattr(api_server_module, "config")
-                # Remove the mock from sys.modules
-                del sys.modules["api_server.config"]
-                # Also clear any submodules that might reference the mock
-                modules_to_remove = [
-                    key
-                    for key in sys.modules.keys()
-                    if key.startswith("api_server.config.")
-                ]
-                for key in modules_to_remove:
-                    del sys.modules[key]
-        # Force reload the real module to ensure fresh state
-        if "api_server.config" not in sys.modules:
-            importlib.import_module("api_server.config")
-        else:
-            # Only reload if it was a mock, otherwise just ensure state is correct
-            config_module = sys.modules["api_server.config"]
-            is_mock = (
-                hasattr(config_module, "_mock_name")
-                or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
-            )
-            if is_mock:
-                importlib.reload(sys.modules["api_server.config"])
-        # Ensure _config_manager and ConfigManager._instance are properly initialized
-        real_config = sys.modules["api_server.config"]
-        # Check if _config_manager is a mock and reset if needed
-        cm = getattr(real_config, "_config_manager", None)
-        if cm is not None and (
-            hasattr(cm, "_mock_name") or str(type(cm)) == "<class 'unittest.mock.Mock'>"
-        ):
-            real_config._config_manager = None
-        # Check if ConfigManager._instance is a mock and reset if needed
-        if hasattr(real_config, "ConfigManager") and hasattr(
-            real_config.ConfigManager, "_instance"
-        ):
-            inst = getattr(real_config.ConfigManager, "_instance", None)
-            if inst is not None and (
-                hasattr(inst, "_mock_name")
-                or str(type(inst)) == "<class 'unittest.mock.Mock'>"
-            ):
-                real_config.ConfigManager._instance = None
diff --git a/test/serve/server/conftest.py b/test/serve/server/conftest.py
new file mode 100644
index 000000000..197a365e5
--- /dev/null
+++ b/test/serve/server/conftest.py
@@ -0,0 +1,88 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+from pathlib import Path
+
+# Repo root is parent of test/serve/server; ensure project is importable
+_repo_root = Path(__file__).resolve().parent.parent.parent
+if str(_repo_root) not in sys.path:
+    sys.path.insert(0, str(_repo_root))
+
+# Store original earth2studio.serve.server.config module to restore if mocked
+_original_config_module = None
+
+_CONFIG_MODULE = "earth2studio.serve.server.config"
+
+
+def pytest_configure(config):
+    """Store the original config module before any tests run"""
+    global _original_config_module
+    if _CONFIG_MODULE in sys.modules:
+        _original_config_module = sys.modules[_CONFIG_MODULE]
+
+
+def pytest_runtest_setup(item):
+    """Restore the real config module before test_config.py tests"""
+    if "test_config" not in str(item.fspath):
+        return
+    import importlib
+
+    if _CONFIG_MODULE in sys.modules:
+        config_module = sys.modules[_CONFIG_MODULE]
+        is_mock = (
+            hasattr(config_module, "_mock_name")
+            or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
+            or (
+                hasattr(config_module, "__class__")
+                and "Mock" in str(config_module.__class__)
+            )
+        )
+        if is_mock:
+            if "earth2studio.serve.server" in sys.modules:
+                server_module = sys.modules["earth2studio.serve.server"]
+                if (
+                    hasattr(server_module, "config")
+                    and server_module.config is config_module
+                ):
+                    delattr(server_module, "config")
+            del sys.modules[_CONFIG_MODULE]
+            modules_to_remove = [
+                k for k in sys.modules if k.startswith(_CONFIG_MODULE + ".")
+            ]
+            for k in modules_to_remove:
+                del sys.modules[k]
+    if _CONFIG_MODULE not in sys.modules:
+        importlib.import_module(_CONFIG_MODULE)
+    else:
+        config_module = sys.modules[_CONFIG_MODULE]
+        is_mock = (
+            hasattr(config_module, "_mock_name")
+            or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
+        )
+        if is_mock:
+            importlib.reload(sys.modules[_CONFIG_MODULE])
+    real_config = sys.modules[_CONFIG_MODULE]
+    # Check if ConfigManager._instance is a mock and reset if needed
+    if hasattr(real_config, "ConfigManager") and hasattr(
+        real_config.ConfigManager, "_instance"
+    ):
+        inst = getattr(real_config.ConfigManager, "_instance", None)
+        if inst is not None and (
+            hasattr(inst, "_mock_name")
+            or str(type(inst)) == "<class 'unittest.mock.Mock'>"
+        ):
+            real_config.ConfigManager._instance = None
diff --git a/test/serve/server/test_cleanup_daemon.py b/test/serve/server/test_cleanup_daemon.py
new file mode 100644
index 000000000..fb4800639
--- /dev/null
+++ b/test/serve/server/test_cleanup_daemon.py
@@ -0,0 +1,775 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+from datetime import datetime, timedelta, timezone
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from earth2studio.serve.server.cleanup_daemon import (
+    _delete_result_files,
+    _process_expired_key,
+    cleanup_expired_results,
+    main,
+)
+from earth2studio.serve.server.config import get_config
+from earth2studio.serve.server.workflow import WorkflowStatus
+
+
+class TestDeleteResultFiles:
+    """Test suite for _delete_result_files function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    def test_delete_workflow_files_with_workflow_name(self, mock_redis):
+        """Test deletion of workflow execution files including workflow-specific directories and metadata"""
+        # Setup
+        workflow_name = "custom_workflow"
+        execution_id = "exec_456"
+        combined_id = f"{workflow_name}:{execution_id}"
+        zip_filename = f"workflow_{combined_id}.zip"
+
+        mock_redis.get.return_value = zip_filename
+
+        # Mock zip file
+        mock_zip_file = MagicMock()
+        mock_zip_file.exists.return_value = True
+        mock_zip_file.unlink = MagicMock()
+
+        # Mock metadata file for workflow
+        mock_metadata_file = MagicMock()
+        mock_metadata_file.is_file.return_value = True
+        mock_metadata_file.is_dir.return_value = False
+        mock_metadata_file.name = f"metadata_{combined_id}.json"
+        mock_metadata_file.unlink = MagicMock()
+
+        # Mock workflow directory
+        mock_workflow_dir = MagicMock()
+        mock_workflow_dir.exists.return_value = True
+
+        mock_workflow_subdir = MagicMock()
+        mock_workflow_subdir.is_dir.return_value = True
+        mock_workflow_subdir.is_file.return_value = False
+        mock_workflow_subdir.name = f"output_{execution_id}"
+
+        mock_workflow_dir.iterdir.return_value = [mock_workflow_subdir]
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_file
+        mock_zip_dir.iterdir.return_value = [mock_metadata_file]
+
+        with patch(
+            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
+        ) as mock_rmtree:
+            # Execute
+            _delete_result_files(
+                mock_redis,
+                combined_id,
+                execution_id,
+                mock_output_dir,
+                mock_zip_dir,
+                workflow_name=workflow_name,
+            )
+
+            # Verify zip file deleted
+            mock_zip_file.unlink.assert_called_once()
+
+            # Verify metadata file deleted
+            mock_metadata_file.unlink.assert_called_once()
+
+            # Verify workflow-specific directory deleted
+            mock_rmtree.assert_called_once_with(mock_workflow_subdir)
+
+    def test_delete_files_no_zip_in_redis(self, mock_redis):
+        """Test deletion when no zip file is found in Redis"""
+        # Setup
+        result_id = "result_789"
+        mock_redis.get.return_value = None
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        # Execute - should not raise exception
+        _delete_result_files(
+            mock_redis, result_id, result_id, mock_output_dir, mock_zip_dir
+        )
+
+        # Verify Redis was queried
+        mock_redis.get.assert_called_once()
+
+    def test_delete_result_files_zip_file_missing(self, mock_redis):
+        """When zip filename is in Redis but file does not exist, no unlink is called."""
+        result_id = "req_123"
+        mock_redis.get.return_value = "missing.zip"
+
+        mock_zip_path = MagicMock()
+        mock_zip_path.exists.return_value = False
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_path
+
+        _delete_result_files(
+            mock_redis, result_id, result_id, mock_output_dir, mock_zip_dir
+        )
+
+        mock_zip_path.unlink.assert_not_called()
+
+    def test_delete_result_files_legacy_path_deletes_metadata_and_dirs(
+        self, mock_redis
+    ):
+        """Without workflow_name, still deletes metadata files and raw/zip dirs matching search_id."""
+        search_id = "exec_789"
+        result_id = search_id
+        mock_redis.get.return_value = None
+
+        mock_metadata_file = MagicMock()
+        mock_metadata_file.is_file.return_value = True
+        mock_metadata_file.is_dir.return_value = False
+        mock_metadata_file.name = f"metadata_{search_id}.json"
+        mock_metadata_file.unlink = MagicMock()
+
+        mock_raw_dir = MagicMock()
+        mock_raw_dir.is_dir.return_value = True
+        mock_raw_dir.name = f"output_{search_id}"
+        mock_raw_dir.__str__ = lambda self: f"output_{search_id}"
+
+        mock_zip_subdir = MagicMock()
+        mock_zip_subdir.is_dir.return_value = True
+        mock_zip_subdir.name = f"results_{search_id}"
+        mock_zip_subdir.__str__ = lambda self: f"results_{search_id}"
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = [mock_raw_dir]
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = [mock_metadata_file, mock_zip_subdir]
+
+        with patch(
+            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
+        ) as mock_rmtree:
+            _delete_result_files(
+                mock_redis,
+                result_id,
+                search_id,
+                mock_output_dir,
+                mock_zip_dir,
+            )
+
+        mock_metadata_file.unlink.assert_called_once()
+        assert mock_rmtree.call_count == 2  # raw dir + zip_dir subdir
+        rmtree_calls = {str(c[0][0]) for c in mock_rmtree.call_args_list}
+        assert f"output_{search_id}" in rmtree_calls or mock_rmtree.call_count == 2
+
+    def test_delete_result_files_workflow_dir_not_exists(self, mock_redis):
+        """When workflow_name is set but workflow subdirectory does not exist, no rmtree on it."""
+        workflow_name = "my_wf"
+        execution_id = "exec_1"
+        combined_id = f"{workflow_name}:{execution_id}"
+        mock_redis.get.return_value = None
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_workflow_dir = MagicMock()
+        mock_workflow_dir.exists.return_value = False  # workflow dir not present
+        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        with patch(
+            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
+        ) as mock_rmtree:
+            _delete_result_files(
+                mock_redis,
+                combined_id,
+                execution_id,
+                mock_output_dir,
+                mock_zip_dir,
+                workflow_name=workflow_name,
+            )
+
+        mock_workflow_dir.iterdir.assert_not_called()
+        mock_rmtree.assert_not_called()
+
+
+class TestProcessExpiredKey:
+    """Test suite for _process_expired_key function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    @pytest.fixture
+    def mock_delete_func(self):
+        """Create a mock delete function"""
+        return MagicMock()
+
+    def test_process_expired_key_success(self, mock_redis, mock_delete_func):
+        """Test successful processing of an expired key"""
+        # Setup
+        key = "workflow_execution:wf1:exec_123"
+        current_time = datetime.now(timezone.utc)
+        old_time = current_time - timedelta(hours=25)  # 25 hours ago
+        results_ttl_hours = 24
+
+        request_data = {
+            "status": WorkflowStatus.COMPLETED,
+            "end_time": old_time.isoformat(),
+            "execution_id": "exec_123",
+        }
+
+        mock_redis.get.return_value = json.dumps(request_data)
+        config = get_config()
+
+        # Execute
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key=key,
+            current_time=current_time,
+            results_ttl_hours=results_ttl_hours,
+            retention_ttl=config.redis.retention_ttl,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        # Verify
+        assert result is True
+        mock_delete_func.assert_called_once_with(mock_redis, key)
+        mock_redis.setex.assert_called_once()
+        args = mock_redis.setex.call_args[0]
+        assert args[0] == key
+        assert args[1] == config.redis.retention_ttl
+        updated_data = json.loads(args[2])
+        assert updated_data["status"] == WorkflowStatus.EXPIRED
+
+    def test_process_expired_key_not_expired(self, mock_redis, mock_delete_func):
+        """Test processing of a key that is not yet expired"""
+        # Setup
+        key = "workflow_execution:wf1:exec_456"
+        current_time = datetime.now(timezone.utc)
+        recent_time = current_time - timedelta(hours=12)  # 12 hours ago
+        results_ttl_hours = 24
+
+        request_data = {
+            "status": WorkflowStatus.COMPLETED,
+            "end_time": recent_time.isoformat(),
+            "execution_id": "exec_456",
+        }
+
+        mock_redis.get.return_value = json.dumps(request_data)
+
+        # Execute
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key=key,
+            current_time=current_time,
+            results_ttl_hours=results_ttl_hours,
+            retention_ttl=604800,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        # Verify - should not clean up
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+    def test_process_expired_key_key_missing(self, mock_redis, mock_delete_func):
+        """When Redis returns None for the key, return False without calling delete."""
+        mock_redis.get.return_value = None
+
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key="workflow_execution:wf1:exec_123",
+            current_time=datetime.now(timezone.utc),
+            results_ttl_hours=24,
+            retention_ttl=604800,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+    def test_process_expired_key_wrong_status(self, mock_redis, mock_delete_func):
+        """When status is not expected (e.g. RUNNING), return False."""
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=25)).isoformat()
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.RUNNING, "end_time": old_time}
+        )
+
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key="workflow_execution:wf1:exec_123",
+            current_time=current_time,
+            results_ttl_hours=24,
+            retention_ttl=604800,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+    def test_process_expired_key_no_end_time(self, mock_redis, mock_delete_func):
+        """When end_time and completion_time are missing, return False."""
+        mock_redis.get.return_value = json.dumps({"status": WorkflowStatus.COMPLETED})
+
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key="workflow_execution:wf1:exec_123",
+            current_time=datetime.now(timezone.utc),
+            results_ttl_hours=24,
+            retention_ttl=604800,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+    def test_process_expired_key_uses_completion_time_fallback(
+        self, mock_redis, mock_delete_func
+    ):
+        """When end_time is missing but completion_time is present, use it and expire."""
+        config = get_config()
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+        mock_redis.get.return_value = json.dumps(
+            {
+                "status": WorkflowStatus.COMPLETED,
+                "completion_time": old_time,
+            }
+        )
+
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key="workflow_execution:wf1:exec_old",
+            current_time=current_time,
+            results_ttl_hours=24,
+            retention_ttl=config.redis.retention_ttl,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        assert result is True
+        mock_delete_func.assert_called_once()
+        mock_redis.setex.assert_called_once()
+        updated = json.loads(mock_redis.setex.call_args[0][2])
+        assert updated["status"] == WorkflowStatus.EXPIRED
+
+    def test_process_expired_key_invalid_timestamp(self, mock_redis, mock_delete_func):
+        """When timestamp is invalid, log warning and return False."""
+        mock_redis.get.return_value = json.dumps(
+            {
+                "status": WorkflowStatus.COMPLETED,
+                "end_time": "not-a-valid-timestamp",
+            }
+        )
+
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key="workflow_execution:wf1:exec_123",
+            current_time=datetime.now(timezone.utc),
+            results_ttl_hours=24,
+            retention_ttl=604800,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+
+class TestCleanupExpiredResults:
+    """Test suite for cleanup_expired_results function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    @pytest.fixture
+    def cleanup_args(self):
+        """Default args for cleanup_expired_results (no module-level config)."""
+        config = get_config()
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+            exists=lambda: False
+        )
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+        return {
+            "results_ttl_hours": config.server.results_ttl_hours,
+            "retention_ttl": config.redis.retention_ttl,
+            "default_output_dir": mock_output_dir,
+            "results_zip_dir": mock_zip_dir,
+        }
+
+    def test_cleanup_expired_results_workflow_only(self, mock_redis, cleanup_args):
+        """Test cleanup with workflow executions only"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+        recent_time = (current_time - timedelta(hours=12)).isoformat()
+
+        # Mock Redis keys - only workflow keys
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf1:exec_old",
+            "workflow_execution:wf2:exec_recent",
+        ]
+
+        # Mock Redis get responses
+        def get_side_effect(key):
+            if key == "workflow_execution:wf1:exec_old":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+                )
+            elif key == "workflow_execution:wf2:exec_recent":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
+            # Execute
+            cleanup_expired_results(mock_redis, **cleanup_args)
+
+            # Verify - should process 1 workflow (exec_old), skip exec_recent
+            assert mock_redis.setex.call_count == 1
+
+            # Verify the expired key was updated
+            setex_calls = mock_redis.setex.call_args_list
+            expired_keys = [call[0][0] for call in setex_calls]
+            assert "workflow_execution:wf1:exec_old" in expired_keys
+
+    def test_cleanup_expired_results_no_keys(self, mock_redis, cleanup_args):
+        """Test cleanup when there are no keys in Redis"""
+        # Setup
+        mock_redis.keys.return_value = []
+
+        # Execute
+        cleanup_expired_results(mock_redis, **cleanup_args)
+
+        # Verify - no cleanup operations
+        mock_redis.setex.assert_not_called()
+
+    def test_cleanup_expired_results_all_recent(self, mock_redis, cleanup_args):
+        """Test cleanup when all workflow keys are recent (not expired)"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        recent_time = (current_time - timedelta(hours=5)).isoformat()
+
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf1:exec_1",
+            "workflow_execution:wf2:exec_2",
+        ]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
+        )
+
+        # Execute
+        cleanup_expired_results(mock_redis, **cleanup_args)
+
+        # Verify - no cleanup operations (not expired)
+        mock_redis.setex.assert_not_called()
+
+    def test_cleanup_expired_results_redis_get_failure(self, mock_redis, cleanup_args):
+        """Test cleanup handles Redis get failures gracefully"""
+        # Setup
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf_fail:exec_1",
+            "workflow_execution:wf_ok:exec_2",
+        ]
+
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        # First key causes exception, second succeeds
+        def get_side_effect(key):
+            if key == "workflow_execution:wf_fail:exec_1":
+                raise Exception("Redis connection error")
+            elif key == "workflow_execution:wf_ok:exec_2":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        with (patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"),):
+            # Execute - should not raise exception
+            cleanup_expired_results(mock_redis, **cleanup_args)
+
+            # Verify - exec_2 should still be processed despite exec_1 error
+            assert mock_redis.setex.call_count == 1
+            assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf_ok:exec_2"
+
+    def test_cleanup_expired_results_delete_files_failure(
+        self, mock_redis, cleanup_args
+    ):
+        """Test cleanup handles file deletion failures gracefully and does not mark as expired"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        mock_redis.keys.return_value = ["workflow_execution:wf_fail:exec_fail"]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+        )
+
+        with patch(
+            "earth2studio.serve.server.cleanup_daemon._delete_result_files"
+        ) as mock_delete:
+            mock_delete.side_effect = Exception("File deletion error")
+
+            # Execute - should handle exception and continue
+            cleanup_expired_results(mock_redis, **cleanup_args)
+
+            # Verify delete was attempted
+            mock_delete.assert_called_once()
+
+            # Status should NOT be updated if file deletion fails
+            # (we want to retry cleanup on next run)
+            assert mock_redis.setex.call_count == 0
+
+    def test_cleanup_expired_results_workflow_execution(self, mock_redis, cleanup_args):
+        """Test cleanup of workflow execution keys"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        mock_redis.keys.return_value = ["workflow_execution:my_workflow:exec_123"]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+        )
+
+        with patch(
+            "earth2studio.serve.server.cleanup_daemon._delete_result_files"
+        ) as mock_delete:
+            # Execute
+            cleanup_expired_results(mock_redis, **cleanup_args)
+
+            # Verify workflow was cleaned up
+            assert mock_redis.setex.call_count == 1
+            assert (
+                mock_redis.setex.call_args[0][0]
+                == "workflow_execution:my_workflow:exec_123"
+            )
+
+            # Verify delete was called with correct parameters
+            mock_delete.assert_called_once()
+            call_args = mock_delete.call_args
+            assert call_args[1]["result_id"] == "my_workflow:exec_123"
+            assert call_args[1]["search_id"] == "exec_123"
+            assert call_args[1]["workflow_name"] == "my_workflow"
+
+    def test_cleanup_expired_results_custom_ttl_config(self, mock_redis):
+        """Test cleanup with custom TTL configuration"""
+        # Custom 48 hour TTL (pass as args; no module-level config)
+        config = get_config()
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+            exists=lambda: False
+        )
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        current_time = datetime.now(timezone.utc)
+        # 30 hours ago - should NOT be expired with 48 hour TTL
+        time_30h_ago = (current_time - timedelta(hours=30)).isoformat()
+        # 50 hours ago - should be expired with 48 hour TTL
+        time_50h_ago = (current_time - timedelta(hours=50)).isoformat()
+
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf_30h:exec_30h",
+            "workflow_execution:wf_50h:exec_50h",
+        ]
+
+        def get_side_effect(key):
+            if key == "workflow_execution:wf_30h:exec_30h":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": time_30h_ago}
+                )
+            elif key == "workflow_execution:wf_50h:exec_50h":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": time_50h_ago}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
+            # Execute with 48 hour TTL
+            cleanup_expired_results(
+                mock_redis,
+                results_ttl_hours=48,
+                retention_ttl=config.redis.retention_ttl,
+                default_output_dir=mock_output_dir,
+                results_zip_dir=mock_zip_dir,
+            )
+
+            # Verify - only exec_50h should be cleaned up
+            assert mock_redis.setex.call_count == 1
+            assert (
+                mock_redis.setex.call_args[0][0] == "workflow_execution:wf_50h:exec_50h"
+            )
+
+    def test_cleanup_expired_results_malformed_key_skipped(
+        self, mock_redis, cleanup_args
+    ):
+        """Keys with fewer than 3 parts (e.g. 'workflow_execution:incomplete') are skipped."""
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        mock_redis.keys.return_value = [
+            "workflow_execution:incomplete",  # only 2 parts
+            "workflow_execution:wf1:exec_old",
+        ]
+
+        def get_side_effect(key):
+            if key == "workflow_execution:wf1:exec_old":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
+            cleanup_expired_results(mock_redis, **cleanup_args)
+
+            # Only the valid key (exec_old) should be processed; malformed key is skipped
+        assert mock_redis.setex.call_count == 1
+        assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf1:exec_old"
+
+
+class TestCleanupDaemonMain:
+    """Tests for main() daemon entrypoint."""
+
+    def test_main_redis_connection_failure_exits_with_one(self, tmp_path):
+        """When Redis connection or ping fails, main() calls sys.exit(1)."""
+        mock_config = MagicMock()
+        mock_config.paths.default_output_dir = str(tmp_path / "out")
+        mock_config.paths.results_zip_dir = str(tmp_path / "zip")
+        mock_config.server.results_ttl_hours = 24
+        mock_config.server.cleanup_watchdog_sec = 60
+        mock_config.redis.retention_ttl = 604800
+        mock_config.redis.host = "localhost"
+        mock_config.redis.port = 6379
+        mock_config.redis.db = 0
+        mock_config.redis.password = None
+        mock_config.redis.decode_responses = True
+        mock_config.redis.socket_connect_timeout = 5
+        mock_config.redis.socket_timeout = 5
+
+        mock_redis_instance = MagicMock()
+        mock_redis_instance.ping.side_effect = Exception("Connection refused")
+
+        with (
+            patch(
+                "earth2studio.serve.server.cleanup_daemon.get_config",
+                return_value=mock_config,
+            ),
+            patch(
+                "earth2studio.serve.server.cleanup_daemon.redis.Redis",
+                return_value=mock_redis_instance,
+            ),
+            patch("earth2studio.serve.server.cleanup_daemon.sys.exit") as mock_exit,
+        ):
+            mock_exit.side_effect = SystemExit(1)
+            with pytest.raises(SystemExit):
+                main()
+            mock_exit.assert_called_once_with(1)
+
+    def test_main_runs_one_cycle_then_handles_keyboard_interrupt(self, tmp_path):
+        """When time.sleep raises KeyboardInterrupt (e.g. SIGINT), main() exits gracefully and closes Redis."""
+        mock_config = MagicMock()
+        mock_config.paths.default_output_dir = str(tmp_path / "out")
+        mock_config.paths.results_zip_dir = str(tmp_path / "zip")
+        mock_config.server.results_ttl_hours = 24
+        mock_config.server.cleanup_watchdog_sec = 60
+        mock_config.redis.retention_ttl = 604800
+        mock_config.redis.host = "localhost"
+        mock_config.redis.port = 6379
+        mock_config.redis.db = 0
+        mock_config.redis.password = None
+        mock_config.redis.decode_responses = True
+        mock_config.redis.socket_connect_timeout = 5
+        mock_config.redis.socket_timeout = 5
+
+        mock_redis_instance = MagicMock()
+        mock_redis_instance.ping.return_value = True
+        mock_redis_instance.close = MagicMock()
+
+        with (
+            patch(
+                "earth2studio.serve.server.cleanup_daemon.get_config",
+                return_value=mock_config,
+            ),
+            patch(
+                "earth2studio.serve.server.cleanup_daemon.redis.Redis",
+                return_value=mock_redis_instance,
+            ),
+            patch("earth2studio.serve.server.cleanup_daemon.cleanup_expired_results"),
+            patch("earth2studio.serve.server.cleanup_daemon.signal.signal"),
+            patch(
+                "earth2studio.serve.server.cleanup_daemon.time.sleep",
+                side_effect=KeyboardInterrupt,
+            ),
+        ):
+            main()
+            mock_redis_instance.close.assert_called_once()
diff --git a/test/serve/test_config.py b/test/serve/server/test_config.py
similarity index 96%
rename from test/serve/test_config.py
rename to test/serve/server/test_config.py
index 9dd7e40a5..854b1c248 100644
--- a/test/serve/test_config.py
+++ b/test/serve/server/test_config.py
@@ -14,12 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Unit tests for api_server.config module.
-
-Tests configuration management, environment variable overrides, and singleton behavior.
-"""
-
 import logging
 import os
 import sys
@@ -48,8 +42,8 @@
 mock_omegaconf.OmegaConf.to_container = MagicMock(return_value={})
 
 # Import config after mocking dependencies (noqa: E402 - import after mocks is intentional)
-# Note: service/inferenceserver path is added by conftest.py
-from api_server.config import (  # noqa: E402
+# Note: serve/server path is added by conftest.py
+from earth2studio.serve.server.config import (  # noqa: E402
     AppConfig,
     ConfigManager,
     CORSConfig,
@@ -183,7 +177,7 @@ def test_reset_config_creates_new_instance(self) -> None:
         # Reset clears the global
         reset_config()
         # Manually clear the instance to verify reset worked
-        from api_server.config import ConfigManager
+        from earth2studio.serve.server.config import ConfigManager
 
         old_instance = ConfigManager._instance
         ConfigManager._instance = None
@@ -195,9 +189,9 @@ def test_reset_config_creates_new_instance(self) -> None:
 class TestConfigManagerInitialization:
     """Test ConfigManager initialization and fallback behavior"""
 
-    @patch("api_server.config.initialize_config_dir")
-    @patch("api_server.config.compose")
-    @patch("api_server.config.GlobalHydra")
+    @patch("earth2studio.serve.server.config.initialize_config_dir")
+    @patch("earth2studio.serve.server.config.compose")
+    @patch("earth2studio.serve.server.config.GlobalHydra")
     def test_initialize_config_success(
         self,
         mock_global_hydra: MagicMock,
@@ -213,7 +207,7 @@ def test_initialize_config_success(
         mock_compose.side_effect = [mock_cfg, mock_workflow_cfg]
 
         # Mock OmegaConf.to_container
-        with patch("api_server.config.OmegaConf") as mock_omega:
+        with patch("earth2studio.serve.server.config.OmegaConf") as mock_omega:
             mock_omega.to_container.side_effect = [
                 {"redis": {"host": "test_host"}},
                 {"workflow1": {}},
@@ -225,8 +219,8 @@ def test_initialize_config_success(
             assert config is not None
             assert isinstance(config, AppConfig)
 
-    @patch("api_server.config.initialize_config_dir")
-    @patch("api_server.config.compose")
+    @patch("earth2studio.serve.server.config.initialize_config_dir")
+    @patch("earth2studio.serve.server.config.compose")
     def test_initialize_config_fallback_to_defaults(
         self, mock_compose: MagicMock, mock_init: MagicMock
     ) -> None:
@@ -521,8 +515,8 @@ def test_setup_logging_adds_execution_id_filter(self) -> None:
 class TestGetWorkflowConfig:
     """Test get_workflow_config function"""
 
-    @patch("api_server.config.initialize_config_dir")
-    @patch("api_server.config.compose")
+    @patch("earth2studio.serve.server.config.initialize_config_dir")
+    @patch("earth2studio.serve.server.config.compose")
     def test_get_workflow_config_returns_dict(
         self, mock_compose: MagicMock, mock_init: MagicMock
     ) -> None:
@@ -532,7 +526,7 @@ def test_get_workflow_config_returns_dict(
         mock_workflow_cfg = MagicMock()
         mock_compose.side_effect = [MagicMock(), mock_workflow_cfg]
 
-        with patch("api_server.config.OmegaConf") as mock_omega:
+        with patch("earth2studio.serve.server.config.OmegaConf") as mock_omega:
             mock_omega.to_container.side_effect = [
                 {},  # config
                 {"test_workflow": {"param": "value"}},  # workflow_config
diff --git a/test/serve/server/test_cpu_worker.py b/test/serve/server/test_cpu_worker.py
new file mode 100644
index 000000000..45e2c9553
--- /dev/null
+++ b/test/serve/server/test_cpu_worker.py
@@ -0,0 +1,1403 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import sys
+import tempfile
+import zipfile
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Literal
+from unittest.mock import Mock, patch
+
+import pytest
+import redis
+
+
+# Create mock config classes before importing cpu_worker
+@dataclass
+class MockRedisConfig:
+    """Mock Redis configuration"""
+
+    host: str = "localhost"
+    port: int = 6379
+    db: int = 0
+    password: str | None = None
+    socket_connect_timeout: int = 5
+    socket_timeout: int = 5
+    decode_responses: bool = True
+    retention_ttl: int = 604800  # Redis key retention TTL in seconds (7 days)
+
+
+@dataclass
+class MockQueueConfig:
+    """Mock queue configuration"""
+
+    name: str = "inference"
+    result_zip_queue_name: str = "result_zip"
+    object_storage_queue_name: str = "object_storage"
+    finalize_metadata_queue_name: str = "finalize_metadata"
+    max_size: int = 10
+    default_timeout: str = "1h"
+    job_timeout: str = "2h"
+
+
+# Secure temp dir for mock path defaults (S108)
+_secure_test_dir = tempfile.mkdtemp(prefix="e2s_testing_")
+
+
+@dataclass
+class MockPathsConfig:
+    """Mock paths configuration"""
+
+    default_output_dir: str = field(default_factory=lambda: _secure_test_dir)
+    results_zip_dir: str = field(default_factory=lambda: _secure_test_dir)
+    output_format: Literal["zarr", "netcdf4"] = "zarr"
+    result_zip_enabled: bool = False
+
+
+@dataclass
+class MockLoggingConfig:
+    """Mock logging configuration"""
+
+    level: str = "INFO"
+    format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+
+
+@dataclass
+class MockServerConfig:
+    """Mock server configuration"""
+
+    host: str = "0.0.0.0"  # noqa: S104
+    port: int = 8000
+    workers: int = 1
+    title: str = "REST API"
+    description: str = "REST API for running workflows"
+    version: str = "1.0.0"
+    docs_url: str = "/docs"
+    redoc_url: str = "/redoc"
+
+
+@dataclass
+class MockCORSConfig:
+    """Mock CORS configuration"""
+
+    allow_origins: list = field(default_factory=lambda: ["*"])
+    allow_credentials: bool = True
+    allow_methods: list = field(default_factory=lambda: ["*"])
+    allow_headers: list = field(default_factory=lambda: ["*"])
+
+
+@dataclass
+class MockObjectStorageConfig:
+    """Mock object storage configuration"""
+
+    enabled: bool = False
+    bucket: str | None = None
+    region: str = "us-east-1"
+    prefix: str = "outputs"
+    access_key_id: str | None = None
+    secret_access_key: str | None = None
+    session_token: str | None = None
+    cloudfront_domain: str | None = None
+    cloudfront_key_pair_id: str | None = None
+    cloudfront_private_key_path: str | None = None
+    signed_url_expires_in: int = 3600
+
+
+@dataclass
+class MockAppConfig:
+    """Mock root configuration"""
+
+    redis: MockRedisConfig = field(default_factory=MockRedisConfig)
+    queue: MockQueueConfig = field(default_factory=MockQueueConfig)
+    paths: MockPathsConfig = field(default_factory=MockPathsConfig)
+    logging: MockLoggingConfig = field(default_factory=MockLoggingConfig)
+    server: MockServerConfig = field(default_factory=MockServerConfig)
+    cors: MockCORSConfig = field(default_factory=MockCORSConfig)
+    object_storage: MockObjectStorageConfig = field(
+        default_factory=MockObjectStorageConfig
+    )
+
+
+# Create a mock config module
+mock_config_module = Mock()
+mock_config_module.AppConfig = MockAppConfig
+mock_config_module.get_config = Mock(return_value=MockAppConfig())
+mock_config_module.get_config_manager = Mock()
+
+pytest.importorskip("earth2studio.serve.server")
+
+# Inject the mock before importing cpu_worker
+sys.modules["earth2studio.serve.server.config"] = mock_config_module
+
+# Now import the module under test
+from earth2studio.serve.server.cpu_worker import (  # noqa: E402  # noqa: E402
+    FileManifestEntry,
+    ResultMetadata,
+    build_file_manifest,
+    create_results_zip,
+    fail_workflow,
+    process_finalize_metadata,
+    process_object_storage_upload,
+    process_result_zip,
+)
+from earth2studio.serve.server.workflow import WorkflowResult  # noqa: E402
+
+
+class TestCreateResultsZip:
+    """Test suite for create_results_zip function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Mock Redis client"""
+        return Mock(spec=redis.Redis)
+
+    @pytest.fixture
+    def inference_request(self):
+        """Sample inference request data"""
+        return {
+            "id": "test_req_123",
+            "type": "deterministic",
+            "status": "completed",
+            "completion_time": "2024-01-01T12:00:00Z",
+            "execution_time_seconds": 45.5,
+            "created_at": "2024-01-01T11:00:00Z",
+            "peak_memory_usage": "2.5GB",
+            "device": "cuda:0",
+            "request": {
+                "workflow_type": "deterministic",
+                "time": "2024-01-01T00:00:00Z",
+                "nsteps": 10,
+                "prognostic": {"model_type": "fcn"},
+                "data": {"source_type": "gfs"},
+                "io": {"backend_type": "zarr"},
+            },
+        }
+
+    def test_create_results_zip_with_single_file(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip with a single output file"""
+        # Create a temporary output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data content")
+        file_size = output_file.stat().st_size
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_123",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip file was created - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_123"
+
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify Redis was updated (4 times: metadata, results_zip_dir, zip, output_path)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+
+        # First call: metadata (stored in Redis for finalize worker)
+        assert calls[0][0][0] == "inference_request:test_req_123:pending_metadata"
+
+        # Second call: results_zip_dir path
+        assert calls[1][0][0] == "inference_request:test_req_123:results_zip_dir"
+        assert calls[1][0][2] == str(results_zip_dir)
+
+        # Third call: zip filename
+        assert calls[2][0][0] == "inference_request_zips:test_req_123:zip_file"
+        assert calls[2][0][2] == zip_filename
+
+        # Fourth call: output path
+        assert calls[3][0][0] == "inference_request:test_req_123:output_path"
+        assert calls[3][0][1] == 86400  # 24 hours
+        assert calls[3][0][2] == str(output_file)
+
+        # Verify zip contents - metadata.json is NOT in the zip
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert "metadata.json" not in file_list  # metadata is now separate
+            assert "output.nc" in file_list
+
+        # Verify metadata is stored in Redis (not as file - file is created by finalize worker)
+        # Get the metadata from the first setex call
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify metadata structure
+        assert metadata["request_id"] == "test_req_123"
+        assert metadata["status"] == "completed"
+        assert metadata["workflow_type"] == "deterministic"
+        assert metadata["execution_time_seconds"] == 45.5
+        assert metadata["device"] == "cuda:0"
+        assert "zip_created_at" in metadata
+
+        # Verify parameters field
+        assert "parameters" in metadata
+        assert metadata["parameters"] == inference_request["request"]
+        assert metadata["parameters"]["workflow_type"] == "deterministic"
+        assert metadata["parameters"]["nsteps"] == 10
+
+        # Verify output_files manifest includes both the file AND the zip
+        assert "output_files" in metadata
+        assert isinstance(metadata["output_files"], list)
+        assert len(metadata["output_files"]) == 2  # output.nc + zip file entry
+
+        # Convert to dict for easier checking
+        files_dict = {f["path"]: f["size"] for f in metadata["output_files"]}
+
+        # Check the output file entry
+        assert "output.nc" in files_dict
+        assert files_dict["output.nc"] == file_size
+
+        # Check the zip file entry (path = request_id)
+        assert "test_req_123" in files_dict
+        assert files_dict["test_req_123"] == zip_path.stat().st_size
+
+    def test_create_results_zip_with_directory(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip with a directory containing multiple files"""
+        # Create a temporary output directory with files
+        output_dir = tmp_path / "output_data"
+        output_dir.mkdir()
+
+        file1 = output_dir / "data1.nc"
+        file1.write_text("data 1 content")
+        file1_size = file1.stat().st_size
+
+        file2 = output_dir / "data2.nc"
+        file2.write_text("data 2 content with more text")
+        file2_size = file2.stat().st_size
+
+        # Create subdirectory
+        subdir = output_dir / "plots"
+        subdir.mkdir()
+        file3 = subdir / "plot.png"
+        file3.write_bytes(b"fake png data")
+        file3_size = file3.stat().st_size
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_456",
+            output_path=output_dir,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip file was created - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_456"
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify zip contents - metadata.json is NOT in the zip
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert "metadata.json" not in file_list
+
+            # Verify files exist in zip
+            assert "output_data/data1.nc" in file_list
+            assert "output_data/data2.nc" in file_list
+            assert "output_data/plots/plot.png" in file_list
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify output_files manifest (3 files + 1 zip entry)
+        assert "output_files" in metadata
+        output_files = metadata["output_files"]
+        assert len(output_files) == 4  # 3 files + zip entry
+
+        # Convert to dict for easier checking
+        files_dict = {f["path"]: f["size"] for f in output_files}
+
+        # Verify all files are in manifest with correct sizes
+        assert "output_data/data1.nc" in files_dict
+        assert files_dict["output_data/data1.nc"] == file1_size
+
+        assert "output_data/data2.nc" in files_dict
+        assert files_dict["output_data/data2.nc"] == file2_size
+
+        assert "output_data/plots/plot.png" in files_dict
+        assert files_dict["output_data/plots/plot.png"] == file3_size
+
+        # Verify zip file entry (path = request_id)
+        assert "test_req_456" in files_dict
+        assert files_dict["test_req_456"] == zip_path.stat().st_size
+
+    def test_create_results_zip_with_nonexistent_output(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip when output path doesn't exist"""
+        # Use nonexistent path
+        output_path = tmp_path / "nonexistent"
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_789",
+            output_path=output_path,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should still create zip - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_789"
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify zip contents - should be empty (no output files)
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert len(file_list) == 0  # No files in zip
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # output_files should only contain the zip entry
+        assert "output_files" in metadata
+        assert len(metadata["output_files"]) == 1  # Only zip entry
+        assert metadata["output_files"][0]["path"] == "test_req_789"
+
+    def test_create_results_zip_parameters_validation(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that parameters are correctly stored in metadata"""
+        # Create a temporary output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Modify inference request to have specific parameters
+        inference_request["request"] = {
+            "workflow_type": "ensemble",
+            "time": "2024-01-15T00:00:00Z",
+            "nsteps": 20,
+            "nensemble": 10,
+            "prognostic": {"model_type": "graphcast"},
+            "data": {"source_type": "era5"},
+            "io": {"backend_type": "netcdf"},
+            "perturbation": {"method": "spherical_gaussian"},
+        }
+
+        # Call create_results_zip
+        result = create_results_zip(
+            request_id="test_req_params",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+        assert result is not None
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify parameters field exists and matches
+        assert "parameters" in metadata
+        params = metadata["parameters"]
+
+        assert params["workflow_type"] == "ensemble"
+        assert params["time"] == "2024-01-15T00:00:00Z"
+        assert params["nsteps"] == 20
+        assert params["nensemble"] == 10
+        assert params["prognostic"]["model_type"] == "graphcast"
+        assert params["data"]["source_type"] == "era5"
+        assert params["io"]["backend_type"] == "netcdf"
+        assert params["perturbation"]["method"] == "spherical_gaussian"
+
+    def test_create_results_zip_file_manifest_structure(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that file manifest has correct structure"""
+        # Create output directory with multiple files
+        output_dir = tmp_path / "outputs"
+        output_dir.mkdir()
+
+        for i in range(5):
+            file = output_dir / f"file_{i}.dat"
+            file.write_text(f"content {i}" * 100)
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_manifest",
+            output_path=output_dir,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify metadata is stored in Redis
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        manifest = metadata["output_files"]
+
+        # Verify manifest structure (5 files + 1 zip entry)
+        assert isinstance(manifest, list)
+        assert len(manifest) == 6  # 5 files + zip entry
+
+        # Find the zip entry (path = request_id)
+        zip_entry = None
+        file_entries = []
+        for entry in manifest:
+            if entry["path"] == "test_manifest":
+                zip_entry = entry
+            else:
+                file_entries.append(entry)
+
+        assert zip_entry is not None
+        assert len(file_entries) == 5
+
+        # Verify file entries structure
+        zip_path = results_zip_dir / zip_filename
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            for entry in file_entries:
+                assert isinstance(entry, dict)
+                assert "path" in entry
+                assert "size" in entry
+                assert isinstance(entry["path"], str)
+                assert isinstance(entry["size"], int)
+                assert entry["size"] > 0
+
+                # Verify path starts with output directory name
+                assert entry["path"].startswith("outputs/")
+
+                # Verify size matches actual file in zip
+                actual_size = zf.getinfo(entry["path"]).file_size
+                assert entry["size"] == actual_size
+
+    def test_create_results_zip_missing_request_parameters(self, mock_redis, tmp_path):
+        """Test handling when request doesn't have parameters field"""
+        # Create inference request without 'request' field
+        inference_request = {
+            "id": "test_req_no_params",
+            "type": "deterministic",
+            "status": "completed",
+        }
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_no_params",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip was created
+        assert zip_filename is not None
+
+        # Verify metadata stored in Redis - parameters should be None
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Parameters should be None or not present
+        assert metadata.get("parameters") is None
+
+    def test_create_results_zip_redis_failure(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test handling when Redis operation fails"""
+        # Make Redis setex raise an exception
+        mock_redis.setex.side_effect = Exception("Redis connection failed")
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip - should handle exception gracefully
+        zip_filename = create_results_zip(
+            request_id="test_redis_fail",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should return None since storing in Redis failed
+        assert zip_filename is None
+
+    def test_create_results_zip_exception_handling(self, mock_redis, tmp_path):
+        """Test general exception handling"""
+        # Use invalid inference request that will cause issues
+        invalid_request = None
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip with invalid data
+        zip_filename = create_results_zip(
+            request_id="test_exception",
+            output_path=output_file,
+            inference_request=invalid_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should return None on exception
+        assert zip_filename is None
+
+    def test_create_results_zip_metadata_completeness(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that all expected metadata fields are present"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_metadata",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        # Verify metadata stored in Redis
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Check all expected fields
+        required_fields = [
+            "request_id",
+            "status",
+            "completion_time",
+            "execution_time_seconds",
+            "workflow_type",
+            "created_at",
+            "peak_memory_usage",
+            "device",
+            "zip_created_at",
+            "parameters",
+            "output_files",
+        ]
+
+        for f in required_fields:
+            assert f in metadata, f"Missing required field: {f}"
+
+        # Verify zip_created_at is a valid timestamp
+        zip_created_at = metadata["zip_created_at"]
+        assert zip_created_at.endswith("Z")
+        # Should be parseable as ISO format
+        datetime.fromisoformat(zip_created_at.replace("Z", "+00:00"))
+
+    def test_create_results_zip_manifest_includes_zip_entry(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that the manifest includes an entry for the zip file itself"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data content")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        request_id = "test_zip_entry"
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id=request_id,
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        zip_path = results_zip_dir / zip_filename
+        zip_size = zip_path.stat().st_size
+
+        # Verify metadata stored in Redis
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Find the zip entry in manifest
+        zip_entry = None
+        for entry in metadata["output_files"]:
+            if entry["path"] == request_id:
+                zip_entry = entry
+                break
+
+        # Verify zip entry exists and has correct size
+        assert zip_entry is not None, "Zip file entry not found in manifest"
+        assert zip_entry["path"] == request_id
+        assert zip_entry["size"] == zip_size
+
+    def test_create_results_zip_output_path_stored_in_redis(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that output path is stored in Redis"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_output_path",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        # Verify Redis setex was called 4 times (metadata, results_zip_dir, zip, output_path)
+        assert mock_redis.setex.call_count == 4
+
+        # Check the output path call (4th call)
+        calls = mock_redis.setex.call_args_list
+        output_path_call = calls[3]
+
+        assert (
+            output_path_call[0][0] == "inference_request:test_output_path:output_path"
+        )
+        assert output_path_call[0][1] == 86400  # 24 hours
+        assert output_path_call[0][2] == str(output_file)
+
+    def test_create_results_zip_manifest_only_no_zip(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test create_results_zip with create_zip=False: no zip file, manifest only."""
+        output_dir = tmp_path / "out"
+        output_dir.mkdir()
+        (output_dir / "a.nc").write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        result = create_results_zip(
+            request_id="req_manifest_only",
+            output_path=output_dir,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+            create_zip=False,
+        )
+
+        assert result is None  # No zip filename when create_zip=False
+        assert not (results_zip_dir / "req_manifest_only").exists()
+        # Metadata should still be stored (manifest only)
+        assert mock_redis.setex.call_count >= 3
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+        assert len(metadata["output_files"]) == 1  # Just the one file, no zip entry
+
+    def test_create_results_zip_with_workflow_result(self, mock_redis, tmp_path):
+        """Test create_results_zip with WorkflowResult (custom workflow) path."""
+        output_file = tmp_path / "out.nc"
+        output_file.write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        workflow_result = WorkflowResult(
+            workflow_name="my_workflow",
+            execution_id="exec_1",
+            status="completed",
+            start_time="2024-01-01T10:00:00Z",
+            end_time="2024-01-01T11:00:00Z",
+            execution_time_seconds=60.0,
+            metadata={
+                "peak_memory_usage": "1GB",
+                "device": "cuda:0",
+                "parameters": {"nsteps": 5},
+            },
+        )
+
+        zip_filename = create_results_zip(
+            request_id="my_workflow:exec_1",
+            output_path=output_file,
+            inference_request=workflow_result,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+        metadata_json = mock_redis.setex.call_args_list[0][0][2]
+        metadata = json.loads(metadata_json)
+        assert metadata.get("workflow_name") == "my_workflow"
+        # from_workflow_result uses workflow_result.execution_id for request_id when set
+        assert metadata.get("request_id") == "exec_1"
+        assert metadata.get("peak_memory_usage") == "1GB"
+        assert metadata.get("parameters", {}).get("nsteps") == 5
+
+    def test_create_results_zip_invalid_type_returns_none(self, mock_redis, tmp_path):
+        """Test that invalid inference_request type is handled (exception caught, returns None)."""
+        output_file = tmp_path / "out.nc"
+        output_file.write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        result = create_results_zip(
+            request_id="req_invalid",
+            output_path=output_file,
+            inference_request=[],  # list is not dict or WorkflowResult
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert result is None
+
+
+class TestFailWorkflow:
+    """Tests for fail_workflow."""
+
+    def test_fail_workflow_returns_error_dict(self):
+        """fail_workflow returns success=False and error message."""
+        out = fail_workflow("w", "e1", "Something failed")
+        assert out["success"] is False
+        assert out["error"] == "Something failed"
+
+    def test_fail_workflow_updates_redis_when_workflow_registered(self):
+        """When workflow is in registry, _update_execution_data is called."""
+        mock_workflow_class = Mock()
+        mock_workflow_class._update_execution_data = Mock()
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            mock_registry.get_workflow_class.return_value = mock_workflow_class
+            with patch("earth2studio.serve.server.cpu_worker.redis_client"):
+                out = fail_workflow("my_wf", "exec_1", "Failed")
+
+        assert out["success"] is False
+        mock_workflow_class._update_execution_data.assert_called_once()
+        call_args = mock_workflow_class._update_execution_data.call_args
+        assert call_args[0][1] == "my_wf"
+        assert call_args[0][2] == "exec_1"
+        assert call_args[0][3]["status"] == "failed"
+        assert call_args[0][3]["error_message"] == "Failed"
+
+    def test_fail_workflow_exception_still_returns_dict(self):
+        """When _update_execution_data raises, we still return error dict."""
+        mock_workflow_class = Mock()
+        mock_workflow_class._update_execution_data.side_effect = Exception("Redis down")
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            mock_registry.get_workflow_class.return_value = mock_workflow_class
+            out = fail_workflow("w", "e1", "Err")
+
+        assert out["success"] is False
+        assert out["error"] == "Err"
+
+
+class TestBuildFileManifest:
+    """Tests for build_file_manifest."""
+
+    def test_build_file_manifest_single_file(self, tmp_path):
+        """Single file returns one entry."""
+        f = tmp_path / "a.nc"
+        f.write_text("x")
+        entries = build_file_manifest(f)
+        assert len(entries) == 1
+        assert entries[0].path == "a.nc"
+        assert entries[0].size == f.stat().st_size
+
+    def test_build_file_manifest_directory_with_subdirs(self, tmp_path):
+        """Directory with subdirs is scanned recursively."""
+        d = tmp_path / "out"
+        d.mkdir()
+        (d / "a.nc").write_text("a")
+        sub = d / "sub"
+        sub.mkdir()
+        (sub / "b.nc").write_text("bb")
+        entries = build_file_manifest(d)
+        assert len(entries) == 2
+        paths = {e.path for e in entries}
+        assert "out/a.nc" in paths
+        assert "out/sub/b.nc" in paths
+
+    def test_build_file_manifest_with_arc_prefix(self, tmp_path):
+        """arc_prefix is applied to paths."""
+        f = tmp_path / "f.nc"
+        f.write_text("x")
+        entries = build_file_manifest(f, arc_prefix="prefix")
+        assert len(entries) == 1
+        assert entries[0].path == "prefix/f.nc"
+
+    def test_build_file_manifest_nonexistent_returns_empty(self, tmp_path):
+        """Nonexistent path returns empty list."""
+        entries = build_file_manifest(tmp_path / "nonexistent")
+        assert entries == []
+
+
+class TestResultMetadata:
+    """Tests for ResultMetadata factory methods and to_dict."""
+
+    def test_from_workflow_result(self):
+        """ResultMetadata.from_workflow_result sets workflow_name and metadata."""
+        wr = WorkflowResult(
+            workflow_name="custom_wf",
+            execution_id="exec_1",
+            status="completed",
+            start_time="2024-01-01T10:00:00Z",
+            end_time="2024-01-01T11:00:00Z",
+            execution_time_seconds=30.0,
+            metadata={"peak_memory_usage": "2GB", "parameters": {"n": 1}},
+        )
+        manifest = [FileManifestEntry(path="out.nc", size=100)]
+        meta = ResultMetadata.from_workflow_result(
+            wr, "exec_1", manifest, "2024-01-01T12:00:00Z"
+        )
+        assert meta.request_id == "exec_1"
+        assert meta.workflow_name == "custom_wf"
+        assert meta.status == "completed"
+        assert meta.parameters == {"n": 1}
+        assert meta.output_files == manifest
+
+    def test_from_legacy_dict(self):
+        """ResultMetadata.from_legacy_dict sets workflow_type from dict."""
+        req = {
+            "status": "completed",
+            "completion_time": "2024-01-01T11:00:00Z",
+            "execution_time_seconds": 10.0,
+            "type": "deterministic",
+            "created_at": "2024-01-01T10:00:00Z",
+            "request": {"nsteps": 5},
+        }
+        manifest = [FileManifestEntry(path="out.zarr", size=200)]
+        meta = ResultMetadata.from_legacy_dict(
+            req, "req_123", manifest, "2024-01-01T12:00:00Z"
+        )
+        assert meta.request_id == "req_123"
+        assert meta.workflow_type == "deterministic"
+        assert meta.parameters == {"nsteps": 5}
+
+    def test_to_dict_includes_workflow_name_and_type(self):
+        """to_dict includes workflow_name or workflow_type when set."""
+        meta = ResultMetadata(
+            request_id="r1",
+            status="completed",
+            completion_time=None,
+            execution_time_seconds=None,
+            workflow_name="my_wf",
+            workflow_type=None,
+            output_files=[],
+        )
+        d = meta.to_dict()
+        assert d["workflow_name"] == "my_wf"
+        assert "workflow_type" not in d
+
+        meta2 = ResultMetadata(
+            request_id="r2",
+            status="completed",
+            completion_time=None,
+            execution_time_seconds=None,
+            workflow_type="ensemble",
+            output_files=[],
+        )
+        d2 = meta2.to_dict()
+        assert d2["workflow_type"] == "ensemble"
+        assert "workflow_name" not in d2
+
+    def test_to_dict_includes_signed_url_and_remote_path(self):
+        """to_dict includes signed_url and remote_path when set."""
+        meta = ResultMetadata(
+            request_id="r1",
+            status="completed",
+            completion_time=None,
+            execution_time_seconds=None,
+            signed_url="https://s3.example.com/signed",
+            remote_path="outputs/r1/out.nc",
+            output_files=[],
+        )
+        d = meta.to_dict()
+        assert d["signed_url"] == "https://s3.example.com/signed"
+        assert d["remote_path"] == "outputs/r1/out.nc"
+
+
+class TestProcessResultZip:
+    """Tests for process_result_zip RQ worker function."""
+
+    def test_process_result_zip_success_returns_zip_filename(self, tmp_path):
+        """process_result_zip with valid workflow and create_zip returns zip filename."""
+        output_file = tmp_path / "out.nc"
+        output_file.write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        mock_workflow_class = Mock()
+        mock_workflow_class._get_execution_data.return_value = {
+            "id": "wf:exec_1",
+            "type": "deterministic",
+            "status": "completed",
+        }
+        mock_workflow_class._update_execution_data = Mock()
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.redis_client"
+            ) as mock_redis:
+                with patch(
+                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
+                ) as mock_queue_next:
+                    mock_registry.get_workflow_class.return_value = mock_workflow_class
+                    mock_redis.setex = Mock()
+                    mock_queue_next.return_value = "job_123"
+
+                    result = process_result_zip(
+                        workflow_name="test_wf",
+                        execution_id="exec_1",
+                        output_path_str=str(output_file),
+                        results_zip_dir_str=str(results_zip_dir),
+                        create_zip=True,
+                    )
+
+                    assert result is not None
+                    assert result == "test_wf:exec_1"  # zip filename is request_id
+                    mock_queue_next.assert_called_once_with(
+                        redis_client=mock_redis,
+                        current_stage="result_zip",
+                        workflow_name="test_wf",
+                        execution_id="exec_1",
+                        output_path_str=str(output_file),
+                        results_zip_dir_str=str(results_zip_dir),
+                    )
+
+    def test_process_result_zip_workflow_not_found_raises(self, tmp_path):
+        """process_result_zip when workflow not in registry raises after fail_workflow."""
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.fail_workflow"
+            ) as mock_fail:
+                mock_registry.get_workflow_class.return_value = None
+
+                with pytest.raises(ValueError, match="not found in registry"):
+                    process_result_zip(
+                        workflow_name="nonexistent",
+                        execution_id="e1",
+                        output_path_str=str(tmp_path / "out"),
+                        results_zip_dir_str=str(tmp_path / "zip"),
+                    )
+                mock_fail.assert_called_once()
+
+    def test_process_result_zip_queue_next_stage_returns_none(self, tmp_path):
+        """When queue_next_stage returns None, fail_workflow is called and function returns None."""
+        output_file = tmp_path / "out.nc"
+        output_file.write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        mock_workflow_class = Mock()
+        mock_workflow_class._get_execution_data.return_value = {
+            "type": "deterministic",
+            "status": "completed",
+        }
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.redis_client"
+            ) as mock_redis:
+                with patch(
+                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
+                ) as mock_queue_next:
+                    with patch(
+                        "earth2studio.serve.server.cpu_worker.fail_workflow"
+                    ) as mock_fail:
+                        mock_registry.get_workflow_class.return_value = (
+                            mock_workflow_class
+                        )
+                        mock_redis.setex = Mock()
+                        mock_queue_next.return_value = None
+
+                        result = process_result_zip(
+                            workflow_name="test_wf",
+                            execution_id="exec_1",
+                            output_path_str=str(output_file),
+                            results_zip_dir_str=str(results_zip_dir),
+                            create_zip=True,
+                        )
+
+        assert result is None
+        mock_fail.assert_called_once()
+        assert "queue next pipeline stage" in mock_fail.call_args[0][2].lower()
+
+    def test_process_result_zip_manifest_only_returns_manifest_only(self, tmp_path):
+        """When create_zip=False and flow succeeds, returns 'manifest_only'."""
+        output_file = tmp_path / "out.nc"
+        output_file.write_text("data")
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        mock_workflow_class = Mock()
+        mock_workflow_class._get_execution_data.return_value = {
+            "type": "deterministic",
+            "status": "completed",
+        }
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.redis_client"
+            ) as mock_redis:
+                with patch(
+                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
+                ) as mock_queue_next:
+                    mock_registry.get_workflow_class.return_value = mock_workflow_class
+                    mock_redis.setex = Mock()
+                    mock_queue_next.return_value = "job_123"
+
+                    result = process_result_zip(
+                        workflow_name="test_wf",
+                        execution_id="exec_1",
+                        output_path_str=str(output_file),
+                        results_zip_dir_str=str(results_zip_dir),
+                        create_zip=False,
+                    )
+
+        assert result == "manifest_only"
+
+    def test_process_result_zip_create_fails_returns_none(self, tmp_path):
+        """When create_results_zip returns None (create_zip=True), fail_workflow and return None."""
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+        # Nonexistent output path so create_results_zip may still build manifest but we can mock return
+        output_path_str = str(tmp_path / "nonexistent")
+
+        mock_workflow_class = Mock()
+        mock_workflow_class._get_execution_data.return_value = {
+            "type": "deterministic",
+            "status": "completed",
+        }
+
+        with patch(
+            "earth2studio.serve.server.cpu_worker.workflow_registry"
+        ) as mock_registry:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.redis_client"
+            ) as mock_redis:
+                with patch("earth2studio.serve.server.cpu_worker.queue_next_stage"):
+                    with patch(
+                        "earth2studio.serve.server.cpu_worker.create_results_zip"
+                    ) as mock_create:
+                        with patch(
+                            "earth2studio.serve.server.cpu_worker.fail_workflow"
+                        ) as mock_fail:
+                            mock_registry.get_workflow_class.return_value = (
+                                mock_workflow_class
+                            )
+                            mock_redis.setex = Mock()
+                            mock_create.return_value = None  # zip creation failed
+
+                            result = process_result_zip(
+                                workflow_name="test_wf",
+                                execution_id="exec_1",
+                                output_path_str=output_path_str,
+                                results_zip_dir_str=str(results_zip_dir),
+                                create_zip=True,
+                            )
+
+        assert result is None
+        mock_fail.assert_called_once()
+        assert "create results zip" in mock_fail.call_args[0][2].lower()
+
+
+class TestProcessFinalizeMetadata:
+    """Tests for process_finalize_metadata RQ worker function."""
+
+    def test_process_finalize_metadata_success(self, tmp_path):
+        """process_finalize_metadata writes metadata file and updates workflow status."""
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+        request_id = "my_wf:exec_1"
+
+        metadata_key = f"inference_request:{request_id}:pending_metadata"
+        results_zip_dir_key = f"inference_request:{request_id}:results_zip_dir"
+        storage_info_key = f"inference_request:{request_id}:storage_info"
+
+        pending_metadata = {
+            "request_id": request_id,
+            "status": "completed",
+            "output_files": [],
+            "storage_type": "server",
+        }
+        mock_workflow_class = Mock()
+        mock_workflow_class._update_execution_data = Mock()
+
+        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.workflow_registry"
+            ) as mock_registry:
+                mock_redis.get.side_effect = lambda k: {
+                    metadata_key: json.dumps(pending_metadata),
+                    results_zip_dir_key: str(results_zip_dir),
+                    storage_info_key: None,
+                }.get(k)
+                mock_registry.get_workflow_class.return_value = mock_workflow_class
+
+                result = process_finalize_metadata(
+                    workflow_name="my_wf",
+                    execution_id="exec_1",
+                )
+
+        assert result is not None
+        assert result["success"] is True
+        assert "metadata_path" in result
+        metadata_path = tmp_path / "results" / f"metadata_{request_id}.json"
+        assert metadata_path.exists()
+        mock_workflow_class._update_execution_data.assert_called_once()
+        mock_redis.delete.assert_any_call(metadata_key)
+        mock_redis.delete.assert_any_call(results_zip_dir_key)
+
+    def test_process_finalize_metadata_missing_pending_metadata_returns_failure(self):
+        """When pending metadata or results_zip_dir is missing in Redis, returns fail_workflow dict."""
+        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.fail_workflow"
+            ) as mock_fail:
+                mock_redis.get.return_value = None
+                mock_fail.return_value = {
+                    "success": False,
+                    "error": "Pending metadata not found",
+                }
+
+                result = process_finalize_metadata(
+                    workflow_name="my_wf",
+                    execution_id="exec_1",
+                )
+
+        assert result is not None and result.get("success") is False
+        mock_fail.assert_called_once()
+        assert "not found" in mock_fail.call_args[0][2].lower()
+
+    def test_process_finalize_metadata_workflow_not_found_returns_failure(
+        self, tmp_path
+    ):
+        """When workflow class is not in registry, returns fail_workflow dict."""
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+        request_id = "my_wf:exec_1"
+        metadata_key = f"inference_request:{request_id}:pending_metadata"
+        results_zip_dir_key = f"inference_request:{request_id}:results_zip_dir"
+
+        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.workflow_registry"
+            ) as mock_registry:
+                with patch(
+                    "earth2studio.serve.server.cpu_worker.fail_workflow"
+                ) as mock_fail:
+                    mock_redis.get.side_effect = lambda k: {
+                        metadata_key: json.dumps({"request_id": request_id}),
+                        results_zip_dir_key: str(results_zip_dir),
+                    }.get(k)
+                    mock_registry.get_workflow_class.return_value = None
+                    mock_fail.return_value = {
+                        "success": False,
+                        "error": "Workflow class not found",
+                    }
+
+                    result = process_finalize_metadata(
+                        workflow_name="my_wf",
+                        execution_id="exec_1",
+                    )
+
+        assert result is not None and result.get("success") is False
+        mock_fail.assert_called_once()
+        assert "not found" in mock_fail.call_args[0][2].lower()
+
+    def test_process_finalize_metadata_with_storage_info(self, tmp_path):
+        """When storage_info is in Redis, it is applied to metadata and key is deleted."""
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+        request_id = "my_wf:exec_1"
+        storage_info_key = f"inference_request:{request_id}:storage_info"
+        pending_metadata = {"request_id": request_id, "status": "completed"}
+        storage_info = {
+            "storage_type": "s3",
+            "remote_path": "s3://bucket/prefix",
+            "signed_url": "https://signed.example.com",
+        }
+
+        mock_workflow_class = Mock()
+        mock_workflow_class._update_execution_data = Mock()
+
+        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.workflow_registry"
+            ) as mock_registry:
+                mock_redis.get.side_effect = lambda k: {
+                    f"inference_request:{request_id}:pending_metadata": json.dumps(
+                        pending_metadata
+                    ),
+                    f"inference_request:{request_id}:results_zip_dir": str(
+                        results_zip_dir
+                    ),
+                    storage_info_key: json.dumps(storage_info),
+                }.get(k)
+                mock_registry.get_workflow_class.return_value = mock_workflow_class
+
+                result = process_finalize_metadata(
+                    workflow_name="my_wf",
+                    execution_id="exec_1",
+                )
+
+        assert result is not None
+        assert result["storage_type"] == "s3"
+        mock_redis.delete.assert_any_call(storage_info_key)
+        metadata_path = tmp_path / "results" / f"metadata_{request_id}.json"
+        data = json.loads(metadata_path.read_text())
+        assert data.get("signed_url") == "https://signed.example.com"
+        assert data.get("remote_path") == "s3://bucket/prefix"
+
+
+class TestProcessObjectStorageUpload:
+    """Tests for process_object_storage_upload RQ worker function."""
+
+    def test_process_object_storage_upload_disabled_queues_finalize(self, tmp_path):
+        """When object storage is disabled, skips upload and queues finalize_metadata."""
+        output_dir = tmp_path / "out"
+        output_dir.mkdir()
+        (output_dir / "f.nc").write_text("data")
+
+        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
+            with patch("earth2studio.serve.server.cpu_worker.config") as mock_config:
+                with patch(
+                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
+                ) as mock_queue_next:
+                    mock_config.object_storage.enabled = False
+                    mock_config.object_storage.bucket = None
+                    mock_redis.setex = Mock()
+                    mock_queue_next.return_value = "job_1"
+
+                    result = process_object_storage_upload(
+                        workflow_name="wf",
+                        execution_id="exec_1",
+                        output_path_str=str(output_dir),
+                    )
+
+        assert result is not None
+        assert result["success"] is True
+        assert result["storage_type"] == "server"
+        mock_queue_next.assert_called_once_with(
+            redis_client=mock_redis,
+            current_stage="object_storage",
+            workflow_name="wf",
+            execution_id="exec_1",
+            output_path_str=str(output_dir),
+        )
+
+    def test_process_object_storage_upload_output_path_missing_returns_failure(self):
+        """When object storage enabled but output path does not exist, returns fail_workflow dict."""
+        with patch("earth2studio.serve.server.cpu_worker.config") as mock_config:
+            with patch(
+                "earth2studio.serve.server.cpu_worker.fail_workflow"
+            ) as mock_fail:
+                mock_config.object_storage.enabled = True
+                mock_config.object_storage.bucket = "my-bucket"
+                mock_fail.return_value = {
+                    "success": False,
+                    "error": "Output path does not exist",
+                }
+
+                result = process_object_storage_upload(
+                    workflow_name="wf",
+                    execution_id="exec_1",
+                    output_path_str="/nonexistent/path",
+                )
+
+        assert result is not None and result.get("success") is False
+        mock_fail.assert_called_once()
+        assert "does not exist" in mock_fail.call_args[0][2]
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/test/serve/server/test_object_storage.py b/test/serve/server/test_object_storage.py
new file mode 100644
index 000000000..907441cc2
--- /dev/null
+++ b/test/serve/server/test_object_storage.py
@@ -0,0 +1,294 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import base64
+import sys
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from earth2studio.serve.server.object_storage import (
+    MSCObjectStorage,
+    ObjectStorage,
+    ObjectStorageError,
+    UploadResult,
+)
+
+
+class TestUploadResult:
+    """Tests for UploadResult dataclass."""
+
+    def test_repr_success(self):
+        """__repr__ shows SUCCESS when success is True."""
+        r = UploadResult(
+            success=True,
+            files_uploaded=10,
+            total_bytes=1000,
+            destination="s3://bucket/prefix",
+            errors=[],
+        )
+        assert "SUCCESS" in repr(r)
+        assert "files=10" in repr(r)
+        assert "bytes=1000" in repr(r)
+
+    def test_repr_failed(self):
+        """__repr__ shows FAILED when success is False."""
+        r = UploadResult(
+            success=False,
+            files_uploaded=0,
+            total_bytes=0,
+            destination="s3://bucket/prefix",
+            errors=["error1"],
+        )
+        assert "FAILED" in repr(r)
+
+
+class TestObjectStorageError:
+    """Tests for ObjectStorageError."""
+
+    def test_is_exception(self):
+        """ObjectStorageError is a subclass of Exception."""
+        assert issubclass(ObjectStorageError, Exception)
+
+    def test_raise_and_catch(self):
+        """Can raise and catch ObjectStorageError with message."""
+        with pytest.raises(ObjectStorageError, match="test message"):
+            raise ObjectStorageError("test message")
+
+
+class TestObjectStorage:
+    """Tests for ObjectStorage abstract base class."""
+
+    def test_cannot_instantiate_directly(self):
+        """ObjectStorage cannot be instantiated (abstract)."""
+        with pytest.raises(TypeError):
+            ObjectStorage()
+
+
+class TestMSCObjectStorage:
+    """Tests for MSCObjectStorage (with mocked multistorageclient)."""
+
+    @pytest.fixture
+    def mock_msc(self):
+        """Mock multistorageclient module so MSCObjectStorage can be instantiated."""
+        mock_module = MagicMock()
+        mock_module.StorageClientConfig.from_dict.side_effect = [
+            MagicMock(),
+            MagicMock(),
+        ]
+        mock_module.StorageClient.return_value = MagicMock()
+        with patch.dict(sys.modules, {"multistorageclient": mock_module}):
+            yield mock_module
+
+    def test_init_requires_multistorageclient(self):
+        """MSCObjectStorage raises ImportError if multistorageclient is not available."""
+        import builtins
+
+        real_import = builtins.__import__
+
+        def fail_msc(name, *args, **kwargs):
+            if name == "multistorageclient":
+                raise ImportError("No module named 'multistorageclient'")
+            return real_import(name, *args, **kwargs)
+
+        with patch.object(builtins, "__import__", side_effect=fail_msc):
+            with pytest.raises(ImportError, match="multi-storage-client"):
+                MSCObjectStorage(bucket="my-bucket")
+
+    def test_upload_directory_raises_when_dir_not_found(self, mock_msc):
+        """upload_directory raises FileNotFoundError when path does not exist."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+
+        with pytest.raises(FileNotFoundError, match="not found"):
+            storage.upload_directory(
+                local_directory="/nonexistent/path",
+                remote_prefix="prefix",
+            )
+
+    def test_upload_directory_raises_when_path_not_directory(self, mock_msc):
+        """upload_directory raises ValueError when path is not a directory."""
+        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
+            file_path = f.name
+        try:
+            storage = MSCObjectStorage(bucket="b", region="us-east-1")
+
+            with pytest.raises(ValueError, match="not a directory"):
+                storage.upload_directory(
+                    local_directory=file_path,
+                    remote_prefix="prefix",
+                )
+        finally:
+            Path(file_path).unlink(missing_ok=True)
+
+    def test_upload_directory_success_returns_upload_result(self, mock_msc):
+        """upload_directory returns UploadResult with success when sync_from succeeds."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            (Path(tmpdir) / "f1.txt").write_text("hello")
+            storage = MSCObjectStorage(bucket="b", region="us-east-1")
+            storage._s3_client.sync_from.return_value = None
+
+            result = storage.upload_directory(
+                local_directory=tmpdir,
+                remote_prefix="prefix",
+            )
+
+            assert isinstance(result, UploadResult)
+            assert result.success is True
+            assert result.files_uploaded == 1
+            assert result.total_bytes == 5
+            assert "s3://b/prefix" in result.destination
+            assert result.errors == []
+
+    def test_upload_directory_failure_appends_errors(self, mock_msc):
+        """upload_directory returns success=False and appends error when sync_from raises."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            (Path(tmpdir) / "f1.txt").write_text("x")
+            storage = MSCObjectStorage(bucket="b", region="us-east-1")
+            storage._s3_client.sync_from.side_effect = Exception("sync failed")
+
+            result = storage.upload_directory(
+                local_directory=tmpdir,
+                remote_prefix="prefix",
+            )
+
+            assert result.success is False
+            assert result.files_uploaded == 0
+            assert len(result.errors) == 1
+            assert "sync failed" in result.errors[0]
+
+    def test_upload_file_returns_false_when_file_not_found(self, mock_msc):
+        """upload_file returns False when local file does not exist."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+
+        assert (
+            storage.upload_file(
+                local_file="/nonexistent/file.txt",
+                remote_key="key.txt",
+            )
+            is False
+        )
+
+    def test_upload_file_returns_true_on_success(self, mock_msc):
+        """upload_file returns True when upload succeeds."""
+        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
+            f.write(b"data")
+            path = f.name
+        try:
+            storage = MSCObjectStorage(bucket="b", region="us-east-1")
+
+            result = storage.upload_file(
+                local_file=path,
+                remote_key="key.txt",
+            )
+            assert result is True
+            storage._s3_client.upload_file.assert_called_once()
+        finally:
+            Path(path).unlink(missing_ok=True)
+
+    def test_upload_file_returns_false_on_exception(self, mock_msc):
+        """upload_file returns False when upload_file raises."""
+        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
+            path = f.name
+        try:
+            storage = MSCObjectStorage(bucket="b", region="us-east-1")
+            storage._s3_client.upload_file.side_effect = Exception("upload failed")
+
+            result = storage.upload_file(
+                local_file=path,
+                remote_key="key.txt",
+            )
+            assert result is False
+        finally:
+            Path(path).unlink(missing_ok=True)
+
+    def test_file_exists_returns_true_when_info_succeeds(self, mock_msc):
+        """file_exists returns True when info() does not raise."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+        storage._s3_client.info.return_value = None
+
+        assert storage.file_exists("my/key") is True
+
+    def test_file_exists_returns_false_when_file_not_found(self, mock_msc):
+        """file_exists returns False when info() raises FileNotFoundError."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+        storage._s3_client.info.side_effect = FileNotFoundError()
+
+        assert storage.file_exists("my/key") is False
+
+    def test_delete_file_returns_true_on_success(self, mock_msc):
+        """delete_file returns True when delete succeeds."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+
+        assert storage.delete_file("my/key") is True
+        storage._s3_client.delete.assert_called_once()
+
+    def test_delete_file_returns_false_when_file_not_found(self, mock_msc):
+        """delete_file returns False when delete raises FileNotFoundError."""
+        storage = MSCObjectStorage(bucket="b", region="us-east-1")
+        storage._s3_client.delete.side_effect = FileNotFoundError()
+
+        assert storage.delete_file("my/key") is False
+
+    def test_url_safe_b64(self, mock_msc):
+        """_url_safe_b64 produces URL-safe base64 (no +, /, =)."""
+        _ = MSCObjectStorage(
+            bucket="b", region="us-east-1"
+        )  # need instance so mock_msc context is active
+        raw = b"hello"
+        result = MSCObjectStorage._url_safe_b64(raw)
+        # Standard b64 could have +, /, =
+        assert "+" not in result
+        assert "/" not in result
+        assert "=" not in result
+        # Decode and check round-trip (with URL-safe mapping)
+        decoded = base64.urlsafe_b64decode(
+            result.replace("-", "+").replace("_", "=").replace("~", "/")
+        )
+        assert decoded == raw
+
+    def test_generate_signed_url_raises_when_cloudfront_not_configured(self, mock_msc):
+        """generate_signed_url raises ObjectStorageError when CloudFront not configured."""
+        storage = MSCObjectStorage(
+            bucket="b",
+            region="us-east-1",
+            cloudfront_domain=None,
+            cloudfront_key_pair_id=None,
+            cloudfront_private_key=None,
+        )
+
+        with pytest.raises(ObjectStorageError, match="CloudFront configuration"):
+            storage.generate_signed_url("key.txt")
+
+    def test_generate_signed_url_returns_url_when_configured(self, mock_msc):
+        """generate_signed_url returns URL string when CloudFront is configured."""
+        storage = MSCObjectStorage(
+            bucket="b",
+            region="us-east-1",
+            cloudfront_domain="d123.cloudfront.net",
+            cloudfront_key_pair_id="KP123",
+            cloudfront_private_key="-----BEGIN RSA PRIVATE KEY-----\nMIIEow==\n-----END RSA PRIVATE KEY-----",
+        )
+        storage._rsa_signer = MagicMock(return_value=b"signature")
+
+        url = storage.generate_signed_url("path/to/file", expires_in=3600)
+
+        assert isinstance(url, str)
+        assert "d123.cloudfront.net" in url
+        assert "Policy=" in url
+        assert "Signature=" in url
+        assert "Key-Pair-Id=KP123" in url
diff --git a/test/serve/server/test_server_main.py b/test/serve/server/test_server_main.py
new file mode 100644
index 000000000..6c9894ff0
--- /dev/null
+++ b/test/serve/server/test_server_main.py
@@ -0,0 +1,2132 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from datetime import datetime, timezone
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+from pydantic import Field
+
+# Set API environment variable before importing main (DANGER!!! REMOVE THIS)
+os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
+
+# Patch FastAPI route creation to handle union return types
+# This fixes the issue where FastAPI can't handle dict[str, Any] | StreamingResponse
+# ruff: noqa: E402
+import fastapi  # type: ignore[import-untyped]
+import fastapi.routing  # type: ignore[import-untyped]
+from fastapi.exceptions import FastAPIError  # type: ignore[import-untyped]
+
+_original_route_init = fastapi.routing.APIRoute.__init__
+
+
+def _patched_route_init(self, *args, **kwargs):
+    """Patched APIRoute.__init__ that handles union return types with StreamingResponse"""
+    import inspect
+
+    from starlette.responses import StreamingResponse
+
+    # Check if the endpoint has a union return type with StreamingResponse
+    endpoint = kwargs.get("endpoint") or (args[1] if len(args) > 1 else None)
+    if endpoint:
+        sig = inspect.signature(endpoint)
+        return_annotation = sig.return_annotation
+        if (
+            hasattr(return_annotation, "__args__")
+            and StreamingResponse in return_annotation.__args__
+        ):
+            # Set response_model=None to skip response model generation
+            kwargs["response_model"] = None
+
+    try:
+        return _original_route_init(self, *args, **kwargs)
+    except FastAPIError as e:
+        if "Invalid args for response field" in str(e):
+            # Retry with response_model=None
+            kwargs["response_model"] = None
+            return _original_route_init(self, *args, **kwargs)
+        raise
+
+
+# Apply the patch before any imports of earth2studio.serve.server.main
+fastapi.routing.APIRoute.__init__ = _patched_route_init
+
+
+class TestWorkflowSchemaEndpoint:
+    """Tests for GET /v1/workflows/{workflow_name}/schema endpoint"""
+
+    @pytest.fixture
+    def mock_workflow_class(self):
+        """Create a mock workflow class with Parameters"""
+        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
+
+        class TestWorkflowParameters(WorkflowParameters):
+            """Test parameters for unit testing"""
+
+            forecast_times: list[str] = Field(
+                default=["2024-01-01T00:00:00"],
+                description="List of forecast initialization times",
+            )
+            nsteps: int = Field(
+                default=10,
+                ge=1,
+                le=100,
+                description="Number of forecast steps",
+            )
+            model_type: str = Field(
+                default="fcn",
+                description="Model type to use",
+            )
+
+        class TestWorkflow(Workflow):
+            name = "test_workflow"
+            description = "Test workflow for unit testing"
+            Parameters = TestWorkflowParameters
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestWorkflowParameters
+            ) -> TestWorkflowParameters:
+                return TestWorkflowParameters.validate(parameters)
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        return TestWorkflow
+
+    @pytest.fixture
+    def client(self, mock_workflow_class):
+        """Create test client with mocked dependencies"""
+        # Mock Redis and other dependencies at their source locations
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue"),
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            # Setup mock async Redis
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+
+            # Setup mock sync Redis
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import workflow_registry
+
+            # Register the test workflow
+            workflow_registry._workflows["test_workflow"] = mock_workflow_class
+
+            with TestClient(app, raise_server_exceptions=False) as client:
+                yield client
+
+            # Cleanup
+            if "test_workflow" in workflow_registry._workflows:
+                del workflow_registry._workflows["test_workflow"]
+
+    def test_schema_endpoint_returns_valid_json_schema(
+        self, client, mock_workflow_class
+    ):
+        """Test that schema endpoint returns valid JSON Schema"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        # Verify it's a valid JSON Schema structure
+        assert "type" in schema
+        assert schema["type"] == "object"
+        assert "properties" in schema
+        assert "title" in schema
+
+    def test_schema_endpoint_includes_all_parameters(self, client, mock_workflow_class):
+        """Test that schema includes all defined parameters"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check all parameters are present
+        assert "forecast_times" in properties
+        assert "nsteps" in properties
+        assert "model_type" in properties
+
+    def test_schema_endpoint_includes_field_metadata(self, client, mock_workflow_class):
+        """Test that schema includes field descriptions and constraints"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check forecast_times has correct type
+        assert properties["forecast_times"]["type"] == "array"
+        assert "description" in properties["forecast_times"]
+
+        # Check nsteps has constraints
+        nsteps = properties["nsteps"]
+        assert nsteps["type"] == "integer"
+        assert "description" in nsteps
+        # Pydantic uses "minimum" and "maximum" for ge/le constraints
+        assert nsteps.get("minimum") == 1 or nsteps.get("exclusiveMinimum") == 0
+        assert nsteps.get("maximum") == 100 or nsteps.get("exclusiveMaximum") == 101
+
+    def test_schema_endpoint_includes_defaults(self, client, mock_workflow_class):
+        """Test that schema includes default values"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check defaults are included
+        assert properties["forecast_times"]["default"] == ["2024-01-01T00:00:00"]
+        assert properties["nsteps"]["default"] == 10
+        assert properties["model_type"]["default"] == "fcn"
+
+    def test_schema_endpoint_workflow_not_found(self, client):
+        """Test that 404 is returned for non-existent workflow"""
+        response = client.get("/v1/workflows/nonexistent_workflow/schema")
+
+        assert response.status_code == 404
+        error = response.json()
+        assert "not found" in error["detail"].lower()
+
+    def test_schema_endpoint_openapi_compatibility(self, client, mock_workflow_class):
+        """Test that returned schema is OpenAPI 3.1 compatible (JSON Schema draft 2020-12)"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        # OpenAPI 3.1 / JSON Schema required fields
+        assert "type" in schema
+        assert "properties" in schema
+
+        # Schema should be directly usable (no wrapper objects)
+        assert "workflow_name" not in schema
+        assert "parameters_schema" not in schema
+
+
+class TestWorkflowParameterValidation:
+    """Test workflow parameter validation at submission time"""
+
+    @pytest.fixture
+    def client(self):
+        """Create test client"""
+        from typing import Any, Literal
+        from unittest.mock import MagicMock, patch
+
+        from pydantic import Field
+
+        from earth2studio.serve.server.main import app
+        from earth2studio.serve.server.workflow import (
+            Workflow,
+            WorkflowParameters,
+            workflow_registry,
+        )
+
+        # Create test workflow parameter classes with Literal validation
+        class TestDeterministicParams(WorkflowParameters):
+            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
+            nsteps: int = Field(default=6, ge=1, le=100)
+            model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
+            data_source: Literal["gfs"] = Field(default="gfs")
+            output_format: Literal["zarr"] = Field(default="zarr")
+            plot_variable: Literal["t2m", "msl", "u10m", "v10m", "tcwv", "z500"] = (
+                Field(default="t2m")
+            )
+
+        class TestDiagnosticParams(WorkflowParameters):
+            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
+            nsteps: int = Field(default=6, ge=1, le=100)
+            prognostic_model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
+            diagnostic_model_type: Literal["precipitation_afno"] = Field(
+                default="precipitation_afno"
+            )
+            data_source: Literal["gfs"] = Field(default="gfs")
+            plot_variable: Literal[
+                "tp", "t2m", "msl", "u10m", "v10m", "tcwv", "z500"
+            ] = Field(default="tp")
+
+        # Create mock workflow classes
+        class TestDeterministicWorkflow(Workflow):
+            name = "deterministic_workflow"
+            description = "Test deterministic workflow"
+            Parameters = TestDeterministicParams
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestDeterministicParams
+            ) -> TestDeterministicParams:
+                try:
+                    return TestDeterministicParams.validate(parameters)
+                except Exception as e:
+                    raise ValueError(f"Invalid parameters: {e}") from e
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        class TestDiagnosticWorkflow(Workflow):
+            name = "diagnostic_workflow"
+            description = "Test diagnostic workflow"
+            Parameters = TestDiagnosticParams
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestDiagnosticParams
+            ) -> TestDiagnosticParams:
+                try:
+                    return TestDiagnosticParams.validate(parameters)
+                except Exception as e:
+                    raise ValueError(f"Invalid parameters: {e}") from e
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        # Register test workflows directly in the registry
+        workflow_registry._workflows["deterministic_workflow"] = (
+            TestDeterministicWorkflow
+        )
+        workflow_registry._workflows["diagnostic_workflow"] = TestDiagnosticWorkflow
+
+        # Create mocks
+        mock_redis = MagicMock()
+        mock_queue = MagicMock()
+        mock_queue.__len__ = MagicMock(return_value=0)
+        mock_queue.max_size = 100
+
+        with patch("earth2studio.serve.server.main.redis_sync_client", mock_redis):
+            with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+                test_client = TestClient(app)
+                yield test_client, mock_queue, mock_redis
+
+    def test_valid_deterministic_workflow_parameters(self, client):
+        """Test that valid parameters for deterministic workflow are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "output_format": "zarr",
+                    "plot_variable": "t2m",
+                }
+            },
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "queued"
+
+    def test_invalid_model_type_rejected(self, client):
+        """Test that invalid model_type is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "invalid_model",  # Invalid value
+                    "data_source": "gfs",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "model_type" in data["detail"]
+
+    def test_invalid_data_source_rejected(self, client):
+        """Test that invalid data_source is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "invalid_source",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "data_source" in data["detail"]
+
+    def test_invalid_output_format_rejected(self, client):
+        """Test that invalid output_format is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "output_format": "invalid_format",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "output_format" in data["detail"]
+
+    def test_invalid_plot_variable_rejected(self, client):
+        """Test that invalid plot_variable is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "plot_variable": "invalid_variable",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "plot_variable" in data["detail"]
+
+    def test_invalid_nsteps_range_rejected(self, client):
+        """Test that nsteps outside valid range is rejected"""
+        test_client, _, _ = client
+
+        # Test nsteps too low
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 0,  # Below minimum of 1
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+        # Test nsteps too high
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 101,  # Above maximum of 100
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+    def test_diagnostic_workflow_invalid_model_types_rejected(self, client):
+        """Test that invalid model types for diagnostic workflow are rejected"""
+        test_client, _, _ = client
+
+        # Test invalid prognostic model
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "invalid_prognostic",  # Invalid
+                    "diagnostic_model_type": "precipitation_afno",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "prognostic_model_type" in data["detail"]
+
+        # Test invalid diagnostic model
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "fcn",
+                    "diagnostic_model_type": "invalid_diagnostic",  # Invalid
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "diagnostic_model_type" in data["detail"]
+
+    def test_valid_diagnostic_workflow_parameters(self, client):
+        """Test that valid parameters for diagnostic workflow are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "fcn",
+                    "diagnostic_model_type": "precipitation_afno",
+                    "data_source": "gfs",
+                    "plot_variable": "tp",
+                }
+            },
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "queued"
+
+    def test_multiple_invalid_parameters_rejected(self, client):
+        """Test that multiple invalid parameters are caught"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 101,  # Invalid: too high
+                    "model_type": "invalid_model",  # Invalid value
+                    "data_source": "invalid_source",  # Invalid value
+                    "output_format": "invalid_format",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+    def test_valid_forecast_times_iso8601_accepted(self, client):
+        """Test that valid ISO 8601 forecast_times are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        # Test various valid ISO 8601 formats
+        valid_times = [
+            ["2024-01-01T00:00:00"],
+            ["2024-01-01T00:00:00Z"],
+            ["2024-01-01T00:00:00+00:00"],
+            ["2024-01-01T00:00:00.123456"],
+            ["2024-01-01 00:00:00"],  # Space separator also accepted
+            ["2024-01-01T00:00:00", "2024-01-02T00:00:00", "2024-01-03T00:00:00"],
+        ]
+
+        for times in valid_times:
+            response = test_client.post(
+                "/v1/infer/deterministic_workflow",
+                json={
+                    "parameters": {
+                        "forecast_times": times,
+                        "nsteps": 10,
+                        "model_type": "fcn",
+                    }
+                },
+            )
+
+            assert response.status_code == 200, f"Failed for times: {times}"
+            data = response.json()
+            assert data["status"] == "queued"
+
+    def test_invalid_forecast_times_rejected(self, client):
+        """Test that invalid forecast_times formats are rejected"""
+        test_client, _, _ = client
+
+        # Test various invalid formats
+        invalid_times = [
+            (["2024-01-01"], "date without time"),
+            (["01/01/2024"], "wrong date format"),
+            (["not-a-date"], "completely invalid"),
+            (["2024-13-01T00:00:00"], "invalid month"),
+            (["2024-01-32T00:00:00"], "invalid day"),
+            (["2024-01-01T25:00:00"], "invalid hour"),
+        ]
+
+        for times, description in invalid_times:
+            response = test_client.post(
+                "/v1/infer/deterministic_workflow",
+                json={
+                    "parameters": {
+                        "forecast_times": times,
+                        "nsteps": 10,
+                        "model_type": "fcn",
+                    }
+                },
+            )
+
+            assert (
+                response.status_code == 422
+            ), f"Expected 422 for {description}, got {response.status_code}"
+            data = response.json()
+            assert (
+                "Invalid parameters" in data["detail"]
+            ), f"Expected validation error for {description}"
+            assert (
+                "forecast_times" in data["detail"]
+            ), f"Expected forecast_times in error for {description}"
+
+    def test_forecast_times_mixed_valid_invalid_rejected(self, client):
+        """Test that forecast_times with one invalid value fails"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": [
+                        "2024-01-01T00:00:00",  # Valid
+                        "not-a-date",  # Invalid
+                        "2024-01-03T00:00:00",  # Valid
+                    ],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "forecast_times" in data["detail"]
+
+    def test_forecast_times_wrong_type_rejected(self, client):
+        """Test that forecast_times with non-string values are rejected"""
+        test_client, _, _ = client
+
+        # Test with integer
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": [12345],  # Not a string
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "forecast_times" in data["detail"]
+
+
+class TestWorkflowExecutionRequest:
+    """Tests for WorkflowExecutionRequest validation"""
+
+    def test_valid_request_with_parameters(self):
+        """Test that valid request with parameters field is accepted"""
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Valid request
+        request = WorkflowExecutionRequest(
+            parameters={"field1": "value1", "field2": 123}
+        )
+        assert request.parameters == {"field1": "value1", "field2": 123}
+
+    def test_valid_request_with_empty_parameters(self):
+        """Test that request with empty parameters dict is accepted"""
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Empty parameters is valid
+        request = WorkflowExecutionRequest(parameters={})
+        assert request.parameters == {}
+
+    def test_valid_request_without_parameters_uses_default(self):
+        """Test that request without parameters field uses default empty dict"""
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # No parameters provided - should use default
+        request = WorkflowExecutionRequest()
+        assert request.parameters == {}
+
+    def test_invalid_request_with_extra_fields(self):
+        """Test that request with unknown fields is rejected"""
+        from pydantic import ValidationError
+
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Request with unknown field should be rejected
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"valid": "param"}, unknown_field="should_fail"
+            )
+
+    def test_invalid_request_with_multiple_extra_fields(self):
+        """Test that request with multiple unknown fields is rejected"""
+        from pydantic import ValidationError
+
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Multiple unknown fields
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"valid": "param"},
+                extra1="bad",
+                extra2="also_bad",
+                extra3="still_bad",
+            )
+
+    def test_invalid_request_flat_structure(self):
+        """Test that flat request structure (without parameters wrapper) is rejected"""
+        from pydantic import ValidationError
+
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Flat structure - fields that should be inside 'parameters'
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                forecast_times=["2024-01-01"],
+                nsteps=6,
+                model_type="fcn",
+            )
+
+    def test_invalid_request_mixed_structure(self):
+        """Test that mixed structure (parameters + extra fields) is rejected"""
+        from pydantic import ValidationError
+
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Mixed: valid parameters field + invalid extra fields
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"forecast_times": ["2024-01-01"], "nsteps": 6},
+                workflow_type="deterministic",  # This should not be at this level
+                device="auto",  # This should not be at this level
+            )
+
+    def test_parameters_field_type_validation(self):
+        """Test that parameters field must be a dict"""
+        from pydantic import ValidationError
+
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # parameters must be a dict, not a string
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters="not_a_dict")
+
+        # parameters must be a dict, not a list
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters=["not", "a", "dict"])
+
+        # parameters must be a dict, not None
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters=None)
+
+    def test_valid_request_serialization(self):
+        """Test that valid request can be serialized to dict"""
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        request = WorkflowExecutionRequest(
+            parameters={"forecast_times": ["2024-01-01"], "nsteps": 10}
+        )
+
+        # Serialize to dict
+        data = request.model_dump()
+        assert isinstance(data, dict)
+        assert data == {"parameters": {"forecast_times": ["2024-01-01"], "nsteps": 10}}
+
+    def test_valid_request_with_nested_parameters(self):
+        """Test that request with nested parameter structures is accepted"""
+        from earth2studio.serve.server.main import WorkflowExecutionRequest
+
+        # Complex nested parameters
+        request = WorkflowExecutionRequest(
+            parameters={
+                "forecast_times": ["2024-01-01", "2024-01-02"],
+                "nsteps": 10,
+                "config": {
+                    "model": {"type": "fcn", "version": "1.0"},
+                    "data": {"source": "gfs", "cache": True},
+                },
+                "options": ["opt1", "opt2", "opt3"],
+            }
+        )
+
+        assert request.parameters["nsteps"] == 10
+        assert request.parameters["config"]["model"]["type"] == "fcn"
+        assert len(request.parameters["options"]) == 3
+
+
+class TestQueuePosition:
+    """Tests for queue position functionality"""
+
+    @pytest.fixture
+    def mock_queue(self):
+        """Create a mock RQ queue with job IDs"""
+        from unittest.mock import MagicMock
+
+        queue = MagicMock()
+        queue.job_ids = [
+            "workflow1_exec_001",
+            "workflow2_exec_002",
+            "workflow1_exec_003",
+            "workflow3_exec_004",
+        ]
+        return queue
+
+    def test_get_queue_position_first_in_queue(self, mock_queue):
+        """Test getting position for first job in queue"""
+        from unittest.mock import patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow1_exec_001")
+            assert position == 0  # 0-indexed: first job is at position 0
+
+    def test_get_queue_position_middle_of_queue(self, mock_queue):
+        """Test getting position for job in middle of queue"""
+        from unittest.mock import patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow1_exec_003")
+            assert position == 2  # 0-indexed: third job is at position 2
+
+    def test_get_queue_position_last_in_queue(self, mock_queue):
+        """Test getting position for last job in queue"""
+        from unittest.mock import patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow3_exec_004")
+            assert position == 3  # 0-indexed: fourth job is at position 3
+
+    def test_get_queue_position_not_in_queue(self, mock_queue):
+        """Test getting position for job not in queue returns None"""
+        from unittest.mock import patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            position = get_queue_position("nonexistent_job")
+            assert position is None
+
+    def test_get_queue_position_no_queue(self):
+        """Test getting position when queue is not initialized returns None"""
+        from unittest.mock import patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        with patch("earth2studio.serve.server.main.inference_queue", None):
+            position = get_queue_position("any_job")
+            assert position is None
+
+    def test_get_queue_position_empty_queue(self):
+        """Test getting position when queue is empty"""
+        from unittest.mock import MagicMock, patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        empty_queue = MagicMock()
+        empty_queue.job_ids = []
+
+        with patch("earth2studio.serve.server.main.inference_queue", empty_queue):
+            position = get_queue_position("any_job")
+            assert position is None
+
+    def test_get_queue_position_exception_handling(self):
+        """Test that exceptions in get_queue_position are handled gracefully"""
+        from unittest.mock import MagicMock, PropertyMock, patch
+
+        from earth2studio.serve.server.main import get_queue_position
+
+        queue_with_error = MagicMock()
+        type(queue_with_error).job_ids = PropertyMock(
+            side_effect=Exception("Queue connection error")
+        )
+
+        with patch("earth2studio.serve.server.main.inference_queue", queue_with_error):
+            position = get_queue_position("any_job")
+            assert position is None
+
+
+class TestWorkflowExecutionWithQueuePosition:
+    """Tests for workflow execution endpoint with queue position"""
+
+    @pytest.fixture
+    def mock_workflow_class(self):
+        """Create a mock workflow class"""
+        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
+
+        class TestWorkflowParameters(WorkflowParameters):
+            """Test parameters"""
+
+            test_param: str = "default"
+
+        class TestWorkflow(Workflow):
+            name = "test_workflow"
+            description = "Test workflow"
+            Parameters = TestWorkflowParameters
+
+            @classmethod
+            def validate_parameters(cls, parameters):
+                return TestWorkflowParameters.validate(parameters)
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        return TestWorkflow
+
+    @pytest.fixture
+    def client(self, mock_workflow_class):
+        """Create test client with mocked dependencies"""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            # Setup mock async Redis
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+
+            # Setup mock sync Redis
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_instance.setex = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            # Setup mock queue
+            mock_queue = MagicMock()
+            mock_queue.__len__ = MagicMock(return_value=0)
+            mock_queue.job_ids = []
+            mock_queue_class.return_value = mock_queue
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import workflow_registry
+
+            # Register the test workflow
+            workflow_registry._workflows["test_workflow"] = mock_workflow_class
+
+            with TestClient(app, raise_server_exceptions=False) as client:
+                yield client, mock_queue, mock_sync_instance
+
+            # Cleanup
+            if "test_workflow" in workflow_registry._workflows:
+                del workflow_registry._workflows["test_workflow"]
+
+    def test_execute_workflow_includes_queue_position(self, client):
+        """Test that POST /v1/infer/{workflow_name} includes queue position"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_123"
+
+        # Before enqueue: queue size is 0
+        mock_queue.__len__ = MagicMock(return_value=0)
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Redis llen returns 0 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        # Patch the inference_queue at module level
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is included in response
+        assert "position" in data
+        assert data["position"] == 0  # Redis llen returns 0 (first job, position 0)
+        assert data["status"] == "queued"
+
+    def test_execute_workflow_position_with_existing_jobs(self, client):
+        """Test that position is correct when there are existing jobs in queue"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_456"
+
+        # Before enqueue: queue has 2 existing jobs
+        mock_queue.__len__ = MagicMock(return_value=2)
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Redis llen returns 2 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=2)
+
+        # Patch the inference_queue at module level
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is correct
+        assert "position" in data
+        assert data["position"] == 2  # Redis llen returns 2 (third job, position 2)
+        assert data["status"] == "queued"
+
+    def test_execute_workflow_position_with_five_existing_jobs(self, client):
+        """Test that position is correct with multiple existing jobs in queue"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_789"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Before enqueue: queue has 5 existing jobs
+        mock_queue.__len__ = MagicMock(return_value=5)
+
+        # Redis llen returns 5 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=5)
+
+        # Patch the inference_queue at module level
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is correct
+        assert "position" in data
+        assert data["position"] == 5  # Redis llen returns 5 (sixth job, position 5)
+        assert data["status"] == "queued"
+
+    def test_get_workflow_status_includes_position_when_queued(self, client):
+        """Test that GET /v1/infer/{workflow_name}/{execution_id}/status includes position when status is queued"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup queue with multiple jobs
+        execution_id = "exec_456"
+        job_id = f"test_workflow_{execution_id}"
+
+        # Set up mock queue job_ids to include our job at position 1
+        mock_queue.job_ids = ["other_job_1", job_id, "other_job_2"]
+
+        # Mock Redis response for queued execution
+        import json
+        from datetime import datetime, timezone
+        from unittest.mock import patch
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "queued",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        # Patch the inference_queue for get_queue_position to use
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is included and correct
+        assert "position" in data
+        assert data["position"] == 1  # Second in queue (0-indexed, so position 1)
+        assert data["status"] == "queued"
+
+    def test_get_workflow_status_queued_but_not_in_queue(self, client):
+        """Test race condition: status is QUEUED but job not in queue (worker picked it up)"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_race"
+
+        # Set up mock queue with empty job_ids (job already picked up by worker)
+        mock_queue.job_ids = []
+
+        # Mock Redis response with QUEUED status (worker hasn't updated it yet)
+        import json
+        from datetime import datetime, timezone
+        from unittest.mock import patch
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "queued",  # Still queued in Redis
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        # Patch the inference_queue for get_queue_position to use
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Should be treated as RUNNING since job is not in queue
+        assert data["status"] == "running"
+        # Position should be None for running status
+        if "position" in data:
+            assert data["position"] is None
+
+    def test_get_workflow_status_no_position_when_running(self, client):
+        """Test that position is None when workflow is running (not queued)"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_789"
+
+        # Mock Redis response for running execution
+        import json
+        from datetime import datetime, timezone
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "running",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Position should be None or not included for non-queued status
+        # (The model allows None, so it could be None or absent)
+        if "position" in data:
+            assert data["position"] is None
+        assert data["status"] == "running"
+
+    def test_get_workflow_status_no_position_when_completed(self, client):
+        """Test that position is None when workflow is completed"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_999"
+
+        # Mock Redis response for completed execution
+        import json
+        from datetime import datetime, timezone
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "completed",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "end_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Position should be None for completed workflows
+        if "position" in data:
+            assert data["position"] is None
+        assert data["status"] == "completed"
+
+
+class TestHealthAndProbes:
+    """Tests for /health, /readiness, /liveness, and /metrics endpoints."""
+
+    @pytest.fixture
+    def client_with_mocks(self):
+        """Client with mocked Redis and RQ for probe endpoints."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue"),
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_redis.return_value = mock_async_instance
+
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            from earth2studio.serve.server.main import app
+
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c
+
+    def test_liveness_returns_alive(self, client_with_mocks):
+        """GET /liveness returns status alive."""
+        response = client_with_mocks.get("/liveness")
+        assert response.status_code == 200
+        assert response.json() == {"status": "alive"}
+
+    def test_health_healthy_when_script_returns_zero(self, client_with_mocks):
+        """GET /health and /readiness return healthy when status script exits 0."""
+        with patch(
+            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
+        ) as mock_exec:
+            mock_proc = MagicMock()
+            mock_proc.returncode = 0
+            mock_proc.communicate = AsyncMock(return_value=(b"", b""))
+            mock_exec.return_value = mock_proc
+
+            for path in ("/health", "/readiness"):
+                response = client_with_mocks.get(path)
+                assert response.status_code == 200
+                data = response.json()
+                assert data["status"] == "healthy"
+                assert "timestamp" in data
+
+    def test_health_unhealthy_when_script_returns_nonzero(self, client_with_mocks):
+        """GET /health returns 503 when status script exits non-zero."""
+        with patch(
+            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
+        ) as mock_exec:
+            mock_proc = MagicMock()
+            mock_proc.returncode = 1
+            mock_proc.communicate = AsyncMock(return_value=(b"", b"stderr"))
+            mock_exec.return_value = mock_proc
+
+            response = client_with_mocks.get("/health")
+            assert response.status_code == 503
+            data = response.json()
+            assert data["detail"]["status"] == "unhealthy"
+
+    def test_metrics_returns_prometheus_format(self, client_with_mocks):
+        """GET /metrics returns Prometheus text format."""
+        with patch("earth2studio.serve.server.main.generate_latest") as mock_gen:
+            mock_gen.return_value = (
+                b"# HELP mock_metric Mock\n# TYPE mock_metric gauge\nmock_metric 1.0\n"
+            )
+
+            response = client_with_mocks.get("/metrics")
+            assert response.status_code == 200
+            assert "mock_metric" in response.text
+            assert "text/plain" in response.headers.get("content-type", "")
+
+
+class TestListWorkflows:
+    """Tests for GET /v1/workflows and /v1/infer/workflows."""
+
+    @pytest.fixture
+    def client_with_workflows(self):
+        """Client with a registered workflow for list_workflows."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue"),
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_redis.return_value = mock_async_instance
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import workflow_registry
+
+            class FakeWorkflow:
+                name = "fake_wf"
+                description = "Fake workflow for list test"
+
+            workflow_registry._workflows["fake_wf"] = FakeWorkflow
+
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c
+
+            if "fake_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["fake_wf"]
+
+    def test_list_workflows_returns_registry(self, client_with_workflows):
+        """GET /v1/workflows returns workflows dict."""
+        response = client_with_workflows.get("/v1/workflows")
+        assert response.status_code == 200
+        data = response.json()
+        assert "workflows" in data
+        assert "fake_wf" in data["workflows"]
+        assert data["workflows"]["fake_wf"] == "Fake workflow for list test"
+
+    def test_list_workflows_infer_path(self, client_with_workflows):
+        """GET /v1/infer/workflows returns same structure."""
+        response = client_with_workflows.get("/v1/infer/workflows")
+        assert response.status_code == 200
+        data = response.json()
+        assert "workflows" in data
+
+
+class TestAdmissionControl:
+    """Tests for check_admission_control (queue full, Redis None)."""
+
+    @pytest.fixture
+    def client_for_admission(self):
+        """Client with mocked queue and redis for admission tests."""
+        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
+
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_redis.return_value = mock_async_instance
+
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.llen = MagicMock(return_value=0)
+            mock_sync_redis.return_value = mock_sync_instance
+
+            mock_queue = MagicMock()
+            mock_queue.enqueue = MagicMock(return_value=MagicMock(id="job_1"))
+            mock_queue_class.return_value = mock_queue
+
+            class AdmitParams(WorkflowParameters):
+                x: int = Field(default=1, ge=0)
+
+            class AdmitWorkflow(Workflow):
+                name = "admit_wf"
+                description = "For admission test"
+                Parameters = AdmitParams
+
+                @classmethod
+                def validate_parameters(
+                    cls, parameters: dict[str, Any] | AdmitParams
+                ) -> AdmitParams:
+                    return AdmitParams.validate(parameters)
+
+                def run(self, parameters, execution_id):
+                    return {"status": "ok"}
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import workflow_registry
+
+            workflow_registry._workflows["admit_wf"] = AdmitWorkflow
+
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c, mock_sync_instance, mock_queue
+
+            if "admit_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["admit_wf"]
+
+    def test_admission_control_redis_none_returns_503(self, client_for_admission):
+        """When redis_sync_client is None, execute returns 503."""
+        client, mock_sync_redis, mock_queue = client_for_admission
+        with patch("earth2studio.serve.server.main.redis_sync_client", None):
+            response = client.post(
+                "/v1/infer/admit_wf",
+                json={"parameters": {}},
+            )
+        assert response.status_code == 503
+        assert "Redis" in response.json().get("detail", "")
+
+    def test_admission_control_queue_full_returns_429(self, client_for_admission):
+        """When queue is full, execute returns 429."""
+        client, mock_sync_redis, mock_queue = client_for_admission
+        mock_sync_redis.llen.return_value = 100
+        with patch("earth2studio.serve.server.main.config") as mock_config:
+            mock_config.queue.max_size = 10
+            mock_config.queue.name = "inference"
+            mock_config.queue.result_zip_queue_name = "result_zip"
+            mock_config.queue.object_storage_queue_name = "object_storage"
+            mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
+            response = client.post(
+                "/v1/infer/admit_wf",
+                json={"parameters": {}},
+            )
+        assert response.status_code == 429
+        assert "full" in response.json().get("detail", "").lower()
+
+    def test_admission_control_llen_raises_returns_500(self, client_for_admission):
+        """When redis_sync_client.llen raises, execute returns 500."""
+        client, mock_sync_redis, mock_queue = client_for_admission
+        mock_sync_redis.llen.side_effect = RuntimeError("Redis connection lost")
+        response = client.post(
+            "/v1/infer/admit_wf",
+            json={"parameters": {}},
+        )
+        assert response.status_code == 500
+        assert "queue" in response.json().get("detail", "").lower()
+
+
+class TestExecuteWorkflowBranches:
+    """Tests for execute_workflow branches (404, 400, 503, 500)."""
+
+    @pytest.fixture
+    def client_exec(self):
+        """Client with one workflow for execute tests."""
+        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
+
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_instance.setex = MagicMock()
+            mock_sync_instance.llen = MagicMock(return_value=0)
+            mock_sync_redis.return_value = mock_sync_instance
+
+            mock_queue = MagicMock()
+            mock_job = MagicMock()
+            mock_job.id = "exec_wf_exec_123"
+            mock_queue.enqueue = MagicMock(return_value=mock_job)
+            mock_queue_class.return_value = mock_queue
+
+            class ExecParams(WorkflowParameters):
+                x: int = Field(default=1, ge=0)
+
+            class ExecWorkflow(Workflow):
+                name = "exec_wf"
+                description = "For execute tests"
+                Parameters = ExecParams
+
+                @classmethod
+                def validate_parameters(
+                    cls, parameters: dict[str, Any] | ExecParams
+                ) -> ExecParams:
+                    return ExecParams.validate(parameters)
+
+                def run(self, parameters, execution_id):
+                    return {"status": "ok"}
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import workflow_registry
+
+            workflow_registry._workflows["exec_wf"] = ExecWorkflow
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c, mock_sync_instance, mock_queue
+            if "exec_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["exec_wf"]
+
+    def test_execute_workflow_not_found_404(self, client_exec):
+        """POST /v1/infer/{workflow_name} with unknown workflow returns 404."""
+        client, _, _ = client_exec
+        response = client.post(
+            "/v1/infer/nonexistent_workflow",
+            json={"parameters": {}},
+        )
+        assert response.status_code == 404
+        assert "not found" in response.json().get("detail", "").lower()
+
+    def test_execute_workflow_validate_parameters_unexpected_error_400(
+        self, client_exec
+    ):
+        """When validate_parameters raises non-ValueError, returns 400."""
+        client, _, _ = client_exec
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
+            mock_wf = MagicMock()
+            mock_wf.validate_parameters.side_effect = AttributeError("no model_dump")
+            mock_registry.get_workflow_class.return_value = mock_wf
+            response = client.post(
+                "/v1/infer/exec_wf",
+                json={"parameters": {"x": 1}},
+            )
+        assert response.status_code == 400
+        assert "validating" in response.json().get("detail", "").lower()
+
+    def test_execute_workflow_inference_queue_none_503(self, client_exec):
+        """When inference_queue is None, returns 503."""
+        client, mock_redis, mock_queue = client_exec
+        with patch("earth2studio.serve.server.main.inference_queue", None):
+            response = client.post(
+                "/v1/infer/exec_wf",
+                json={"parameters": {}},
+            )
+        assert response.status_code == 503
+        assert "queue" in response.json().get("detail", "").lower()
+
+    def test_execute_workflow_llen_raises_after_enqueue_500(self, client_exec):
+        """When llen raises after enqueue (queue position lookup), returns 500."""
+        client, mock_redis, mock_queue = client_exec
+        # First 4 calls: admission (4 queues); 5th: position lookup. Make 5th raise.
+        mock_redis.llen.side_effect = [0, 0, 0, 0, RuntimeError("redis error")]
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = client.post(
+                "/v1/infer/exec_wf",
+                json={"parameters": {}},
+            )
+        assert response.status_code == 500
+        assert "queue position" in response.json().get(
+            "detail", ""
+        ).lower() or "Internal" in response.json().get("detail", "")
+
+    def test_execute_workflow_enqueue_failure_500(self, client_exec):
+        """When enqueue raises, returns 500."""
+        client, mock_redis, mock_queue = client_exec
+        mock_queue.enqueue.side_effect = RuntimeError("RQ enqueue failed")
+        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
+            response = client.post(
+                "/v1/infer/exec_wf",
+                json={"parameters": {}},
+            )
+        assert response.status_code == 500
+        assert "enqueue" in response.json().get("detail", "").lower()
+
+
+class TestHealthMetricsSchemaExceptions:
+    """Tests for health, metrics, and schema exception paths."""
+
+    @pytest.fixture
+    def client_probes(self):
+        """Client for probe endpoints."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue"),
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_redis.return_value = mock_async_instance
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+            from earth2studio.serve.server.main import app
+
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c
+
+    def test_health_check_subprocess_exception_500(self, client_probes):
+        """When create_subprocess_exec raises, health returns 500."""
+        with patch(
+            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
+        ) as mock_exec:
+            mock_exec.side_effect = FileNotFoundError("status.sh not found")
+            response = client_probes.get("/health")
+        assert response.status_code == 500
+        assert "Health check failed" in response.json().get("detail", "")
+
+    def test_metrics_generate_latest_raises_500(self, client_probes):
+        """When generate_latest raises, metrics returns 500."""
+        with patch("earth2studio.serve.server.main.generate_latest") as mock_gen:
+            mock_gen.side_effect = RuntimeError("metrics error")
+            response = client_probes.get("/metrics")
+        assert response.status_code == 500
+        detail = response.json().get("detail", {})
+        if isinstance(detail, dict):
+            assert "metrics" in str(detail).lower()
+        else:
+            assert "metrics" in detail.lower()
+
+    def test_schema_generation_exception_500(self, client_probes):
+        """When model_json_schema raises, schema endpoint returns 500."""
+        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
+
+        class BadSchemaParams(WorkflowParameters):
+            x: int = Field(default=1)
+
+        class BadSchemaWorkflow(Workflow):
+            name = "bad_schema_wf"
+            description = "Bad schema"
+            Parameters = BadSchemaParams
+
+            @classmethod
+            def validate_parameters(cls, parameters):
+                return BadSchemaParams.validate(parameters)
+
+            def run(self, parameters, execution_id):
+                return {"status": "ok"}
+
+        from earth2studio.serve.server.workflow import workflow_registry
+
+        workflow_registry._workflows["bad_schema_wf"] = BadSchemaWorkflow
+        try:
+            with patch.object(
+                BadSchemaParams,
+                "model_json_schema",
+                side_effect=RuntimeError("schema error"),
+            ):
+                response = client_probes.get("/v1/workflows/bad_schema_wf/schema")
+            assert response.status_code == 500
+            detail = response.json().get("detail", {})
+            assert "schema" in str(detail).lower()
+        finally:
+            if "bad_schema_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["bad_schema_wf"]
+
+
+class TestGetWorkflowStatusBranches:
+    """Tests for get_workflow_status branches (404 ValueError, 500 Exception)."""
+
+    @pytest.fixture
+    def client_status(self):
+        """Client with workflow for status tests."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+            mock_queue_class.return_value = MagicMock()
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import (
+                Workflow,
+                WorkflowParameters,
+                workflow_registry,
+            )
+
+            class StatusParams(WorkflowParameters):
+                x: int = Field(default=1)
+
+            class StatusWorkflow(Workflow):
+                name = "status_wf"
+                description = "Status test"
+                Parameters = StatusParams
+
+                @classmethod
+                def validate_parameters(cls, parameters):
+                    return StatusParams.validate(parameters)
+
+                def run(self, parameters, execution_id):
+                    return {"status": "ok"}
+
+            workflow_registry._workflows["status_wf"] = StatusWorkflow
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c, mock_sync_instance
+            if "status_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["status_wf"]
+
+    def test_get_workflow_status_value_error_404(self, client_status):
+        """When _get_execution_data raises ValueError, returns 404."""
+        client, mock_redis = client_status
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(
+                side_effect=ValueError("Execution not found")
+            )
+            mock_registry.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/status_wf/exec_123/status")
+        assert response.status_code == 404
+
+    def test_get_workflow_status_exception_500(self, client_status):
+        """When _get_execution_data raises Exception, returns 500."""
+        client, mock_redis = client_status
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(
+                side_effect=RuntimeError("Redis error")
+            )
+            mock_registry.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/status_wf/exec_123/status")
+        assert response.status_code == 500
+        assert "status" in response.json().get("detail", "").lower()
+
+
+class TestGetWorkflowResultsBranches:
+    """Tests for get_workflow_results (EXPIRED, 202, FAILED, CANCELLED, 404, 200, 500)."""
+
+    @pytest.fixture
+    def client_results(self, tmp_path):
+        """Client and tmp dir for results tests."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+            mock_queue_class.return_value = MagicMock()
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import (
+                Workflow,
+                WorkflowParameters,
+                WorkflowStatus,
+                workflow_registry,
+            )
+
+            class ResultsParams(WorkflowParameters):
+                x: int = Field(default=1)
+
+            class ResultsWorkflow(Workflow):
+                name = "results_wf"
+                description = "Results test"
+                Parameters = ResultsParams
+
+                @classmethod
+                def validate_parameters(cls, parameters):
+                    return ResultsParams.validate(parameters)
+
+                def run(self, parameters, execution_id):
+                    return {"status": "ok"}
+
+            workflow_registry._workflows["results_wf"] = ResultsWorkflow
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c, mock_sync_instance, tmp_path, WorkflowStatus
+            if "results_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["results_wf"]
+
+    def _make_execution_data(self, status, execution_id="exec_1"):
+        from earth2studio.serve.server.workflow import WorkflowResult
+
+        return WorkflowResult(
+            workflow_name="results_wf",
+            execution_id=execution_id,
+            status=status,
+            start_time=datetime.now(timezone.utc).isoformat(),
+            end_time=(
+                datetime.now(timezone.utc).isoformat()
+                if status in ("completed", "failed", "expired", "cancelled")
+                else None
+            ),
+            error_message="failed" if status == "failed" else None,
+        )
+
+    def test_get_workflow_results_expired_400(self, client_results):
+        """When status is EXPIRED, returns 400."""
+        client, mock_redis, _, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.EXPIRED)
+        mock_redis.get.return_value = json.dumps(exec_data.model_dump()).encode()
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 400
+        assert "expired" in response.json().get("detail", {}).get("error", "").lower()
+
+    def test_get_workflow_results_still_running_202(self, client_results):
+        """When status is RUNNING, returns 202."""
+        client, mock_redis, _, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.RUNNING)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 202
+
+    def test_get_workflow_results_failed_404(self, client_results):
+        """When status is FAILED, returns 404."""
+        client, mock_redis, _, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.FAILED)
+        exec_data.error_message = "Something failed"
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 404
+        assert "failed" in response.json().get("detail", {}).get("error", "").lower()
+
+    def test_get_workflow_results_cancelled_404(self, client_results):
+        """When status is CANCELLED, returns 404."""
+        client, mock_redis, _, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.CANCELLED)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 404
+        assert "cancelled" in response.json().get("detail", {}).get("error", "").lower()
+
+    def test_get_workflow_results_metadata_not_found_404(self, client_results):
+        """When status is COMPLETED but metadata file missing, returns 404."""
+        client, mock_redis, tmp_path, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 404
+        assert "Metadata" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_results_success_200(self, client_results):
+        """When status is COMPLETED and metadata exists, returns 200 with JSON."""
+        client, mock_redis, tmp_path, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
+        metadata_path = tmp_path / "metadata_results_wf:exec_1.json"
+        metadata_path.write_text(
+            json.dumps({"request_id": "results_wf:exec_1", "status": "completed"})
+        )
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 200
+        assert response.json().get("request_id") == "results_wf:exec_1"
+
+    def test_get_workflow_results_value_error_400(self, client_results):
+        """When _get_execution_data raises ValueError, returns 400."""
+        client, _, _, _ = client_results
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(
+                side_effect=ValueError("Invalid key")
+            )
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 400
+
+    def test_get_workflow_results_exception_500(self, client_results):
+        """When reading metadata raises, returns 500."""
+        client, mock_redis, tmp_path, WorkflowStatus = client_results
+        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
+        metadata_path = tmp_path / "metadata_results_wf:exec_1.json"
+        metadata_path.write_text("not valid json {{")
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/results_wf/exec_1/results")
+        assert response.status_code == 500
+
+
+class TestGetWorkflowResultFileBranches:
+    """Tests for get_workflow_result_file (404, 503, 403, 400, 200)."""
+
+    @pytest.fixture
+    def client_file(self, tmp_path):
+        """Client and tmp dir for result file tests."""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("earth2studio.serve.server.workflow.register_all_workflows"),
+        ):
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+            mock_queue_class.return_value = MagicMock()
+
+            from earth2studio.serve.server.main import app
+            from earth2studio.serve.server.workflow import (
+                Workflow,
+                WorkflowParameters,
+                WorkflowStatus,
+                workflow_registry,
+            )
+
+            class FileParams(WorkflowParameters):
+                x: int = Field(default=1)
+
+            class FileWorkflow(Workflow):
+                name = "file_wf"
+                description = "File test"
+                Parameters = FileParams
+
+                @classmethod
+                def validate_parameters(cls, parameters):
+                    return FileParams.validate(parameters)
+
+                def run(self, parameters, execution_id):
+                    return {"status": "ok"}
+
+            workflow_registry._workflows["file_wf"] = FileWorkflow
+            with TestClient(app, raise_server_exceptions=False) as c:
+                yield c, mock_async_instance, mock_sync_instance, tmp_path, WorkflowStatus
+            if "file_wf" in workflow_registry._workflows:
+                del workflow_registry._workflows["file_wf"]
+
+    def _exec_data(self, status=None):
+        from earth2studio.serve.server.workflow import (
+            WorkflowResult,
+            WorkflowStatus,
+        )
+
+        if status is None:
+            status = WorkflowStatus.COMPLETED
+        return WorkflowResult(
+            workflow_name="file_wf",
+            execution_id="exec_1",
+            status=status,
+            start_time=datetime.now(timezone.utc).isoformat(),
+            end_time=datetime.now(timezone.utc).isoformat(),
+        )
+
+    def test_get_workflow_result_file_workflow_not_found_404(self, client_file):
+        """When workflow not in registry, returns 404."""
+        client, *_ = client_file
+        response = client.get("/v1/infer/nonexistent_wf/exec_1/results/out.nc")
+        assert response.status_code == 404
+
+    def test_get_workflow_result_file_status_not_completed_404(self, client_file):
+        """When status is not COMPLETED, returns 404."""
+        client, mock_async, mock_sync, _, WorkflowStatus = client_file
+        exec_data = self._exec_data(WorkflowStatus.RUNNING)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            mock_wf = MagicMock()
+            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+            mock_reg.get_workflow_class.return_value = mock_wf
+            response = client.get("/v1/infer/file_wf/exec_1/results/out.nc")
+        assert response.status_code == 404
+        assert (
+            "not available"
+            in response.json().get("detail", {}).get("error", "").lower()
+        )
+
+    def test_get_workflow_result_file_redis_none_503(self, client_file):
+        """When redis_client (async) is None for file request, returns 503."""
+        client, mock_async, mock_sync, _, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", None):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/file_wf/exec_1/results/out.nc")
+        assert response.status_code == 503
+
+    def test_get_workflow_result_file_zip_not_in_redis_404(self, client_file):
+        """When filepath equals request_id but zip not in Redis, returns 404."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        mock_async.get = AsyncMock(return_value=None)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
+                    mock_wf = MagicMock()
+                    mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                    mock_reg.get_workflow_class.return_value = mock_wf
+                    response = client.get(
+                        "/v1/infer/file_wf/exec_1/results/file_wf:exec_1"
+                    )
+        assert response.status_code == 404
+        assert "Zip" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_result_file_output_dir_not_found_404(self, client_file):
+        """When output_path_key not in Redis, returns 404."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        mock_async.get = AsyncMock(return_value=None)
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/file_wf/exec_1/results/somefile.txt")
+        assert response.status_code == 404
+        assert "Output directory" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_result_file_path_traversal_403(self, client_file):
+        """When filepath escapes output dir, returns 403 (or 404 if path invalid)."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        output_dir = tmp_path / "output"
+        output_dir.mkdir()
+        # Create a file inside output_dir; request it via path that escapes (..)
+        inner = output_dir / "inner.txt"
+        inner.write_text("data")
+        mock_async.get = AsyncMock(return_value=str(output_dir))
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                # Path ../output/inner.txt from output_dir resolves to output_dir/inner.txt
+                # Use path that clearly escapes: .. with resolved path outside
+                response = client.get(
+                    "/v1/infer/file_wf/exec_1/results/..%2F..%2F..%2Fetc%2Fpasswd"
+                )
+        # Path traversal must not succeed: either 403 (access denied) or 404
+        assert response.status_code in (403, 404)
+        if response.status_code == 403:
+            assert "Access denied" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_result_file_not_found_404(self, client_file):
+        """When file does not exist in output dir, returns 404."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        output_dir = tmp_path / "output"
+        output_dir.mkdir()
+        mock_async.get = AsyncMock(return_value=str(output_dir))
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get(
+                    "/v1/infer/file_wf/exec_1/results/nonexistent.txt"
+                )
+        assert response.status_code == 404
+        assert "File not found" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_result_file_not_a_file_400(self, client_file):
+        """When path is a directory, returns 400."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        output_dir = tmp_path / "output"
+        output_dir.mkdir()
+        subdir = output_dir / "subdir"
+        subdir.mkdir()
+        mock_async.get = AsyncMock(return_value=str(output_dir))
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/file_wf/exec_1/results/subdir")
+        assert response.status_code == 400
+        assert "Not a file" in response.json().get("detail", {}).get("error", "")
+
+    def test_get_workflow_result_file_stream_file_200(self, client_file):
+        """When file exists, returns 200 with streamed content."""
+        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
+        exec_data = self._exec_data()
+        output_dir = tmp_path / "output"
+        output_dir.mkdir()
+        out_file = output_dir / "data.txt"
+        out_file.write_text("hello world")
+        mock_async.get = AsyncMock(return_value=str(output_dir))
+        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
+            with patch("earth2studio.serve.server.main.redis_client", mock_async):
+                mock_wf = MagicMock()
+                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
+                mock_reg.get_workflow_class.return_value = mock_wf
+                response = client.get("/v1/infer/file_wf/exec_1/results/data.txt")
+        assert response.status_code == 200
+        assert response.text == "hello world"
+
+
+class TestMainEntrypoint:
+    """Test main module entrypoint (covers line 1044)."""
+
+    def test_main_entrypoint_calls_uvicorn(self):
+        """When run as __main__, uvicorn.run is called."""
+        import runpy
+
+        import earth2studio.serve.server.main as main_module
+
+        with patch("uvicorn.run") as mock_run:
+            runpy.run_path(main_module.__file__, run_name="__main__")
+            mock_run.assert_called_once()
+            call = mock_run.call_args
+            # runpy re-executes the module so app is a new instance; check type and args
+            assert call[0][0].__class__.__name__ == "FastAPI"
+            assert call[1]["host"] == main_module.config.server.host
+            assert call[1]["port"] == main_module.config.server.port
diff --git a/test/serve/server/test_utils.py b/test/serve/server/test_utils.py
new file mode 100644
index 000000000..3e3b663ff
--- /dev/null
+++ b/test/serve/server/test_utils.py
@@ -0,0 +1,266 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest.mock import MagicMock, patch
+
+from earth2studio.serve.server.utils import (
+    get_inference_request_metadata_key,
+    get_inference_request_output_path_key,
+    get_inference_request_zip_key,
+    get_results_zip_dir_key,
+    get_signed_url_key,
+    queue_next_stage,
+)
+
+
+class TestRedisKeyFunctions:
+    """Tests for Redis key helper functions."""
+
+    def test_get_inference_request_zip_key(self):
+        """get_inference_request_zip_key returns expected key format."""
+        assert get_inference_request_zip_key("req_1") == (
+            "inference_request_zips:req_1:zip_file"
+        )
+
+    def test_get_inference_request_output_path_key(self):
+        """get_inference_request_output_path_key returns expected key format."""
+        assert get_inference_request_output_path_key("req_1") == (
+            "inference_request:req_1:output_path"
+        )
+
+    def test_get_inference_request_metadata_key(self):
+        """get_inference_request_metadata_key returns expected key format."""
+        assert get_inference_request_metadata_key("req_1") == (
+            "inference_request:req_1:pending_metadata"
+        )
+
+    def test_get_results_zip_dir_key(self):
+        """get_results_zip_dir_key returns expected key format."""
+        assert get_results_zip_dir_key("req_1") == (
+            "inference_request:req_1:results_zip_dir"
+        )
+
+    def test_get_signed_url_key(self):
+        """get_signed_url_key returns expected key format."""
+        assert get_signed_url_key("req_1") == ("inference_request:req_1:signed_url")
+
+
+class TestQueueNextStage:
+    """Tests for queue_next_stage."""
+
+    def test_unknown_stage_returns_none(self):
+        """Unknown current_stage logs error and returns None."""
+        mock_redis = MagicMock()
+        result = queue_next_stage(
+            redis_client=mock_redis,
+            current_stage="unknown_stage",  # type: ignore[arg-type]
+            workflow_name="wf",
+            execution_id="exec_1",
+            output_path_str="/path",
+        )
+        assert result is None
+
+    def test_inference_stage_queues_result_zip(self):
+        """current_stage=inference enqueues process_result_zip to result_zip queue."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.paths.result_zip_enabled = True
+        mock_config.object_storage.enabled = False
+        mock_config.queue.result_zip_queue_name = "result_zip"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_job = MagicMock()
+        mock_job.id = "job_123"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.return_value = mock_job
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            result = queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="inference",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+                results_zip_dir_str="/zip",
+            )
+
+        assert result == "job_123"
+        mock_queue.enqueue.assert_called_once()
+        call_kw = mock_queue.enqueue.call_args
+        assert (
+            call_kw[0][0] == "earth2studio.serve.server.cpu_worker.process_result_zip"
+        )
+        assert call_kw[0][1:5] == ("wf", "exec_1", "/out", "/zip")
+        assert call_kw[0][5] is True  # create_zip
+
+    def test_inference_stage_create_zip_false_when_object_storage_enabled(self):
+        """When object_storage enabled, create_zip is False for inference stage."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.paths.result_zip_enabled = True
+        mock_config.object_storage.enabled = True
+        mock_config.queue.result_zip_queue_name = "result_zip"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_job = MagicMock()
+        mock_job.id = "job_123"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.return_value = mock_job
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="inference",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+                results_zip_dir_str="/zip",
+            )
+
+        assert mock_queue.enqueue.call_args[0][5] is False  # create_zip
+
+    def test_result_zip_stage_object_storage_enabled_queues_object_storage(self):
+        """current_stage=result_zip with object_storage enabled enqueues process_object_storage_upload."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.object_storage.enabled = True
+        mock_config.queue.object_storage_queue_name = "object_storage"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_job = MagicMock()
+        mock_job.id = "job_456"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.return_value = mock_job
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            result = queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="result_zip",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+            )
+
+        assert result == "job_456"
+        mock_queue.enqueue.assert_called_once()
+        assert "process_object_storage_upload" in mock_queue.enqueue.call_args[0][0]
+        assert mock_queue.enqueue.call_args[0][1:4] == ("wf", "exec_1", "/out")
+
+    def test_result_zip_stage_object_storage_disabled_queues_finalize_metadata(self):
+        """current_stage=result_zip with object_storage disabled enqueues process_finalize_metadata."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.object_storage.enabled = False
+        mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_job = MagicMock()
+        mock_job.id = "job_789"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.return_value = mock_job
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            result = queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="result_zip",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+            )
+
+        assert result == "job_789"
+        mock_queue.enqueue.assert_called_once()
+        assert "process_finalize_metadata" in mock_queue.enqueue.call_args[0][0]
+        assert mock_queue.enqueue.call_args[0][1:3] == ("wf", "exec_1")
+
+    def test_object_storage_stage_queues_finalize_metadata(self):
+        """current_stage=object_storage enqueues process_finalize_metadata."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_job = MagicMock()
+        mock_job.id = "job_final"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.return_value = mock_job
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            result = queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="object_storage",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+            )
+
+        assert result == "job_final"
+        mock_queue.enqueue.assert_called_once()
+        assert "process_finalize_metadata" in mock_queue.enqueue.call_args[0][0]
+
+    def test_enqueue_exception_returns_none(self):
+        """When Queue.enqueue raises, queue_next_stage returns None."""
+        mock_redis = MagicMock()
+        mock_config = MagicMock()
+        mock_config.paths.result_zip_enabled = False
+        mock_config.object_storage.enabled = False
+        mock_config.queue.result_zip_queue_name = "result_zip"
+        mock_config.queue.default_timeout = "1h"
+        mock_config.queue.job_timeout = "2h"
+        mock_queue = MagicMock()
+        mock_queue.enqueue.side_effect = Exception("RQ error")
+
+        with (
+            patch(
+                "earth2studio.serve.server.utils.get_config", return_value=mock_config
+            ),
+            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
+        ):
+            result = queue_next_stage(
+                redis_client=mock_redis,
+                current_stage="inference",
+                workflow_name="wf",
+                execution_id="exec_1",
+                output_path_str="/out",
+                results_zip_dir_str="/zip",
+            )
+
+        assert result is None
diff --git a/test/serve/server/test_worker.py b/test/serve/server/test_worker.py
new file mode 100644
index 000000000..f8b4e02b8
--- /dev/null
+++ b/test/serve/server/test_worker.py
@@ -0,0 +1,241 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from earth2studio.serve.server.worker import get_output_path, run_custom_workflow
+
+
+class TestGetOutputPath:
+    """Tests for get_output_path."""
+
+    def test_returns_path_with_default_backend_when_io_config_none(self):
+        """When io_config is None, backend_type defaults to zarr."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            default_dir = Path(tmpdir)
+            with (
+                patch(
+                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
+                    default_dir,
+                ),
+            ):
+                out = get_output_path(
+                    io_config=None,
+                    timestamp="2024-01-01T12:00:00Z",
+                    workflow_type="my_workflow",
+                    request_id="req_123",
+                )
+            assert out.suffix == ".zarr"
+            assert out.name == "forecast.zarr"
+            assert "my_workflow" in str(out)
+            assert "req_123" in str(out)
+            assert out.parent.exists()
+
+    def test_uses_backend_type_from_io_config(self):
+        """When io_config has backend_type, output file uses that extension."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            default_dir = Path(tmpdir)
+            with (
+                patch(
+                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
+                    default_dir,
+                ),
+            ):
+                out = get_output_path(
+                    io_config={"backend_type": "netcdf4"},
+                    timestamp="2024-01-01T00:00:00",
+                    workflow_type="wf",
+                    request_id="r1",
+                )
+            assert out.suffix == ".netcdf4" or "netcdf4" in out.name
+            assert out.name == "forecast.netcdf4"
+
+    def test_normalizes_timestamp_in_path(self):
+        """Timestamp is normalized (colons, Z, + removed) in directory name."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            default_dir = Path(tmpdir)
+            with (
+                patch(
+                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
+                    default_dir,
+                ),
+            ):
+                out = get_output_path(
+                    io_config=None,
+                    timestamp="2024-01-01T12:00:00+00:00",
+                    workflow_type="w",
+                    request_id="r",
+                )
+            # Directory should not contain : or + or Z
+            dir_name = out.parent.name
+            assert ":" not in dir_name
+            assert "Z" not in dir_name
+            assert "+" not in dir_name
+
+
+class TestRunCustomWorkflow:
+    """Tests for run_custom_workflow."""
+
+    def test_raises_value_error_when_workflow_not_in_registry(self):
+        """Raises ValueError when workflow name is not registered."""
+        with patch(
+            "earth2studio.serve.server.worker.workflow_registry"
+        ) as mock_registry:
+            mock_registry.get_workflow_class.return_value = None
+
+            with pytest.raises(ValueError, match="not found in registry"):
+                run_custom_workflow(
+                    workflow_name="nonexistent",
+                    execution_id="exec_1",
+                    parameters={},
+                )
+
+    def test_raises_value_error_when_workflow_cannot_be_instantiated(self):
+        """Raises ValueError when registry.get returns None (instantiation failed)."""
+        with patch(
+            "earth2studio.serve.server.worker.workflow_registry"
+        ) as mock_registry:
+            mock_registry.get_workflow_class.return_value = MagicMock()
+            mock_registry.get.return_value = None
+
+            with pytest.raises(ValueError, match="could not be instantiated"):
+                run_custom_workflow(
+                    workflow_name="my_workflow",
+                    execution_id="exec_1",
+                    parameters={},
+                )
+
+    def test_success_returns_result_and_queues_next_stage(self):
+        """On success, returns workflow result and queue_next_stage is called."""
+        mock_workflow_class = MagicMock()
+        mock_workflow = MagicMock()
+        mock_workflow.run.return_value = {"result": "ok"}
+        with tempfile.TemporaryDirectory() as tmpdir:
+            safe_out = Path(tmpdir) / "out"
+            safe_results_zip = Path(tmpdir) / "results_zip"
+            mock_workflow.get_output_path.return_value = safe_out
+
+            with (
+                patch(
+                    "earth2studio.serve.server.worker.workflow_registry"
+                ) as mock_registry,
+                patch(
+                    "earth2studio.serve.server.worker.redis_client",
+                    MagicMock(),
+                ),
+                patch(
+                    "earth2studio.serve.server.worker.queue_next_stage",
+                    return_value="job_123",
+                ) as mock_queue,
+                patch(
+                    "earth2studio.serve.server.worker.RESULTS_ZIP_DIR",
+                    safe_results_zip,
+                ),
+            ):
+                mock_registry.get_workflow_class.return_value = mock_workflow_class
+                mock_registry.get.return_value = mock_workflow
+
+                result = run_custom_workflow(
+                    workflow_name="my_workflow",
+                    execution_id="exec_1",
+                    parameters={"key": "value"},
+                )
+
+            assert result == {"result": "ok"}
+            mock_workflow.run.assert_called_once_with({"key": "value"}, "exec_1")
+            mock_queue.assert_called_once()
+            call_kw = mock_queue.call_args[1]
+            assert call_kw["workflow_name"] == "my_workflow"
+            assert call_kw["execution_id"] == "exec_1"
+            assert call_kw["current_stage"] == "inference"
+
+    def test_raises_runtime_error_when_queue_next_stage_fails(self):
+        """When queue_next_stage returns None, raises RuntimeError and updates status."""
+        mock_workflow_class = MagicMock()
+        mock_workflow = MagicMock()
+        mock_workflow.run.return_value = {}
+        with tempfile.TemporaryDirectory() as tmpdir:
+            safe_out = Path(tmpdir) / "out"
+            safe_results_zip = Path(tmpdir) / "results_zip"
+            mock_workflow.get_output_path.return_value = safe_out
+
+            with (
+                patch(
+                    "earth2studio.serve.server.worker.workflow_registry"
+                ) as mock_registry,
+                patch(
+                    "earth2studio.serve.server.worker.redis_client",
+                    MagicMock(),
+                ),
+                patch(
+                    "earth2studio.serve.server.worker.queue_next_stage",
+                    return_value=None,
+                ),
+                patch(
+                    "earth2studio.serve.server.worker.RESULTS_ZIP_DIR",
+                    safe_results_zip,
+                ),
+            ):
+                mock_registry.get_workflow_class.return_value = mock_workflow_class
+                mock_registry.get.return_value = mock_workflow
+
+                with pytest.raises(RuntimeError, match="Failed to queue next pipeline"):
+                    run_custom_workflow(
+                        workflow_name="my_workflow",
+                        execution_id="exec_1",
+                        parameters={},
+                    )
+
+        # Status should have been updated to FAILED
+        mock_workflow_class._update_execution_data.assert_called()
+        calls = mock_workflow_class._update_execution_data.call_args_list
+        # Last call before raise is the one with status FAILED
+        fail_calls = [c for c in calls if c[0][3].get("status") == "failed"]
+        assert len(fail_calls) >= 1
+
+    def test_on_run_exception_updates_status_and_reraises(self):
+        """When workflow.run raises, status is updated to FAILED and exception is re-raised."""
+        mock_workflow_class = MagicMock()
+        mock_workflow = MagicMock()
+        mock_workflow.run.side_effect = ValueError("run failed")
+
+        with (
+            patch(
+                "earth2studio.serve.server.worker.workflow_registry"
+            ) as mock_registry,
+            patch(
+                "earth2studio.serve.server.worker.redis_client",
+                MagicMock(),
+            ),
+        ):
+            mock_registry.get_workflow_class.return_value = mock_workflow_class
+            mock_registry.get.return_value = mock_workflow
+
+            with pytest.raises(ValueError, match="run failed"):
+                run_custom_workflow(
+                    workflow_name="my_workflow",
+                    execution_id="exec_1",
+                    parameters={},
+                )
+
+        mock_workflow_class._update_execution_data.assert_called()
+        calls = mock_workflow_class._update_execution_data.call_args_list
+        fail_updates = [c for c in calls if c[0][3].get("status") == "failed"]
+        assert len(fail_updates) >= 1
diff --git a/test/serve/test_workflow.py b/test/serve/server/test_workflow.py
similarity index 97%
rename from test/serve/test_workflow.py
rename to test/serve/server/test_workflow.py
index 36fc7af4f..4fb7e4259 100644
--- a/test/serve/test_workflow.py
+++ b/test/serve/server/test_workflow.py
@@ -24,8 +24,10 @@
 
 import pytest
 import redis  # type: ignore[import-untyped]
-from api_server.config import get_config  # type: ignore[import-untyped]
-from api_server.workflow import (  # type: ignore[import-untyped]
+from pydantic import Field, ValidationError  # type: ignore[import-untyped]
+
+from earth2studio.serve.server.config import get_config  # type: ignore[import-untyped]
+from earth2studio.serve.server.workflow import (  # type: ignore[import-untyped]
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
@@ -36,10 +38,25 @@
     register_all_workflows,
     workflow_registry,
 )
-from pydantic import Field, ValidationError  # type: ignore[import-untyped]
 
-# imitate API server environment
-os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
+
+@pytest.fixture(scope="module", autouse=True)
+def api_active_env():  # type: ignore[no-untyped-def]
+    """
+    Set EARTH2STUDIO_API_ACTIVE=1 for tests in this module only; restore afterward.
+
+    Ensures workflow registration is enabled during these tests without
+    affecting other test modules.
+    """
+    old = os.environ.pop("EARTH2STUDIO_API_ACTIVE", None)
+    os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
+    try:
+        yield
+    finally:
+        if old is not None:
+            os.environ["EARTH2STUDIO_API_ACTIVE"] = old
+        else:
+            os.environ.pop("EARTH2STUDIO_API_ACTIVE", None)
 
 
 def get_test_config(base_path: str | None = None):  # type: ignore[no-untyped-def]
@@ -61,11 +78,13 @@ def setup_test_paths():  # type: ignore[no-untyped-def]
         test_config = get_test_config(tmpdir)
         with (
             patch(
-                "api_server.config.get_config",
+                "earth2studio.serve.server.workflow.get_config",
                 return_value=test_config,
             ),
-            patch("api_server.config.get_config", return_value=test_config),
-            patch("api_server.workflow.workflow.config", test_config),
+            patch(
+                "earth2studio.serve.server.config.get_config", return_value=test_config
+            ),
+            patch("earth2studio.serve.server.workflow.config", test_config),
         ):
             yield test_config
 
@@ -1630,7 +1649,7 @@ def test_discover_and_register_from_directories_with_python_file(self):
             workflow_file = Path(tmpdir) / "test_workflow_module.py"
             workflow_file.write_text(
                 """
-from api_server.workflow import Workflow, WorkflowParameters, workflow_registry
+from earth2studio.serve.server.workflow import Workflow, WorkflowParameters, workflow_registry
 
 class SimpleWorkflow(Workflow):
     # No __init__ needed - name and description set by registry
@@ -1652,7 +1671,7 @@ def run(self, parameters, execution_id):
             successful, failed = self.registry.discover_and_register_from_directories(
                 [tmpdir], include_builtin=False
             )
-            from api_server.workflow import workflow_registry
+            from earth2studio.serve.server.workflow import workflow_registry
 
             assert successful == 1
             assert failed == 0
@@ -1686,7 +1705,7 @@ def test_discover_and_register_filters_init_files(self):
             assert successful == 0
             assert failed == 0
 
-    @patch("api_server.workflow.workflow.Path")
+    @patch("earth2studio.serve.server.workflow.Path")
     def test_discover_and_register_with_builtin(self, mock_path):
         """Test discovering workflows with built-in workflows"""
         # Mock the builtin workflows directory to not exist
@@ -1709,7 +1728,7 @@ def test_auto_register_workflows(self):
 
         with (
             patch(
-                "api_server.workflow.workflow.parse_workflow_directories_from_env",
+                "earth2studio.serve.server.workflow.parse_workflow_directories_from_env",
                 return_value=[],
             ),
             patch.object(
@@ -1729,7 +1748,7 @@ def test_auto_register_workflows_with_error(self):
         mock_redis = Mock(spec=redis.Redis)
 
         with patch(
-            "api_server.workflow.workflow.parse_workflow_directories_from_env",
+            "earth2studio.serve.server.workflow.parse_workflow_directories_from_env",
             side_effect=Exception("Test error"),
         ):
             with pytest.raises(Exception, match="Test error"):
diff --git a/test/serve/test_cleanup_daemon.py b/test/serve/test_cleanup_daemon.py
deleted file mode 100644
index e25cc9944..000000000
--- a/test/serve/test_cleanup_daemon.py
+++ /dev/null
@@ -1,502 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Unit tests for the cleanup daemon.
-
-This module tests the cleanup daemon functionality with mocked Redis and filesystem operations.
-"""
-
-import json
-from datetime import datetime, timedelta, timezone
-from unittest.mock import MagicMock, patch
-
-import pytest
-from api_server.cleanup_daemon import (
-    _delete_result_files,
-    _process_expired_key,
-    cleanup_expired_results,
-)
-from api_server.config import get_config
-from api_server.workflow import WorkflowStatus
-
-
-class TestDeleteResultFiles:
-    """Test suite for _delete_result_files function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    def test_delete_workflow_files_with_workflow_name(self, mock_redis):
-        """Test deletion of workflow execution files including workflow-specific directories and metadata"""
-        # Setup
-        workflow_name = "custom_workflow"
-        execution_id = "exec_456"
-        combined_id = f"{workflow_name}:{execution_id}"
-        zip_filename = f"workflow_{combined_id}.zip"
-
-        mock_redis.get.return_value = zip_filename
-
-        # Mock zip file
-        mock_zip_file = MagicMock()
-        mock_zip_file.exists.return_value = True
-        mock_zip_file.unlink = MagicMock()
-
-        # Mock metadata file for workflow
-        mock_metadata_file = MagicMock()
-        mock_metadata_file.is_file.return_value = True
-        mock_metadata_file.is_dir.return_value = False
-        mock_metadata_file.name = f"metadata_{combined_id}.json"
-        mock_metadata_file.unlink = MagicMock()
-
-        # Mock workflow directory
-        mock_workflow_dir = MagicMock()
-        mock_workflow_dir.exists.return_value = True
-
-        mock_workflow_subdir = MagicMock()
-        mock_workflow_subdir.is_dir.return_value = True
-        mock_workflow_subdir.is_file.return_value = False
-        mock_workflow_subdir.name = f"output_{execution_id}"
-
-        mock_workflow_dir.iterdir.return_value = [mock_workflow_subdir]
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_file
-        mock_zip_dir.iterdir.return_value = [mock_metadata_file]
-
-        with (
-            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
-            patch(
-                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                mock_output_dir,
-            ),
-            patch("api_server.cleanup_daemon.shutil.rmtree") as mock_rmtree,
-        ):
-            # Execute
-            _delete_result_files(
-                mock_redis, combined_id, execution_id, workflow_name=workflow_name
-            )
-
-            # Verify zip file deleted
-            mock_zip_file.unlink.assert_called_once()
-
-            # Verify metadata file deleted
-            mock_metadata_file.unlink.assert_called_once()
-
-            # Verify workflow-specific directory deleted
-            mock_rmtree.assert_called_once_with(mock_workflow_subdir)
-
-    def test_delete_files_no_zip_in_redis(self, mock_redis):
-        """Test deletion when no zip file is found in Redis"""
-        # Setup
-        result_id = "result_789"
-        mock_redis.get.return_value = None
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        with (
-            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
-            patch(
-                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                mock_output_dir,
-            ),
-        ):
-            # Execute - should not raise exception
-            _delete_result_files(mock_redis, result_id, result_id)
-
-            # Verify Redis was queried
-            mock_redis.get.assert_called_once()
-
-
-class TestProcessExpiredKey:
-    """Test suite for _process_expired_key function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    @pytest.fixture
-    def mock_delete_func(self):
-        """Create a mock delete function"""
-        return MagicMock()
-
-    def test_process_expired_key_success(self, mock_redis, mock_delete_func):
-        """Test successful processing of an expired key"""
-        # Setup
-        key = "workflow_execution:wf1:exec_123"
-        current_time = datetime.now(timezone.utc)
-        old_time = current_time - timedelta(hours=25)  # 25 hours ago
-        results_ttl_hours = 24
-
-        request_data = {
-            "status": WorkflowStatus.COMPLETED,
-            "end_time": old_time.isoformat(),
-            "execution_id": "exec_123",
-        }
-
-        mock_redis.get.return_value = json.dumps(request_data)
-        config = get_config()
-
-        # Execute
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key=key,
-            current_time=current_time,
-            results_ttl_hours=results_ttl_hours,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        # Verify
-        assert result is True
-        mock_delete_func.assert_called_once_with(mock_redis, key)
-        mock_redis.setex.assert_called_once()
-        args = mock_redis.setex.call_args[0]
-        assert args[0] == key
-        assert args[1] == config.redis.retention_ttl
-        updated_data = json.loads(args[2])
-        assert updated_data["status"] == WorkflowStatus.EXPIRED
-
-    def test_process_expired_key_not_expired(self, mock_redis, mock_delete_func):
-        """Test processing of a key that is not yet expired"""
-        # Setup
-        key = "workflow_execution:wf1:exec_456"
-        current_time = datetime.now(timezone.utc)
-        recent_time = current_time - timedelta(hours=12)  # 12 hours ago
-        results_ttl_hours = 24
-
-        request_data = {
-            "status": WorkflowStatus.COMPLETED,
-            "end_time": recent_time.isoformat(),
-            "execution_id": "exec_456",
-        }
-
-        mock_redis.get.return_value = json.dumps(request_data)
-
-        # Execute
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key=key,
-            current_time=current_time,
-            results_ttl_hours=results_ttl_hours,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        # Verify - should not clean up
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-
-class TestCleanupExpiredResults:
-    """Test suite for cleanup_expired_results function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    @pytest.fixture
-    def mock_config(self):
-        """Mock the config"""
-        with patch("api_server.cleanup_daemon.config") as mock_cfg:
-            mock_cfg.server.results_ttl_hours = 24
-            mock_cfg.redis.retention_ttl = 604800
-            yield mock_cfg
-
-    def test_cleanup_expired_results_workflow_only(self, mock_redis, mock_config):
-        """Test cleanup with workflow executions only"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-        recent_time = (current_time - timedelta(hours=12)).isoformat()
-
-        # Mock Redis keys - only workflow keys
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf1:exec_old",
-            "workflow_execution:wf2:exec_recent",
-        ]
-
-        # Mock Redis get responses
-        def get_side_effect(key):
-            if key == "workflow_execution:wf1:exec_old":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-                )
-            elif key == "workflow_execution:wf2:exec_recent":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-            exists=lambda: False
-        )
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        with (
-            patch("api_server.cleanup_daemon._delete_result_files"),
-            patch(
-                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                mock_output_dir,
-            ),
-            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
-        ):
-            # Execute
-            cleanup_expired_results(mock_redis)
-
-            # Verify - should process 1 workflow (exec_old), skip exec_recent
-            assert mock_redis.setex.call_count == 1
-
-            # Verify the expired key was updated
-            setex_calls = mock_redis.setex.call_args_list
-            expired_keys = [call[0][0] for call in setex_calls]
-            assert "workflow_execution:wf1:exec_old" in expired_keys
-
-    def test_cleanup_expired_results_no_keys(self, mock_redis, mock_config):
-        """Test cleanup when there are no keys in Redis"""
-        # Setup
-        mock_redis.keys.return_value = []
-
-        # Execute
-        cleanup_expired_results(mock_redis)
-
-        # Verify - no cleanup operations
-        mock_redis.setex.assert_not_called()
-
-    def test_cleanup_expired_results_all_recent(self, mock_redis, mock_config):
-        """Test cleanup when all workflow keys are recent (not expired)"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        recent_time = (current_time - timedelta(hours=5)).isoformat()
-
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf1:exec_1",
-            "workflow_execution:wf2:exec_2",
-        ]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
-        )
-
-        # Execute
-        cleanup_expired_results(mock_redis)
-
-        # Verify - no cleanup operations (not expired)
-        mock_redis.setex.assert_not_called()
-
-    def test_cleanup_expired_results_redis_get_failure(self, mock_redis, mock_config):
-        """Test cleanup handles Redis get failures gracefully"""
-        # Setup
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf_fail:exec_1",
-            "workflow_execution:wf_ok:exec_2",
-        ]
-
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        # First key causes exception, second succeeds
-        def get_side_effect(key):
-            if key == "workflow_execution:wf_fail:exec_1":
-                raise Exception("Redis connection error")
-            elif key == "workflow_execution:wf_ok:exec_2":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-            exists=lambda: False
-        )
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        with (
-            patch("api_server.cleanup_daemon._delete_result_files"),
-            patch(
-                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                mock_output_dir,
-            ),
-            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
-        ):
-            # Execute - should not raise exception
-            cleanup_expired_results(mock_redis)
-
-            # Verify - exec_2 should still be processed despite exec_1 error
-            assert mock_redis.setex.call_count == 1
-            assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf_ok:exec_2"
-
-    def test_cleanup_expired_results_delete_files_failure(
-        self, mock_redis, mock_config
-    ):
-        """Test cleanup handles file deletion failures gracefully and does not mark as expired"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        mock_redis.keys.return_value = ["workflow_execution:wf_fail:exec_fail"]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-        )
-
-        with patch("api_server.cleanup_daemon._delete_result_files") as mock_delete:
-            mock_delete.side_effect = Exception("File deletion error")
-
-            # Execute - should handle exception and continue
-            cleanup_expired_results(mock_redis)
-
-            # Verify delete was attempted
-            mock_delete.assert_called_once()
-
-            # Status should NOT be updated if file deletion fails
-            # (we want to retry cleanup on next run)
-            assert mock_redis.setex.call_count == 0
-
-    def test_cleanup_expired_results_workflow_execution(self, mock_redis, mock_config):
-        """Test cleanup of workflow execution keys"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        mock_redis.keys.return_value = ["workflow_execution:my_workflow:exec_123"]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-        )
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-            exists=lambda: False
-        )
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        with (
-            patch("api_server.cleanup_daemon._delete_result_files") as mock_delete,
-            patch(
-                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                mock_output_dir,
-            ),
-            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
-        ):
-            # Execute
-            cleanup_expired_results(mock_redis)
-
-            # Verify workflow was cleaned up
-            assert mock_redis.setex.call_count == 1
-            assert (
-                mock_redis.setex.call_args[0][0]
-                == "workflow_execution:my_workflow:exec_123"
-            )
-
-            # Verify delete was called with correct parameters
-            mock_delete.assert_called_once()
-            call_args = mock_delete.call_args
-            assert call_args[1]["result_id"] == "my_workflow:exec_123"
-            assert call_args[1]["search_id"] == "exec_123"
-            assert call_args[1]["workflow_name"] == "my_workflow"
-
-    def test_cleanup_expired_results_custom_ttl_config(self, mock_redis):
-        """Test cleanup with custom TTL configuration"""
-        # Setup custom config
-        with patch("api_server.cleanup_daemon.config") as mock_cfg:
-            mock_cfg.server.results_ttl_hours = 48  # Custom 48 hour TTL
-            mock_cfg.redis.retention_ttl = 604800
-
-            current_time = datetime.now(timezone.utc)
-            # 30 hours ago - should NOT be expired with 48 hour TTL
-            time_30h_ago = (current_time - timedelta(hours=30)).isoformat()
-            # 50 hours ago - should be expired with 48 hour TTL
-            time_50h_ago = (current_time - timedelta(hours=50)).isoformat()
-
-            mock_redis.keys.return_value = [
-                "workflow_execution:wf_30h:exec_30h",
-                "workflow_execution:wf_50h:exec_50h",
-            ]
-
-            def get_side_effect(key):
-                if key == "workflow_execution:wf_30h:exec_30h":
-                    return json.dumps(
-                        {"status": WorkflowStatus.COMPLETED, "end_time": time_30h_ago}
-                    )
-                elif key == "workflow_execution:wf_50h:exec_50h":
-                    return json.dumps(
-                        {"status": WorkflowStatus.COMPLETED, "end_time": time_50h_ago}
-                    )
-                return None
-
-            mock_redis.get.side_effect = get_side_effect
-
-            mock_output_dir = MagicMock()
-            mock_output_dir.iterdir.return_value = []
-            mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-                exists=lambda: False
-            )
-
-            mock_zip_dir = MagicMock()
-            mock_zip_dir.iterdir.return_value = []
-
-            with (
-                patch("api_server.cleanup_daemon._delete_result_files"),
-                patch(
-                    "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
-                    mock_output_dir,
-                ),
-                patch(
-                    "api_server.cleanup_daemon.RESULTS_ZIP_DIR",
-                    mock_zip_dir,
-                ),
-            ):
-                # Execute
-                cleanup_expired_results(mock_redis)
-
-                # Verify - only exec_50h should be cleaned up
-                assert mock_redis.setex.call_count == 1
-                assert (
-                    mock_redis.setex.call_args[0][0]
-                    == "workflow_execution:wf_50h:exec_50h"
-                )
diff --git a/test/serve/test_cpu_worker.py b/test/serve/test_cpu_worker.py
deleted file mode 100644
index f29836e26..000000000
--- a/test/serve/test_cpu_worker.py
+++ /dev/null
@@ -1,743 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Unit tests for the CPU worker module.
-
-This module tests CPU-intensive functions that can be offloaded from the main worker process.
-"""
-
-import json
-import sys
-import tempfile
-import zipfile
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Literal
-from unittest.mock import Mock
-
-import pytest
-import redis
-
-
-# Create mock config classes before importing cpu_worker
-@dataclass
-class MockRedisConfig:
-    """Mock Redis configuration"""
-
-    host: str = "localhost"
-    port: int = 6379
-    db: int = 0
-    password: str | None = None
-    socket_connect_timeout: int = 5
-    socket_timeout: int = 5
-    decode_responses: bool = True
-    retention_ttl: int = 604800  # Redis key retention TTL in seconds (7 days)
-
-
-@dataclass
-class MockQueueConfig:
-    """Mock queue configuration"""
-
-    name: str = "inference"
-    result_zip_queue_name: str = "result_zip"
-    object_storage_queue_name: str = "object_storage"
-    finalize_metadata_queue_name: str = "finalize_metadata"
-    max_size: int = 10
-    default_timeout: str = "1h"
-    job_timeout: str = "2h"
-
-
-# Secure temp dir for mock path defaults (S108)
-_secure_test_dir = tempfile.mkdtemp(prefix="e2s_testing_")
-
-
-@dataclass
-class MockPathsConfig:
-    """Mock paths configuration"""
-
-    default_output_dir: str = field(default_factory=lambda: _secure_test_dir)
-    results_zip_dir: str = field(default_factory=lambda: _secure_test_dir)
-    output_format: Literal["zarr", "netcdf4"] = "zarr"
-    result_zip_enabled: bool = False
-
-
-@dataclass
-class MockLoggingConfig:
-    """Mock logging configuration"""
-
-    level: str = "INFO"
-    format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-
-
-@dataclass
-class MockServerConfig:
-    """Mock server configuration"""
-
-    host: str = "0.0.0.0"  # noqa: S104
-    port: int = 8000
-    workers: int = 1
-    title: str = "REST API"
-    description: str = "REST API for running workflows"
-    version: str = "1.0.0"
-    docs_url: str = "/docs"
-    redoc_url: str = "/redoc"
-
-
-@dataclass
-class MockCORSConfig:
-    """Mock CORS configuration"""
-
-    allow_origins: list = field(default_factory=lambda: ["*"])
-    allow_credentials: bool = True
-    allow_methods: list = field(default_factory=lambda: ["*"])
-    allow_headers: list = field(default_factory=lambda: ["*"])
-
-
-@dataclass
-class MockObjectStorageConfig:
-    """Mock object storage configuration"""
-
-    enabled: bool = False
-    bucket: str | None = None
-    region: str = "us-east-1"
-    prefix: str = "outputs"
-    access_key_id: str | None = None
-    secret_access_key: str | None = None
-    session_token: str | None = None
-    cloudfront_domain: str | None = None
-    cloudfront_key_pair_id: str | None = None
-    cloudfront_private_key_path: str | None = None
-    signed_url_expires_in: int = 3600
-
-
-@dataclass
-class MockAppConfig:
-    """Mock root configuration"""
-
-    redis: MockRedisConfig = field(default_factory=MockRedisConfig)
-    queue: MockQueueConfig = field(default_factory=MockQueueConfig)
-    paths: MockPathsConfig = field(default_factory=MockPathsConfig)
-    logging: MockLoggingConfig = field(default_factory=MockLoggingConfig)
-    server: MockServerConfig = field(default_factory=MockServerConfig)
-    cors: MockCORSConfig = field(default_factory=MockCORSConfig)
-    object_storage: MockObjectStorageConfig = field(
-        default_factory=MockObjectStorageConfig
-    )
-
-
-# Create a mock config module
-mock_config_module = Mock()
-mock_config_module.AppConfig = MockAppConfig
-mock_config_module.get_config = Mock(return_value=MockAppConfig())
-mock_config_module.get_config_manager = Mock()
-
-# Inject the mock before importing cpu_worker
-sys.modules["api_server.config"] = mock_config_module
-
-# Now import the module under test
-from api_server.cpu_worker import create_results_zip  # noqa: E402
-
-
-class TestCreateResultsZip:
-    """Test suite for create_results_zip function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Mock Redis client"""
-        return Mock(spec=redis.Redis)
-
-    @pytest.fixture
-    def inference_request(self):
-        """Sample inference request data"""
-        return {
-            "id": "test_req_123",
-            "type": "deterministic",
-            "status": "completed",
-            "completion_time": "2024-01-01T12:00:00Z",
-            "execution_time_seconds": 45.5,
-            "created_at": "2024-01-01T11:00:00Z",
-            "peak_memory_usage": "2.5GB",
-            "device": "cuda:0",
-            "request": {
-                "workflow_type": "deterministic",
-                "time": "2024-01-01T00:00:00Z",
-                "nsteps": 10,
-                "prognostic": {"model_type": "fcn"},
-                "data": {"source_type": "gfs"},
-                "io": {"backend_type": "zarr"},
-            },
-        }
-
-    def test_create_results_zip_with_single_file(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip with a single output file"""
-        # Create a temporary output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data content")
-        file_size = output_file.stat().st_size
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_123",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip file was created - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_123"
-
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify Redis was updated (4 times: metadata, results_zip_dir, zip, output_path)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-
-        # First call: metadata (stored in Redis for finalize worker)
-        assert calls[0][0][0] == "inference_request:test_req_123:pending_metadata"
-
-        # Second call: results_zip_dir path
-        assert calls[1][0][0] == "inference_request:test_req_123:results_zip_dir"
-        assert calls[1][0][2] == str(results_zip_dir)
-
-        # Third call: zip filename
-        assert calls[2][0][0] == "inference_request_zips:test_req_123:zip_file"
-        assert calls[2][0][2] == zip_filename
-
-        # Fourth call: output path
-        assert calls[3][0][0] == "inference_request:test_req_123:output_path"
-        assert calls[3][0][1] == 86400  # 24 hours
-        assert calls[3][0][2] == str(output_file)
-
-        # Verify zip contents - metadata.json is NOT in the zip
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert "metadata.json" not in file_list  # metadata is now separate
-            assert "output.nc" in file_list
-
-        # Verify metadata is stored in Redis (not as file - file is created by finalize worker)
-        # Get the metadata from the first setex call
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify metadata structure
-        assert metadata["request_id"] == "test_req_123"
-        assert metadata["status"] == "completed"
-        assert metadata["workflow_type"] == "deterministic"
-        assert metadata["execution_time_seconds"] == 45.5
-        assert metadata["device"] == "cuda:0"
-        assert "zip_created_at" in metadata
-
-        # Verify parameters field
-        assert "parameters" in metadata
-        assert metadata["parameters"] == inference_request["request"]
-        assert metadata["parameters"]["workflow_type"] == "deterministic"
-        assert metadata["parameters"]["nsteps"] == 10
-
-        # Verify output_files manifest includes both the file AND the zip
-        assert "output_files" in metadata
-        assert isinstance(metadata["output_files"], list)
-        assert len(metadata["output_files"]) == 2  # output.nc + zip file entry
-
-        # Convert to dict for easier checking
-        files_dict = {f["path"]: f["size"] for f in metadata["output_files"]}
-
-        # Check the output file entry
-        assert "output.nc" in files_dict
-        assert files_dict["output.nc"] == file_size
-
-        # Check the zip file entry (path = request_id)
-        assert "test_req_123" in files_dict
-        assert files_dict["test_req_123"] == zip_path.stat().st_size
-
-    def test_create_results_zip_with_directory(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip with a directory containing multiple files"""
-        # Create a temporary output directory with files
-        output_dir = tmp_path / "output_data"
-        output_dir.mkdir()
-
-        file1 = output_dir / "data1.nc"
-        file1.write_text("data 1 content")
-        file1_size = file1.stat().st_size
-
-        file2 = output_dir / "data2.nc"
-        file2.write_text("data 2 content with more text")
-        file2_size = file2.stat().st_size
-
-        # Create subdirectory
-        subdir = output_dir / "plots"
-        subdir.mkdir()
-        file3 = subdir / "plot.png"
-        file3.write_bytes(b"fake png data")
-        file3_size = file3.stat().st_size
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_456",
-            output_path=output_dir,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip file was created - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_456"
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify zip contents - metadata.json is NOT in the zip
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert "metadata.json" not in file_list
-
-            # Verify files exist in zip
-            assert "output_data/data1.nc" in file_list
-            assert "output_data/data2.nc" in file_list
-            assert "output_data/plots/plot.png" in file_list
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify output_files manifest (3 files + 1 zip entry)
-        assert "output_files" in metadata
-        output_files = metadata["output_files"]
-        assert len(output_files) == 4  # 3 files + zip entry
-
-        # Convert to dict for easier checking
-        files_dict = {f["path"]: f["size"] for f in output_files}
-
-        # Verify all files are in manifest with correct sizes
-        assert "output_data/data1.nc" in files_dict
-        assert files_dict["output_data/data1.nc"] == file1_size
-
-        assert "output_data/data2.nc" in files_dict
-        assert files_dict["output_data/data2.nc"] == file2_size
-
-        assert "output_data/plots/plot.png" in files_dict
-        assert files_dict["output_data/plots/plot.png"] == file3_size
-
-        # Verify zip file entry (path = request_id)
-        assert "test_req_456" in files_dict
-        assert files_dict["test_req_456"] == zip_path.stat().st_size
-
-    def test_create_results_zip_with_nonexistent_output(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip when output path doesn't exist"""
-        # Use nonexistent path
-        output_path = tmp_path / "nonexistent"
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_789",
-            output_path=output_path,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should still create zip - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_789"
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify zip contents - should be empty (no output files)
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert len(file_list) == 0  # No files in zip
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # output_files should only contain the zip entry
-        assert "output_files" in metadata
-        assert len(metadata["output_files"]) == 1  # Only zip entry
-        assert metadata["output_files"][0]["path"] == "test_req_789"
-
-    def test_create_results_zip_parameters_validation(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that parameters are correctly stored in metadata"""
-        # Create a temporary output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Modify inference request to have specific parameters
-        inference_request["request"] = {
-            "workflow_type": "ensemble",
-            "time": "2024-01-15T00:00:00Z",
-            "nsteps": 20,
-            "nensemble": 10,
-            "prognostic": {"model_type": "graphcast"},
-            "data": {"source_type": "era5"},
-            "io": {"backend_type": "netcdf"},
-            "perturbation": {"method": "spherical_gaussian"},
-        }
-
-        # Call create_results_zip
-        result = create_results_zip(
-            request_id="test_req_params",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-        assert result is not None
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify parameters field exists and matches
-        assert "parameters" in metadata
-        params = metadata["parameters"]
-
-        assert params["workflow_type"] == "ensemble"
-        assert params["time"] == "2024-01-15T00:00:00Z"
-        assert params["nsteps"] == 20
-        assert params["nensemble"] == 10
-        assert params["prognostic"]["model_type"] == "graphcast"
-        assert params["data"]["source_type"] == "era5"
-        assert params["io"]["backend_type"] == "netcdf"
-        assert params["perturbation"]["method"] == "spherical_gaussian"
-
-    def test_create_results_zip_file_manifest_structure(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that file manifest has correct structure"""
-        # Create output directory with multiple files
-        output_dir = tmp_path / "outputs"
-        output_dir.mkdir()
-
-        for i in range(5):
-            file = output_dir / f"file_{i}.dat"
-            file.write_text(f"content {i}" * 100)
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_manifest",
-            output_path=output_dir,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify metadata is stored in Redis
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        manifest = metadata["output_files"]
-
-        # Verify manifest structure (5 files + 1 zip entry)
-        assert isinstance(manifest, list)
-        assert len(manifest) == 6  # 5 files + zip entry
-
-        # Find the zip entry (path = request_id)
-        zip_entry = None
-        file_entries = []
-        for entry in manifest:
-            if entry["path"] == "test_manifest":
-                zip_entry = entry
-            else:
-                file_entries.append(entry)
-
-        assert zip_entry is not None
-        assert len(file_entries) == 5
-
-        # Verify file entries structure
-        zip_path = results_zip_dir / zip_filename
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            for entry in file_entries:
-                assert isinstance(entry, dict)
-                assert "path" in entry
-                assert "size" in entry
-                assert isinstance(entry["path"], str)
-                assert isinstance(entry["size"], int)
-                assert entry["size"] > 0
-
-                # Verify path starts with output directory name
-                assert entry["path"].startswith("outputs/")
-
-                # Verify size matches actual file in zip
-                actual_size = zf.getinfo(entry["path"]).file_size
-                assert entry["size"] == actual_size
-
-    def test_create_results_zip_missing_request_parameters(self, mock_redis, tmp_path):
-        """Test handling when request doesn't have parameters field"""
-        # Create inference request without 'request' field
-        inference_request = {
-            "id": "test_req_no_params",
-            "type": "deterministic",
-            "status": "completed",
-        }
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_no_params",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip was created
-        assert zip_filename is not None
-
-        # Verify metadata stored in Redis - parameters should be None
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Parameters should be None or not present
-        assert metadata.get("parameters") is None
-
-    def test_create_results_zip_redis_failure(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test handling when Redis operation fails"""
-        # Make Redis setex raise an exception
-        mock_redis.setex.side_effect = Exception("Redis connection failed")
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip - should handle exception gracefully
-        zip_filename = create_results_zip(
-            request_id="test_redis_fail",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should return None since storing in Redis failed
-        assert zip_filename is None
-
-    def test_create_results_zip_exception_handling(self, mock_redis, tmp_path):
-        """Test general exception handling"""
-        # Use invalid inference request that will cause issues
-        invalid_request = None
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip with invalid data
-        zip_filename = create_results_zip(
-            request_id="test_exception",
-            output_path=output_file,
-            inference_request=invalid_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should return None on exception
-        assert zip_filename is None
-
-    def test_create_results_zip_metadata_completeness(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that all expected metadata fields are present"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_metadata",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        # Verify metadata stored in Redis
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Check all expected fields
-        required_fields = [
-            "request_id",
-            "status",
-            "completion_time",
-            "execution_time_seconds",
-            "workflow_type",
-            "created_at",
-            "peak_memory_usage",
-            "device",
-            "zip_created_at",
-            "parameters",
-            "output_files",
-        ]
-
-        for f in required_fields:
-            assert f in metadata, f"Missing required field: {field}"
-
-        # Verify zip_created_at is a valid timestamp
-        zip_created_at = metadata["zip_created_at"]
-        assert zip_created_at.endswith("Z")
-        # Should be parseable as ISO format
-        datetime.fromisoformat(zip_created_at.replace("Z", "+00:00"))
-
-    def test_create_results_zip_manifest_includes_zip_entry(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that the manifest includes an entry for the zip file itself"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data content")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        request_id = "test_zip_entry"
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id=request_id,
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        zip_path = results_zip_dir / zip_filename
-        zip_size = zip_path.stat().st_size
-
-        # Verify metadata stored in Redis
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Find the zip entry in manifest
-        zip_entry = None
-        for entry in metadata["output_files"]:
-            if entry["path"] == request_id:
-                zip_entry = entry
-                break
-
-        # Verify zip entry exists and has correct size
-        assert zip_entry is not None, "Zip file entry not found in manifest"
-        assert zip_entry["path"] == request_id
-        assert zip_entry["size"] == zip_size
-
-    def test_create_results_zip_output_path_stored_in_redis(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that output path is stored in Redis"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_output_path",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        # Verify Redis setex was called 4 times (metadata, results_zip_dir, zip, output_path)
-        assert mock_redis.setex.call_count == 4
-
-        # Check the output path call (4th call)
-        calls = mock_redis.setex.call_args_list
-        output_path_call = calls[3]
-
-        assert (
-            output_path_call[0][0] == "inference_request:test_output_path:output_path"
-        )
-        assert output_path_call[0][1] == 86400  # 24 hours
-        assert output_path_call[0][2] == str(output_file)
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])
diff --git a/test/serve/test_main.py b/test/serve/test_main.py
deleted file mode 100644
index 964fc9922..000000000
--- a/test/serve/test_main.py
+++ /dev/null
@@ -1,1213 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Unit tests for api_server.main module.
-
-Tests the FastAPI endpoints including the workflow schema endpoint.
-"""
-
-import os
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from fastapi.testclient import TestClient
-from pydantic import Field
-
-# Set API environment variable before importing main
-os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
-
-# Patch FastAPI route creation to handle union return types
-# This fixes the issue where FastAPI can't handle dict[str, Any] | StreamingResponse
-# ruff: noqa: E402
-import fastapi  # type: ignore[import-untyped]
-import fastapi.routing  # type: ignore[import-untyped]
-from fastapi.exceptions import FastAPIError  # type: ignore[import-untyped]
-
-_original_route_init = fastapi.routing.APIRoute.__init__
-
-
-def _patched_route_init(self, *args, **kwargs):
-    """Patched APIRoute.__init__ that handles union return types with StreamingResponse"""
-    import inspect
-
-    from starlette.responses import StreamingResponse
-
-    # Check if the endpoint has a union return type with StreamingResponse
-    endpoint = kwargs.get("endpoint") or (args[1] if len(args) > 1 else None)
-    if endpoint:
-        sig = inspect.signature(endpoint)
-        return_annotation = sig.return_annotation
-        if (
-            hasattr(return_annotation, "__args__")
-            and StreamingResponse in return_annotation.__args__
-        ):
-            # Set response_model=None to skip response model generation
-            kwargs["response_model"] = None
-
-    try:
-        return _original_route_init(self, *args, **kwargs)
-    except FastAPIError as e:
-        if "Invalid args for response field" in str(e):
-            # Retry with response_model=None
-            kwargs["response_model"] = None
-            return _original_route_init(self, *args, **kwargs)
-        raise
-
-
-# Apply the patch before any imports of api_server.main
-fastapi.routing.APIRoute.__init__ = _patched_route_init
-
-
-class TestWorkflowSchemaEndpoint:
-    """Tests for GET /v1/workflows/{workflow_name}/schema endpoint"""
-
-    @pytest.fixture
-    def mock_workflow_class(self):
-        """Create a mock workflow class with Parameters"""
-        from api_server.workflow import Workflow, WorkflowParameters
-
-        class TestWorkflowParameters(WorkflowParameters):
-            """Test parameters for unit testing"""
-
-            forecast_times: list[str] = Field(
-                default=["2024-01-01T00:00:00"],
-                description="List of forecast initialization times",
-            )
-            nsteps: int = Field(
-                default=10,
-                ge=1,
-                le=100,
-                description="Number of forecast steps",
-            )
-            model_type: str = Field(
-                default="fcn",
-                description="Model type to use",
-            )
-
-        class TestWorkflow(Workflow):
-            name = "test_workflow"
-            description = "Test workflow for unit testing"
-            Parameters = TestWorkflowParameters
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestWorkflowParameters
-            ) -> TestWorkflowParameters:
-                return TestWorkflowParameters.validate(parameters)
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        return TestWorkflow
-
-    @pytest.fixture
-    def client(self, mock_workflow_class):
-        """Create test client with mocked dependencies"""
-        # Mock Redis and other dependencies at their source locations
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue"),
-            patch("api_server.workflow.register_all_workflows"),
-        ):
-            # Setup mock async Redis
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-
-            # Setup mock sync Redis
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            from api_server.main import app
-            from api_server.workflow import workflow_registry
-
-            # Register the test workflow
-            workflow_registry._workflows["test_workflow"] = mock_workflow_class
-
-            with TestClient(app, raise_server_exceptions=False) as client:
-                yield client
-
-            # Cleanup
-            if "test_workflow" in workflow_registry._workflows:
-                del workflow_registry._workflows["test_workflow"]
-
-    def test_schema_endpoint_returns_valid_json_schema(
-        self, client, mock_workflow_class
-    ):
-        """Test that schema endpoint returns valid JSON Schema"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        # Verify it's a valid JSON Schema structure
-        assert "type" in schema
-        assert schema["type"] == "object"
-        assert "properties" in schema
-        assert "title" in schema
-
-    def test_schema_endpoint_includes_all_parameters(self, client, mock_workflow_class):
-        """Test that schema includes all defined parameters"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check all parameters are present
-        assert "forecast_times" in properties
-        assert "nsteps" in properties
-        assert "model_type" in properties
-
-    def test_schema_endpoint_includes_field_metadata(self, client, mock_workflow_class):
-        """Test that schema includes field descriptions and constraints"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check forecast_times has correct type
-        assert properties["forecast_times"]["type"] == "array"
-        assert "description" in properties["forecast_times"]
-
-        # Check nsteps has constraints
-        nsteps = properties["nsteps"]
-        assert nsteps["type"] == "integer"
-        assert "description" in nsteps
-        # Pydantic uses "minimum" and "maximum" for ge/le constraints
-        assert nsteps.get("minimum") == 1 or nsteps.get("exclusiveMinimum") == 0
-        assert nsteps.get("maximum") == 100 or nsteps.get("exclusiveMaximum") == 101
-
-    def test_schema_endpoint_includes_defaults(self, client, mock_workflow_class):
-        """Test that schema includes default values"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check defaults are included
-        assert properties["forecast_times"]["default"] == ["2024-01-01T00:00:00"]
-        assert properties["nsteps"]["default"] == 10
-        assert properties["model_type"]["default"] == "fcn"
-
-    def test_schema_endpoint_workflow_not_found(self, client):
-        """Test that 404 is returned for non-existent workflow"""
-        response = client.get("/v1/workflows/nonexistent_workflow/schema")
-
-        assert response.status_code == 404
-        error = response.json()
-        assert "not found" in error["detail"].lower()
-
-    def test_schema_endpoint_openapi_compatibility(self, client, mock_workflow_class):
-        """Test that returned schema is OpenAPI 3.1 compatible (JSON Schema draft 2020-12)"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        # OpenAPI 3.1 / JSON Schema required fields
-        assert "type" in schema
-        assert "properties" in schema
-
-        # Schema should be directly usable (no wrapper objects)
-        assert "workflow_name" not in schema
-        assert "parameters_schema" not in schema
-
-
-class TestWorkflowParameterValidation:
-    """Test workflow parameter validation at submission time"""
-
-    @pytest.fixture
-    def client(self):
-        """Create test client"""
-        from typing import Any, Literal
-        from unittest.mock import MagicMock, patch
-
-        from api_server.main import app
-        from api_server.workflow import (
-            Workflow,
-            WorkflowParameters,
-            workflow_registry,
-        )
-        from pydantic import Field
-
-        # Create test workflow parameter classes with Literal validation
-        class TestDeterministicParams(WorkflowParameters):
-            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
-            nsteps: int = Field(default=6, ge=1, le=100)
-            model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
-            data_source: Literal["gfs"] = Field(default="gfs")
-            output_format: Literal["zarr"] = Field(default="zarr")
-            plot_variable: Literal["t2m", "msl", "u10m", "v10m", "tcwv", "z500"] = (
-                Field(default="t2m")
-            )
-
-        class TestDiagnosticParams(WorkflowParameters):
-            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
-            nsteps: int = Field(default=6, ge=1, le=100)
-            prognostic_model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
-            diagnostic_model_type: Literal["precipitation_afno"] = Field(
-                default="precipitation_afno"
-            )
-            data_source: Literal["gfs"] = Field(default="gfs")
-            plot_variable: Literal[
-                "tp", "t2m", "msl", "u10m", "v10m", "tcwv", "z500"
-            ] = Field(default="tp")
-
-        # Create mock workflow classes
-        class TestDeterministicWorkflow(Workflow):
-            name = "deterministic_workflow"
-            description = "Test deterministic workflow"
-            Parameters = TestDeterministicParams
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestDeterministicParams
-            ) -> TestDeterministicParams:
-                try:
-                    return TestDeterministicParams.validate(parameters)
-                except Exception as e:
-                    raise ValueError(f"Invalid parameters: {e}") from e
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        class TestDiagnosticWorkflow(Workflow):
-            name = "diagnostic_workflow"
-            description = "Test diagnostic workflow"
-            Parameters = TestDiagnosticParams
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestDiagnosticParams
-            ) -> TestDiagnosticParams:
-                try:
-                    return TestDiagnosticParams.validate(parameters)
-                except Exception as e:
-                    raise ValueError(f"Invalid parameters: {e}") from e
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        # Register test workflows directly in the registry
-        workflow_registry._workflows["deterministic_workflow"] = (
-            TestDeterministicWorkflow
-        )
-        workflow_registry._workflows["diagnostic_workflow"] = TestDiagnosticWorkflow
-
-        # Create mocks
-        mock_redis = MagicMock()
-        mock_queue = MagicMock()
-        mock_queue.__len__ = MagicMock(return_value=0)
-        mock_queue.max_size = 100
-
-        with patch("api_server.main.redis_sync_client", mock_redis):
-            with patch("api_server.main.inference_queue", mock_queue):
-                test_client = TestClient(app)
-                yield test_client, mock_queue, mock_redis
-
-    def test_valid_deterministic_workflow_parameters(self, client):
-        """Test that valid parameters for deterministic workflow are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "output_format": "zarr",
-                    "plot_variable": "t2m",
-                }
-            },
-        )
-
-        assert response.status_code == 200
-        data = response.json()
-        assert data["status"] == "queued"
-
-    def test_invalid_model_type_rejected(self, client):
-        """Test that invalid model_type is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "invalid_model",  # Invalid value
-                    "data_source": "gfs",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "model_type" in data["detail"]
-
-    def test_invalid_data_source_rejected(self, client):
-        """Test that invalid data_source is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "invalid_source",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "data_source" in data["detail"]
-
-    def test_invalid_output_format_rejected(self, client):
-        """Test that invalid output_format is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "output_format": "invalid_format",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "output_format" in data["detail"]
-
-    def test_invalid_plot_variable_rejected(self, client):
-        """Test that invalid plot_variable is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "plot_variable": "invalid_variable",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "plot_variable" in data["detail"]
-
-    def test_invalid_nsteps_range_rejected(self, client):
-        """Test that nsteps outside valid range is rejected"""
-        test_client, _, _ = client
-
-        # Test nsteps too low
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 0,  # Below minimum of 1
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-        # Test nsteps too high
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 101,  # Above maximum of 100
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-    def test_diagnostic_workflow_invalid_model_types_rejected(self, client):
-        """Test that invalid model types for diagnostic workflow are rejected"""
-        test_client, _, _ = client
-
-        # Test invalid prognostic model
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "invalid_prognostic",  # Invalid
-                    "diagnostic_model_type": "precipitation_afno",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "prognostic_model_type" in data["detail"]
-
-        # Test invalid diagnostic model
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "fcn",
-                    "diagnostic_model_type": "invalid_diagnostic",  # Invalid
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "diagnostic_model_type" in data["detail"]
-
-    def test_valid_diagnostic_workflow_parameters(self, client):
-        """Test that valid parameters for diagnostic workflow are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "fcn",
-                    "diagnostic_model_type": "precipitation_afno",
-                    "data_source": "gfs",
-                    "plot_variable": "tp",
-                }
-            },
-        )
-
-        assert response.status_code == 200
-        data = response.json()
-        assert data["status"] == "queued"
-
-    def test_multiple_invalid_parameters_rejected(self, client):
-        """Test that multiple invalid parameters are caught"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 101,  # Invalid: too high
-                    "model_type": "invalid_model",  # Invalid value
-                    "data_source": "invalid_source",  # Invalid value
-                    "output_format": "invalid_format",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-    def test_valid_forecast_times_iso8601_accepted(self, client):
-        """Test that valid ISO 8601 forecast_times are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        # Test various valid ISO 8601 formats
-        valid_times = [
-            ["2024-01-01T00:00:00"],
-            ["2024-01-01T00:00:00Z"],
-            ["2024-01-01T00:00:00+00:00"],
-            ["2024-01-01T00:00:00.123456"],
-            ["2024-01-01 00:00:00"],  # Space separator also accepted
-            ["2024-01-01T00:00:00", "2024-01-02T00:00:00", "2024-01-03T00:00:00"],
-        ]
-
-        for times in valid_times:
-            response = test_client.post(
-                "/v1/infer/deterministic_workflow",
-                json={
-                    "parameters": {
-                        "forecast_times": times,
-                        "nsteps": 10,
-                        "model_type": "fcn",
-                    }
-                },
-            )
-
-            assert response.status_code == 200, f"Failed for times: {times}"
-            data = response.json()
-            assert data["status"] == "queued"
-
-    def test_invalid_forecast_times_rejected(self, client):
-        """Test that invalid forecast_times formats are rejected"""
-        test_client, _, _ = client
-
-        # Test various invalid formats
-        invalid_times = [
-            (["2024-01-01"], "date without time"),
-            (["01/01/2024"], "wrong date format"),
-            (["not-a-date"], "completely invalid"),
-            (["2024-13-01T00:00:00"], "invalid month"),
-            (["2024-01-32T00:00:00"], "invalid day"),
-            (["2024-01-01T25:00:00"], "invalid hour"),
-        ]
-
-        for times, description in invalid_times:
-            response = test_client.post(
-                "/v1/infer/deterministic_workflow",
-                json={
-                    "parameters": {
-                        "forecast_times": times,
-                        "nsteps": 10,
-                        "model_type": "fcn",
-                    }
-                },
-            )
-
-            assert (
-                response.status_code == 422
-            ), f"Expected 422 for {description}, got {response.status_code}"
-            data = response.json()
-            assert (
-                "Invalid parameters" in data["detail"]
-            ), f"Expected validation error for {description}"
-            assert (
-                "forecast_times" in data["detail"]
-            ), f"Expected forecast_times in error for {description}"
-
-    def test_forecast_times_mixed_valid_invalid_rejected(self, client):
-        """Test that forecast_times with one invalid value fails"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": [
-                        "2024-01-01T00:00:00",  # Valid
-                        "not-a-date",  # Invalid
-                        "2024-01-03T00:00:00",  # Valid
-                    ],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "forecast_times" in data["detail"]
-
-    def test_forecast_times_wrong_type_rejected(self, client):
-        """Test that forecast_times with non-string values are rejected"""
-        test_client, _, _ = client
-
-        # Test with integer
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": [12345],  # Not a string
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "forecast_times" in data["detail"]
-
-
-class TestWorkflowExecutionRequest:
-    """Tests for WorkflowExecutionRequest validation"""
-
-    def test_valid_request_with_parameters(self):
-        """Test that valid request with parameters field is accepted"""
-        from api_server.main import WorkflowExecutionRequest
-
-        # Valid request
-        request = WorkflowExecutionRequest(
-            parameters={"field1": "value1", "field2": 123}
-        )
-        assert request.parameters == {"field1": "value1", "field2": 123}
-
-    def test_valid_request_with_empty_parameters(self):
-        """Test that request with empty parameters dict is accepted"""
-        from api_server.main import WorkflowExecutionRequest
-
-        # Empty parameters is valid
-        request = WorkflowExecutionRequest(parameters={})
-        assert request.parameters == {}
-
-    def test_valid_request_without_parameters_uses_default(self):
-        """Test that request without parameters field uses default empty dict"""
-        from api_server.main import WorkflowExecutionRequest
-
-        # No parameters provided - should use default
-        request = WorkflowExecutionRequest()
-        assert request.parameters == {}
-
-    def test_invalid_request_with_extra_fields(self):
-        """Test that request with unknown fields is rejected"""
-        from api_server.main import WorkflowExecutionRequest
-        from pydantic import ValidationError
-
-        # Request with unknown field should be rejected
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"valid": "param"}, unknown_field="should_fail"
-            )
-
-    def test_invalid_request_with_multiple_extra_fields(self):
-        """Test that request with multiple unknown fields is rejected"""
-        from api_server.main import WorkflowExecutionRequest
-        from pydantic import ValidationError
-
-        # Multiple unknown fields
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"valid": "param"},
-                extra1="bad",
-                extra2="also_bad",
-                extra3="still_bad",
-            )
-
-    def test_invalid_request_flat_structure(self):
-        """Test that flat request structure (without parameters wrapper) is rejected"""
-        from api_server.main import WorkflowExecutionRequest
-        from pydantic import ValidationError
-
-        # Flat structure - fields that should be inside 'parameters'
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                forecast_times=["2024-01-01"],
-                nsteps=6,
-                model_type="fcn",
-            )
-
-    def test_invalid_request_mixed_structure(self):
-        """Test that mixed structure (parameters + extra fields) is rejected"""
-        from api_server.main import WorkflowExecutionRequest
-        from pydantic import ValidationError
-
-        # Mixed: valid parameters field + invalid extra fields
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"forecast_times": ["2024-01-01"], "nsteps": 6},
-                workflow_type="deterministic",  # This should not be at this level
-                device="auto",  # This should not be at this level
-            )
-
-    def test_parameters_field_type_validation(self):
-        """Test that parameters field must be a dict"""
-        from api_server.main import WorkflowExecutionRequest
-        from pydantic import ValidationError
-
-        # parameters must be a dict, not a string
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters="not_a_dict")
-
-        # parameters must be a dict, not a list
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters=["not", "a", "dict"])
-
-        # parameters must be a dict, not None
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters=None)
-
-    def test_valid_request_serialization(self):
-        """Test that valid request can be serialized to dict"""
-        from api_server.main import WorkflowExecutionRequest
-
-        request = WorkflowExecutionRequest(
-            parameters={"forecast_times": ["2024-01-01"], "nsteps": 10}
-        )
-
-        # Serialize to dict
-        data = request.model_dump()
-        assert isinstance(data, dict)
-        assert data == {"parameters": {"forecast_times": ["2024-01-01"], "nsteps": 10}}
-
-    def test_valid_request_with_nested_parameters(self):
-        """Test that request with nested parameter structures is accepted"""
-        from api_server.main import WorkflowExecutionRequest
-
-        # Complex nested parameters
-        request = WorkflowExecutionRequest(
-            parameters={
-                "forecast_times": ["2024-01-01", "2024-01-02"],
-                "nsteps": 10,
-                "config": {
-                    "model": {"type": "fcn", "version": "1.0"},
-                    "data": {"source": "gfs", "cache": True},
-                },
-                "options": ["opt1", "opt2", "opt3"],
-            }
-        )
-
-        assert request.parameters["nsteps"] == 10
-        assert request.parameters["config"]["model"]["type"] == "fcn"
-        assert len(request.parameters["options"]) == 3
-
-
-class TestQueuePosition:
-    """Tests for queue position functionality"""
-
-    @pytest.fixture
-    def mock_queue(self):
-        """Create a mock RQ queue with job IDs"""
-        from unittest.mock import MagicMock
-
-        queue = MagicMock()
-        queue.job_ids = [
-            "workflow1_exec_001",
-            "workflow2_exec_002",
-            "workflow1_exec_003",
-            "workflow3_exec_004",
-        ]
-        return queue
-
-    def test_get_queue_position_first_in_queue(self, mock_queue):
-        """Test getting position for first job in queue"""
-        from unittest.mock import patch
-
-        from api_server.main import get_queue_position
-
-        with patch("api_server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow1_exec_001")
-            assert position == 0  # 0-indexed: first job is at position 0
-
-    def test_get_queue_position_middle_of_queue(self, mock_queue):
-        """Test getting position for job in middle of queue"""
-        from unittest.mock import patch
-
-        from api_server.main import get_queue_position
-
-        with patch("api_server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow1_exec_003")
-            assert position == 2  # 0-indexed: third job is at position 2
-
-    def test_get_queue_position_last_in_queue(self, mock_queue):
-        """Test getting position for last job in queue"""
-        from unittest.mock import patch
-
-        from api_server.main import get_queue_position
-
-        with patch("api_server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow3_exec_004")
-            assert position == 3  # 0-indexed: fourth job is at position 3
-
-    def test_get_queue_position_not_in_queue(self, mock_queue):
-        """Test getting position for job not in queue returns None"""
-        from unittest.mock import patch
-
-        from api_server.main import get_queue_position
-
-        with patch("api_server.main.inference_queue", mock_queue):
-            position = get_queue_position("nonexistent_job")
-            assert position is None
-
-    def test_get_queue_position_no_queue(self):
-        """Test getting position when queue is not initialized returns None"""
-        from unittest.mock import patch
-
-        from api_server.main import get_queue_position
-
-        with patch("api_server.main.inference_queue", None):
-            position = get_queue_position("any_job")
-            assert position is None
-
-    def test_get_queue_position_empty_queue(self):
-        """Test getting position when queue is empty"""
-        from unittest.mock import MagicMock, patch
-
-        from api_server.main import get_queue_position
-
-        empty_queue = MagicMock()
-        empty_queue.job_ids = []
-
-        with patch("api_server.main.inference_queue", empty_queue):
-            position = get_queue_position("any_job")
-            assert position is None
-
-    def test_get_queue_position_exception_handling(self):
-        """Test that exceptions in get_queue_position are handled gracefully"""
-        from unittest.mock import MagicMock, PropertyMock, patch
-
-        from api_server.main import get_queue_position
-
-        queue_with_error = MagicMock()
-        type(queue_with_error).job_ids = PropertyMock(
-            side_effect=Exception("Queue connection error")
-        )
-
-        with patch("api_server.main.inference_queue", queue_with_error):
-            position = get_queue_position("any_job")
-            assert position is None
-
-
-class TestWorkflowExecutionWithQueuePosition:
-    """Tests for workflow execution endpoint with queue position"""
-
-    @pytest.fixture
-    def mock_workflow_class(self):
-        """Create a mock workflow class"""
-        from api_server.workflow import Workflow, WorkflowParameters
-
-        class TestWorkflowParameters(WorkflowParameters):
-            """Test parameters"""
-
-            test_param: str = "default"
-
-        class TestWorkflow(Workflow):
-            name = "test_workflow"
-            description = "Test workflow"
-            Parameters = TestWorkflowParameters
-
-            @classmethod
-            def validate_parameters(cls, parameters):
-                return TestWorkflowParameters.validate(parameters)
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        return TestWorkflow
-
-    @pytest.fixture
-    def client(self, mock_workflow_class):
-        """Create test client with mocked dependencies"""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("api_server.workflow.register_all_workflows"),
-        ):
-            # Setup mock async Redis
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-
-            # Setup mock sync Redis
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_instance.setex = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            # Setup mock queue
-            mock_queue = MagicMock()
-            mock_queue.__len__ = MagicMock(return_value=0)
-            mock_queue.job_ids = []
-            mock_queue_class.return_value = mock_queue
-
-            from api_server.main import app
-            from api_server.workflow import workflow_registry
-
-            # Register the test workflow
-            workflow_registry._workflows["test_workflow"] = mock_workflow_class
-
-            with TestClient(app, raise_server_exceptions=False) as client:
-                yield client, mock_queue, mock_sync_instance
-
-            # Cleanup
-            if "test_workflow" in workflow_registry._workflows:
-                del workflow_registry._workflows["test_workflow"]
-
-    def test_execute_workflow_includes_queue_position(self, client):
-        """Test that POST /v1/infer/{workflow_name} includes queue position"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_123"
-
-        # Before enqueue: queue size is 0
-        mock_queue.__len__ = MagicMock(return_value=0)
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Redis llen returns 0 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        # Patch the inference_queue at module level
-        with patch("api_server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is included in response
-        assert "position" in data
-        assert data["position"] == 0  # Redis llen returns 0 (first job, position 0)
-        assert data["status"] == "queued"
-
-    def test_execute_workflow_position_with_existing_jobs(self, client):
-        """Test that position is correct when there are existing jobs in queue"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_456"
-
-        # Before enqueue: queue has 2 existing jobs
-        mock_queue.__len__ = MagicMock(return_value=2)
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Redis llen returns 2 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=2)
-
-        # Patch the inference_queue at module level
-        with patch("api_server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is correct
-        assert "position" in data
-        assert data["position"] == 2  # Redis llen returns 2 (third job, position 2)
-        assert data["status"] == "queued"
-
-    def test_execute_workflow_position_with_five_existing_jobs(self, client):
-        """Test that position is correct with multiple existing jobs in queue"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_789"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Before enqueue: queue has 5 existing jobs
-        mock_queue.__len__ = MagicMock(return_value=5)
-
-        # Redis llen returns 5 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=5)
-
-        # Patch the inference_queue at module level
-        with patch("api_server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is correct
-        assert "position" in data
-        assert data["position"] == 5  # Redis llen returns 5 (sixth job, position 5)
-        assert data["status"] == "queued"
-
-    def test_get_workflow_status_includes_position_when_queued(self, client):
-        """Test that GET /v1/infer/{workflow_name}/{execution_id}/status includes position when status is queued"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup queue with multiple jobs
-        execution_id = "exec_456"
-        job_id = f"test_workflow_{execution_id}"
-
-        # Set up mock queue job_ids to include our job at position 1
-        mock_queue.job_ids = ["other_job_1", job_id, "other_job_2"]
-
-        # Mock Redis response for queued execution
-        import json
-        from datetime import datetime, timezone
-        from unittest.mock import patch
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "queued",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        # Patch the inference_queue for get_queue_position to use
-        with patch("api_server.main.inference_queue", mock_queue):
-            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is included and correct
-        assert "position" in data
-        assert data["position"] == 1  # Second in queue (0-indexed, so position 1)
-        assert data["status"] == "queued"
-
-    def test_get_workflow_status_queued_but_not_in_queue(self, client):
-        """Test race condition: status is QUEUED but job not in queue (worker picked it up)"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_race"
-
-        # Set up mock queue with empty job_ids (job already picked up by worker)
-        mock_queue.job_ids = []
-
-        # Mock Redis response with QUEUED status (worker hasn't updated it yet)
-        import json
-        from datetime import datetime, timezone
-        from unittest.mock import patch
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "queued",  # Still queued in Redis
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        # Patch the inference_queue for get_queue_position to use
-        with patch("api_server.main.inference_queue", mock_queue):
-            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Should be treated as RUNNING since job is not in queue
-        assert data["status"] == "running"
-        # Position should be None for running status
-        if "position" in data:
-            assert data["position"] is None
-
-    def test_get_workflow_status_no_position_when_running(self, client):
-        """Test that position is None when workflow is running (not queued)"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_789"
-
-        # Mock Redis response for running execution
-        import json
-        from datetime import datetime, timezone
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "running",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Position should be None or not included for non-queued status
-        # (The model allows None, so it could be None or absent)
-        if "position" in data:
-            assert data["position"] is None
-        assert data["status"] == "running"
-
-    def test_get_workflow_status_no_position_when_completed(self, client):
-        """Test that position is None when workflow is completed"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_999"
-
-        # Mock Redis response for completed execution
-        import json
-        from datetime import datetime, timezone
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "completed",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "end_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Position should be None for completed workflows
-        if "position" in data:
-            assert data["position"] is None
-        assert data["status"] == "completed"
diff --git a/test/utils/test_coords.py b/test/utils/test_coords.py
index 4d8134225..8a526303c 100644
--- a/test/utils/test_coords.py
+++ b/test/utils/test_coords.py
@@ -26,7 +26,12 @@
     handshake_dim,
     handshake_size,
 )
-from earth2studio.utils.coords import map_coords, split_coords
+from earth2studio.utils.coords import (
+    cat_coords,
+    map_coords,
+    split_coords,
+    tile_coords,
+)
 
 
 @pytest.mark.parametrize(
@@ -300,15 +305,26 @@ def test_convert_multidim_to_singledim_error():
 
 def test_handshake_coords():
     """Test handshake_coords function"""
-    coords1 = OrderedDict([("lat", np.array([1, 2, 3]))])
-    coords2 = OrderedDict([("lat", np.array([1, 2, 3]))])
+    coords1 = OrderedDict(
+        [
+            ("lat", np.array([1, 2, 3])),
+            ("lon", np.array([4, 5, 6])),
+        ]
+    )
+    coords2 = OrderedDict(
+        [
+            ("lat", np.array([1, 2, 3])),
+            ("lon", np.array([4, 5, 6])),
+        ]
+    )
 
     # Should pass
     handshake_coords(coords1, coords2, "lat")
+    handshake_coords(coords1, coords2, ["lat", "lon"])
 
     # Test missing dimension in input coords
     with pytest.raises(KeyError, match="not found in input coordinates"):
-        handshake_coords(coords1, coords2, "lon")
+        handshake_coords(coords1, coords2, "variable")
 
     # Test missing dimension in target coords
     coords2 = OrderedDict([("lon", np.array([1, 2, 3]))])
@@ -420,3 +436,425 @@ def test_convert_multidim_to_singledim_additional():
         ValueError, match="Assumed that if an n-dimensional coordinate exists"
     ):
         convert_multidim_to_singledim(coords)
+
+
+def test_tile_coords():
+    """Test tiling function for expanding dimensions"""
+
+    x = torch.randn(2, 721, 1440)
+    coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    target_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lead_time": np.array([0, 1, 2, 3, 4]),
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 4, 5, 2, 721, 1440)
+    assert "variable" in result_coords
+    assert "time" in result_coords
+
+
+def test_tile_coords_ignores_common_dims():
+    x = torch.randn(2, 721, 1440)
+    coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    # target_coords has "variable", "lat", "lon" which should be ignored
+    # Only "ensemble", "time", "lead_time" should be used for tiling
+    target_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lead_time": np.array([0, 1, 2, 3, 4]),
+            "variable": np.array(["different", "values"]),  # Should be ignored
+            "lat": np.linspace(0, 90, 100),  # Should be ignored
+            "lon": np.linspace(0, 180, 200),  # Should be ignored
+        }
+    )
+
+    result, result_coords = tile_coords(x, coords, target_coords)
+    # Should tile to (3, 4, 5, 2, 721, 1440) - leading dims from target_coords (not in coords)
+    assert result.shape == (3, 4, 5, 2, 721, 1440)
+    # Should use coords values, not target_coords values for common dims
+    assert np.array_equal(result_coords["variable"], coords["variable"])
+    assert np.array_equal(result_coords["lat"], coords["lat"])
+    assert np.array_equal(result_coords["lon"], coords["lon"])
+
+
+def test_tile_coords_edge_cases():
+    # Test: no leading dimensions (all target_coords dims exist in coords)
+    x = torch.randn(2, 3, 4)
+    coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.array([0, 1, 2]),
+            "c": np.array([0, 1, 2, 3]),
+        }
+    )
+    target_coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.array([0, 1, 2]),
+            "c": np.array([0, 1, 2, 3]),
+        }
+    )
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == x.shape
+    assert result_coords == coords
+
+    # Test: common dimensions in target_coords are ignored (uses coords values)
+    x = torch.randn(2, 721, 1440)
+    coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    target_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "variable": np.array(["different"]),  # Should be ignored
+            "lat": np.linspace(0, 90, 100),  # Should be ignored
+            "lon": np.linspace(0, 180, 200),  # Should be ignored
+        }
+    )
+    # Only "ensemble" and "time" from target_coords should be used for tiling
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 4, 2, 721, 1440)
+    assert np.array_equal(result_coords["variable"], coords["variable"])
+    assert np.array_equal(result_coords["lat"], coords["lat"])
+    assert np.array_equal(result_coords["lon"], coords["lon"])
+
+    # Test: single dimension x
+    x = torch.randn(721)
+    coords = OrderedDict({"lat": np.linspace(90, -90, 721)})
+    target_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lat": np.linspace(0, 90, 100),  # Should be ignored
+        }
+    )
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 4, 721)
+    assert "lat" in result_coords
+    assert np.array_equal(result_coords["lat"], coords["lat"])
+
+    # Test: empty tensors (0-sized dimensions)
+    x = torch.randn(0, 721, 1440)
+    coords = OrderedDict(
+        {
+            "variable": np.array([]),  # Empty array
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    target_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "variable": np.array([1, 2, 3]),  # Should be ignored
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 4, 0, 721, 1440)
+    assert len(result_coords["variable"]) == 0
+
+    # Test: mismatched coordinate values (common dims use coords values)
+    x = torch.randn(2, 10)
+    coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.arange(10),
+        }
+    )
+    target_coords = OrderedDict(
+        {
+            "c": np.array([0, 1, 2]),
+            "a": np.array([10, 20]),  # Should be ignored
+            "b": np.arange(10, 20),  # Should be ignored
+        }
+    )
+    result, result_coords = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 2, 10)
+    assert np.array_equal(result_coords["a"], coords["a"])
+    assert np.array_equal(result_coords["b"], coords["b"])
+
+
+def test_tile_coords_validation():
+    """Test that tile_coords validates new dims lead common dims"""
+    x = torch.randn(2, 10)
+    coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.arange(10),
+        }
+    )
+
+    # Valid: new dims before common dims
+    target_coords = OrderedDict(
+        {
+            "c": np.array([0, 1, 2]),
+            "a": np.array([10, 20]),
+            "b": np.arange(10, 20),
+        }
+    )
+    result, _ = tile_coords(x, coords, target_coords)
+    assert result.shape == (3, 2, 10)
+
+    # Invalid: new dim after common dim
+    target_coords_bad = OrderedDict(
+        {
+            "a": np.array([10, 20]),
+            "c": np.array([0, 1, 2]),  # New dim after common dim
+            "b": np.arange(10, 20),
+        }
+    )
+    with pytest.raises(ValueError):
+        tile_coords(x, coords, target_coords_bad)
+
+
+def test_cat_coords():
+    xx = torch.randn(1, 2, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["u10m", "v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(1, 1, 721, 1440)
+    coy = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="variable")
+
+    assert result.shape == (1, 3, 721, 1440)
+    assert len(result_coords["variable"]) == 3
+    assert np.array_equal(result_coords["variable"], ["u10m", "v10m", "msl"])
+
+
+def test_cat_coords_different_dims():
+    """Test concatenation along different dimensions"""
+    # Test concatenation along "time" dimension
+    xx = torch.randn(2, 3, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0, 1]),
+            "variable": np.array(["u10m", "v10m", "msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(1, 3, 721, 1440)
+    coy = OrderedDict(
+        {
+            "time": np.array([2]),
+            "variable": np.array(["u10m", "v10m", "msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="time")
+    assert result.shape == (3, 3, 721, 1440)
+    assert len(result_coords["time"]) == 3
+    assert np.array_equal(result_coords["time"], [0, 1, 2])
+
+    # Test concatenation along "batch" dimension
+    xx = torch.randn(2, 3, 721, 1440)
+    cox = OrderedDict(
+        {
+            "batch": np.array([0, 1]),
+            "variable": np.array(["u10m", "v10m", "msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(1, 3, 721, 1440)
+    coy = OrderedDict(
+        {
+            "batch": np.array([2]),
+            "variable": np.array(["u10m", "v10m", "msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="batch")
+    assert result.shape == (3, 3, 721, 1440)
+    assert len(result_coords["batch"]) == 3
+    assert np.array_equal(result_coords["batch"], [0, 1, 2])
+
+
+def test_cat_coords_multiple():
+    xx = torch.randn(1, 1, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["u10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(1, 1, 721, 1440)
+    coy = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    zz = torch.randn(1, 1, 721, 1440)
+    coz = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = cat_coords((xx, yy, zz), (cox, coy, coz), dim="variable")
+    assert result.shape == (1, 3, 721, 1440)
+    assert len(result_coords["variable"]) == 3
+    assert np.array_equal(result_coords["variable"], ["u10m", "v10m", "msl"])
+
+
+def test_cat_coords_single():
+    """Test concatenation with a single tensor (should return as-is)"""
+    xx = torch.randn(1, 2, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["u10m", "v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = cat_coords((xx,), (cox,), dim="variable")
+    assert torch.equal(result, xx)  # Should return the same tensor
+    for dim in cox.keys():
+        assert np.all(result_coords[dim] == cox[dim])
+
+
+def test_cat_coords_errors():
+    """Test error cases for cat_coords"""
+    # Test length mismatch
+    xx = torch.randn(1, 2, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["u10m", "v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    yy = torch.randn(1, 1, 721, 1440)
+    coy = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    with pytest.raises(ValueError):
+        cat_coords((xx, yy), (cox,), dim="variable")
+
+    # Test empty input
+    with pytest.raises(ValueError):
+        cat_coords((), (), dim="variable")
+
+    xx = torch.randn(1, 2, 721, 1440)
+    cox = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["u10m", "v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(1, 1, 721, 1440)
+    coy = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    # Test missing dimension in first coords (handshake_dim on cox)
+    with pytest.raises(KeyError):
+        cat_coords((xx, yy), (cox, coy), dim="nonexistent")
+
+    # Test cox has extra dim (key-equality check catches mismatched dimension sets)
+    cox_with_extra = cox.copy()
+    cox_with_extra["extra_dim"] = np.array([0])
+    xx_extra = torch.randn(1, 2, 1, 721, 1440)
+    with pytest.raises(KeyError):
+        cat_coords((xx_extra, yy), (cox_with_extra, coy), dim="extra_dim")
+
+    # Test mismatched non-cat coords (handshake_coords catches shape mismatch)
+    yy_bad = torch.randn(1, 1, 361, 1440)
+    coy_bad = OrderedDict(
+        {
+            "time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 361),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    with pytest.raises(ValueError):
+        cat_coords((xx, yy_bad), (cox, coy_bad), dim="variable")
+
+    # Test mismatched dimension names
+    yy_bad = torch.randn(1, 1, 361, 1440)
+    coy_bad = OrderedDict(
+        {
+            "lead_time": np.array([0]),
+            "variable": np.array(["msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    with pytest.raises(ValueError):
+        cat_coords((xx, yy_bad), (cox, coy_bad), dim="variable")
diff --git a/tox-full.ini b/tox-full.ini
index a2c79803d..58b3a0c4f 100644
--- a/tox-full.ini
+++ b/tox-full.ini
@@ -47,6 +47,8 @@ package = editable
 basepython = python3.12
 passenv =
     EARTH2STUDIO_CACHE
+    EARTH2STUDIO_DATA_CACHE
+    EARTH2STUDIO_MODEL_CACHE
     EARTH2STUDIO_PACKAGE_TIMEOUT
     TESTMON_DATAFILE
     UV_CACHE_DIR
diff --git a/tox.ini b/tox.ini
index f3ae05d6b..98e5ef839 100644
--- a/tox.ini
+++ b/tox.ini
@@ -9,6 +9,8 @@ envlist =
     test-stats
     test-px-models
     test-dx-models
+    test-da-models
+    test-px-models-ace2
 
 [testenv]
 # Base configuration for all environments
@@ -18,6 +20,8 @@ package = editable
 basepython = python3.12
 passenv =
     EARTH2STUDIO_CACHE
+    EARTH2STUDIO_DATA_CACHE
+    EARTH2STUDIO_MODEL_CACHE
     EARTH2STUDIO_PACKAGE_TIMEOUT
     TESTMON_DATAFILE
     UV_CACHE_DIR
@@ -52,11 +56,7 @@ description = Run tests for the serve
 extras = serve
 commands =
     pytest {posargs:-s --cov --cov-append --slow --testmon} \
-        test/serve/test_config.py \
-        test/serve/test_cleanup_daemon.py \
-        test/serve/test_cpu_worker.py \
-        test/serve/test_main.py \
-        test/serve/test_workflow.py
+        test/serve
 
 [testenv:test-data]
 runner = uv-venv-lock-runner
@@ -100,6 +100,21 @@ commands =
     pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
         test/models/dx
 
+[testenv:test-da-models]
+runner = uv-venv-lock-runner
+description = Run tests for da models using all env
+extras = all
+commands =
+    pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
+        test/models/da
+
 # =====================
 # Conflict environments
 # =====================
+[testenv:test-px-models-ace2]
+runner = uv-venv-lock-runner
+description = Run tests for px models with conflicts
+extras = ace2
+commands =
+    pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
+        test/models/px/test_ace2.py
diff --git a/uv.lock b/uv.lock
index e83b710f9..2c59325f9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
@@ -27,14 +27,27 @@ resolution-markers = [
     "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
     "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
 ]
+conflicts = [[
+    { package = "earth2studio", extra = "ace2" },
+    { package = "earth2studio", extra = "atlas" },
+], [
+    { package = "earth2studio", extra = "ace2" },
+    { package = "earth2studio", extra = "fcn3" },
+], [
+    { package = "earth2studio", extra = "ace2" },
+    { package = "earth2studio", extra = "perturbation" },
+], [
+    { package = "earth2studio", extra = "ace2" },
+    { package = "earth2studio", extra = "sfno" },
+]]
 
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 version = "2.4.0"
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+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
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+sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" },
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+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
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 [[package]]
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+sdist = { url = "https://files.pythonhosted.org/packages/18/94/332629387f4a9fc691cac9c0cb078af877bfaba415b1a16411377f6ea310/aiobotocore-3.1.3.tar.gz", hash = "sha256:b1b6a95aa4c17410090f4adf16fd45e45a898140c83d4e9d554602f9310408c0", size = 122675, upload-time = "2026-02-14T12:11:01.745Z" }
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+    { url = "https://files.pythonhosted.org/packages/14/29/a3e75834009121ebb695dc24f9fe804566b1bcc9b7d46f6fbe56fe972c6a/aiobotocore-3.1.3-py3-none-any.whl", hash = "sha256:3afc93bf14de304dbd4a2c90f36fb3ce6348b06a5a1ec7f87261be628d7876d9", size = 87717, upload-time = "2026-02-14T12:10:59.898Z" },
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 [[package]]
 name = "earth2grid"
-version = "2025.11.1+torch210"
+version = "2025.11.1+torch29"
 source = { git = "https://github.com/NVlabs/earth2grid.git?rev=11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3#11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3" }
 dependencies = [
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@@ -1706,7 +1610,6 @@ dependencies = [
 [package.optional-dependencies]
 ace2 = [
     { name = "fme" },
-    { name = "h5py" },
     { name = "pandas" },
     { name = "scipy" },
 ]
@@ -1724,29 +1627,29 @@ aifsens = [
 ]
 all = [
     { name = "anemoi-models" },
-    { name = "cbottle", marker = "python_full_version >= '3.12'" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
     { name = "cdsapi" },
     { name = "cfgrib" },
     { name = "cucim-cu12" },
+    { name = "cudf-cu12" },
     { name = "cupy-cuda12x" },
     { name = "dm-haiku" },
     { name = "dm-tree" },
     { name = "earth2grid" },
     { name = "earthkit-regrid" },
     { name = "eccodes" },
+    { name = "eccodeslib" },
     { name = "ecmwf-opendata" },
     { name = "einops" },
     { name = "flash-attn" },
     { name = "flax" },
-    { name = "fme" },
     { name = "globus-sdk" },
     { name = "graphcast" },
-    { name = "h5py" },
     { name = "httpx" },
     { name = "hydra-core" },
     { name = "importlib-metadata" },
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-    { name = "intake-esgf", version = "2026.1.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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+    { name = "intake-esgf", version = "2026.1.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
     { name = "jax", extra = ["cuda12"] },
     { name = "makani" },
     { name = "microsoft-aurora" },
@@ -1764,37 +1667,35 @@ all = [
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     { name = "pystac-client" },
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-    { name = "rioxarray", version = "0.20.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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     { name = "scikit-image" },
     { name = "scipy" },
     { name = "timm" },
-    { name = "torch-harmonics" },
+    { name = "torch-harmonics", version = "0.8.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-12-earth2studio-ace2'" },
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 ]
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     { name = "natten" },
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
     { name = "timm" },
-    { name = "torch-harmonics" },
+    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
 ]
 aurora = [
     { name = "microsoft-aurora" },
 ]
 cbottle = [
-    { name = "cbottle", marker = "python_full_version >= '3.12'" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
     { name = "earth2grid" },
 ]
 corrdiff = [
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     { name = "nvidia-physicsnemo" },
     { name = "nvtx" },
-    { name = "pytz" },
     { name = "scipy" },
 ]
 cyclone = [
@@ -1806,24 +1707,35 @@ da-interp = [
     { name = "cudf-cu12" },
     { name = "cupy-cuda12x" },
 ]
+da-stormcast = [
+    { name = "cudf-cu12" },
+    { name = "cupy-cuda12x" },
+    { name = "einops" },
+    { name = "nvidia-physicsnemo" },
+    { name = "nvtx" },
+    { name = "omegaconf" },
+    { name = "pyproj" },
+    { name = "scipy" },
+]
 data = [
     { name = "cdsapi" },
     { name = "cfgrib" },
     { name = "eccodes" },
+    { name = "eccodeslib" },
     { name = "ecmwf-opendata" },
     { name = "globus-sdk" },
     { name = "httpx" },
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-    { name = "intake-esgf", version = "2026.1.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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+    { name = "rasterio", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "rioxarray", version = "0.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "rioxarray", version = "0.20.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
     { name = "scipy" },
 ]
 dlesym = [
@@ -1831,24 +1743,20 @@ dlesym = [
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     { name = "nvidia-physicsnemo" },
     { name = "omegaconf" },
-    { name = "pytz" },
 ]
 dlwp = [
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     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
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-    { name = "pytz" },
 ]
 fcn3 = [
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     { name = "moviepy" },
-    { name = "pytz" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
-    { name = "torch-harmonics" },
+    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
 ]
 fengwu = [
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@@ -1869,7 +1777,6 @@ graphcast = [
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 interp-modafno = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 pangu = [
     { name = "onnx" },
@@ -1878,32 +1785,29 @@ pangu = [
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 perturbation = [
     { name = "scipy" },
-    { name = "torch-harmonics" },
+    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
 ]
 precip-afno = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 precip-afno-v2 = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 serve = [
     { name = "aiofiles" },
-    { name = "azure-identity" },
-    { name = "azure-storage-blob" },
-    { name = "cryptography" },
     { name = "fastapi" },
     { name = "hiredis" },
     { name = "httpx" },
     { name = "hydra-core" },
-    { name = "multi-storage-client" },
     { name = "prometheus-client" },
     { name = "pydantic" },
     { name = "python-dotenv" },
     { name = "python-multipart" },
     { name = "redis" },
+    { name = "requests" },
     { name = "rq" },
+    { name = "types-redis" },
+    { name = "urllib3" },
     { name = "uvicorn", extra = ["standard"] },
 ]
 sfno = [
@@ -1912,15 +1816,13 @@ sfno = [
     { name = "pynvml" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
-    { name = "torch-harmonics" },
+    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
 ]
 solarradiation-afno = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 statistics = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 stormcast = [
     { name = "einops" },
@@ -1928,7 +1830,6 @@ stormcast = [
     { name = "nvtx" },
     { name = "omegaconf" },
     { name = "pyproj" },
-    { name = "pytz" },
     { name = "scipy" },
 ]
 stormscope = [
@@ -1936,7 +1837,6 @@ stormscope = [
     { name = "natten" },
     { name = "nvidia-physicsnemo" },
     { name = "nvtx" },
-    { name = "pytz" },
     { name = "scipy" },
 ]
 utils = [
@@ -1945,7 +1845,6 @@ utils = [
 ]
 windgust-afno = [
     { name = "nvidia-physicsnemo" },
-    { name = "pytz" },
 ]
 
 [package.dev-dependencies]
@@ -1971,6 +1870,7 @@ dev = [
     { name = "ruff" },
     { name = "tox" },
     { name = "tox-uv" },
+    { name = "types-redis" },
     { name = "types-requests" },
 ]
 docs = [
@@ -1978,12 +1878,12 @@ docs = [
     { name = "jupytext" },
     { name = "matplotlib" },
     { name = "myst-parser" },
-    { name = "pydata-sphinx-theme" },
+    { name = "nvidia-sphinx-theme" },
     { name = "scipy" },
-    { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
-    { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
-    { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
-    { name = "sphinx-autodoc-typehints", version = "3.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "sphinx-autodoc-typehints", version = "3.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
     { name = "sphinx-design" },
     { name = "sphinx-favicon" },
     { name = "sphinx-gallery" },
@@ -1996,22 +1896,22 @@ requires-dist = [
     { name = "anemoi-models", marker = "extra == 'aifs'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'aifsens'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'all'", specifier = "==0.5.1" },
-    { name = "azure-identity", marker = "extra == 'serve'", specifier = ">=1.15.0" },
-    { name = "azure-storage-blob", marker = "extra == 'serve'", specifier = ">=12.19.0" },
-    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'all'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
-    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'cbottle'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'all'", git = "https://github.com/NVlabs/cBottle.git?rev=8b8b358466e6b2f50d1779009790002ceb596e72" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'cbottle'", git = "https://github.com/NVlabs/cBottle.git?rev=8b8b358466e6b2f50d1779009790002ceb596e72" },
     { name = "cdsapi", marker = "extra == 'all'", specifier = ">=0.7.5" },
     { name = "cdsapi", marker = "extra == 'data'", specifier = ">=0.7.5" },
     { name = "cfgrib", marker = "extra == 'all'", git = "https://github.com/NickGeneva/cfgrib.git?rev=6a9af3173d7050b5fa5d221755f97e6ecb8c3433" },
     { name = "cfgrib", marker = "extra == 'data'", git = "https://github.com/NickGeneva/cfgrib.git?rev=6a9af3173d7050b5fa5d221755f97e6ecb8c3433" },
     { name = "cftime" },
-    { name = "cryptography", marker = "extra == 'serve'", specifier = ">=41.0.0" },
     { name = "cucim-cu12", marker = "extra == 'all'", specifier = ">=25.4.0" },
     { name = "cucim-cu12", marker = "extra == 'cyclone'", specifier = ">=25.4.0" },
+    { name = "cudf-cu12", marker = "extra == 'all'", specifier = "==26.2.*" },
     { name = "cudf-cu12", marker = "extra == 'da-interp'", specifier = "==26.2.*" },
+    { name = "cudf-cu12", marker = "extra == 'da-stormcast'", specifier = "==26.2.*" },
     { name = "cupy-cuda12x", marker = "extra == 'all'", specifier = "<14.0.0" },
     { name = "cupy-cuda12x", marker = "extra == 'cyclone'", specifier = "<14.0.0" },
     { name = "cupy-cuda12x", marker = "extra == 'da-interp'", specifier = "<14.0.0" },
+    { name = "cupy-cuda12x", marker = "extra == 'da-stormcast'", specifier = "<14.0.0" },
     { name = "dm-haiku", marker = "extra == 'all'", specifier = ">=0.0.14" },
     { name = "dm-haiku", marker = "extra == 'graphcast'", specifier = ">=0.0.14" },
     { name = "dm-tree", marker = "extra == 'all'", specifier = ">=0.1.9" },
@@ -2024,8 +1924,10 @@ requires-dist = [
     { name = "earthkit-regrid", marker = "extra == 'aifs'", specifier = "==0.4.0" },
     { name = "earthkit-regrid", marker = "extra == 'aifsens'", specifier = "==0.4.0" },
     { name = "earthkit-regrid", marker = "extra == 'all'", specifier = "==0.4.0" },
-    { name = "eccodes", marker = "extra == 'all'", specifier = ">=2.38.0" },
-    { name = "eccodes", marker = "extra == 'data'", specifier = ">=2.38.0" },
+    { name = "eccodes", marker = "extra == 'all'", specifier = ">=2.39.2" },
+    { name = "eccodes", marker = "extra == 'data'", specifier = ">=2.39.2" },
+    { name = "eccodeslib", marker = "extra == 'all'", specifier = ">=2.39.2" },
+    { name = "eccodeslib", marker = "extra == 'data'", specifier = ">=2.39.2" },
     { name = "ecmwf-opendata", marker = "extra == 'aifs'", specifier = ">=0.3.3" },
     { name = "ecmwf-opendata", marker = "extra == 'aifsens'", specifier = ">=0.3.3" },
     { name = "ecmwf-opendata", marker = "extra == 'all'", specifier = ">=0.3.3" },
@@ -2034,6 +1936,7 @@ requires-dist = [
     { name = "einops", marker = "extra == 'all'", specifier = ">=0.8.1" },
     { name = "einops", marker = "extra == 'atlas'" },
     { name = "einops", marker = "extra == 'corrdiff'", specifier = ">=0.8.1" },
+    { name = "einops", marker = "extra == 'da-stormcast'", specifier = ">=0.8.1" },
     { name = "einops", marker = "extra == 'stormcast'", specifier = ">=0.8.1" },
     { name = "fastapi", marker = "extra == 'serve'", specifier = ">=0.104.0" },
     { name = "flash-attn", marker = "extra == 'aifs'" },
@@ -2042,7 +1945,6 @@ requires-dist = [
     { name = "flax", marker = "extra == 'all'", specifier = ">=0.10.6" },
     { name = "flax", marker = "extra == 'graphcast'", specifier = ">=0.10.6" },
     { name = "fme", marker = "extra == 'ace2'", git = "https://github.com/ai2cm/ace.git?rev=e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" },
-    { name = "fme", marker = "extra == 'all'", git = "https://github.com/ai2cm/ace.git?rev=e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" },
     { name = "fsspec", specifier = ">=2024.2.0" },
     { name = "gcsfs" },
     { name = "globus-sdk", marker = "extra == 'all'", specifier = "<4.0.0" },
@@ -2051,8 +1953,6 @@ requires-dist = [
     { name = "graphcast", marker = "extra == 'graphcast'", git = "https://github.com/google-deepmind/graphcast.git?rev=7077d40a36db6541e3ed72ccaed1c0d202fa6014" },
     { name = "h5netcdf", specifier = ">=1.0.0" },
     { name = "h5py", specifier = ">=3.2.0" },
-    { name = "h5py", marker = "extra == 'ace2'", specifier = "<=3.14.0" },
-    { name = "h5py", marker = "extra == 'all'", specifier = "<=3.14.0" },
     { name = "hiredis", marker = "extra == 'serve'", specifier = ">=2.0.0" },
     { name = "httpx", marker = "extra == 'all'", specifier = "==0.28.1" },
     { name = "httpx", marker = "extra == 'data'", specifier = "==0.28.1" },
@@ -2068,15 +1968,14 @@ requires-dist = [
     { name = "jax", extras = ["cuda12"], marker = "extra == 'all'", specifier = ">=0.4.26" },
     { name = "jax", extras = ["cuda12"], marker = "extra == 'graphcast'", specifier = ">=0.4.26" },
     { name = "loguru" },
-    { name = "makani", marker = "extra == 'all'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
-    { name = "makani", marker = "extra == 'fcn3'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
-    { name = "makani", marker = "extra == 'sfno'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
+    { name = "makani", marker = "extra == 'all'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
+    { name = "makani", marker = "extra == 'fcn3'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
+    { name = "makani", marker = "extra == 'sfno'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
     { name = "microsoft-aurora", marker = "extra == 'all'", specifier = ">=1.6.0" },
     { name = "microsoft-aurora", marker = "extra == 'aurora'", specifier = ">=1.6.0" },
     { name = "moviepy", marker = "extra == 'all'" },
     { name = "moviepy", marker = "extra == 'fcn3'" },
     { name = "moviepy", marker = "extra == 'sfno'" },
-    { name = "multi-storage-client", marker = "extra == 'serve'", specifier = ">=0.44.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'all'", specifier = ">=0.33.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'data'", specifier = ">=0.33.0" },
     { name = "natten", marker = "extra == 'all'" },
@@ -2084,28 +1983,30 @@ requires-dist = [
     { name = "natten", marker = "extra == 'stormscope'" },
     { name = "nest-asyncio" },
     { name = "netcdf4", specifier = ">=1.6.4,<1.7.3" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = "==1.3.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.2.0,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.3.0,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'atlas'", specifier = ">=1.3.0,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'corrdiff'", specifier = ">=1.2.0,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'dlesym'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'dlwp'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'fcn'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'interp-modafno'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno-v2'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'solarradiation-afno'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'statistics'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'stormcast'", specifier = ">=1.0.1,<2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'stormscope'", specifier = "==1.3.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'windgust-afno'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.3.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'atlas'", specifier = ">=1.3.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'corrdiff'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'da-stormcast'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'dlesym'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'dlwp'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'fcn'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'interp-modafno'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno-v2'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'solarradiation-afno'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'statistics'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'stormcast'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'stormscope'", specifier = ">=2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'windgust-afno'", specifier = ">=1.0.1" },
     { name = "nvtx", marker = "extra == 'all'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'corrdiff'", specifier = ">=0.2.11" },
+    { name = "nvtx", marker = "extra == 'da-stormcast'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'stormcast'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'stormscope'", specifier = ">=0.2.11" },
     { name = "omegaconf", marker = "extra == 'all'", specifier = ">=2.3.0" },
+    { name = "omegaconf", marker = "extra == 'da-stormcast'", specifier = ">=2.3.0" },
     { name = "omegaconf", marker = "extra == 'dlesym'", specifier = ">=2.3.0" },
     { name = "omegaconf", marker = "extra == 'stormcast'", specifier = ">=2.3.0" },
     { name = "onnx", marker = "extra == 'all'" },
@@ -2123,7 +2024,6 @@ requires-dist = [
     { name = "pandas", specifier = "<3.0" },
     { name = "pandas", marker = "extra == 'ace2'", specifier = ">=2.3.2" },
     { name = "pandas", marker = "extra == 'all'", specifier = "<3.0" },
-    { name = "pandas", marker = "extra == 'all'", specifier = ">=2.3.2" },
     { name = "pandas", marker = "extra == 'data'", specifier = "<3.0" },
     { name = "planetary-computer", marker = "extra == 'all'", specifier = ">=0.5.0" },
     { name = "planetary-computer", marker = "extra == 'data'", specifier = ">=0.5.0" },
@@ -2134,6 +2034,7 @@ requires-dist = [
     { name = "pynvml", marker = "extra == 'all'", specifier = ">=12.0.0" },
     { name = "pynvml", marker = "extra == 'sfno'", specifier = ">=12.0.0" },
     { name = "pyproj", marker = "extra == 'all'", specifier = ">=3.7.1" },
+    { name = "pyproj", marker = "extra == 'da-stormcast'", specifier = ">=3.7.1" },
     { name = "pyproj", marker = "extra == 'data'", specifier = ">=3.7.1" },
     { name = "pyproj", marker = "extra == 'stormcast'", specifier = ">=3.7.1" },
     { name = "pystac-client", marker = "extra == 'all'", specifier = ">=0.9.0" },
@@ -2141,24 +2042,10 @@ requires-dist = [
     { name = "python-dotenv" },
     { name = "python-dotenv", marker = "extra == 'serve'", specifier = ">=1.0.0" },
     { name = "python-multipart", marker = "extra == 'serve'", specifier = ">=0.0.6" },
-    { name = "pytz", marker = "extra == 'all'" },
-    { name = "pytz", marker = "extra == 'atlas'" },
-    { name = "pytz", marker = "extra == 'corrdiff'" },
-    { name = "pytz", marker = "extra == 'dlesym'" },
-    { name = "pytz", marker = "extra == 'dlwp'" },
-    { name = "pytz", marker = "extra == 'fcn'" },
-    { name = "pytz", marker = "extra == 'fcn3'" },
-    { name = "pytz", marker = "extra == 'interp-modafno'" },
-    { name = "pytz", marker = "extra == 'precip-afno'" },
-    { name = "pytz", marker = "extra == 'precip-afno-v2'" },
-    { name = "pytz", marker = "extra == 'solarradiation-afno'" },
-    { name = "pytz", marker = "extra == 'statistics'" },
-    { name = "pytz", marker = "extra == 'stormcast'" },
-    { name = "pytz", marker = "extra == 'stormscope'" },
-    { name = "pytz", marker = "extra == 'windgust-afno'" },
     { name = "rasterio", marker = "extra == 'all'", specifier = ">=1.3.9" },
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 with-cuda = [
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@@ -9002,25 +9207,25 @@ dependencies = [
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From ca01c57dd09e4ef0b60e1fc34f89b5479f4db8ef Mon Sep 17 00:00:00 2001
From: Ramu Ramamurthy <rramamurthy@nvidia.com>
Date: Thu, 12 Mar 2026 14:19:18 -0700
Subject: [PATCH 24/24] Revert "merge upstream/main"

This reverts commit 8750f5a161138e57a9897b1810cd29f36234a924.
---
 .cursor/rules/e2s-000-python-style-guide.mdc  |   12 -
 .cursor/rules/e2s-002-api-documentation.mdc   |    1 -
 .cursor/rules/e2s-009-prognostic-models.mdc   |    2 -
 .github/ISSUE_TEMPLATE/bug_report.yml         |    2 +-
 .github/ISSUE_TEMPLATE/chore.yml              |   59 -
 .../ISSUE_TEMPLATE/documentation_request.yml  |    2 +-
 .github/ISSUE_TEMPLATE/feature_request.yml    |    2 +-
 .gitlab-ci.yml                                |   64 +-
 CHANGELOG.md                                  |   15 -
 Makefile                                      |    2 +-
 docs/conf.py                                  |    3 +-
 docs/modules/models.rst                       |   25 -
 docs/templates/dataassim.rst                  |   23 -
 docs/userguide/about/install.md               |   62 +-
 earth2studio/data/__init__.py                 |    2 +-
 earth2studio/data/ufs.py                      |    7 +
 earth2studio/data/utils.py                    |  144 +-
 earth2studio/data/xr.py                       |   22 -
 earth2studio/io/zarr.py                       |    3 +-
 earth2studio/models/da/__init__.py            |   18 -
 earth2studio/models/da/base.py                |  188 -
 earth2studio/models/da/interp.py              |  589 --
 earth2studio/models/da/sda_stormcast.py       |  914 --
 earth2studio/models/da/utils.py               |  167 -
 earth2studio/models/dx/cbottle_sr.py          |    7 +-
 earth2studio/models/dx/corrdiff.py            |   19 +-
 earth2studio/models/dx/corrdiff_cmip6.py      |   16 +-
 earth2studio/models/nn/afno_ssrd.py           |    2 +-
 earth2studio/models/nn/atlas.py               |   17 +-
 earth2studio/models/nn/stormscope_util.py     |   17 +-
 earth2studio/models/px/aifsens.py             |    5 +-
 earth2studio/models/px/cbottle_video.py       |   12 +-
 earth2studio/models/px/dlesym.py              |    2 +-
 earth2studio/models/px/fcn.py                 |   24 +-
 .../models/px/graphcast_operational.py        |  105 +-
 earth2studio/models/px/graphcast_small.py     |  107 +-
 earth2studio/models/px/stormcast.py           |   21 +-
 earth2studio/models/px/stormscope.py          |    2 +-
 earth2studio/perturbation/gaussian.py         |   18 +-
 earth2studio/perturbation/spherical.py        |   12 +-
 earth2studio/run.py                           |   13 +-
 earth2studio/serve/client/__init__.py         |   15 -
 earth2studio/serve/client/client.py           |  550 -
 earth2studio/serve/client/e2client.py         |  479 -
 earth2studio/serve/client/exceptions.py       |  137 -
 earth2studio/serve/client/fsspec_utils.py     |  302 -
 earth2studio/serve/client/models.py           |  314 -
 earth2studio/utils/coords.py                  |  409 +-
 earth2studio/utils/time.py                    |   63 +-
 earth2studio/utils/type.py                    |    9 -
 examples/16_cbottle_super_resolution.py       |    1 -
 examples/21_stormcast_sda.py                  |  488 -
 pyproject.toml                                |   95 +-
 recipes/hens/cfg/storm_bernd.yaml             |    2 +-
 recipes/hens/src/hens_ensemble.py             |   13 +-
 recipes/hens/src/hens_utilities.py            |   50 +
 recipes/s2s/src/s2s_ensemble.py               |   24 +-
 recipes/s2s/src/s2s_utilities.py              |   51 +-
 serve/Dockerfile                              |   29 +-
 serve/client/README.md                        |  442 -
 serve/client/examples/basic_forecast.py       |  116 -
 serve/client/examples/diagnostic_analysis.py  |  155 -
 serve/client/examples/downscaled_forecast.py  |  128 -
 serve/client/pyproject.toml                   |   88 -
 serve/client/requirements.txt                 |   14 -
 serve/{server => inferenceserver}/Makefile    |    2 +-
 serve/{server => inferenceserver}/README.md   |    4 +-
 .../README_earth2workflows.md                 |    2 +-
 .../README_object_storage.md                  |    0
 .../README_workflows.md                       |   10 +-
 .../api_server/__init__.py}                   |   20 +-
 .../api_server}/cleanup_daemon.py             |   84 +-
 .../api_server}/conf/config.yaml              |    0
 .../api_server}/conf/profiles.json            |    0
 .../api_server}/conf/workflows.yaml           |    0
 .../inferenceserver/api_server}/config.py     |   39 +-
 .../inferenceserver/api_server}/cpu_worker.py |   25 +-
 .../api_server}/example_workflows/__init__.py |    0
 .../deterministic_earth2_workflow.py          |    2 +-
 .../example_workflows/deterministic_fcn.py    |    2 +-
 .../deterministic_workflow.py                 |    2 +-
 .../example_workflows/diagnostic_workflow.py  |    2 +-
 .../example_user_workflow.py                  |    2 +-
 .../example_workflows/foundry_fcn3.py         |    2 +-
 .../foundry_fcn3_stormscope_goes.py           |   20 +-
 .../stormcast_fcn3_workflow.py                |    2 +-
 .../inferenceserver/api_server}/main.py       |  261 +-
 .../api_server}/object_storage.py             |  207 +-
 .../planetary_computer/pc_client.py           |    0
 ...plate-collection-fcn3-stormscope-goes.json |    0
 .../template-collection-fcn3.json             |    0
 ...template-feature-fcn3-stormscope-goes.json |    0
 .../template-feature-fcn3.json                |    0
 .../inferenceserver/api_server}/utils.py      |   16 +-
 .../inferenceserver/api_server}/worker.py     |   68 +-
 .../api_server/workflow}/__init__.py          |   30 +-
 .../api_server/workflow}/e2workflow.py        |   12 +-
 .../api_server/workflow}/workflow.py          |  433 +-
 serve/{server => inferenceserver}/env.example |    0
 .../{server => inferenceserver}/openapi.yaml  |    0
 .../requirements.txt                          |    4 -
 .../scripts/profile_workflow.sh               |    0
 .../scripts/redis.conf                        |    0
 .../scripts/start_api_server.sh               |   21 +-
 .../scripts/start_redis.sh                    |    0
 .../scripts/startup.sh                        |   20 +-
 .../scripts/status.sh                         |    2 +-
 .../scripts/stop_api_server.sh                |    2 +-
 .../scripts/stop_redis.sh                     |    0
 .../{server => }/test/unit/test_e2workflow.py |   89 +-
 .../test/unit/test_e2workflow_params.py       |   65 +-
 test/Dockerfile                               |    2 +-
 test/data/test_data_utils.py                  |  181 +-
 test/data/test_goes.py                        |    1 -
 test/models/da/test_da_interp.py              |  359 -
 test/models/da/test_da_sda_stormcast.py       |  537 -
 test/models/da/test_utils.py                  |  204 -
 test/models/px/test_ace2.py                   |    2 -
 test/models/px/test_aurora.py                 |    6 +-
 test/models/px/test_dxwrapper.py              |    1 -
 test/models/px/test_fcn.py                    |    1 +
 test/models/px/test_graphcast.py              |   27 +-
 test/serve/client/conftest.py                 |  135 -
 test/serve/client/test_client.py              |  444 -
 test/serve/client/test_e2client.py            |  663 --
 test/serve/client/test_fsspec_utils.py        |  378 -
 test/serve/conftest.py                        |  112 +
 test/serve/server/conftest.py                 |   88 -
 test/serve/server/test_cleanup_daemon.py      |  775 --
 test/serve/server/test_cpu_worker.py          | 1403 ---
 test/serve/server/test_object_storage.py      |  294 -
 test/serve/server/test_server_main.py         | 2132 ----
 test/serve/server/test_utils.py               |  266 -
 test/serve/server/test_worker.py              |  241 -
 test/serve/test_cleanup_daemon.py             |  502 +
 test/serve/{server => }/test_config.py        |   30 +-
 test/serve/test_cpu_worker.py                 |  743 ++
 test/serve/test_main.py                       | 1213 +++
 test/serve/{server => }/test_workflow.py      |   45 +-
 test/utils/test_coords.py                     |  446 +-
 tox-full.ini                                  |    2 -
 tox.ini                                       |   25 +-
 uv.lock                                       | 8991 ++++++++---------
 143 files changed, 7992 insertions(+), 20408 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/chore.yml
 delete mode 100644 docs/templates/dataassim.rst
 delete mode 100644 earth2studio/models/da/__init__.py
 delete mode 100644 earth2studio/models/da/base.py
 delete mode 100644 earth2studio/models/da/interp.py
 delete mode 100644 earth2studio/models/da/sda_stormcast.py
 delete mode 100644 earth2studio/models/da/utils.py
 delete mode 100644 earth2studio/serve/client/__init__.py
 delete mode 100644 earth2studio/serve/client/client.py
 delete mode 100644 earth2studio/serve/client/e2client.py
 delete mode 100644 earth2studio/serve/client/exceptions.py
 delete mode 100644 earth2studio/serve/client/fsspec_utils.py
 delete mode 100644 earth2studio/serve/client/models.py
 delete mode 100644 examples/21_stormcast_sda.py
 delete mode 100644 serve/client/README.md
 delete mode 100644 serve/client/examples/basic_forecast.py
 delete mode 100644 serve/client/examples/diagnostic_analysis.py
 delete mode 100644 serve/client/examples/downscaled_forecast.py
 delete mode 100644 serve/client/pyproject.toml
 delete mode 100644 serve/client/requirements.txt
 rename serve/{server => inferenceserver}/Makefile (96%)
 rename serve/{server => inferenceserver}/README.md (99%)
 rename serve/{server => inferenceserver}/README_earth2workflows.md (98%)
 rename serve/{server => inferenceserver}/README_object_storage.md (100%)
 rename serve/{server => inferenceserver}/README_workflows.md (98%)
 rename serve/{client/Makefile => inferenceserver/api_server/__init__.py} (65%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/cleanup_daemon.py (82%)
 rename serve/{server => inferenceserver/api_server}/conf/config.yaml (100%)
 rename serve/{server => inferenceserver/api_server}/conf/profiles.json (100%)
 rename serve/{server => inferenceserver/api_server}/conf/workflows.yaml (100%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/config.py (95%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/cpu_worker.py (98%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/__init__.py (100%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/deterministic_earth2_workflow.py (96%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/deterministic_fcn.py (99%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/deterministic_workflow.py (99%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/diagnostic_workflow.py (99%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/example_user_workflow.py (99%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/foundry_fcn3.py (100%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/foundry_fcn3_stormscope_goes.py (97%)
 rename serve/{server => inferenceserver/api_server}/example_workflows/stormcast_fcn3_workflow.py (98%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/main.py (83%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/object_storage.py (90%)
 rename serve/{server => inferenceserver/api_server}/planetary_computer/pc_client.py (100%)
 rename serve/{server => inferenceserver/api_server}/planetary_computer/template-collection-fcn3-stormscope-goes.json (100%)
 rename serve/{server => inferenceserver/api_server}/planetary_computer/template-collection-fcn3.json (100%)
 rename serve/{server => inferenceserver/api_server}/planetary_computer/template-feature-fcn3-stormscope-goes.json (100%)
 rename serve/{server => inferenceserver/api_server}/planetary_computer/template-feature-fcn3.json (100%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/utils.py (94%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server}/worker.py (79%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server/workflow}/__init__.py (62%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server/workflow}/e2workflow.py (97%)
 rename {earth2studio/serve/server => serve/inferenceserver/api_server/workflow}/workflow.py (75%)
 rename serve/{server => inferenceserver}/env.example (100%)
 rename serve/{server => inferenceserver}/openapi.yaml (100%)
 rename serve/{server => inferenceserver}/requirements.txt (80%)
 rename serve/{server => inferenceserver}/scripts/profile_workflow.sh (100%)
 rename serve/{server => inferenceserver}/scripts/redis.conf (100%)
 rename serve/{server => inferenceserver}/scripts/start_api_server.sh (92%)
 rename serve/{server => inferenceserver}/scripts/start_redis.sh (100%)
 rename serve/{server => inferenceserver}/scripts/startup.sh (59%)
 rename serve/{server => inferenceserver}/scripts/status.sh (98%)
 rename serve/{server => inferenceserver}/scripts/stop_api_server.sh (96%)
 rename serve/{server => inferenceserver}/scripts/stop_redis.sh (100%)
 rename serve/{server => }/test/unit/test_e2workflow.py (82%)
 rename serve/{server => }/test/unit/test_e2workflow_params.py (86%)
 delete mode 100644 test/models/da/test_da_interp.py
 delete mode 100644 test/models/da/test_da_sda_stormcast.py
 delete mode 100644 test/models/da/test_utils.py
 delete mode 100644 test/serve/client/conftest.py
 delete mode 100644 test/serve/client/test_client.py
 delete mode 100644 test/serve/client/test_e2client.py
 delete mode 100644 test/serve/client/test_fsspec_utils.py
 create mode 100644 test/serve/conftest.py
 delete mode 100644 test/serve/server/conftest.py
 delete mode 100644 test/serve/server/test_cleanup_daemon.py
 delete mode 100644 test/serve/server/test_cpu_worker.py
 delete mode 100644 test/serve/server/test_object_storage.py
 delete mode 100644 test/serve/server/test_server_main.py
 delete mode 100644 test/serve/server/test_utils.py
 delete mode 100644 test/serve/server/test_worker.py
 create mode 100644 test/serve/test_cleanup_daemon.py
 rename test/serve/{server => }/test_config.py (96%)
 create mode 100644 test/serve/test_cpu_worker.py
 create mode 100644 test/serve/test_main.py
 rename test/serve/{server => }/test_workflow.py (97%)

diff --git a/.cursor/rules/e2s-000-python-style-guide.mdc b/.cursor/rules/e2s-000-python-style-guide.mdc
index d885e42a2..eb6f2cc04 100644
--- a/.cursor/rules/e2s-000-python-style-guide.mdc
+++ b/.cursor/rules/e2s-000-python-style-guide.mdc
@@ -75,17 +75,6 @@ This rule enforces the coding standards for Earth2Studio as defined in [style.md
 - Run `make lint` to check linting
 - Pre-commit hooks will automatically format and lint code
 
-## Logging
-
-- Print statements should always be done via [loguru.logger](https://loguru.readthedocs.io/en/stable/api.html) or using [rich.console](https://rich.readthedocs.io/en/stable/reference/console.html)
-- Generally, debugging messages should be placed behind a verbose toggle
-- For tqdm progress bars be sure to use the following snippet at the top of the file to ensure progress bars are not disrupted
-
-  ```
-  logger.remove()
-  add(lambda msg: tqdm.write(msg, end=""), colorize=True)
-  ```
-
 ## Code Quality Reminders
 
 - Always include type hints for function parameters and return values
@@ -93,4 +82,3 @@ This rule enforces the coding standards for Earth2Studio as defined in [style.md
 - Prefer modern Python 3.10+ type syntax (`list[str]`, `str | None`)
 - Ensure all files have the SPDX license header at the top
 - Follow black formatting (run `make format` if unsure)
-- Do not use Python's native print() inside any files of the earth2studio folder
diff --git a/.cursor/rules/e2s-002-api-documentation.mdc b/.cursor/rules/e2s-002-api-documentation.mdc
index 65882aba3..707efdaf0 100644
--- a/.cursor/rules/e2s-002-api-documentation.mdc
+++ b/.cursor/rules/e2s-002-api-documentation.mdc
@@ -238,7 +238,6 @@ class CorrelatedSphericalGaussian:
 - Use periods for complete sentences, omit for "by default" phrases
 - Include `Raises` section when exceptions are raised
 - Include `Warning` section for classes with important warnings
-- Do not put doc strings at the top of python files, only license headers should be above imports
 
 ## Tools
 
diff --git a/.cursor/rules/e2s-009-prognostic-models.mdc b/.cursor/rules/e2s-009-prognostic-models.mdc
index ee53da683..e71df34dd 100644
--- a/.cursor/rules/e2s-009-prognostic-models.mdc
+++ b/.cursor/rules/e2s-009-prognostic-models.mdc
@@ -328,8 +328,6 @@ def to(self, device: torch.device | str) -> PrognosticModel:
 - Call `super().to(device)` for PyTorch module
 - Move any custom buffers/parameters to device
 - Return `self` for chaining
-- Torch.nn.Module parent class addresses this requirement most of the time
-- Generally its good to have `self.register_buffer("device_buffer", torch.empty(0))` in their init to help track what the current device of the model is
 
 ## Data Operations on GPU
 
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index d74d80c90..0b2dfb9fe 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.github/ISSUE_TEMPLATE/chore.yml b/.github/ISSUE_TEMPLATE/chore.yml
deleted file mode 100644
index abe1d7022..000000000
--- a/.github/ISSUE_TEMPLATE/chore.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-name: Chore
-description: Maintenance task, refactor, cleanup, or other housekeeping work
-title: "🧹[CHORE]: "
-labels: ["chore", "? - Needs Triage"]
-
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to help keep Earth2Studio clean and maintainable!
-        - By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/NVIDIA/earth2studio/blob/main/CONTRIBUTING.md)
-        - You also confirm that you have searched the [open issues](https://github.com/NVIDIA/earth2studio/issues) and have found no duplicates for this request
-
-  - type: dropdown
-    id: chore_type
-    attributes:
-      label: What type of chore is this?
-      options:
-        - Refactor
-        - Cleanup
-        - Dependency Update
-        - CI/CD
-        - Testing
-        - Other
-    validations:
-      required: true
-
-  - type: dropdown
-    id: criticality
-    attributes:
-      label: How would you describe the priority of this chore
-      options:
-        - Critical (blocking other work)
-        - Medium
-        - Low (nice to have)
-    validations:
-      required: true
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Please provide a clear description of the chore.
-      description: Describe what needs to be done and why. Include relevant file paths, modules, or areas of the codebase affected.
diff --git a/.github/ISSUE_TEMPLATE/documentation_request.yml b/.github/ISSUE_TEMPLATE/documentation_request.yml
index 8b07bd757..38f540cdf 100644
--- a/.github/ISSUE_TEMPLATE/documentation_request.yml
+++ b/.github/ISSUE_TEMPLATE/documentation_request.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index 0c734d3a5..d6aa3f0a7 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
 # SPDX-FileCopyrightText: All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 096bcd82d..4baf555e6 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -122,6 +122,13 @@ test-data:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+      changes:
+        paths:
+          - earth2studio/data/**/*
+          - earth2studio/lexicon/**/*
+          - test/data/**/*
+          - test/lexicon/**/*
+        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -148,6 +155,11 @@ test-perturb:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+      changes:
+        paths:
+          - earth2studio/perturbation/**/*
+          - test/perturbation/**/*
+        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -170,6 +182,11 @@ test-stats:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+      changes:
+        paths:
+          - earth2studio/statistics/**/*
+          - test/statistics/**/*
+        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -192,29 +209,11 @@ test-px-models:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-    - <<: *test_rule_non_github_with_changes
-      changes:
-        paths:
-          - earth2studio/models/px/**/*
-          - test/models/px/**/*
-        compare_to: 'refs/heads/main'
-    - <<: *test_rule_changes_only
       changes:
         paths:
           - earth2studio/models/px/**/*
           - test/models/px/**/*
         compare_to: 'refs/heads/main'
-    - <<: *test_rule_never
-
-# TODO: REMOVE, CONFLICT CURRENTLY
-test-px-ace2:
-  extends:
-    - .test:base
-  variables:
-    TOX_ENV: test-px-models-ace2
-  rules:
-    - <<: *test_rule_always_if_pytest_all
-    - <<: *test_rule_main_with_changes
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
@@ -237,39 +236,22 @@ test-dx-models:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
-    - <<: *test_rule_non_github_with_changes
       changes:
         paths:
           - earth2studio/models/dx/**/*
           - test/models/dx/**/*
         compare_to: 'refs/heads/main'
-    - <<: *test_rule_changes_only
+    - <<: *test_rule_non_github_with_changes
       changes:
         paths:
           - earth2studio/models/dx/**/*
           - test/models/dx/**/*
         compare_to: 'refs/heads/main'
-    - <<: *test_rule_never
-
-test-da-models:
-  extends:
-    - .test:base
-  variables:
-    TOX_ENV: test-da-models
-  rules:
-    - <<: *test_rule_always_if_pytest_all
-    - <<: *test_rule_main_with_changes
-    - <<: *test_rule_non_github_with_changes
-      changes:
-        paths:
-          - earth2studio/models/da/**/*
-          - test/models/da/**/*
-        compare_to: 'refs/heads/main'
     - <<: *test_rule_changes_only
       changes:
         paths:
-          - earth2studio/models/da/**/*
-          - test/models/da/**/*
+          - earth2studio/models/dx/**/*
+          - test/models/dx/**/*
         compare_to: 'refs/heads/main'
     - <<: *test_rule_never
 
@@ -281,6 +263,12 @@ test-serve:
   rules:
     - <<: *test_rule_always_if_pytest_all
     - <<: *test_rule_main_with_changes
+      changes:
+        paths:
+          - earth2studio/serve/**/*
+          - test/serve/**/*
+          - serve/**/*
+        compare_to: 'refs/heads/main'
     - <<: *test_rule_non_github_with_changes
       changes:
         paths:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5b5e9b0c8..36273bd9f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,39 +15,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added Earth2Studio base schema for dataframe sources
 - Added Planetary Computer data source for ECMWF IFS analysis data
 - Added accumulated variable support to NCAR ERA5 data source
-- Added routine to tile tensors to higher-dimensional tensors
-- Added routine to concatenate tensors along specified coordinate dimension
 - Added Planetary Computer data source for GOES cloud and moisture imagery
 - Added ability to have seperate data and model cache locations via env variables
 - Added random dataframe source
 - Added base reflectivity to MRMS data source
-- Added `fetch_dataframe` utility function
-- Added data assimilation model class
-- Added equirectangular interpolation data assimilation model
-- Added StormCast SDA model
-- Adding Beta serve utils with inference server and client implementations
 
 ### Changed
 
 - Changed available date for ARCO data source to be dynamically updated
 - Changed ISD data frame return to master schema
-- handshake_coords is now accepting list of dimensions while remaining backwards-compatible
 - Updated CBottle infill to mixture of model checkpoints
-- Updated GraphCastOperational and GraphCastSmall latitude input / output to be [90,-90]
-- Updated GraphCast models to support multiple time inputs, multiple times will be looped not batched
 
 ### Deprecated
 
 ### Removed
 
-- Removed device from cbottle SR load_model api
-
 ### Fixed
 
-- Fixed timezone bug in `CBottleVideo` that converted UTC time to local system time
 - Bug in cbottle datasource resulting identical samples for multiple samples
 - Bug in StormCast loading out-of-date model package, introduced in `5518edecbabee371c824b34f0f2ec269a4d6094f`
-- Bug in spherical perturbations which did not use lmax from the SHT transform
 
 ### Security
 
@@ -57,7 +43,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Updated CBottle repo to NVlabs location
 - Updated Makani dependency hash to later version with pyproject toml fix
 - Updated ACE2 dependency hash with setuptools fix
-- Updated models using PhysicsNeMo to compliance with `nvidia-physicsnemo>=2.0`
 
 ## [0.12.1] - 2026-01-29
 
diff --git a/Makefile b/Makefile
index 8d6efa653..ef427b48f 100644
--- a/Makefile
+++ b/Makefile
@@ -101,7 +101,7 @@ docs-dev:
 # Example DOCKER_REPO?=nvcr.io/dycvht5ows21
 E2S_RELEASE_TAG?=0.12.0
 E2S_IMAGE_NAME=$(DOCKER_REPO)/earth2studio-scicomp
-E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260311.0
+E2S_IMAGE_TAG=v$(E2S_RELEASE_TAG).20260223.0
 container-service:
 	@test -n "$(DOCKER_REPO)" || (echo "DOCKER_REPO is not set!" && exit 1)
 	DOCKER_BUILDKIT=1 docker build -t $(E2S_IMAGE_NAME):$(E2S_IMAGE_TAG) -f serve/Dockerfile .
diff --git a/docs/conf.py b/docs/conf.py
index ee64c639e..6ed0ed6a8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -50,7 +50,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 version = ".".join(release.split(".")[:2])
 project = "Earth2Studio"
-copyright = "2026, NVIDIA"
+copyright = "2025, NVIDIA"
 author = "NVIDIA"
 
 # -- General configuration ---------------------------------------------------
@@ -83,7 +83,6 @@
     "css/nvidia-sphinx-theme.css",
 ]
 html_theme_options = {
-    "public_docs_features": True,
     "logo": {
         "text": "Earth2Studio",
         "image_light": "_static/NVIDIA-Logo-V-ForScreen-ForLightBG.png",
diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 3df2f16a0..1d1f89e77 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -96,31 +96,6 @@ etc.
    WindgustAFNO
    Identity
 
-:mod:`earth2studio.models.da`: Data Assimilation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Data assimilation models are a class of models that incorporate observational data
-into model states or grids. These models process sparse, irregularly-distributed
-observations (typically from DataFrames) and map them to regular grids or model
-coordinate systems (typically as DataArrays). Data assimilation models support both
-stateless and stateful operation, allowing them to process observations independently
-or maintain internal state across time steps.
-
-.. warning ::
-
-   Data Assimilation models are a new addition to Earth2Studio and APIs might be subject
-   to change without warning while the implementation is hardened.
-
-.. currentmodule:: earth2studio.models.da
-
-.. autosummary::
-   :nosignatures:
-   :toctree: generated/models/da/
-   :template: dataassim.rst
-
-   InterpEquirectangular
-   StormCastSDA
-
 :mod:`earth2studio.models`: Utilities
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/templates/dataassim.rst b/docs/templates/dataassim.rst
deleted file mode 100644
index c3e9907c5..000000000
--- a/docs/templates/dataassim.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. currentmodule:: {{module}}
-
-.. autoclass:: {{ objname }}
-
-   {% block methods %}
-    .. automethod:: __call__
-    .. automethod:: create_generator
-    .. automethod:: load_default_package
-    .. automethod:: load_model
-   {% endblock %}
-
-.. _sphx_glr_backref_{{module}}.{{objname}}:
-
-.. minigallery:: {{module}}.{{objname}}
-      :add-heading:
-      :heading-level: ^
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/docs/userguide/about/install.md b/docs/userguide/about/install.md
index 4905ce138..443c8fe7b 100644
--- a/docs/userguide/about/install.md
+++ b/docs/userguide/about/install.md
@@ -163,7 +163,6 @@ can take a long time to compile.
 :::{tab-item} pip
 
 ```bash
-pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
 pip install earth2studio[atlas]
 ```
 
@@ -268,7 +267,7 @@ for known suggestions/fixes related to this install process.
 
 ```bash
 export FORCE_CUDA_EXTENSION=1
-pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
+pip install --no-build-isolation torch-harmonics==0.8.0
 pip install "makani @ git+https://github.com/NVIDIA/makani.git@fd1a62b9f27aebbc793f0cbafd838a3811de43fd"
 pip install earth2studio[fcn3]
 ```
@@ -278,6 +277,7 @@ pip install earth2studio[fcn3]
 
 ```bash
 export FORCE_CUDA_EXTENSION=1
+uv add torch-harmonics==0.8.0 --no-build-isolation
 uv add earth2studio --extra fcn3
 ```
 
@@ -375,7 +375,6 @@ installed manually.
 :::{tab-item} pip
 
 ```bash
-pip install --no-build-isolation "torch-harmonics @ git+https://github.com/NVIDIA/torch-harmonics.git@a632ca748a12bd9f74dbc1e00653317810991f74"
 pip install "makani @ git+https://github.com/NVIDIA/makani.git@fd1a62b9f27aebbc793f0cbafd838a3811de43fd"
 pip install earth2studio[sfno]
 ```
@@ -642,63 +641,6 @@ uv add earth2studio --extra windgust-afno
 :::::
 ::::::
 
-#### Data Assimilation
-
-:::{admonition} Warning
-:class: warning
-
-Data assimilation model APIs are currently **in Beta** and may change in future
-releases. Expect possible breaking changes as these APIs mature.
-:::
-
-:::{admonition} Warning
-:class: warning
-
-All data assimilation models require [CuPy](https://docs.cupy.dev/en/stable/) and [cuDF](https://docs.rapids.ai/api/cudf/stable/),
-which are CUDA-dependent libraries.
-The default installation uses CUDA 12 (i.e., `cupy-cuda12x`, `cudf-cu12`).
-If your system uses a different CUDA version, you may need to adjust the dependencies.
-:::
-
-::::::{tab-set}
-:::::{tab-item} InterpEquirectangular
-::::{tab-set}
-:::{tab-item} pip
-
-```bash
-pip install earth2studio[da-interp]
-```
-
-:::
-:::{tab-item} uv
-
-```bash
-uv add earth2studio --extra da-interp
-```
-
-:::
-::::
-:::::
-:::::{tab-item} StormCast SDA
-::::{tab-set}
-:::{tab-item} pip
-
-```bash
-pip install earth2studio[da-stormcast]
-```
-
-:::
-:::{tab-item} uv
-
-```bash
-uv add earth2studio --extra da-stormcast
-```
-
-:::
-::::
-:::::
-::::::
-
 ### Submodule Dependencies
 
 A few features in various submodules require some specific dependencies that have been
diff --git a/earth2studio/data/__init__.py b/earth2studio/data/__init__.py
index a27049741..f33b6038c 100644
--- a/earth2studio/data/__init__.py
+++ b/earth2studio/data/__init__.py
@@ -41,7 +41,7 @@
 from .rx import CosineSolarZenith, LandSeaMask, SurfaceGeoPotential
 from .time_window import TimeWindow
 from .ufs import UFSObsConv, UFSObsSat
-from .utils import datasource_to_file, fetch_data, fetch_dataframe, prep_data_array
+from .utils import datasource_to_file, fetch_data, prep_data_array
 from .wb2 import WB2ERA5, WB2Climatology, WB2ERA5_32x64, WB2ERA5_121x240
 from .xr import (
     DataArrayDirectory,
diff --git a/earth2studio/data/ufs.py b/earth2studio/data/ufs.py
index b84566bc8..b3335b23b 100644
--- a/earth2studio/data/ufs.py
+++ b/earth2studio/data/ufs.py
@@ -733,3 +733,10 @@ def _transform_column(
     def _add_task_columns(self, df: pd.DataFrame, task: _GSIAsyncTask) -> None:
         """Add satellite column for satellite data."""
         df["satellite"] = task.satellite
+
+
+if __name__ == "__main__":
+
+    ds = UFSObsSat(satellites=["npp"], tolerance=timedelta(hours=6))
+    df = ds(datetime(2024, 2, 1), ["atms"], ["lon", "variable"])
+    print(df)
diff --git a/earth2studio/data/utils.py b/earth2studio/data/utils.py
index aa8bcb54f..ba155969f 100644
--- a/earth2studio/data/utils.py
+++ b/earth2studio/data/utils.py
@@ -43,39 +43,18 @@
 from fsspec.utils import isfilelike
 from loguru import logger
 
-from earth2studio.data.base import (
-    DataFrameSource,
-    DataSource,
-    ForecastFrameSource,
-    ForecastSource,
-)
+from earth2studio.data.base import DataSource, ForecastSource
 from earth2studio.utils.interp import LatLonInterpolation
 from earth2studio.utils.time import (
     leadtimearray_to_timedelta,
     timearray_to_datetime,
     to_time_array,
 )
-from earth2studio.utils.type import (
-    CoordSystem,
-    FieldArray,
-    LeadTimeArray,
-    TimeArray,
-    VariableArray,
-)
+from earth2studio.utils.type import CoordSystem, LeadTimeArray, TimeArray, VariableArray
 
 if TYPE_CHECKING:
     from fsspec.implementations.cache_mapper import AbstractCacheMapper
 
-try:
-    import cupy as cp
-except ImportError:
-    cp = None
-
-try:
-    import cudf
-except ImportError:
-    cudf = None
-
 
 def fetch_data(
     source: DataSource | ForecastSource,
@@ -83,18 +62,16 @@ def fetch_data(
     variable: VariableArray,
     lead_time: LeadTimeArray = np.array([np.timedelta64(0, "h")]),
     device: torch.device = "cpu",
-    interp_to: CoordSystem | None = None,
+    interp_to: CoordSystem = None,
     interp_method: str = "nearest",
-    legacy: bool = True,
-) -> tuple[torch.Tensor, CoordSystem] | xr.DataArray:
-    """Utility function to fetch data arrays from particular sources and load data on
-    the target device. If desired, xarray interpolation/regridding in the spatial
-    domain can be used by passing a target coordinate system via the optional
-    `interp_to` argument.
+) -> tuple[torch.Tensor, CoordSystem]:
+    """Utility function to fetch data for models and load data on the target device.
+    If desired, xarray interpolation/regridding in the spatial domain can be used
+    by passing a target coordinate system via the optional `interp_to` argument.
 
     Parameters
     ----------
-    source : DataSource | ForecastSource
+    source : DataSource
         The data source to fetch from
     time : TimeArray
         Timestamps to return data for (UTC).
@@ -104,24 +81,20 @@ def fetch_data(
         Lead times to fetch for each provided time, by default
         np.array(np.timedelta64(0, "h"))
     device : torch.device, optional
-        Torch device to load data tensor to, by default "cpu"
+        Torch devive to load data tensor to, by default "cpu"
     interp_to : CoordSystem, optional
         If provided, the fetched data will be interpolated to the coordinates
         specified by lat/lon arrays in this CoordSystem
     interp_method : str
         Interpolation method to use with xarray (by default 'nearest')
-    legacy : bool, optional
-        If True (default), returns tuple of (torch.Tensor, CoordSystem).
-        If False, returns xr.DataArray with numpy arrays for CPU or cupy arrays for CUDA.
 
     Returns
     -------
-    tuple[torch.Tensor, CoordSystem] | xr.DataArray
-        If legacy=True: Tuple containing output tensor and coordinate OrderedDict.
-        If legacy=False: xr.DataArray with numpy arrays (CPU) or cupy arrays (CUDA).
+    tuple[torch.Tensor, CoordSystem]
+        Tuple containing output tensor and coordinate OrderedDict
     """
+
     sig = signature(source.__call__)
-    device = torch.device(device)
 
     if "lead_time" in sig.parameters:
         # Working with a Forecast Data Source
@@ -139,90 +112,12 @@ def fetch_data(
 
         da = xr.concat(da, "lead_time")
 
-    if legacy:
-        return prep_data_array(
-            da,
-            device=device,
-            interp_to=interp_to,
-            interp_method=interp_method,
-        )
-
-    # Non-legacy path: return xr.DataArray
-    else:
-        if interp_to is not None:
-            raise ValueError(
-                "The interp_to argument is not supported when legacy is False. Set legacy=True to use interpolation."
-            )
-        # Convert to cupy arrays if CUDA device and cupy is available
-        if device.type == "cuda":
-            if cp is not None:
-                with cp.cuda.Device(device.index or 0):
-                    da = da.copy(data=cp.asarray(da.values))
-            else:
-                raise ImportError(
-                    "cupy is required when using device='cuda' with legacy=False. "
-                    "Install cupy or use legacy=True."
-                )
-        return da
-
-
-def fetch_dataframe(
-    source: DataFrameSource | ForecastFrameSource,
-    time: TimeArray,
-    variable: VariableArray,
-    fields: FieldArray | None = None,
-    lead_time: LeadTimeArray = np.array([np.timedelta64(0, "h")]),
-    device: torch.device = "cpu",
-) -> pd.DataFrame | cudf.DataFrame:
-    """Utility function to fetch data frames from particular sources
-
-    Parameters
-    ----------
-    source : DataFrameSource | ForecastFrameSource
-        The data source to fetch from
-    time : TimeArray
-        Timestamps to return data for (UTC).
-    variable : VariableArray
-        Strings or list of strings that refer to variables to return
-    fields : FieldArray | None
-        Array of strings indicating which fields to return in data frame, if None all
-        possible fields will be returned, by default None
-    lead_time : LeadTimeArray, optional
-        Lead times to fetch for each provided time, by default
-        np.array(np.timedelta64(0, "h"))
-    device : torch.device, optional
-        Torch device to load data tensor to, by default "cpu"
-
-    Returns
-    -------
-    pd.DataFrame | cudf.DataFrame
-        Pandas dataframe if device is CPU, cudf DataFrame if device is CUDA
-    """
-    sig = signature(source.__call__)
-
-    if "lead_time" in sig.parameters:
-        # Working with a ForecastFrameSource
-        df = source(time, lead_time, variable, fields=fields)  # type: ignore
-    else:
-        # Combine all adjusted times and get unique values using broadcasting
-        all_times = (time[:, None] + lead_time).flatten()
-        unique_times = np.unique(all_times)
-        df = source(unique_times, variable, fields=fields)  # type: ignore
-    # Add request meta-data
-    df.attrs = {"request_time": time, "request_lead_time": lead_time}
-
-    # Convert to appropriate format based on device
-    device = torch.device(device)
-    if device.type == "cuda":
-        if cudf is None:
-            raise ImportError(
-                "cudf is required for CUDA device. Install with: pip install cudf"
-            )
-        with cp.cuda.Device(device.index):
-            result = cudf.from_pandas(df)
-        return result
-    else:
-        return df
+    return prep_data_array(
+        da,
+        device=device,
+        interp_to=interp_to,
+        interp_method=interp_method,
+    )
 
 
 def prep_data_array(
@@ -344,9 +239,6 @@ def prep_data_inputs(
     if isinstance(variable, str):
         variable = [variable]
 
-    if isinstance(variable, np.ndarray):
-        variable = variable.astype(str).tolist()
-
     if isinstance(time, datetime):
         time = [time]
 
diff --git a/earth2studio/data/xr.py b/earth2studio/data/xr.py
index d15e9df51..afcc4098e 100644
--- a/earth2studio/data/xr.py
+++ b/earth2studio/data/xr.py
@@ -187,28 +187,6 @@ def __call__(
 
         return self.da.sel(time=time, variable=variable)
 
-    async def fetch(  # type: ignore[override]
-        self,
-        time: datetime | list[datetime] | TimeArray,
-        variable: str | list[str] | VariableArray,
-    ) -> xr.DataArray:
-        """Async function to get data
-
-        Parameters
-        ----------
-        time : datetime | list[datetime] | TimeArray
-            Timestamps to return data for (UTC).
-        variable : str | list[str] | VariableArray
-            String, list of strings or array of strings that refer to variables to
-            return. Must be in the GFS lexicon.
-
-        Returns
-        -------
-        xr.DataArray
-            Inference output data array
-        """
-        return self.__call__(time, variable)
-
 
 class DataSetFile:
     """A local xarray dataset file data source. This file should be compatible with
diff --git a/earth2studio/io/zarr.py b/earth2studio/io/zarr.py
index 608ccdcfa..6894ce81b 100644
--- a/earth2studio/io/zarr.py
+++ b/earth2studio/io/zarr.py
@@ -202,8 +202,7 @@ def add_array(
             if name in self.root and not kwargs.get("overwrite", False):
                 raise RuntimeError(
                     f"{name} is already in Zarr Store. "
-                    + "To overwrite Zarr array pass overwrite=True to this function or"
-                    + " backend_kwargs = {'overwrite': True} to the ZarrBackend constructor"
+                    + "To overwrite Zarr array pass overwrite=True to this function"
                 )
 
             di = di.cpu().numpy() if di is not None else None
diff --git a/earth2studio/models/da/__init__.py b/earth2studio/models/da/__init__.py
deleted file mode 100644
index cbb768276..000000000
--- a/earth2studio/models/da/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .interp import InterpEquirectangular
-from .sda_stormcast import StormCastSDA
diff --git a/earth2studio/models/da/base.py b/earth2studio/models/da/base.py
deleted file mode 100644
index e0819ed81..000000000
--- a/earth2studio/models/da/base.py
+++ /dev/null
@@ -1,188 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-from collections.abc import Generator
-from typing import Any, Protocol, runtime_checkable
-
-import pandas as pd
-import xarray as xr
-
-from earth2studio.utils.type import CoordSystem, FrameSchema
-
-try:
-    import cudf
-except ImportError:
-    cudf = None  # type: ignore[assignment, misc]
-
-
-@runtime_checkable
-class AssimilationModel(Protocol):
-    """Data assimilation model interface"""
-
-    def __call__(
-        self,
-        *args: pd.DataFrame | xr.DataArray | None,
-    ) -> tuple[pd.DataFrame | xr.DataArray, ...]:
-        """Stateless iteration for the data assimilation model.
-
-        Processes observations and returns assimilated data without maintaining
-        internal state between calls. This method is suitable for independent
-        processing of observation batches.
-
-        Parameters
-        ----------
-        *args : pd.DataFrame | xr.DataArray | None
-            Variable number of observation arguments. Each argument can be a
-            DataFrame (pandas or cudf DataFrame) or xarray DataArray
-            containing observation data. None can be passed for optional
-            arguments when no input data is available.
-
-        Returns
-        -------
-        tuple[pd.DataFrame | xr.DataArray, ...]
-            Assimilated data output. Can return a combination of DataFrames or
-            xarray DataArrays depending on the particular model. Output is expect to be
-            on the same device as the model.
-        """
-
-    def create_generator(
-        self,
-        *args: pd.DataFrame | xr.DataArray,
-    ) -> Generator[
-        tuple[pd.DataFrame | xr.DataArray, ...],
-        tuple[pd.DataFrame | xr.DataArray | None, ...],
-        None,
-    ]:
-        """Creates a generator which accepts collection of input observations and
-        outputs a collection of assimilated data. Used for both stateless and stateful
-        iterations of the data assimilation model
-
-        The generator accepts observations (DataFrame or DataArray) via the send()
-        method and yields assimilated data (DataFrame or DataArray) as output.
-        Supports any number of arguments (variadic).
-
-        Parameters
-        ----------
-        *args : pd.DataFrame | xr.DataArray
-            Variable number of initialization arguments, if any are required by
-            the model. Each argument can be a DataFrame (pandas or cudf
-            DataFrame) or xarray DataArray containing initial state data.
-
-        Yields
-        ------
-        tuple[pd.DataFrame | xr.DataArray, ...]
-            Generator yields multiple arguments of assimilated data. Each argument
-            can be a DataFrame (PyArrow Table or cudf DataFrame) or xarray DataArray.
-            Supports any number of arguments.
-
-        Receives
-        --------
-        tuple[pd.DataFrame | xr.DataArray | None, ...]
-            Observations sent via generator.send() as multiple arguments. Each
-            argument can be a DataFrame (PyArrow Table or cudf DataFrame) or xarray
-            DataArray. None is sent initially to start the generator and can also be
-            sent for iterations where no input data is available. Supports any number
-            of arguments.
-
-        Examples
-        --------
-        >>> generator = model.create_generator()
-        >>> generator.send(None)  # Prime the generator
-        >>> # Process observations over time
-        >>> for obs in observations:
-        ...     result = generator.send(obs)  # Send observations, receive assimilated data
-        ...     # result is a tuple of DataFrames or DataArrays
-        >>> generator.close()  # Clean up
-        """
-        pass
-
-    def init_coords(self) -> tuple[FrameSchema | CoordSystem, ...] | None:
-        """Initialization coordinate system required by the assimilation model.
-
-        Specifies the coordinate system(s) for initial state data that must be provided
-        before the model can process observations. The returned coordinate systems should
-        match the expected input format for the first argument(s) passed to ``__call__``
-        or sent to ``create_generator`` when initializing the model state.
-
-        Returns
-        -------
-        tuple[FrameSchema | CoordSystem, ...] | None
-            Tuple of coordinate systems or frame schemas defining the structure of
-            required initialization data. Returns ``None`` if the model does not require
-            initialization data (e.g., stateless models).
-        """
-        pass
-
-    def input_coords(self) -> tuple[FrameSchema | CoordSystem, ...]:
-        """Input coordinate system of assimilation model.
-
-        For DataFrame inputs, this should return a PyArrow schema (or a wrapper
-        containing schema and constraints). For tensor inputs, this should return
-        a CoordSystem.
-
-        Returns
-        -------
-        tuple[FrameSchema | CoordSystem, ...]
-            Tuple of coordinate systems or frame schemas, one for each input argument
-            that __call__ or create_generator accepts
-        """
-        pass
-
-    def output_coords(
-        self,
-        input_coords: tuple[FrameSchema | CoordSystem, ...],
-        *args: Any,
-        **kwargs: Any,
-    ) -> tuple[FrameSchema | CoordSystem, ...]:
-        """Output coordinate system of the assimilation model given an input coordinate
-        system.
-
-        Parameters
-        ----------
-        input_coords : tuple[FrameSchema | CoordSystem, ...]
-            Input coordinate system tuple. FrameSchema (OrderedDict mapping field names
-            to numpy arrays) for DataFrame inputs, or CoordSystem (OrderedDict mapping
-            dimension names to coordinate arrays) for tensor inputs
-        *args
-            Additional positional arguments
-        **kwargs
-            Additional keyword arguments, typically including request metadata such as
-            request_time and request_lead_time from DataFrame attrs
-
-        Returns
-        -------
-        tuple[FrameSchema | CoordSystem, ...]
-            Tuple of coordinate systems or frame schemas, one for each output argument
-            that __call__ or create_generator returns
-        """
-        pass
-
-    def to(self, device: Any) -> AssimilationModel:
-        """Moves assimilation model onto inference device, this is typically satisfied
-        via `torch.nn.Module`.
-
-        Parameters
-        ----------
-        device : Any
-            Object representing the inference device, typically `torch.device` or str
-
-        Returns
-        -------
-        AssimilationModel
-            Returns instance of prognostic
-        """
-        pass
diff --git a/earth2studio/models/da/interp.py b/earth2studio/models/da/interp.py
deleted file mode 100644
index f51c2aacb..000000000
--- a/earth2studio/models/da/interp.py
+++ /dev/null
@@ -1,589 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from collections.abc import Generator
-from typing import Any
-
-import numpy as np
-import pandas as pd
-import torch
-import xarray as xr
-from loguru import logger
-
-from earth2studio.models.da.utils import (
-    dfseries_to_torch,
-    filter_time_range,
-    validate_observation_fields,
-)
-from earth2studio.utils.imports import (
-    OptionalDependencyFailure,
-    check_optional_dependencies,
-)
-from earth2studio.utils.time import normalize_time_tolerance
-from earth2studio.utils.type import CoordSystem, FrameSchema, TimeTolerance
-
-try:
-    import cupy as cp
-except ImportError:
-    OptionalDependencyFailure("gpu")
-    cp = None  # type: ignore[assignment]
-
-
-@check_optional_dependencies()
-class InterpEquirectangular(torch.nn.Module):
-    """Interpolation assimilation model that interpolates sparse observations to a
-    lat-lon grid.
-
-    This is a sample implementation of an AssimilationModel that:
-    - Accepts DataFrames with observations (time, lat, lon, observation, variable)
-    - Interpolates observations to a regular lat-lon grid using specified method
-    - Validates input observations against schema constraints
-    - Supports interpolation methods: 'nearest' or 'smolyak' (Smolyak sparse grid algorithm)
-
-    Parameters
-    ----------
-    lat : np.ndarray | None, optional
-        Latitude coordinates for output grid, by default None (uses default grid over
-        CONUS)
-    lon : np.ndarray | None, optional
-        Longitude coordinates for output grid, by default None (uses default
-        grid over CONUS)
-    interp_method : str, optional
-        Interpolation method to use: 'nearest' or 'smolyak', by default "smolyak"
-    time_tolerance : TimeTolerance, optional
-        Time tolerance for filtering observations. Observations within the tolerance
-        window around each requested time will be used for interpolation, by default
-        np.timedelta64(10, "m")
-
-    Raises
-    ------
-    ValueError
-        If interp_method is not one of the supported methods
-    """
-
-    # Acceptable variables for this model
-    VARIABLES = ["t2m", "u10m", "v10m", "sp"]
-
-    def __init__(
-        self,
-        lat: np.ndarray | None = None,
-        lon: np.ndarray | None = None,
-        interp_method: str = "smolyak",
-        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
-    ) -> None:
-        if interp_method not in ["nearest", "smolyak"]:
-            raise ValueError(
-                f"interp_method must be one of ['nearest', 'smolyak'], "
-                f"got {interp_method}"
-            )
-        super().__init__()
-        self._lat = (
-            lat if lat is not None else np.linspace(25.0, 50.0, 101, dtype=np.float32)
-        )
-        self._lon = (
-            lon if lon is not None else np.linspace(235.0, 295.0, 241, dtype=np.float32)
-        )
-        self.interp_method = interp_method
-        self._tolerance = normalize_time_tolerance(time_tolerance)
-        self.register_buffer("device_buffer", torch.empty(0), persistent=False)
-
-    def init_coords(self) -> None:
-        """Initialization coords (not required)"""
-        return None
-
-    def input_coords(self) -> tuple[FrameSchema]:
-        """Input coordinate system specifying required DataFrame fields.
-
-        Returns
-        -------
-         tuple[FrameSchema]
-            Tuple containing coordinate system dictionary with field names as keys:
-            - variable: array of acceptable variable values
-            - time, lat, lon, observation: empty arrays (dynamic dimensions)
-        """
-        return (
-            FrameSchema(
-                {
-                    "time": np.empty(0, dtype="datetime64[ns]"),
-                    "lat": np.empty(0, dtype=np.float32),
-                    "lon": np.empty(0, dtype=np.float32),
-                    "observation": np.empty(0, dtype=np.float32),
-                    "variable": np.array(self.VARIABLES, dtype=str),
-                }
-            ),
-        )
-
-    def output_coords(
-        self,
-        input_coords: tuple[CoordSystem],
-        request_time: np.ndarray,
-        **kwargs: Any,
-    ) -> tuple[CoordSystem]:
-        """Output coordinate system for assimilated data.
-
-        Parameters
-        ----------
-        input_coords : tuple[CoordSystem, ...]
-            Input coordinate system (CoordSystem for DataFrame input)
-
-        Returns
-        -------
-        tuple[CoordSystem]
-            Coordinate system dictionary with time, variable, lat, and lon dimensions
-
-        Raises
-        ------
-        ValueError
-            If input_coords are not valid
-        """
-        # Extract variables from first input coord system
-        if len(input_coords) > 0 and "variable" in input_coords[0]:
-            variables = input_coords[0]["variable"]
-        else:
-            variables = self.VARIABLES
-
-        return (
-            CoordSystem(
-                {
-                    "time": request_time,
-                    "variable": np.array(variables, dtype=str),
-                    "lat": self._lat,
-                    "lon": self._lon,
-                }
-            ),
-        )
-
-    def __call__(self, obs: pd.DataFrame) -> xr.DataArray:
-        """Stateless forward pass"""
-        input_coords = self.input_coords()
-        (output_coords,) = self.output_coords(input_coords, **obs.attrs)
-        # Validate observation types match input_coords
-        validate_observation_fields(obs, required_fields=list(input_coords[0].keys()))
-        return self._interpolate_dataframe(obs, output_coords)
-
-    def create_generator(self) -> Generator[
-        xr.DataArray,
-        pd.DataFrame,
-        None,
-    ]:
-        """Creates a generator which accepts collection of input observations and
-        outputs a collection of assimilated data.
-
-        Parameters
-        ----------
-        x : AssimilationInput
-            Input configuration for the assimilation model
-
-        Yields
-        ------
-        xr.DataArray
-            Assimilated data output the generator yields
-
-        Receives
-        --------
-        *DataFrame
-            Observations sent via generator.send()
-        """
-        # Validate input configuration
-
-        # Initialize generator - first yield is None to start
-        observations = yield None  # type: ignore[misc]
-        try:
-            while True:
-                da = self.__call__(observations)
-                observations = yield da
-        except GeneratorExit:
-            logger.debug("InterpEquirectangular clean up complete.")
-
-    def _interpolate_dataframe(
-        self, df: pd.DataFrame, output_coords: CoordSystem
-    ) -> xr.DataArray:
-        """Interpolate DataFrame observations to a regular lat-lon grid.
-
-        Parameters
-        ----------
-        df : pd.DataFrame
-            Input observations DataFrame with columns: time, lat, lon, observation, variable
-        output_coords : CoordSystem
-            Output coordinate system with time, variable, lat, lon dimensions
-
-        Returns
-        -------
-        xr.DataArray
-            Interpolated data array on the output grid
-        """
-        variables = output_coords["variable"]
-        lat_grid = output_coords["lat"]
-        lon_grid = output_coords["lon"]
-        time_coords = output_coords["time"]
-
-        # Initialize output array: (time, variable, lat, lon)
-        n_time = len(time_coords)
-        n_var = len(variables)
-        n_lat = len(lat_grid)
-        n_lon = len(lon_grid)
-
-        device = self.device_buffer.device
-        lat_grid_t = torch.tensor(lat_grid, dtype=torch.float32, device=device)
-        lon_grid_t = torch.tensor(lon_grid, dtype=torch.float32, device=device)
-
-        # Create meshgrid using torch
-        lon_mesh_t, lat_mesh_t = torch.meshgrid(lon_grid_t, lat_grid_t, indexing="ij")
-        # Stack and reshape to [n_lat * n_lon, 2] with (lat, lon) pairs
-        target_points_t = torch.stack(
-            [lat_mesh_t.T.ravel(), lon_mesh_t.T.ravel()], dim=1
-        )
-
-        # Initialize output tensor
-        interpolated_data = torch.full(
-            (n_time, n_var, n_lat, n_lon),
-            float("nan"),
-            dtype=torch.float32,
-            device=device,
-        )
-
-        # Process each time step separately
-        for t_idx, request_time in enumerate(time_coords):
-            # Filter observations within tolerance window for this time step
-            time_filtered_df = filter_time_range(
-                df, request_time, self._tolerance, time_column="time"
-            )
-
-            # Group observations by variable and interpolate each
-            for var_idx, variable in enumerate(variables):
-                var_df = time_filtered_df[
-                    time_filtered_df["variable"] == variable
-                ].copy()
-
-                if len(var_df) == 0:
-                    # No observations for this variable at this time, leave as NaN
-                    continue
-
-                # Extract observation points and values, convert to torch
-                source_lat = dfseries_to_torch(
-                    var_df["lat"], dtype=torch.float32, device=device
-                )
-                source_lon = dfseries_to_torch(
-                    var_df["lon"], dtype=torch.float32, device=device
-                )
-                source_points_t = torch.stack([source_lat, source_lon], dim=1)
-                values_t = dfseries_to_torch(
-                    var_df["observation"], dtype=torch.float32, device=device
-                )
-
-                # Interpolate to grid using PyTorch
-                grid_values = self._interpolate_scattered(
-                    source_points_t, values_t, target_points_t, n_lat, n_lon
-                )
-
-                # Assign to this time step
-                interpolated_data[t_idx, var_idx, :, :] = grid_values
-
-        # Create DataArray (convert tensor to numpy or cupy based on device)
-        if device.type == "cuda" and cp is not None:
-            data_array = cp.asarray(interpolated_data)  # Zero-copy
-        else:
-            data_array = interpolated_data.cpu().numpy()
-
-        da = xr.DataArray(
-            data=data_array,
-            dims=["time", "variable", "lat", "lon"],
-            coords={
-                "time": time_coords,
-                "variable": variables,
-                "lat": lat_grid,
-                "lon": lon_grid,
-            },
-        )
-
-        return da
-
-    def _interpolate_scattered(
-        self,
-        source_points: torch.Tensor,
-        values: torch.Tensor,
-        target_points: torch.Tensor,
-        n_lat: int,
-        n_lon: int,
-    ) -> torch.Tensor:
-        """Interpolate scattered points to target grid using PyTorch.
-
-        Accounts for periodic boundary condition in longitude.
-        Automatically converts longitude from -180,180 range to 0,360 range if needed.
-
-        Parameters
-        ----------
-        source_points : torch.Tensor
-            Source points [N, 2] with (lat, lon) coordinates. Longitude can be in
-            -180,180 or 0,360 range.
-        values : torch.Tensor
-            Values at source points [N]
-        target_points : torch.Tensor
-            Target grid points [M, 2] with (lat, lon) coordinates. Longitude can be in
-            -180,180 or 0,360 range.
-        n_lat : int
-            Number of latitude points in output grid
-        n_lon : int
-            Number of longitude points in output grid
-
-        Returns
-        -------
-        torch.Tensor
-            Interpolated values [n_lat, n_lon]
-        """
-        target_lat = target_points[:, 0:1]  # [M, 1]
-        target_lon = target_points[:, 1:2]  # [M, 1]
-        source_lat = source_points[:, 0:1].T  # [1, N]
-        source_lon = source_points[:, 1:2].T  # [1, N]
-
-        # Normalize longitude from -180,180 to 0,360 range
-        # Convert negative longitudes to positive: -180 -> 180, -90 -> 270, etc.
-        target_lon = torch.where(target_lon < 0, target_lon + 360.0, target_lon)
-        source_lon = torch.where(source_lon < 0, source_lon + 360.0, source_lon)
-
-        # Latitude distance (not periodic)
-        lat_diff = target_lat - source_lat  # [M, N]
-        lat_dist_sq = lat_diff**2
-
-        # Longitude distance with periodic boundary (0-360)
-        lon_diff = target_lon - source_lon  # [M, N]
-        # Compute periodic distance: min(|diff|, 360 - |diff|)
-        lon_diff_abs = torch.abs(lon_diff)
-        lon_dist = torch.minimum(lon_diff_abs, 360.0 - lon_diff_abs)
-        lon_dist_sq = lon_dist**2
-        distances = torch.sqrt(lat_dist_sq + lon_dist_sq)
-
-        if self.interp_method == "nearest":
-            # Nearest neighbor interpolation using distance
-            nearest_indices = torch.argmin(distances, dim=1)
-            interpolated = values[nearest_indices]
-        elif self.interp_method == "smolyak":
-            interpolated = self._smolyak_interpolate(
-                source_points, values, target_points
-            )
-        else:
-            raise ValueError(f"Unsupported interpolation type: {self.interp_method}")
-
-        # Reshape to grid
-        return interpolated.reshape(n_lat, n_lon)
-
-    def _chebyshev_polynomials(self, x: torch.Tensor, degree: int) -> torch.Tensor:
-        """Evaluate Chebyshev polynomials T_0(x) through T_degree(x) using recurrence.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            Input values [..., N]
-        degree : int
-            Maximum polynomial degree
-
-        Returns
-        -------
-        torch.Tensor
-            Chebyshev polynomial values [..., N, degree+1]
-        """
-        shape = x.shape
-        out = torch.zeros(*shape, degree + 1, dtype=x.dtype, device=x.device)
-
-        # T_0(x) = 1
-        out[..., 0] = 1.0
-
-        if degree >= 1:
-            # T_1(x) = x
-            out[..., 1] = x
-
-            # Recurrence: T_n(x) = 2*x*T_{n-1}(x) - T_{n-2}(x)
-            for n in range(2, degree + 1):
-                out[..., n] = 2.0 * x * out[..., n - 1] - out[..., n - 2]
-
-        return out
-
-    def _smolyak_interpolate(
-        self,
-        source_points: torch.Tensor,
-        values: torch.Tensor,
-        target_points: torch.Tensor,
-    ) -> torch.Tensor:
-        """Interpolate using Smolyak's algorithm with Chebyshev polynomial basis.
-
-        Vectorized implementation using Smolyak sparse grid structure for efficient
-        polynomial interpolation. Uses tensor products of Chebyshev polynomials.
-
-        Parameters
-        ----------
-        source_points : torch.Tensor
-            Source points [N, 2] with (lat, lon) coordinates
-        values : torch.Tensor
-            Values at source points [N]
-        target_points : torch.Tensor
-            Target points [M, 2] with (lat, lon) coordinates
-
-        Returns
-        -------
-        torch.Tensor
-            Interpolated values [M]
-        """
-        device = source_points.device
-        N = source_points.shape[0]
-        M = target_points.shape[0]
-        d = 2  # 2D: lat and lon
-
-        # Normalize coordinates to [-1, 1]^2 for Chebyshev polynomials
-        source_lat = source_points[:, 0]
-        source_lon = source_points[:, 1]
-        target_lat = target_points[:, 0]
-        target_lon = target_points[:, 1]
-
-        # Normalize longitude to 0-360 range first
-        source_lon = torch.where(source_lon < 0, source_lon + 360.0, source_lon)
-        target_lon = torch.where(target_lon < 0, target_lon + 360.0, target_lon)
-
-        # Get bounds with padding
-        lat_min, lat_max = source_lat.min().item(), source_lat.max().item()
-        lon_min, lon_max = source_lon.min().item(), source_lon.max().item()
-
-        lat_range = lat_max - lat_min
-        lon_range = lon_max - lon_min
-        lat_min -= lat_range * 0.1
-        lat_max += lat_range * 0.1
-        lon_min -= lon_range * 0.1
-        lon_max += lon_range * 0.1
-
-        # Clamp to valid ranges
-        lat_min = max(lat_min, -90.0)
-        lat_max = min(lat_max, 90.0)
-        lon_min = max(lon_min, 0.0)
-        lon_max = min(lon_max, 360.0)
-
-        # Normalize to [-1, 1]
-        source_lat_norm = 2.0 * (source_lat - lat_min) / (lat_max - lat_min) - 1.0
-        source_lon_norm = 2.0 * (source_lon - lon_min) / (lon_max - lon_min) - 1.0
-        target_lat_norm = 2.0 * (target_lat - lat_min) / (lat_max - lat_min) - 1.0
-        target_lon_norm = 2.0 * (target_lon - lon_min) / (lon_max - lon_min) - 1.0
-
-        # Determine Smolyak level mu adaptively based on number of points
-        # For 2D: mu=1->5, mu=2->13, mu=3->29, mu=4->65, mu=5->145, mu=6->321, mu=7->705 points
-        mu_thresholds = [(500, 7), (200, 6), (100, 5), (50, 4), (25, 3), (9, 2)]
-        mu = 1
-        max_degree = 1
-        for threshold, level in mu_thresholds:
-            if N >= threshold:
-                mu = level
-                max_degree = level
-                break
-
-        # Generate Smolyak indices: combinations where d < sum(i) <= d + mu
-        # Vectorized generation
-        i1_range = torch.arange(1, mu + 2, device=device)
-        i2_range = torch.arange(1, mu + 2, device=device)
-        i1_grid, i2_grid = torch.meshgrid(i1_range, i2_range, indexing="ij")
-        mask = (d < (i1_grid + i2_grid)) & ((i1_grid + i2_grid) <= (d + mu))
-        smol_i1 = i1_grid[mask]
-        smol_i2 = i2_grid[mask]
-
-        # Compute m_i(i) = 2^(i-1) + 1 for i > 1, else special cases
-        def m_i(i: torch.Tensor) -> torch.Tensor:
-            result = torch.where(
-                i == 1,
-                torch.tensor(1, device=device),
-                torch.where(i == 0, torch.tensor(0, device=device), (2 ** (i - 1) + 1)),
-            )
-            return result
-
-        # Get degrees for each dimension
-        deg1_all = torch.clamp(m_i(smol_i1) - 1, max=max_degree)
-        deg2_all = torch.clamp(m_i(smol_i2) - 1, max=max_degree)
-
-        # Build unique basis terms (deg_lat, deg_lon) pairs
-        basis_terms = set()
-        for idx in range(len(smol_i1)):
-            d1 = int(deg1_all[idx].item())
-            d2 = int(deg2_all[idx].item())
-            for d1_val in range(d1 + 1):
-                for d2_val in range(d2 + 1):
-                    if d1_val + d2_val <= max_degree:
-                        basis_terms.add((d1_val, d2_val))
-
-        # Fallback if no terms
-        if len(basis_terms) == 0:
-            for deg_lat in range(max_degree + 1):
-                for deg_lon in range(max_degree + 1):
-                    if deg_lat + deg_lon <= max_degree:
-                        basis_terms.add((deg_lat, deg_lon))
-
-        # Evaluate Chebyshev polynomials for source points
-        cheb_lat = self._chebyshev_polynomials(
-            source_lat_norm, max_degree
-        )  # [N, max_degree+1]
-        cheb_lon = self._chebyshev_polynomials(
-            source_lon_norm, max_degree
-        )  # [N, max_degree+1]
-
-        # Build basis matrix vectorized
-        basis_list = []
-        for deg_lat, deg_lon in sorted(basis_terms):
-            basis_list.append(cheb_lat[:, deg_lat] * cheb_lon[:, deg_lon])
-
-        if len(basis_list) == 0:
-            basis_matrix = torch.ones(N, 1, device=device)
-        else:
-            basis_matrix = torch.stack(basis_list, dim=1)  # [N, n_basis]
-
-        # Limit basis size to avoid overfitting
-        if basis_matrix.shape[1] > N:
-            basis_matrix = basis_matrix[:, :N]
-
-        # Solve for coefficients using regularized least squares (vectorized)
-        reg = 1e-6 * torch.eye(basis_matrix.shape[1], device=device)
-        ATA = basis_matrix.T @ basis_matrix + reg
-        ATb = basis_matrix.T @ values.unsqueeze(1)
-        try:
-            coeffs = torch.linalg.solve(ATA, ATb).squeeze(1)  # [n_basis]
-        except Exception:
-            # Fallback to SVD
-            U, S, Vt = torch.linalg.svd(basis_matrix, full_matrices=False)
-            threshold_svd = 1e-6 * S[0] if S[0] > 0 else 1e-6
-            S_inv = torch.where(S > threshold_svd, 1.0 / S, torch.zeros_like(S))
-            coeffs = (Vt.T @ (S_inv[:, None] * (U.T @ values.unsqueeze(1)))).squeeze(1)
-
-        # Evaluate polynomial at target points (vectorized)
-        cheb_lat_target = self._chebyshev_polynomials(
-            target_lat_norm, max_degree
-        )  # [M, max_degree+1]
-        cheb_lon_target = self._chebyshev_polynomials(
-            target_lon_norm, max_degree
-        )  # [M, max_degree+1]
-
-        # Build target basis matrix with same structure
-        target_basis_list = []
-        for deg_lat, deg_lon in sorted(basis_terms):
-            target_basis_list.append(
-                cheb_lat_target[:, deg_lat] * cheb_lon_target[:, deg_lon]
-            )
-
-        if len(target_basis_list) == 0:
-            target_basis = torch.ones(M, 1, device=device)
-        else:
-            target_basis = torch.stack(target_basis_list, dim=1)  # [M, n_basis]
-            # Match coefficient size
-            if target_basis.shape[1] > len(coeffs):
-                target_basis = target_basis[:, : len(coeffs)]
-            elif target_basis.shape[1] < len(coeffs):
-                coeffs = coeffs[: target_basis.shape[1]]
-
-        # Evaluate: target_basis @ coeffs (fully vectorized)
-        interpolated = target_basis @ coeffs  # [M]
-
-        return interpolated
diff --git a/earth2studio/models/da/sda_stormcast.py b/earth2studio/models/da/sda_stormcast.py
deleted file mode 100644
index 59980d2b0..000000000
--- a/earth2studio/models/da/sda_stormcast.py
+++ /dev/null
@@ -1,914 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import warnings
-from collections import OrderedDict
-from collections.abc import Generator
-from itertools import product
-
-import numpy as np
-import pandas as pd
-import torch
-import xarray as xr
-import zarr
-from loguru import logger
-
-from earth2studio.data import GFS_FX, HRRR, DataSource, ForecastSource, fetch_data
-from earth2studio.models.auto import AutoModelMixin, Package
-from earth2studio.models.da.base import AssimilationModel
-from earth2studio.models.da.utils import filter_time_range
-from earth2studio.utils import (
-    handshake_coords,
-    handshake_dim,
-    handshake_size,
-)
-from earth2studio.utils.imports import (
-    OptionalDependencyFailure,
-    check_optional_dependencies,
-)
-from earth2studio.utils.time import normalize_time_tolerance
-from earth2studio.utils.type import CoordSystem, FrameSchema, TimeTolerance
-
-try:
-    import cupy as cp
-except ImportError:
-    cp = None
-
-try:
-    from scipy.spatial import cKDTree
-except ImportError:
-    OptionalDependencyFailure("stormcast")
-    cKDTree = None
-
-try:
-    from omegaconf import OmegaConf
-    from physicsnemo.diffusion.guidance import (
-        DataConsistencyDPSGuidance,
-        DPSScorePredictor,
-    )
-    from physicsnemo.diffusion.noise_schedulers import EDMNoiseScheduler
-    from physicsnemo.diffusion.preconditioners import EDMPreconditioner
-    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecond
-    from physicsnemo.diffusion.samplers import sample
-    from physicsnemo.models.diffusion_unets import StormCastUNet
-except ImportError:
-    OptionalDependencyFailure("stormcast")
-    StormCastUNet = None
-    EDMPreconditioner = None
-    OmegaConf = None
-
-
-# Variables used in StormCastV1 paper
-VARIABLES = (
-    ["u10m", "v10m", "t2m", "msl"]
-    + [
-        var + str(level)
-        for var, level in product(
-            ["u", "v", "t", "q", "Z", "p"],
-            map(
-                lambda x: str(x) + "hl",
-                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 20, 25, 30],
-            ),
-        )
-        if not ((var == "p") and (int(level.replace("hl", "")) > 20))
-    ]
-    + [
-        "refc",
-    ]
-)
-
-CONDITIONING_VARIABLES = ["u10m", "v10m", "t2m", "tcwv", "sp", "msl"] + [
-    var + str(level)
-    for var, level in product(["u", "v", "z", "t", "q"], [1000, 850, 500, 250])
-]
-
-INVARIANTS = ["lsm", "orography"]
-
-
-@check_optional_dependencies()
-class StormCastSDA(torch.nn.Module, AutoModelMixin):
-    """StormCast with score-based data assimilation (SDA) using diffusion posterior
-    sampling for convection-allowing regional forecasts. Combines a regression and
-    diffusion model with DPS guidance to assimilate observations during inference.
-    Model time step size is 1 hour, taking as input:
-
-    - High-resolution (3km) HRRR state over the central United States (99 vars)
-    - High-resolution land-sea mask and orography invariants
-    - Coarse resolution (25km) global state (26 vars)
-    - Point observations for data assimilation
-
-    The high-resolution grid is the HRRR Lambert conformal projection.
-    Coarse-resolution inputs are regridded to the HRRR grid internally.
-
-    Note
-    ----
-    For more information see the following references:
-
-    - https://arxiv.org/abs/2408.10958
-    - https://huggingface.co/nvidia/stormcast-v1-era5-hrrr
-    - https://arxiv.org/abs/2306.10574
-
-    Parameters
-    ----------
-    regression_model : torch.nn.Module
-        Deterministic model used to make an initial prediction
-    diffusion_model : torch.nn.Module
-        Generative model correcting the deterministic prediciton
-    means : torch.Tensor
-        Mean value of each input high-resolution variable
-    stds : torch.Tensor
-        Standard deviation of each input high-resolution variable
-    invariants : torch.Tensor
-        Static invariant  quantities
-    hrrr_lat_lim : tuple[int, int], optional
-        HRRR grid latitude limits, defaults to be the StormCastV1 region in central
-        United States, by default (273, 785)
-    hrrr_lon_lim : tuple[int, int], optional
-        HRRR grid longitude limits, defaults to be the StormCastV1 region in central
-        United States,, by default (579, 1219)
-    variables : np.array, optional
-        High-resolution variables, by default np.array(VARIABLES)
-    conditioning_means : torch.Tensor | None, optional
-        Means to normalize conditioning data, by default None
-    conditioning_stds : torch.Tensor | None, optional
-        Standard deviations to normalize conditioning data, by default None
-    conditioning_variables : np.array, optional
-        Global variables for conditioning, by default np.array(CONDITIONING_VARIABLES)
-    conditioning_data_source : DataSource | ForecastSource | None, optional
-        Data Source to use for global conditioning. Required for running in iterator mode, by default None
-    time_tolerance : TimeTolerance, optional
-        Time tolerance for filtering observations. Observations within the tolerance
-        window around each requested time will be used for data assimilation,
-        by default np.timedelta64(10, "m")
-    sampler_steps : int, optional
-        Number of diffusion sampler steps, by default 36
-    sampler_args : dict[str, float  |  int] | None, optional
-        Arguments to pass to the diffusion sampler, by default None
-    sda_std_obs : float, optional
-        Observation noise standard deviation for DPS guidance, by default 0.1
-    sda_gamma : float, optional
-        SDA scaling factor for DPS guidance, by default 0.001
-    """
-
-    def __init__(
-        self,
-        regression_model: torch.nn.Module,
-        diffusion_model: torch.nn.Module,
-        means: torch.Tensor,
-        stds: torch.Tensor,
-        invariants: torch.Tensor,
-        hrrr_lat_lim: tuple[int, int] = (273, 785),
-        hrrr_lon_lim: tuple[int, int] = (579, 1219),
-        variables: np.array = np.array(VARIABLES),
-        conditioning_means: torch.Tensor | None = None,
-        conditioning_stds: torch.Tensor | None = None,
-        conditioning_variables: np.array = np.array(CONDITIONING_VARIABLES),
-        conditioning_data_source: DataSource | ForecastSource | None = None,
-        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
-        sampler_steps: int = 36,
-        sampler_args: dict[str, float | int] | None = None,
-        sda_std_obs: float = 0.1,
-        sda_gamma: float = 0.001,
-    ):
-        super().__init__()
-        self.regression_model = regression_model
-        self.diffusion_model = diffusion_model
-        self.register_buffer("means", means)
-        self.register_buffer("stds", stds)
-        self.register_buffer("invariants", invariants)
-        self.register_buffer("device_buffer", torch.empty(0))
-        self.sampler_steps = sampler_steps
-        self.sampler_args = {
-            "sigma_min": 0.002,
-            "sigma_max": 800,
-            "rho": 7,
-            "S_churn": 0.0,
-            "S_min": 0.0,
-            "S_max": float("inf"),
-            "S_noise": 1,
-        }
-        if sampler_args is not None:
-            self.sampler_args.update(sampler_args)
-        self._tolerance = normalize_time_tolerance(time_tolerance)
-        self.sda_std_obs = sda_std_obs
-        self.sda_dps_norm = 2
-        self.sda_gamma = sda_gamma
-
-        hrrr_lat, hrrr_lon = HRRR.grid()
-        self.lat = hrrr_lat[
-            hrrr_lat_lim[0] : hrrr_lat_lim[1], hrrr_lon_lim[0] : hrrr_lon_lim[1]
-        ]
-        self.lon = hrrr_lon[
-            hrrr_lat_lim[0] : hrrr_lat_lim[1], hrrr_lon_lim[0] : hrrr_lon_lim[1]
-        ]
-
-        self.hrrr_x = HRRR.HRRR_X[hrrr_lon_lim[0] : hrrr_lon_lim[1]]
-        self.hrrr_y = HRRR.HRRR_Y[hrrr_lat_lim[0] : hrrr_lat_lim[1]]
-
-        # Build ordered boundary polygon from 2D grid perimeter for
-        # point-in-grid testing (top row -> right col -> bottom row -> left col)
-        self._grid_boundary = np.column_stack(
-            [
-                np.concatenate(
-                    [
-                        self.lat[0, :],
-                        self.lat[1:, -1],
-                        self.lat[-1, -2::-1],
-                        self.lat[-2:0:-1, 0],
-                    ]
-                ),
-                np.concatenate(
-                    [
-                        self.lon[0, :],
-                        self.lon[1:, -1],
-                        self.lon[-1, -2::-1],
-                        self.lon[-2:0:-1, 0],
-                    ]
-                ),
-            ]
-        )  # [n_boundary, 2] ordered (lat, lon)
-
-        # Build a KD-tree over (lat, lon) for efficient nearest-grid-point queries
-        # TODO: Make cpu and gpu support
-        self._grid_tree = cKDTree(np.column_stack([self.lat.ravel(), self.lon.ravel()]))
-
-        self.variables = variables
-
-        self.conditioning_variables = conditioning_variables
-        self.conditioning_data_source = conditioning_data_source
-        if conditioning_data_source is None:
-            warnings.warn(
-                "No conditioning data source was provided to StormCast, "
-                + "set the conditioning_data_source attribute of the model "
-                + "before running inference."
-            )
-
-        if conditioning_means is not None:
-            self.register_buffer("conditioning_means", conditioning_means)
-
-        if conditioning_stds is not None:
-            self.register_buffer("conditioning_stds", conditioning_stds)
-
-    @property
-    def device(self) -> torch.device:
-        return self.device_buffer.device
-
-    def init_coords(self) -> tuple[CoordSystem]:
-        """Initialization coordinate system"""
-        return (
-            OrderedDict(
-                {
-                    "time": np.empty(0),
-                    "lead_time": np.array([np.timedelta64(0, "h")]),
-                    "variable": np.array(self.variables),
-                    "hrrr_y": self.hrrr_y,
-                    "hrrr_x": self.hrrr_x,
-                }
-            ),
-        )
-
-    def input_coords(self) -> tuple[FrameSchema]:
-        """Input coordinate system specifying required DataFrame fields."""
-        return (
-            FrameSchema(
-                {
-                    "time": np.empty(0, dtype="datetime64[ns]"),
-                    "lat": np.empty(0, dtype=np.float32),
-                    "lon": np.empty(0, dtype=np.float32),
-                    "observation": np.empty(0, dtype=np.float32),
-                    "variable": np.array(self.variables, dtype=str),
-                }
-            ),
-        )
-
-    def output_coords(self, input_coords: tuple[CoordSystem]) -> tuple[CoordSystem]:
-        """Output coordinate system of the assimilation model
-
-        Parameters
-        ----------
-        input_coords : tuple[CoordSystem]
-            Coordinates of tensor used to initialize the forecast model.
-
-        Returns
-        -------
-        CoordSystem
-            Coordinate system dictionary
-        """
-
-        output_coords = OrderedDict(
-            {
-                "time": np.empty(0),
-                "lead_time": np.array([np.timedelta64(1, "h")]),
-                "variable": np.array(self.variables),
-                "hrrr_y": self.hrrr_y,
-                "hrrr_x": self.hrrr_x,
-            }
-        )
-
-        target_input_coords = self.init_coords()[0]
-
-        handshake_dim(input_coords[0], "hrrr_x", 4)
-        handshake_dim(input_coords[0], "hrrr_y", 3)
-        handshake_dim(input_coords[0], "variable", 2)
-        # Index coords are arbitrary as long its on the HRRR grid, so just check size
-        handshake_size(input_coords[0], "hrrr_y", self.lat.shape[0])
-        handshake_size(input_coords[0], "hrrr_x", self.lat.shape[1])
-        handshake_coords(input_coords[0], target_input_coords, "variable")
-
-        output_coords["time"] = input_coords[0]["time"]
-        output_coords["lead_time"] = (
-            output_coords["lead_time"] + input_coords[0]["lead_time"]
-        )
-        return (output_coords,)
-
-    @classmethod
-    def load_default_package(cls) -> Package:
-        """Load assimilation package"""
-        package = Package(
-            "hf://nvidia/stormcast-v1-era5-hrrr@6c89a0877a0d6b231033d3b0d8b9828a6f833ed8",
-            cache_options={
-                "cache_storage": Package.default_cache("stormcast"),
-                "same_names": True,
-            },
-        )
-        return package
-
-    @classmethod
-    @check_optional_dependencies()
-    def load_model(
-        cls,
-        package: Package,
-        conditioning_data_source: DataSource | ForecastSource = GFS_FX(verbose=False),
-        time_tolerance: TimeTolerance = np.timedelta64(10, "m"),
-        sampler_steps: int = 36,
-        sda_std_obs: float = 0.1,
-        sda_gamma: float = 0.001,
-    ) -> AssimilationModel:
-        """Load assimilation from package
-
-        Parameters
-        ----------
-        package : Package
-            Package to load model from
-        conditioning_data_source : DataSource | ForecastSource, optional
-            Data source to use for global conditioning, by default GFS_FX
-        time_tolerance : TimeTolerance, optional
-            Time tolerance for filtering observations. Observations within the tolerance
-            window around each requested time will be used for data assimilation,
-            by default np.timedelta64(10, "m")
-        sampler_steps : int, optional
-            Number of diffusion sampler steps, by default 36
-        sda_std_obs : float, optional
-            Observation noise standard deviation for DPS guidance, by default 0.1
-        sda_gamma : float, optional
-            SDA scaling factor for DPS guidance, by default 0.001
-
-        Returns
-        -------
-        AssimilationModel
-            Assimilation model
-        """
-        try:
-            package.resolve("config.json")  # HF tracking download statistics
-        except FileNotFoundError:
-            pass
-
-        try:
-            OmegaConf.register_new_resolver("eval", eval)
-        except ValueError:
-            # Likely already registered so skip
-            pass
-
-        # load model registry:
-        config = OmegaConf.load(package.resolve("model.yaml"))
-
-        # TODO: remove strict=False once checkpoints/imports updated to new diffusion API
-        regression = StormCastUNet.from_checkpoint(
-            package.resolve("StormCastUNet.0.0.mdlus"),
-            strict=False,
-        )
-        diffusion = EDMPrecond.from_checkpoint(
-            package.resolve("EDMPrecond.0.0.mdlus"),
-            strict=False,
-        )
-
-        # Load metadata: means, stds, grid
-        store = zarr.storage.ZipStore(package.resolve("metadata.zarr.zip"), mode="r")
-        metadata = xr.open_zarr(store, zarr_format=2)
-
-        variables = metadata["variable"].values
-        conditioning_variables = metadata["conditioning_variable"].values
-
-        # Expand dims and tensorify normalization buffers
-        means = torch.from_numpy(metadata["means"].values[None, :, None, None])
-        stds = torch.from_numpy(metadata["stds"].values[None, :, None, None])
-        conditioning_means = torch.from_numpy(
-            metadata["conditioning_means"].values[None, :, None, None]
-        )
-        conditioning_stds = torch.from_numpy(
-            metadata["conditioning_stds"].values[None, :, None, None]
-        )
-
-        # Load invariants
-        invariants = metadata["invariants"].sel(invariant=config.data.invariants).values
-        invariants = torch.from_numpy(invariants).repeat(1, 1, 1, 1)
-
-        # EDM sampler arguments
-        if config.sampler_args is not None:
-            sampler_args = config.sampler_args
-        else:
-            sampler_args = {}
-
-        return cls(
-            regression,
-            diffusion,
-            means,
-            stds,
-            invariants,
-            variables=variables,
-            conditioning_means=conditioning_means,
-            conditioning_stds=conditioning_stds,
-            conditioning_data_source=conditioning_data_source,
-            conditioning_variables=conditioning_variables,
-            time_tolerance=time_tolerance,
-            sampler_steps=sampler_steps,
-            sampler_args=sampler_args,
-            sda_std_obs=sda_std_obs,
-            sda_gamma=sda_gamma,
-        )
-
-    @torch.no_grad()
-    def _forward(
-        self,
-        x: torch.Tensor,
-        conditioning: torch.Tensor,
-        y_obs: torch.Tensor,
-        mask: torch.Tensor,
-    ) -> torch.Tensor:
-
-        # Scale data
-        if "conditioning_means" in self._buffers:
-            conditioning = conditioning - self.conditioning_means
-        if "conditioning_stds" in self._buffers:
-            conditioning = conditioning / self.conditioning_stds
-
-        x = (x - self.means) / self.stds
-        y_obs = (y_obs - self.means) / self.stds
-
-        # Run regression model
-        invariant_tensor = self.invariants.repeat(x.shape[0], 1, 1, 1)
-        concats = torch.cat((x, conditioning, invariant_tensor), dim=1)
-
-        out = self.regression_model(concats)
-        y_obs = y_obs - out  # Convert to residual obs
-
-        # Concat for diffusion conditioning
-        condition = torch.cat((x, out, invariant_tensor), dim=1)
-        latents = torch.randn_like(x, dtype=torch.float64)
-        latents = self.sampler_args["sigma_max"] * latents  # Initial guess
-
-        def _conditional_diffusion(x: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
-            return self.diffusion_model(x, t, condition=condition)
-
-        scheduler = EDMNoiseScheduler(
-            sigma_min=self.sampler_args["sigma_min"],
-            sigma_max=self.sampler_args["sigma_max"],
-            rho=self.sampler_args["rho"],
-        )
-
-        guidance = DataConsistencyDPSGuidance(
-            mask=mask,
-            y=y_obs,
-            std_y=self.sda_std_obs,
-            norm=self.sda_dps_norm,
-            gamma=self.sda_gamma,
-            sigma_fn=scheduler.sigma,
-            alpha_fn=scheduler.alpha,
-        )
-        score_predictor = DPSScorePredictor(
-            x0_predictor=_conditional_diffusion,
-            x0_to_score_fn=scheduler.x0_to_score,
-            guidances=guidance,
-        )
-        denoiser = scheduler.get_denoiser(score_predictor=score_predictor)
-
-        edm_out = sample(
-            denoiser,
-            latents,
-            noise_scheduler=scheduler,
-            num_steps=self.sampler_steps,
-            solver="edm_stochastic_heun",
-            solver_options={
-                "S_churn": self.sampler_args["S_churn"],
-                "S_min": self.sampler_args["S_min"],
-                "S_max": self.sampler_args["S_max"],
-                "S_noise": self.sampler_args["S_noise"],
-            },
-        )
-
-        out += edm_out
-        out = out * self.stds + self.means
-        return out
-
-    @staticmethod
-    def _points_in_polygon(points: np.ndarray, polygon: np.ndarray) -> np.ndarray:
-        """Vectorized ray casting point-in-polygon test.
-        TODO: Improved this (GPU and reduce memory requirement)
-        make a general purpose util maybe...
-
-        Note
-        ----
-        For more information see the following references:
-        https://observablehq.com/@tmcw/understanding-point-in-polygon
-
-        Parameters
-        ----------
-        points : np.ndarray
-            Points to test, shape [n, 2]
-        polygon : np.ndarray
-            Ordered polygon vertices, shape [m, 2]
-
-        Returns
-        -------
-        np.ndarray
-            Boolean array of shape [n], True if point is inside polygon
-        """
-        px, py = points[:, 0], points[:, 1]  # [n]
-        vx, vy = polygon[:, 0], polygon[:, 1]  # [m]
-        vx_next = np.roll(vx, -1)
-        vy_next = np.roll(vy, -1)
-
-        # For each edge (m) and each point (n), check if horizontal ray crosses
-        # Broadcasting: [m, 1] vs [1, n] -> [m, n]
-        crosses = (vy[:, None] > py[None, :]) != (vy_next[:, None] > py[None, :])
-        dvy = vy_next[:, None] - vy[:, None]
-        safe_dvy = np.where(dvy == 0, 1.0, dvy)  # avoid division by zero; masked later
-        x_intersect = (vx_next[:, None] - vx[:, None]) * (
-            py[None, :] - vy[:, None]
-        ) / safe_dvy + vx[:, None]
-        hits = crosses & (px[None, :] < x_intersect)
-
-        # Odd number of crossings = inside
-        return (np.sum(hits, axis=0) % 2) == 1
-
-    def _build_obs_tensors(
-        self,
-        obs: pd.DataFrame | None,
-        request_time: np.datetime64,
-        device: torch.device,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        n_var = len(self.variables)
-        n_hrrr_y, n_hrrr_x = self.lat.shape
-
-        y_obs = torch.zeros(
-            1, n_var, n_hrrr_y, n_hrrr_x, device=device, dtype=torch.float32
-        )
-        mask = torch.zeros(
-            1, n_var, n_hrrr_y, n_hrrr_x, device=device, dtype=torch.float32
-        )
-
-        if obs is None or len(obs) == 0:
-            return y_obs, mask
-
-        # Filter observations within tolerance window
-        time_filtered = filter_time_range(
-            obs, request_time, self._tolerance, time_column="time"
-        )
-
-        if len(time_filtered) == 0:
-            return y_obs, mask
-
-        # TODO, make native cudf support
-        # Convert to pandas if cudf for reliable string/value access
-        if hasattr(time_filtered, "to_pandas"):
-            time_filtered = time_filtered.to_pandas()
-
-        obs_lat = time_filtered["lat"].values.astype(np.float64)
-        obs_lon = time_filtered["lon"].values.astype(np.float64)
-        obs_var = time_filtered["variable"].values
-        obs_val = time_filtered["observation"].values.astype(np.float32)
-
-        # Normalize lon to 0-360 to match HRRR grid
-        obs_lon = np.where(obs_lon < 0, obs_lon + 360.0, obs_lon)
-
-        # Filter observations to those inside the curvilinear grid boundary
-        # using ray casting point-in-polygon on the precomputed perimeter
-        obs_points = np.column_stack([obs_lat, obs_lon])
-        in_grid = self._points_in_polygon(obs_points, self._grid_boundary)
-
-        if not in_grid.any():
-            return y_obs, mask
-
-        obs_lat = obs_lat[in_grid]
-        obs_lon = obs_lon[in_grid]
-        obs_var = obs_var[in_grid]
-        obs_val = obs_val[in_grid]
-
-        # Find nearest HRRR grid point for each observation using a KD-tree
-        # to avoid allocating the full [n_obs, n_grid] distance matrix.
-        _, nearest_flat = self._grid_tree.query(np.column_stack([obs_lat, obs_lon]))
-        nearest_y = nearest_flat // n_hrrr_x
-        nearest_x = nearest_flat % n_hrrr_x
-
-        # Map variable names to indices
-        var_to_idx = {str(v): i for i, v in enumerate(self.variables)}
-        var_indices = np.array([var_to_idx.get(str(v), -1) for v in obs_var])
-        valid = var_indices >= 0
-
-        # Average multiple observations that map to the same grid cell
-        if valid.any():
-            vi = torch.tensor(var_indices[valid], device=device, dtype=torch.long)
-            yi = torch.tensor(nearest_y[valid], device=device, dtype=torch.long)
-            xi = torch.tensor(nearest_x[valid], device=device, dtype=torch.long)
-            vals = torch.tensor(obs_val[valid], device=device, dtype=torch.float32)
-
-            # Flatten (vi, yi, xi) into a single linear index for scatter ops
-            flat_idx = vi * (n_hrrr_y * n_hrrr_x) + yi * n_hrrr_x + xi
-            flat_sum = torch.zeros(
-                n_var * n_hrrr_y * n_hrrr_x, device=device, dtype=torch.float32
-            )
-            flat_cnt = torch.zeros_like(flat_sum)
-            flat_sum.scatter_add_(0, flat_idx, vals)
-            flat_cnt.scatter_add_(0, flat_idx, torch.ones_like(vals))
-
-            occupied = flat_cnt > 0
-            flat_avg = torch.where(occupied, flat_sum / flat_cnt, flat_sum)
-            y_obs[0] = flat_avg.view(n_var, n_hrrr_y, n_hrrr_x)
-            mask[0] = occupied.float().view(n_var, n_hrrr_y, n_hrrr_x)
-
-        return y_obs, mask
-
-    def _fetch_and_interp_conditioning(self, x: xr.DataArray) -> xr.DataArray:
-        """Fetch global conditioning data and interpolate to HRRR curvilinear grid.
-
-        Parameters
-        ----------
-        x : xr.DataArray
-            Input state DataArray with time and lead_time coordinates
-
-        Returns
-        -------
-        xr.DataArray
-            Conditioning data interpolated onto the HRRR grid
-        """
-        device = self.device
-
-        if self.conditioning_data_source is None:
-            raise RuntimeError(
-                "StormCast has been called without initializing the model's conditioning_data_source"
-            )
-
-        c: xr.DataArray = fetch_data(
-            self.conditioning_data_source,
-            time=x.coords["time"].data,
-            variable=self.conditioning_variables,
-            lead_time=x.coords["lead_time"].data,
-            device=self.device,
-            legacy=False,
-        )
-
-        # Interpolate conditioning from regular lat/lon grid to HRRR curvilinear grid
-        if cp is not None and isinstance(c.data, cp.ndarray):
-            # GPU path: bilinear interpolation using cupy, data stays on GPU
-            with cp.cuda.Device(device.index or 0):
-                data = c.data
-                src_lat = cp.asarray(c.coords["lat"].values, dtype=cp.float64)
-                src_lon = cp.asarray(c.coords["lon"].values, dtype=cp.float64)
-
-                target_lat_cp = cp.asarray(self.lat, dtype=cp.float64)
-                target_lon_cp = cp.asarray(self.lon, dtype=cp.float64)
-
-                # Ensure ascending order for searchsorted (latitude is
-                # commonly descending in weather data, e.g. 90 -> -90)
-                if src_lat[-1] < src_lat[0]:
-                    src_lat = src_lat[::-1]
-                    data = data[..., ::-1, :]
-                if src_lon[-1] < src_lon[0]:
-                    src_lon = src_lon[::-1]
-                    data = data[..., :, ::-1]
-
-                # Compute fractional indices via searchsorted (handles
-                # non-uniform spacing), src_lat and src_lon needs to be acending
-                lat_idx = cp.searchsorted(src_lat, target_lat_cp.ravel()) - 1
-                lat_idx = cp.clip(lat_idx, 0, len(src_lat) - 2)
-                lat_idx = lat_idx.reshape(target_lat_cp.shape)
-
-                lon_idx = cp.searchsorted(src_lon, target_lon_cp.ravel()) - 1
-                lon_idx = cp.clip(lon_idx, 0, len(src_lon) - 2)
-                lon_idx = lon_idx.reshape(target_lon_cp.shape)
-
-                lat0 = lat_idx
-                lon0 = lon_idx
-                lat1 = lat0 + 1
-                lon1 = lon0 + 1
-
-                # Fractional weights between grid cells
-                wlat = (target_lat_cp - src_lat[lat0]) / (src_lat[lat1] - src_lat[lat0])
-                wlon = (target_lon_cp - src_lon[lon0]) / (src_lon[lon1] - src_lon[lon0])
-                wlat = cp.clip(wlat, 0.0, 1.0)
-                wlon = cp.clip(wlon, 0.0, 1.0)
-
-                interp_data = (
-                    data[..., lat0, lon0] * (1 - wlat) * (1 - wlon)
-                    + data[..., lat0, lon1] * (1 - wlat) * wlon
-                    + data[..., lat1, lon0] * wlat * (1 - wlon)
-                    + data[..., lat1, lon1] * wlat * wlon
-                )
-
-            c = xr.DataArray(
-                data=interp_data,
-                dims=["time", "lead_time", "variable", "hrrr_y", "hrrr_x"],
-                coords={
-                    "time": c.coords["time"],
-                    "lead_time": c.coords["lead_time"],
-                    "variable": c.coords["variable"],
-                    "hrrr_y": self.hrrr_y,
-                    "hrrr_x": self.hrrr_x,
-                    "lat": (["hrrr_y", "hrrr_x"], self.lat),
-                    "lon": (["hrrr_y", "hrrr_x"], self.lon),
-                },
-            )
-        else:
-            # CPU path: use xarray's built-in interpolation
-            target_lat = xr.DataArray(self.lat, dims=["hrrr_y", "hrrr_x"])
-            target_lon = xr.DataArray(self.lon, dims=["hrrr_y", "hrrr_x"])
-            c = c.interp(lat=target_lat, lon=target_lon, method="linear")
-            c = c.assign_coords(
-                hrrr_y=("hrrr_y", self.hrrr_y),
-                hrrr_x=("hrrr_x", self.hrrr_x),
-                lat=(["hrrr_y", "hrrr_x"], self.lat),
-                lon=(["hrrr_y", "hrrr_x"], self.lon),
-            )
-
-        return c
-
-    def _to_output_dataarray(
-        self,
-        x_tensor: torch.Tensor,
-        output_coords: tuple[CoordSystem],
-    ) -> xr.DataArray:
-        """Convert output tensor to xr.DataArray with HRRR grid coordinates.
-
-        Parameters
-        ----------
-        x_tensor : torch.Tensor
-            Output tensor from _forward
-        output_coords : tuple[CoordSystem]
-            Output coordinate system from output_coords()
-
-        Returns
-        -------
-        xr.DataArray
-            Output DataArray with cupy backend on GPU or numpy on CPU
-        """
-        device = self.device
-        (oc,) = output_coords
-        if device.type == "cuda" and cp is not None:
-            with cp.cuda.Device(device.index or 0):
-                out_data = cp.asarray(x_tensor.detach())
-        else:
-            out_data = x_tensor.detach().cpu().numpy()
-
-        return xr.DataArray(
-            data=out_data,
-            dims=list(oc.keys()),
-            coords={
-                k: ((["hrrr_y", "hrrr_x"], v) if k in ("lat", "lon") else v)
-                for k, v in oc.items()
-            }
-            | {
-                "lat": (["hrrr_y", "hrrr_x"], self.lat),
-                "lon": (["hrrr_y", "hrrr_x"], self.lon),
-            },
-        )
-
-    # NOTE: @torch.inference_mode() is intentionally omitted here.
-    # DPS guidance requires gradient computation through the denoiser for
-    # the score correction step; inference_mode would disable those gradients.
-    def __call__(
-        self,
-        x: xr.DataArray,
-        obs: pd.DataFrame | None,
-    ) -> xr.DataArray:
-        """Runs assimilation model 1 step.
-
-        Parameters
-        ----------
-        x : xr.DataArray
-            Input state on the HRRR curvilinear grid
-        obs : pd.DataFrame | None
-            Sparse observations DataFrame, or None for no assimilation
-
-        Returns
-        -------
-        xr.DataArray
-            Output state one time-step into the future
-
-        Raises
-        ------
-        RuntimeError
-            If conditioning data source is not initialized
-        """
-        if self.conditioning_data_source is None:
-            raise RuntimeError(
-                "StormCast has been called without initializing the model's conditioning_data_source"
-            )
-
-        device = self.device
-        c = self._fetch_and_interp_conditioning(x)
-
-        x_coords = OrderedDict({dim: x.coords[dim].values for dim in x.dims})
-        output_coords = self.output_coords((x_coords,))
-
-        x_tensor = torch.as_tensor(x.data)
-        c_tensor = torch.as_tensor(c.data)
-
-        for j, t in enumerate(x.coords["time"].data):
-            obs_time = t + output_coords[0]["lead_time"][0]
-            y_obs, mask = self._build_obs_tensors(obs, obs_time, device)
-            x_tensor[j, :] = self._forward(x_tensor[j, :], c_tensor[j, :], y_obs, mask)
-
-        return self._to_output_dataarray(x_tensor, output_coords)
-
-    def create_generator(
-        self, x: xr.DataArray
-    ) -> Generator[xr.DataArray, pd.DataFrame | None, None]:
-        """Creates a generator for iterative forecast with data assimilation.
-
-        The generator yields forecast states and receives observation DataFrames
-        via ``send()``. At each step, conditioning data is fetched, observations
-        are mapped to the HRRR grid, and the diffusion model produces the next
-        forecast step.
-
-        Parameters
-        ----------
-        x : xr.DataArray
-            Initial state on the HRRR curvilinear grid
-
-        Yields
-        ------
-        xr.DataArray
-            Forecast state at each time step
-
-        Receives
-        --------
-        pd.DataFrame | None
-            Observations sent via ``generator.send()``. Pass ``None`` for
-            steps without assimilation.
-
-        Example
-        -------
-        >>> gen = model.create_generator(x0)
-        >>> state = next(gen)           # yields initial state x0
-        >>> state = gen.send(obs_df)    # step 1 with observations
-        >>> state = gen.send(None)      # step 2 without observations
-        """
-        if self.conditioning_data_source is None:
-            raise RuntimeError(
-                "StormCast has been called without initializing the model's "
-                "conditioning_data_source"
-            )
-
-        # Yield the initial state so the caller can inspect it
-        obs = yield x
-
-        try:
-            while True:
-                # Fetch and interpolate conditioning onto HRRR grid
-                c = self._fetch_and_interp_conditioning(x)
-
-                # Compute output coords (advances lead_time by 1h)
-                x_coords = OrderedDict({dim: x.coords[dim].values for dim in x.dims})
-                output_coords = self.output_coords((x_coords,))
-
-                # Zero-copy conversion to torch tensors
-                x_tensor = torch.as_tensor(x.data)
-                c_tensor = torch.as_tensor(c.data)
-
-                # Run forward with observations
-                for j, t in enumerate(x.coords["time"].data):
-                    obs_time = t + output_coords[0]["lead_time"][0]
-                    y_obs, mask = self._build_obs_tensors(obs, obs_time, self.device)
-                    x_tensor[j] = self._forward(x_tensor[j], c_tensor[j], y_obs, mask)
-
-                # Build output DataArray and use as next input
-                x = self._to_output_dataarray(x_tensor, output_coords)
-
-                # Yield forecast result and wait for next observations
-                obs = yield x
-
-        except GeneratorExit:
-            logger.info("StormCast SDA clean up")
diff --git a/earth2studio/models/da/utils.py b/earth2studio/models/da/utils.py
deleted file mode 100644
index c0012e195..000000000
--- a/earth2studio/models/da/utils.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-from datetime import datetime
-
-import numpy as np
-import pandas as pd
-import torch
-from loguru import logger
-
-try:
-    import cudf
-except ImportError:
-    cudf = None
-
-
-def validate_observation_fields(
-    observation: pd.DataFrame | cudf.DataFrame, required_fields: list[str]
-) -> None:
-    """Validate that required fields are present as columns in the DataFrame.
-
-    Parameters
-    ----------
-    observation : pd.DataFrame | cudf.DataFrame
-        DataFrame observation to validate
-    required_fields : list[str]
-        List of required field/column names
-
-    Raises
-    ------
-    ValueError
-        If any required fields are missing from the DataFrame columns
-    """
-    missing_fields = [
-        field for field in required_fields if field not in observation.columns
-    ]
-    if missing_fields:
-        raise ValueError(
-            f"DataFrame missing required fields: {missing_fields}. "
-            f"Available columns: {list(observation.columns)}"
-        )
-
-
-def filter_time_range(
-    df: pd.DataFrame | cudf.DataFrame,
-    request_time: np.datetime64 | datetime | str,
-    tolerance: tuple[np.timedelta64, np.timedelta64],
-    time_column: str = "time",
-) -> pd.DataFrame | cudf.DataFrame:
-    """Filter DataFrame rows where time column is within the specified tolerance range.
-
-    Filters the DataFrame to include only rows where the time column value is within
-    [request_time + lower_bound, request_time + upper_bound]. Ensures the time column
-    is of dtype datetime64[ns].
-
-    Parameters
-    ----------
-    df : pd.DataFrame | cudf.DataFrame
-        DataFrame to filter. Can be pandas or cudf DataFrame.
-    request_time : np.datetime64 | datetime | str
-        Reference time for filtering. Observations within the tolerance window
-        around this time will be included.
-    tolerance : tuple[np.timedelta64, np.timedelta64]
-        Tuple of (lower_bound, upper_bound) time deltas defining the tolerance window.
-    time_column : str, optional
-        Name of the time column in the DataFrame, by default "time"
-
-    Returns
-    -------
-    pd.DataFrame | cudf.DataFrame
-        Filtered DataFrame containing only rows within the time tolerance range.
-        Returns the same DataFrame type as the input (pandas or cudf).
-
-    Raises
-    ------
-    KeyError
-        If the time_column is not present in the DataFrame
-    """
-    if time_column not in df.columns:
-        raise KeyError(
-            f"Time column '{time_column}' not found in DataFrame. "
-            f"Available columns: {list(df.columns)}"
-        )
-
-    request_time_ns = np.datetime64(request_time, "ns")
-
-    # Ensure time column is datetime64[ns]
-    time_series = df[time_column]
-    if time_series.dtype != "datetime64[ns]":
-        df = df.copy()
-        # Use cudf methods if it's a cudf DataFrame, otherwise use pandas
-        if cudf is not None and isinstance(df, cudf.DataFrame):
-            df[time_column] = cudf.to_datetime(time_series).astype("datetime64[ns]")
-        else:
-            df[time_column] = pd.to_datetime(time_series).astype("datetime64[ns]")
-
-    # Calculate time bounds
-    lower_bound, upper_bound = tolerance
-    time_min = request_time_ns + lower_bound
-    time_max = request_time_ns + upper_bound
-
-    time_mask = (df[time_column] >= time_min) & (df[time_column] <= time_max)
-    return df[time_mask]
-
-
-def dfseries_to_torch(
-    series: pd.Series | cudf.Series,
-    dtype: torch.dtype = torch.float32,
-    device: torch.device | str = "cpu",
-) -> torch.Tensor:
-    """Convert a DataFrame series to a torch tensor with zero-copy for cudf.
-
-    If the series is from a cudf DataFrame, uses dlpack for zero-copy transfer.
-    If the series is from pandas but target device is GPU, transfers the data
-    and warns that cudf is not being used.
-
-    Parameters
-    ----------
-    series : pd.Series | cudf.Series
-        Series to convert to torch tensor. Can be pandas or cudf Series.
-    dtype : torch.dtype, optional
-        Desired dtype for the tensor, by default torch.float32
-    device : torch.device | str, optional
-        Target device for the tensor, by default "cpu"
-
-    Returns
-    -------
-    torch.Tensor
-        Torch tensor with the series data on the specified device
-
-    Raises
-    ------
-    ImportError
-        If cudf is required but not available
-    """
-    device = torch.device(device)
-
-    # Check if series is from cudf
-    if cudf is not None and isinstance(series, cudf.Series):
-        # Use dlpack for zero-copy transfer from cudf to torch
-        return torch.from_dlpack(series.values).to(dtype=dtype, device=device)
-
-    # Handle pandas Series
-    if device.type == "cuda":
-        # Warn that cudf is not being used for GPU transfer
-        logger.warning(
-            "Converting pandas Series to GPU tensor. Consider installing cudf "
-            "for zero-copy transfer and better performance."
-        )
-        return torch.tensor(series.values, dtype=dtype, device=device)
-
-    # CPU case - standard conversion
-    return torch.tensor(series.values, dtype=dtype, device=device)
diff --git a/earth2studio/models/dx/cbottle_sr.py b/earth2studio/models/dx/cbottle_sr.py
index b82321d2e..3edcb0c71 100644
--- a/earth2studio/models/dx/cbottle_sr.py
+++ b/earth2studio/models/dx/cbottle_sr.py
@@ -388,6 +388,7 @@ def load_model(
         sigma_max: int = 800,
         seed: int | None = None,
         distilled_model: bool = False,
+        device: str = "cpu",
     ) -> DiagnosticModel:
         """Load diagnostic model from package
 
@@ -412,6 +413,8 @@ def load_model(
         distilled_model : bool, optional
             Whether to use the distilled model, If True, the distilled helper is used,
             enabling generation with fewer sampler steps, by default False
+        device : str
+            Device to load model onto, by default cpu
 
         Returns
         -------
@@ -434,10 +437,10 @@ def load_model(
 
         if distilled_model:
             sr_model = model_cls.from_pretrained(
-                state_path, window_function="KBD", window_alpha=1.0
+                state_path, window_function="KBD", window_alpha=1.0, device=device
             )
         else:
-            sr_model = model_cls.from_pretrained(state_path)
+            sr_model = model_cls.from_pretrained(state_path, device=device)
 
         return cls(
             sr_model,
diff --git a/earth2studio/models/dx/corrdiff.py b/earth2studio/models/dx/corrdiff.py
index 9186714f3..0eff57053 100644
--- a/earth2studio/models/dx/corrdiff.py
+++ b/earth2studio/models/dx/corrdiff.py
@@ -45,28 +45,21 @@
 from earth2studio.utils.type import CoordSystem
 
 try:
-    from physicsnemo import Module as PhysicsNemoModule
-    from physicsnemo.diffusion.generate.legacy_generate import (
+    from physicsnemo.models import Module as PhysicsNemoModule
+    from physicsnemo.utils.corrdiff import (
         diffusion_step,
         regression_step,
     )
-    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecondSR
-    from physicsnemo.diffusion.samplers.legacy_deterministic_sampler import (
+    from physicsnemo.utils.generative import (
+        StackedRandomGenerator,
         deterministic_sampler,
-    )
-    from physicsnemo.diffusion.samplers.legacy_stochastic_sampler import (
         stochastic_sampler,
     )
-    from physicsnemo.diffusion.utils import StackedRandomGenerator
-    from physicsnemo.models.diffusion_unets import UNet
 except ImportError:
     OptionalDependencyFailure("corrdiff")
     PhysicsNemoModule = None
     StackedRandomGenerator = None
     deterministic_sampler = None
-    stochastic_sampler = None
-    EDMPrecondSR = None
-    UNet = None
 
 
 @check_optional_dependencies()
@@ -1401,7 +1394,7 @@ def load_model(cls, package: Package, device: str | None = None) -> DiagnosticMo
         with zipfile.ZipFile(checkpoint_zip, "r") as zip_ref:
             zip_ref.extractall(checkpoint_zip.parent)
 
-        residual = EDMPrecondSR.from_checkpoint(
+        residual = PhysicsNemoModule.from_checkpoint(
             str(
                 checkpoint_zip.parent
                 / Path("corrdiff_inference_package/checkpoints/diffusion.mdlus")
@@ -1414,7 +1407,7 @@ def load_model(cls, package: Package, device: str | None = None) -> DiagnosticMo
             residual = residual.to(device)
         residual = residual.to(memory_format=torch.channels_last)
 
-        regression = UNet.from_checkpoint(
+        regression = PhysicsNemoModule.from_checkpoint(
             str(
                 checkpoint_zip.parent
                 / Path("corrdiff_inference_package/checkpoints/regression.mdlus")
diff --git a/earth2studio/models/dx/corrdiff_cmip6.py b/earth2studio/models/dx/corrdiff_cmip6.py
index 1e83cd9c2..e8bbcabee 100644
--- a/earth2studio/models/dx/corrdiff_cmip6.py
+++ b/earth2studio/models/dx/corrdiff_cmip6.py
@@ -37,20 +37,14 @@
 from earth2studio.utils.type import CoordSystem, LeadTimeArray
 
 try:
-    from physicsnemo.diffusion.generate.legacy_generate import (
-        diffusion_step,
-        regression_step,
-    )
-    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecondSuperResolution
-    from physicsnemo.models.diffusion_unets import UNet
+    from physicsnemo.models import Module as PhysicsNemoModule
+    from physicsnemo.utils.corrdiff import diffusion_step, regression_step
     from physicsnemo.utils.zenith_angle import cos_zenith_angle
 except ImportError:  # pragma: no cover
     OptionalDependencyFailure("corrdiff")
     diffusion_step = None  # type: ignore[assignment]
     regression_step = None  # type: ignore[assignment]
     cos_zenith_angle = None  # type: ignore[assignment]
-    UNet = None  # type: ignore[assignment]
-    EDMPrecondSuperResolution = None  # type: ignore[assignment]
 
 
 class CorrDiffCMIP6(CorrDiff):
@@ -436,14 +430,16 @@ def load_model(
 
         # Load the base CorrDiff model from the package.
         residual = (
-            EDMPrecondSuperResolution.from_checkpoint(
+            PhysicsNemoModule.from_checkpoint(
                 package.resolve("diffusion.mdlus"), strict=False
             )
             .eval()
             .to(device)
         )
         regression = (
-            UNet.from_checkpoint(package.resolve("regression.mdlus"), strict=False)
+            PhysicsNemoModule.from_checkpoint(
+                package.resolve("regression.mdlus"), strict=False
+            )
             .eval()
             .to(device)
         )
diff --git a/earth2studio/models/nn/afno_ssrd.py b/earth2studio/models/nn/afno_ssrd.py
index eba9e7aa7..5b2e180bb 100644
--- a/earth2studio/models/nn/afno_ssrd.py
+++ b/earth2studio/models/nn/afno_ssrd.py
@@ -18,8 +18,8 @@
 import physicsnemo  # noqa: F401 for docs
 import torch
 import torch.nn as nn
-from physicsnemo import Module
 from physicsnemo.models.afno.afno import Block
+from physicsnemo.models.module import Module
 
 Tensor = torch.Tensor
 
diff --git a/earth2studio/models/nn/atlas.py b/earth2studio/models/nn/atlas.py
index 671bebb7a..3346d3cbf 100644
--- a/earth2studio/models/nn/atlas.py
+++ b/earth2studio/models/nn/atlas.py
@@ -45,9 +45,7 @@
 
 @cache
 def get_isht(nlat, nlon, grid="equiangular", norm="ortho", device=None):
-    return InverseRealSHT(
-        nlat, nlon, lmax=nlat, mmax=nlon // 2 + 1, grid=grid, norm=norm
-    ).to(device=device)
+    return InverseRealSHT(nlat, nlon, grid=grid, norm=norm).to(device=device)
 
 
 def spherical_white_noise(shape, scale=3.56, device=None, studentt_deg=None):
@@ -57,21 +55,19 @@ def spherical_white_noise(shape, scale=3.56, device=None, studentt_deg=None):
     if len(shape) < 2:
         raise ValueError(f"Shape must have at least 2 dimensions, got {shape}")
 
-    isht = get_isht(shape[-2], shape[-1], device=device)
-    lmax = isht.lmax
-    mmax = isht.mmax
-
     if studentt_deg is None:
-        noise = torch.randn(shape[0:-2] + [lmax, mmax] + [2], device=device)
+        noise = torch.randn(shape[0:-1] + [(shape[-1] // 2) + 1] + [2], device=device)
     else:
         dist = torch.distributions.studentT.StudentT(studentt_deg)
-        noise = dist.sample(shape[0:-2] + [lmax, mmax] + [2]).to(device)
+        noise = dist.sample(shape[0:-1] + [(shape[-1] // 2) + 1] + [2]).to(device)
 
     noise = (1.0 / math.sqrt(float(shape[-1] * shape[-2]))) * scale * noise
     noise = torch.view_as_complex(noise)
 
     noise = torch.tril(noise)
 
+    isht = get_isht(shape[-2], shape[-1], device=device)
+
     with torch.amp.autocast("cuda", enabled=False):
         return isht(noise)
 
@@ -1309,9 +1305,12 @@ def forward(
         # Conditioning
         t = self.t_embedder(t)
 
+        # print(t.shape)
+
         if self.embedding_table is not None and label is not None:
             t = t + self.embedding_table(label)
 
+        # print(t.shape)
         # Apply DiT blocks
         for j, block in enumerate(self.blocks):
             if j >= self.checkpoint:
diff --git a/earth2studio/models/nn/stormscope_util.py b/earth2studio/models/nn/stormscope_util.py
index 4a9c75f5a..729005397 100644
--- a/earth2studio/models/nn/stormscope_util.py
+++ b/earth2studio/models/nn/stormscope_util.py
@@ -18,7 +18,6 @@
 
 import torch
 import torch.nn as nn
-from loguru import logger
 
 from earth2studio.utils.imports import (
     OptionalDependencyFailure,
@@ -26,7 +25,7 @@
 )
 
 try:
-    from physicsnemo.models import DiT as PNM_DiT
+    from physicsnemo.experimental.models.dit.dit import DiT as PNM_DiT
 except ImportError:
     OptionalDependencyFailure("stormscope")
     PNM_DiT = None  # type: ignore[assignment]
@@ -79,13 +78,13 @@ def __init__(
             self.p_min = p_min
             self.x_offset = x_offset
             self.slope = slope
-            logger.debug(f"sigma_max_dropout: {self.sigma_max_dropout}")
-            logger.debug(f"sigma_min_dropout: {self.sigma_min_dropout}")
-            logger.debug(f"dropout_function_type: {self.dropout_function_type}")
-            logger.debug(f"p_max: {self.p_max}")
-            logger.debug(f"p_min: {self.p_min}")
-            logger.debug(f"x_offset: {self.x_offset}")
-            logger.debug(f"slope: {self.slope}")
+            print(f"sigma_max_dropout: {self.sigma_max_dropout}")
+            print(f"sigma_min_dropout: {self.sigma_min_dropout}")
+            print(f"dropout_function_type: {self.dropout_function_type}")
+            print(f"p_max: {self.p_max}")
+            print(f"p_min: {self.p_min}")
+            print(f"x_offset: {self.x_offset}")
+            print(f"slope: {self.slope}")
         else:
             self.noise_dependent_dropout = False
 
diff --git a/earth2studio/models/px/aifsens.py b/earth2studio/models/px/aifsens.py
index 3168343e9..4f1f48b8e 100644
--- a/earth2studio/models/px/aifsens.py
+++ b/earth2studio/models/px/aifsens.py
@@ -818,8 +818,8 @@ def _fill_input(self, x: torch.Tensor, coords: CoordSystem) -> torch.Tensor:
 
         batch, time, lead, _, height, width = x.shape
 
-        # Prepare output tensor with VARIABLE dimension (zeros to avoid uninitialised memory)
-        out = torch.zeros(
+        # Prepare empty output tensor with VARIABLE dimension
+        out = torch.empty(
             (batch, time, lead, len(VARIABLES), height, width),
             device=x.device,
         )
@@ -848,7 +848,6 @@ def _fill_input(self, x: torch.Tensor, coords: CoordSystem) -> torch.Tensor:
         keep = torch.isin(
             all_indices, self.model.data_indices.data.output.forcing, invert=True
         )
-
         out = out[:, :, :, keep, ...]
 
         # Update coordinates with remaining variable names
diff --git a/earth2studio/models/px/cbottle_video.py b/earth2studio/models/px/cbottle_video.py
index 4453fe0ea..93b12d7a2 100644
--- a/earth2studio/models/px/cbottle_video.py
+++ b/earth2studio/models/px/cbottle_video.py
@@ -16,7 +16,7 @@
 
 from collections import OrderedDict
 from collections.abc import Generator, Iterator
-from datetime import datetime, timezone
+from datetime import datetime
 from enum import IntEnum, StrEnum
 
 import numpy as np
@@ -376,20 +376,14 @@ def reorder(x: torch.Tensor) -> torch.Tensor:
 
         # Set up time tensors
         times0 = [
-            datetime.fromtimestamp(
-                t.astype("datetime64[s]").astype(int), tz=timezone.utc
-            )
+            datetime.fromtimestamp(t.astype("datetime64[s]").astype(int))
             for t in times.reshape(-1)
         ]
         second_of_day = np.array(
             [(t.hour * 3600) + (t.minute * 60) + t.second for t in times0]
         ).reshape(times.shape)
         day_of_year = np.array(
-            [
-                (t - datetime(t.year, 1, 1, tzinfo=timezone.utc)).total_seconds()
-                / (86400.0)
-                for t in times0
-            ]
+            [(t - datetime(t.year, 1, 1)).total_seconds() / (86400.0) for t in times0]
         ).reshape(times.shape)
         second_of_day = torch.tensor(second_of_day.astype(np.float32), device=device)
         day_of_year = torch.tensor(day_of_year.astype(np.float32), device=device)
diff --git a/earth2studio/models/px/dlesym.py b/earth2studio/models/px/dlesym.py
index bcdbbb4d1..8c4be9b29 100644
--- a/earth2studio/models/px/dlesym.py
+++ b/earth2studio/models/px/dlesym.py
@@ -36,7 +36,7 @@
 try:
     import earth2grid
     from omegaconf import OmegaConf
-    from physicsnemo import Module
+    from physicsnemo.models import Module
     from physicsnemo.utils.insolation import insolation
 except ImportError:
     OptionalDependencyFailure("dlesym")
diff --git a/earth2studio/models/px/fcn.py b/earth2studio/models/px/fcn.py
index e25d810e1..17588ec7c 100644
--- a/earth2studio/models/px/fcn.py
+++ b/earth2studio/models/px/fcn.py
@@ -14,8 +14,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import zipfile
 from collections import OrderedDict
 from collections.abc import Generator, Iterator
+from pathlib import Path
 
 import numpy as np
 import torch
@@ -79,7 +81,7 @@ class FCN(torch.nn.Module, AutoModelMixin, PrognosticMixin):
     paper. For additional information see the following resources:
 
     - https://arxiv.org/abs/2202.11214
-    - https://huggingface.co/nvidia/fourcastnet1
+    - https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_fcn
 
     Parameters
     ----------
@@ -175,7 +177,7 @@ def __str__(
     def load_default_package(cls) -> Package:
         """Load prognostic package"""
         return Package(
-            "hf://nvidia/fourcastnet1@c67a63995f6c8e0e557eb3d791f32f437e9b02d5",
+            "ngc://models/nvidia/modulus/modulus_fcn@v0.2",
             cache_options={
                 "cache_storage": Package.default_cache("fcn"),
                 "same_names": True,
@@ -189,16 +191,20 @@ def load_model(
         package: Package,
     ) -> PrognosticModel:
         """Load prognostic from package"""
-        try:
-            package.resolve("config.json")  # HF tracking download statistics
-        except FileNotFoundError:
-            pass
+        fcn_zip = Path(package.resolve("fcn.zip"))
+        # Have to manually unzip here. Should not zip checkpoints in the future
+        with zipfile.ZipFile(fcn_zip, "r") as zip_ref:
+            zip_ref.extractall(fcn_zip.parent)
 
-        model = AFNO.from_checkpoint(package.resolve("fcn.mdlus"))
+        model = AFNO.from_checkpoint(str(fcn_zip.parent / Path("fcn/fcn.mdlus")))
         model.eval()
 
-        local_center = torch.Tensor(np.load(package.resolve("global_means.npy")))
-        local_std = torch.Tensor(np.load(package.resolve("global_stds.npy")))
+        local_center = torch.Tensor(
+            np.load(str(fcn_zip.parent / Path("fcn/global_means.npy")))
+        )
+        local_std = torch.Tensor(
+            np.load(str(fcn_zip.parent / Path("fcn/global_stds.npy")))
+        )
         return cls(model, center=local_center, scale=local_std)
 
     @torch.inference_mode()
diff --git a/earth2studio/models/px/graphcast_operational.py b/earth2studio/models/px/graphcast_operational.py
index e29881d31..cd036fa25 100644
--- a/earth2studio/models/px/graphcast_operational.py
+++ b/earth2studio/models/px/graphcast_operational.py
@@ -237,7 +237,7 @@ def __init__(
                     ]
                 ),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(90, -90, 721, endpoint=True),
+                "lat": np.linspace(-90, 90, 721, endpoint=True),
                 "lon": np.linspace(0, 360, 1440, endpoint=False),
             }
         )
@@ -248,7 +248,7 @@ def __init__(
                 "time": np.empty(0),
                 "lead_time": np.array([np.timedelta64(6, "h")]),
                 "variable": np.array(VARIABLES + ["tp06"]),
-                "lat": np.linspace(90, -90, 721, endpoint=True),
+                "lat": np.linspace(-90, 90, 721, endpoint=True),
                 "lon": np.linspace(0, 360, 1440, endpoint=False),
             }
         )
@@ -456,7 +456,7 @@ def _default_generator(
         # Get device
         device = x.device
 
-        # first batch has 2 times (lead_times)
+        # first batch has 2 times
         coords_out["lead_time"] = coords["lead_time"][1:]
         # Add on zeros slice for tp06
         out = torch.cat(
@@ -469,11 +469,8 @@ def _default_generator(
             # Forward is identity operator
             coords = self.output_coords(coords)
 
-            # Get next prediction from all time iterators
-            results = [
-                self.iterator_result_to_tensor(next(it)) for it in self.iterators
-            ]
-            x = torch.cat(results, dim=1) if len(results) > 1 else results[0]
+            # Get next prediction
+            x = self.iterator_result_to_tensor(next(self.iterator))
 
             # Rear hook
             x, coords = self.rear_hook(x, coords)
@@ -506,37 +503,24 @@ def create_iterator(
         """
 
         with jax.default_device(self.get_jax_device_from_tensor(x)):
-            # Create a separate JAX iterator for each init time
-            # (rollout._get_next_inputs only supports single time)
-            time_dim = list(coords.keys()).index("time")
-            n_times = len(coords["time"])
-            self.iterators = []
-
-            for t in range(n_times):
-                x_t = x.narrow(time_dim, t, 1)
-                coords_t = coords.copy()
-                coords_t["time"] = coords["time"][t : t + 1]
-
-                batch, target_lead_times = self.from_dataarray_to_dataset(
-                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
-                )
+            batch, target_lead_times = self.from_dataarray_to_dataset(
+                xr.DataArray(x.cpu(), coords=coords), 6
+            )
 
-                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                    batch,
-                    target_lead_times=target_lead_times,
-                    **dataclasses.asdict(self.ckpt.task_config),
-                )
+            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                batch,
+                target_lead_times=target_lead_times,
+                **dataclasses.asdict(self.ckpt.task_config),
+            )
 
-                self.iterators.append(
-                    self._chunked_prediction_generator(
-                        predictor_fn=self.run_forward,
-                        rng=self.prng_key,
-                        inputs=inputs,
-                        targets_template=targets * np.nan,
-                        batch=batch,
-                        forcings=forcings,
-                    )
-                )
+            self.iterator = self._chunked_prediction_generator(
+                predictor_fn=self.run_forward,
+                rng=self.prng_key,
+                inputs=inputs,
+                targets_template=targets * np.nan,
+                batch=batch,
+                forcings=forcings,
+            )
 
             yield from self._default_generator(x, coords)
 
@@ -577,9 +561,7 @@ def iterator_result_to_tensor(self, dataset: xr.Dataset) -> torch.Tensor:
                 .T.transpose(..., "time", "lead_time", "variable", "lat", "lon")
             )
 
-        out = torch.from_numpy(dataarray.to_numpy().copy())
-        out = out.flip(-2)  # Flip lat from ascending (-90->90, JAX native) to (90->-90)
-        return out
+        return torch.from_numpy(dataarray.to_numpy().copy())
 
     @staticmethod
     def get_jax_device_from_tensor(x: torch.Tensor) -> "jax.Device":
@@ -618,35 +600,24 @@ def __call__(
             # Map lat and lon if needed
             x, coords = map_coords(x, coords, self.input_coords())
 
-            # Loop over time dimension (JAX model supports single init time only)
-            time_dim = list(coords.keys()).index("time")
-            n_times = len(coords["time"])
-            results = []
-            for t in range(n_times):
-                x_t = x.narrow(time_dim, t, 1)
-                coords_t = coords.copy()
-                coords_t["time"] = coords["time"][t : t + 1]
-
-                data, target_lead_times = self.from_dataarray_to_dataset(
-                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
-                )
-
-                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                    data,
-                    target_lead_times=target_lead_times,
-                    **dataclasses.asdict(self.ckpt.task_config),
-                )
+            data, target_lead_times = self.from_dataarray_to_dataset(
+                xr.DataArray(x.cpu(), coords=coords), 6
+            )
 
-                predictions = rollout.chunked_prediction(
-                    self.run_forward,
-                    rng=self.prng_key,
-                    inputs=inputs,
-                    targets_template=targets * np.nan,
-                    forcings=forcings,
-                )
-                results.append(self.iterator_result_to_tensor(predictions))
+            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                data,
+                target_lead_times=target_lead_times,
+                **dataclasses.asdict(self.ckpt.task_config),
+            )
 
-            out = torch.cat(results, dim=1) if n_times > 1 else results[0]
+            predictions = rollout.chunked_prediction(
+                self.run_forward,
+                rng=self.prng_key,
+                inputs=inputs,
+                targets_template=targets * np.nan,
+                forcings=forcings,
+            )
+            out = self.iterator_result_to_tensor(predictions)
             output_coords = self.output_coords(coords)
 
             # Convert to device
diff --git a/earth2studio/models/px/graphcast_small.py b/earth2studio/models/px/graphcast_small.py
index 0afe99bdf..0650df74d 100644
--- a/earth2studio/models/px/graphcast_small.py
+++ b/earth2studio/models/px/graphcast_small.py
@@ -169,7 +169,7 @@ class GraphCastSmall(torch.nn.Module, AutoModelMixin, PrognosticMixin):
     A smaller, low-resolution version of GraphCast (1 degree resolution, 13 pressure
     levels and a smaller mesh), trained on ERA5 data from 1979 to 2015. This model is
     useful for running with lower memory and compute constraints while maintaining good
-    forecast skill. The model operates on a 1-degree lat-lon grid (pole including)
+    forecast skill. The model operates on a 1-degree lat-lon grid (south-pole including)
     equirectangular grid with 85 variables including:
 
     - Surface variables (2m temperature, 10m winds, etc.)
@@ -238,7 +238,7 @@ def __init__(
                     ]
                 ),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(90, -90, 181, endpoint=True),
+                "lat": np.linspace(-90, 90, 181, endpoint=True),
                 "lon": np.linspace(0, 360, 360, endpoint=False),
             }
         )
@@ -249,7 +249,7 @@ def __init__(
                 "time": np.empty(0),
                 "lead_time": np.array([np.timedelta64(6, "h")]),
                 "variable": np.array(VARIABLES),
-                "lat": np.linspace(90, -90, 181, endpoint=True),
+                "lat": np.linspace(-90, 90, 181, endpoint=True),
                 "lon": np.linspace(0, 360, 360, endpoint=False),
             }
         )
@@ -457,7 +457,7 @@ def _default_generator(
         # Get device
         device = x.device
 
-        # first batch has 2 times (lead_times)
+        # first batch has 2 times
         coords_out = coords.copy()
         coords_out["lead_time"] = coords["lead_time"][1:]
         yield x[:, :, 1:, ...], coords_out
@@ -467,11 +467,8 @@ def _default_generator(
             # Forward is identity operator
             coords = self.output_coords(coords)
 
-            # Get next prediction from all time iterators
-            results = [
-                self.iterator_result_to_tensor(next(it)) for it in self.iterators
-            ]
-            x = torch.cat(results, dim=1) if len(results) > 1 else results[0]
+            # Get next prediction
+            x = self.iterator_result_to_tensor(next(self.iterator))
 
             # Rear hook
             x, coords = self.rear_hook(x, coords)
@@ -504,37 +501,24 @@ def create_iterator(
         """
 
         with jax.default_device(self.get_jax_device_from_tensor(x)):
-            # Create a separate JAX iterator for each init time
-            # (rollout._get_next_inputs only supports single time)
-            time_dim = list(coords.keys()).index("time")
-            n_times = len(coords["time"])
-            self.iterators = []
-
-            for t in range(n_times):
-                x_t = x.narrow(time_dim, t, 1)
-                coords_t = coords.copy()
-                coords_t["time"] = coords["time"][t : t + 1]
-
-                batch, target_lead_times = self.from_dataarray_to_dataset(
-                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
-                )
+            batch, target_lead_times = self.from_dataarray_to_dataset(
+                xr.DataArray(x.cpu(), coords=coords), 6
+            )
 
-                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                    batch,
-                    target_lead_times=target_lead_times,
-                    **dataclasses.asdict(self.ckpt.task_config),
-                )
+            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                batch,
+                target_lead_times=target_lead_times,
+                **dataclasses.asdict(self.ckpt.task_config),
+            )
 
-                self.iterators.append(
-                    self._chunked_prediction_generator(
-                        predictor_fn=self.run_forward,
-                        rng=self.prng_key,
-                        inputs=inputs,
-                        targets_template=targets * np.nan,
-                        batch=batch,
-                        forcings=forcings,
-                    )
-                )
+            self.iterator = self._chunked_prediction_generator(
+                predictor_fn=self.run_forward,
+                rng=self.prng_key,
+                inputs=inputs,
+                targets_template=targets * np.nan,
+                batch=batch,
+                forcings=forcings,
+            )
 
             yield from self._default_generator(x, coords)
 
@@ -575,9 +559,7 @@ def iterator_result_to_tensor(self, dataset: xr.Dataset) -> torch.Tensor:
                 .T.transpose(..., "time", "lead_time", "variable", "lat", "lon")
             )
 
-        out = torch.from_numpy(dataarray.to_numpy().copy())
-        out = out.flip(-2)  # Flip lat from ascending (-90->90, JAX native) to (90->-90)
-        return out
+        return torch.from_numpy(dataarray.to_numpy().copy())
 
     @staticmethod
     def get_jax_device_from_tensor(x: torch.Tensor) -> "jax.Device":
@@ -616,35 +598,24 @@ def __call__(
             # Map lat and lon if needed
             x, coords = map_coords(x, coords, self.input_coords())
 
-            # Loop over time dimension (JAX model supports single init time only)
-            time_dim = list(coords.keys()).index("time")
-            n_times = len(coords["time"])
-            results = []
-            for t in range(n_times):
-                x_t = x.narrow(time_dim, t, 1)
-                coords_t = coords.copy()
-                coords_t["time"] = coords["time"][t : t + 1]
-
-                data, target_lead_times = self.from_dataarray_to_dataset(
-                    xr.DataArray(x_t.cpu(), coords=coords_t), 6
-                )
-
-                inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
-                    data,
-                    target_lead_times=target_lead_times,
-                    **dataclasses.asdict(self.ckpt.task_config),
-                )
+            data, target_lead_times = self.from_dataarray_to_dataset(
+                xr.DataArray(x.cpu(), coords=coords), 6
+            )
 
-                predictions = rollout.chunked_prediction(
-                    self.run_forward,
-                    rng=self.prng_key,
-                    inputs=inputs,
-                    targets_template=targets * np.nan,
-                    forcings=forcings,
-                )
-                results.append(self.iterator_result_to_tensor(predictions))
+            inputs, targets, forcings = data_utils.extract_inputs_targets_forcings(
+                data,
+                target_lead_times=target_lead_times,
+                **dataclasses.asdict(self.ckpt.task_config),
+            )
 
-            out = torch.cat(results, dim=1) if n_times > 1 else results[0]
+            predictions = rollout.chunked_prediction(
+                self.run_forward,
+                rng=self.prng_key,
+                inputs=inputs,
+                targets_template=targets * np.nan,
+                forcings=forcings,
+            )
+            out = self.iterator_result_to_tensor(predictions)
             output_coords = self.output_coords(coords)
 
             # Convert to device
diff --git a/earth2studio/models/px/stormcast.py b/earth2studio/models/px/stormcast.py
index 5a0d2aa73..dea2ec454 100644
--- a/earth2studio/models/px/stormcast.py
+++ b/earth2studio/models/px/stormcast.py
@@ -43,15 +43,11 @@
 
 try:
     from omegaconf import OmegaConf
-    from physicsnemo.diffusion.preconditioners.legacy import EDMPrecond
-    from physicsnemo.diffusion.samplers.legacy_deterministic_sampler import (
-        deterministic_sampler,
-    )
-    from physicsnemo.models.diffusion_unets import StormCastUNet
+    from physicsnemo.models import Module as PhysicsNemoModule
+    from physicsnemo.utils.generative import deterministic_sampler
 except ImportError:
     OptionalDependencyFailure("stormcast")
-    StormCastUNet = None
-    EDMPrecond = None
+    PhysicsNemoModule = None
     OmegaConf = None
     deterministic_sampler = None
 
@@ -291,14 +287,11 @@ def load_model(
         # load model registry:
         config = OmegaConf.load(package.resolve("model.yaml"))
 
-        # TODO: remove strict=False once checkpoints/imports updated to new diffusion API
-        regression = StormCastUNet.from_checkpoint(
-            package.resolve("StormCastUNet.0.0.mdlus"),
-            strict=False,
+        regression = PhysicsNemoModule.from_checkpoint(
+            package.resolve("StormCastUNet.0.0.mdlus")
         )
-        diffusion = EDMPrecond.from_checkpoint(
-            package.resolve("EDMPrecond.0.0.mdlus"),
-            strict=False,
+        diffusion = PhysicsNemoModule.from_checkpoint(
+            package.resolve("EDMPrecond.0.0.mdlus")
         )
 
         # Load metadata: means, stds, grid
diff --git a/earth2studio/models/px/stormscope.py b/earth2studio/models/px/stormscope.py
index 803f96e7c..9e97aed93 100644
--- a/earth2studio/models/px/stormscope.py
+++ b/earth2studio/models/px/stormscope.py
@@ -46,7 +46,7 @@
 from earth2studio.utils.type import CoordSystem
 
 try:
-    from physicsnemo import Module
+    from physicsnemo.models import Module
     from physicsnemo.utils.zenith_angle import cos_zenith_angle
 except ImportError:
     OptionalDependencyFailure("stormscope")
diff --git a/earth2studio/perturbation/gaussian.py b/earth2studio/perturbation/gaussian.py
index 5809db6d9..4270169cd 100644
--- a/earth2studio/perturbation/gaussian.py
+++ b/earth2studio/perturbation/gaussian.py
@@ -232,18 +232,16 @@ def __init__(
         self.isht = InverseRealSHT(
             self.nlat, 2 * self.nlat, grid=grid, norm="backward"
         ).to(dtype=dtype)
-        self.lmax = self.isht.lmax
-        self.mmax = self.isht.mmax
 
         r_earth = 6.371e6
         # kT is defined on slide 7
         self.kT = (length_scale / r_earth) ** 2 / 2
-        F0 = self.calculateF0(self.sigma, self.phi, self.lmax, self.kT)
+        F0 = self.calculateF0(self.sigma, self.phi, self.nlat, self.kT)
 
         prods = (
-            torch.tensor([j * (j + 1) for j in range(0, self.lmax)])
-            .view(self.lmax, 1)
-            .repeat(1, self.mmax)
+            torch.tensor([j * (j + 1) for j in range(0, self.nlat)])
+            .view(self.nlat, 1)
+            .repeat(1, self.nlat + 1)
         )
 
         sigma_n = torch.tril(torch.exp(-self.kT * prods / 2) * F0)
@@ -262,7 +260,7 @@ def __init__(
         # in the call function
         self.gaussian_noise = torch.distributions.normal.Normal(self.mean, self.var)
         xi = self.gaussian_noise.sample(
-            torch.Size((self.N, self.lmax, self.mmax, 2))
+            torch.Size((self.N, self.nlat, self.nlat + 1, 2))
         ).squeeze()
         xi = torch.view_as_complex(xi)
 
@@ -273,13 +271,13 @@ def __init__(
         self.register_buffer("coeff", coeff)
 
     def calculateF0(
-        self, sigma: float, phi: float, lmax: int, kT: float
+        self, sigma: float, phi: float, nlat: int, kT: float
     ) -> torch.Tensor:
         """This function scales the coefficients such that their grid-point standarddeviation is sigma.
         sigma is the desired variance phi is a np.exp(-dt/time_scale)
         """
         numerator = sigma**2 * (1 - (phi**2))
-        wavenumbers = torch.arange(1, lmax)
+        wavenumbers = torch.arange(1, nlat)
         denominator = (2 * wavenumbers + 1) * torch.exp(
             -kT * wavenumbers * (wavenumbers + 1)
         )
@@ -300,7 +298,7 @@ def forward(self, x: torch.Tensor, time: np.datetime64 = None) -> torch.Tensor:
 
             # Sample Gaussian noise. # TODO why??? for next step maybe?
             xi = self.gaussian_noise.sample(
-                torch.Size((self.N, self.lmax, self.mmax, 2))
+                torch.Size((self.N, self.nlat, self.nlat + 1, 2))
             ).squeeze()
             xi = torch.view_as_complex(xi)
 
diff --git a/earth2studio/perturbation/spherical.py b/earth2studio/perturbation/spherical.py
index 29ce383ea..995d53f81 100644
--- a/earth2studio/perturbation/spherical.py
+++ b/earth2studio/perturbation/spherical.py
@@ -203,14 +203,12 @@ def __init__(
             .to(dtype=dtype)
             .to(device=device)
         )
-        self.lmax = self.isht.lmax
-        self.mmax = self.isht.mmax
 
         # Square root of the eigenvalues of C.
         sqrt_eig = (
-            torch.tensor([j * (j + 1) for j in range(self.lmax)], device=device)
-            .view(self.lmax, 1)
-            .repeat(1, self.mmax)
+            torch.tensor([j * (j + 1) for j in range(self.nlat)], device=device)
+            .view(self.nlat, 1)
+            .repeat(1, self.nlat + 1)
         )
         sqrt_eig = torch.tril(
             sigma * (((sqrt_eig / radius**2) + tau**2) ** (-alpha / 2.0))
@@ -234,7 +232,7 @@ def forward(self, N: int, xi: torch.Tensor | None = None) -> torch.Tensor:
         N : int
             Number of functions to sample.
         xi : torch.Tensor, default is None
-            Noise is a complex tensor of size (N, lmax, mmax).
+            Noise is a complex tensor of size (N, nlat, nlat+1).
             If None, new Gaussian noise is sampled.
             If xi is provided, N is ignored.
 
@@ -248,7 +246,7 @@ def forward(self, N: int, xi: torch.Tensor | None = None) -> torch.Tensor:
         if xi is None:
             gaussian_noise = torch.distributions.normal.Normal(self.mean, self.var)
             xi = gaussian_noise.sample(
-                torch.Size((N, self.lmax, self.mmax, 2))
+                torch.Size((N, self.nlat, self.nlat + 1, 2))
             ).squeeze()
             xi = torch.view_as_complex(xi)
 
diff --git a/earth2studio/run.py b/earth2studio/run.py
index 706acaa93..562a5d082 100644
--- a/earth2studio/run.py
+++ b/earth2studio/run.py
@@ -23,7 +23,7 @@
 from loguru import logger
 from tqdm import tqdm
 
-from earth2studio.data import DataSource, ForecastSource, fetch_data
+from earth2studio.data import DataSource, fetch_data
 from earth2studio.io import IOBackend
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
@@ -158,7 +158,7 @@ def diagnostic(
     nsteps: int,
     prognostic: PrognosticModel,
     diagnostic: DiagnosticModel,
-    data: DataSource | ForecastSource,
+    data: DataSource,
     io: IOBackend,
     output_coords: CoordSystem = OrderedDict({}),
     device: torch.device | None = None,
@@ -178,7 +178,7 @@ def diagnostic(
         Prognostic model
     diagnostic: DiagnosticModel
         Diagnostic model, must be on same coordinate axis as prognostic
-    data : DataSource | ForecastSource
+    data : DataSource
         Data source
     io : IOBackend
         IO object
@@ -401,10 +401,9 @@ def ensemble(
 
         # Expand x, coords for ensemble
         mini_batch_size = min(batch_size, nensemble - batch_id)
-        coords = (
-            OrderedDict({"ensemble": np.arange(batch_id, batch_id + mini_batch_size)})
-            | coords0.copy()
-        )
+        coords = {
+            "ensemble": np.arange(batch_id, batch_id + mini_batch_size)
+        } | coords0.copy()
 
         # Unsqueeze x for batching ensemble
         x = x.unsqueeze(0).repeat(mini_batch_size, *([1] * x.ndim))
diff --git a/earth2studio/serve/client/__init__.py b/earth2studio/serve/client/__init__.py
deleted file mode 100644
index 0df5e8698..000000000
--- a/earth2studio/serve/client/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/earth2studio/serve/client/client.py b/earth2studio/serve/client/client.py
deleted file mode 100644
index ecbaa6eec..000000000
--- a/earth2studio/serve/client/client.py
+++ /dev/null
@@ -1,550 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import io
-import json
-import time
-from typing import Any, cast
-from urllib.parse import urljoin
-
-import requests  # type: ignore[import-untyped]
-from requests.adapters import HTTPAdapter  # type: ignore[import-untyped]
-from requests.packages.urllib3.util.retry import Retry  # type: ignore[import-untyped]
-
-from earth2studio.serve.client.exceptions import (
-    APIConnectionError as ClientConnectionError,
-)
-from earth2studio.serve.client.exceptions import (
-    BadRequestError,
-    Earth2StudioAPIError,
-    InferenceRequestNotFoundError,
-    InternalServerError,
-    RequestTimeoutError,
-)
-from earth2studio.serve.client.models import (
-    HealthStatus,
-    InferenceRequest,
-    InferenceRequestResponse,
-    InferenceRequestResults,
-    InferenceRequestStatus,
-    RequestStatus,
-    StorageType,
-)
-
-
-class Earth2StudioClient:
-    """
-    Python client for Earth2Studio REST API.
-
-    Parameters
-    ----------
-    base_url : str, optional
-        Base URL of the Earth2Studio API server. Default is "http://localhost:8000".
-    workflow_name : str, optional
-        Name of the workflow to use. Default is "deterministic_earth2_workflow".
-    timeout : float, optional
-        Request timeout in seconds. Default is 30.0.
-    max_retries : int, optional
-        Maximum number of retries for failed requests. Default is 3.
-    retry_backoff_factor : float, optional
-        Backoff factor for retries. Default is 0.3.
-    token : str, optional
-        Optional authentication token (sent as Bearer token in Authorization header).
-
-    Examples
-    --------
-    >>> client = Earth2StudioClient(base_url="http://localhost:8000")
-    >>> health = client.health_check()
-    >>> print(health.status)
-    healthy
-
-    With authentication:
-
-    >>> client = Earth2StudioClient(base_url="http://localhost:8000", token="your-token")
-    """
-
-    def __init__(
-        self,
-        base_url: str = "http://localhost:8000",
-        workflow_name: str = "deterministic_earth2_workflow",
-        timeout: float = 30.0,
-        max_retries: int = 3,
-        retry_backoff_factor: float = 0.3,
-        token: str | None = None,
-    ):
-        self.base_url = base_url.rstrip("/")
-        self.workflow_name = workflow_name
-        self.timeout = timeout
-        self.token = token
-
-        # Create session with retry strategy
-        self.session = requests.Session()
-        retry_strategy = Retry(
-            total=max_retries,
-            backoff_factor=retry_backoff_factor,
-            status_forcelist=[429, 500, 502, 503, 504],
-        )
-        adapter = HTTPAdapter(max_retries=retry_strategy)
-        self.session.mount("http://", adapter)
-        self.session.mount("https://", adapter)
-
-        # Set default headers
-        headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json",
-        }
-        # Add authorization header if token is provided
-        if token:
-            headers["Authorization"] = f"Bearer {token}"
-        self.session.headers.update(headers)
-
-    def _make_request(
-        self,
-        method: str,
-        endpoint: str,
-        json_data: dict | None = None,
-        params: dict | None = None,
-        return_response: bool = False,
-        stream: bool = False,
-        timeout: float | None = None,
-    ) -> dict | requests.Response:
-        """
-        Make an HTTP request with error handling.
-
-        Parameters
-        ----------
-        method : str
-            HTTP method (GET, POST, etc.).
-        endpoint : str
-            API endpoint path.
-        json_data : dict, optional
-            JSON data for request body.
-        params : dict, optional
-            Query parameters.
-        return_response : bool, optional
-            If True, return raw Response object instead of parsed JSON.
-        stream : bool, optional
-            If True, enable streaming for large responses.
-        timeout : float, optional
-            Timeout in seconds. If None, uses client default.
-
-        Returns
-        -------
-        dict or requests.Response
-            Parsed JSON response (if return_response=False), or raw Response
-            object (if return_response=True).
-
-        Raises
-        ------
-        RequestTimeoutError
-            If the request times out.
-        APIConnectionError
-            If the connection fails.
-        Earth2StudioAPIError
-            For other request failures or non-2xx responses.
-        """
-        url = urljoin(self.base_url + "/", endpoint.lstrip("/"))
-        timeout = self.timeout if timeout is None else timeout
-        try:
-            response = self.session.request(
-                method=method,
-                url=url,
-                json=json_data,
-                params=params,
-                timeout=timeout,
-                stream=stream,
-            )
-        except requests.exceptions.Timeout:
-            raise RequestTimeoutError(
-                f"Request to {url} timed out after {timeout} seconds"
-            )
-        except requests.exceptions.ConnectionError as e:
-            raise ClientConnectionError(f"Failed to connect to {url}: {str(e)}")
-        except requests.exceptions.RequestException as e:
-            raise Earth2StudioAPIError(f"Request failed: {str(e)}")
-
-        # Handle HTTP error status codes
-        if response.status_code == 400:
-            error_data = self._parse_error_response(response)
-            raise BadRequestError(
-                error_data.get("error", "Bad request"),
-                details=error_data.get("details"),
-            )
-        elif response.status_code == 404:
-            error_data = self._parse_error_response(response)
-            raise InferenceRequestNotFoundError(
-                error_data.get("error", "Not found"), details=error_data.get("details")
-            )
-        elif response.status_code == 500:
-            error_data = self._parse_error_response(response)
-            raise InternalServerError(
-                error_data.get("error", "Internal server error"),
-                details=error_data.get("details"),
-            )
-        elif not response.ok:
-            error_data = self._parse_error_response(response)
-            raise Earth2StudioAPIError(
-                error_data.get("error", f"HTTP {response.status_code} error"),
-                status_code=response.status_code,
-                details=error_data.get("details"),
-            )
-
-        # Return raw response if requested (for binary content like zip files)
-        if return_response:
-            return response
-
-        # Otherwise parse as JSON (default behavior)
-        try:
-            return response.json()
-        except json.JSONDecodeError:
-            raise Earth2StudioAPIError("Invalid JSON response from server")
-
-    def _parse_error_response(self, response: requests.Response) -> dict:
-        """
-        Parse error response JSON, fallback to dict with error text.
-
-        Parameters
-        ----------
-        response : requests.Response
-            HTTP response from the API.
-
-        Returns
-        -------
-        dict
-            Parsed JSON body, or {"error": ...} if JSON decode fails.
-        """
-        try:
-            return response.json()
-        except json.JSONDecodeError:
-            return {"error": response.text or f"HTTP {response.status_code} error"}
-
-    def health_check(self) -> HealthStatus:
-        """
-        Check if the API is running and healthy.
-
-        Returns
-        -------
-        HealthStatus
-            Object with status and timestamp.
-
-        Raises
-        ------
-        Earth2StudioAPIError
-            If the API is not accessible.
-        """
-        response_data = self._make_request("GET", "/health")
-        return HealthStatus.from_dict(cast(dict[str, Any], response_data))
-
-    def submit_inference_request(
-        self, request: InferenceRequest
-    ) -> InferenceRequestResponse:
-        """
-        Submit an inference request to be processed.
-
-        Parameters
-        ----------
-        request : InferenceRequest
-            InferenceRequest object with workflow configuration.
-
-        Returns
-        -------
-        InferenceRequestResponse
-            Response with execution ID and initial status.
-
-        Raises
-        ------
-        BadRequestError
-            If the request parameters are invalid.
-        Earth2StudioAPIError
-            For other API errors.
-        """
-        response_data = self._make_request(
-            "POST", f"/v1/infer/{self.workflow_name}", json_data=request.to_dict()
-        )
-        return InferenceRequestResponse.from_dict(cast(dict[str, Any], response_data))
-
-    def get_request_status(self, request_id: str) -> InferenceRequestStatus:
-        """
-        Get the current status of an inference request.
-
-        Parameters
-        ----------
-        request_id : str
-            Unique identifier for the inference request.
-
-        Returns
-        -------
-        InferenceRequestStatus
-            Current progress and status.
-
-        Raises
-        ------
-        InferenceRequestNotFoundError
-            If the request ID is not found.
-        Earth2StudioAPIError
-            For other API errors.
-        """
-        response_data = self._make_request(
-            "GET", f"/v1/infer/{self.workflow_name}/{request_id}/status"
-        )
-        return InferenceRequestStatus.from_dict(cast(dict[str, Any], response_data))
-
-    def get_request_results(
-        self,
-        request_id: str,
-        timeout: float | None = None,
-    ) -> InferenceRequestResults:
-        """
-        Get the results of a completed inference request by downloading the results.
-
-        Parameters
-        ----------
-        request_id : str
-            Unique identifier for the inference request.
-        timeout : float, optional
-            Timeout in seconds for the download request. If None, uses client default.
-
-        Returns
-        -------
-        InferenceRequestResults
-            Results with output file paths. Use result_paths() to get paths.
-
-        Raises
-        ------
-        InferenceRequestNotFoundError
-            If the request ID is not found.
-        Earth2StudioAPIError
-            For other API errors (including 202 if request is still processing).
-
-        Notes
-        -----
-        If the request is still processing, the API returns 202 and this method
-        raises Earth2StudioAPIError. Use get_request_status() to check completion first.
-        """
-        # Use _make_request with return_response=True to get raw response for zip file
-        response = cast(
-            requests.Response,
-            self._make_request(
-                method="GET",
-                endpoint=f"/v1/infer/{self.workflow_name}/{request_id}/results",
-                return_response=True,
-                timeout=timeout,
-            ),
-        )
-
-        # Handle 202 status specially (request still processing)
-        if response.status_code == 202:
-            try:
-                error_data = response.json()
-                raise Earth2StudioAPIError(
-                    error_data.get("message", "Request is still processing"),
-                    status_code=202,
-                    details=error_data.get("status"),
-                )
-            except json.JSONDecodeError:
-                raise Earth2StudioAPIError(
-                    "Request is still processing", status_code=202
-                )
-
-        response_data = response.json()
-        return InferenceRequestResults.from_dict(response_data)
-
-    def wait_for_completion(
-        self,
-        request_id: str,
-        poll_interval: float = 5.0,
-        timeout: float | None = None,
-    ) -> InferenceRequestResults:
-        """
-        Wait for an inference request to complete and return results.
-
-        Parameters
-        ----------
-        request_id : str
-            Unique identifier for the inference request.
-        poll_interval : float, optional
-            How often to check status in seconds. Default is 5.0.
-        timeout : float, optional
-            Maximum time to wait in seconds. None for no timeout.
-
-        Returns
-        -------
-        InferenceRequestResults
-            Results when the request completes successfully.
-
-        Raises
-        ------
-        RequestTimeoutError
-            If timeout is reached.
-        Earth2StudioAPIError
-            If the request fails or other API errors occur.
-        """
-        start_time = time.time()
-
-        while True:
-            status = self.get_request_status(request_id)
-
-            if status.status == RequestStatus.COMPLETED:
-                return self.get_request_results(request_id, timeout)
-            elif status.status == RequestStatus.FAILED:
-                raise Earth2StudioAPIError(
-                    f"Inference request {request_id} failed: {status.error_message}"
-                )
-            elif status.status == RequestStatus.CANCELLED:
-                raise Earth2StudioAPIError("Inference request was cancelled")
-
-            # Check timeout (timeout=0.0 means fail immediately if not complete)
-            if timeout is not None and (time.time() - start_time) > timeout:
-                raise RequestTimeoutError(
-                    f"Request {request_id} did not complete within {timeout} seconds"
-                )
-
-            time.sleep(poll_interval)
-
-    def run_inference_sync(
-        self,
-        request: InferenceRequest,
-        poll_interval: float = 5.0,
-        timeout: float | None = None,
-    ) -> InferenceRequestResults:
-        """
-        Submit an inference request and wait for it to complete.
-
-        Convenience method that combines submit_inference_request() and
-        wait_for_completion().
-
-        Parameters
-        ----------
-        request : InferenceRequest
-            InferenceRequest object with workflow configuration.
-        poll_interval : float, optional
-            How often to check status in seconds. Default is 5.0.
-        timeout : float, optional
-            Maximum time to wait in seconds. None for no timeout.
-
-        Returns
-        -------
-        InferenceRequestResults
-            Results when the request completes successfully.
-        """
-        response = self.submit_inference_request(request)
-        return self.wait_for_completion(response.execution_id, poll_interval, timeout)
-
-    def result_root_path(self, result: InferenceRequestResults) -> str:
-        """
-        Get the root URL path for accessing result files.
-
-        Parameters
-        ----------
-        result : InferenceRequestResults
-            Result object returned by the request.
-
-        Returns
-        -------
-        str
-            Root path URL for result files (e.g. "/v1/infer/workflow_name/exec_123/results/").
-        """
-        return f"/v1/infer/{self.workflow_name}/{result.request_id}/results/"
-
-    def download_result(
-        self,
-        result: InferenceRequestResults,
-        path: str,
-        timeout: float | None = None,
-    ) -> io.BytesIO:
-        """
-        Download a result file from the server or S3.
-
-        Parameters
-        ----------
-        result : InferenceRequestResults
-            Result object containing request metadata.
-        path : str
-            Relative path to the result file (as returned in result_paths).
-        timeout : float, optional
-            Timeout in seconds for the download. If None, uses client default.
-
-        Returns
-        -------
-        io.BytesIO
-            In-memory buffer containing the downloaded file content.
-
-        Raises
-        ------
-        Earth2StudioAPIError
-            If the download fails or other API errors occur.
-        """
-        if result.storage_type == StorageType.S3:
-            if not result.signed_url:
-                raise Earth2StudioAPIError("S3 storage type requires a signed URL")
-
-            from earth2studio.serve.client.fsspec_utils import (
-                create_cloudfront_mapper,
-            )
-
-            mapper = create_cloudfront_mapper(result.signed_url, zarr_path="")
-            # strip out the prefix execution_id from the path
-            parts = path.split("/")
-            if len(parts) < 2:
-                raise Earth2StudioAPIError(
-                    f"Expected S3 result path to include an execution-id prefix, got: {path!r}"
-                )
-            path = "/".join(parts[1:])
-            content = mapper.fs.cat_file(path)
-            return io.BytesIO(content)
-
-        # Default: download from server
-        response = cast(
-            requests.Response,
-            self._make_request(
-                method="GET",
-                endpoint=f"{self.result_root_path(result)}{path}",
-                return_response=True,
-                stream=True,
-                timeout=timeout,
-            ),
-        )
-        # TODO: add support for downloading to tmp file to avoid OOM
-        return io.BytesIO(response.content)
-
-    def close(self) -> None:
-        """
-        Close the underlying HTTP session.
-        """
-        self.session.close()  # type: ignore[no-untyped-call]
-
-    def __enter__(self) -> "Earth2StudioClient":
-        """
-        Context manager entry.
-
-        Returns
-        -------
-        Earth2StudioClient
-            self.
-        """
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: object,
-    ) -> None:
-        """
-        Context manager exit. Closes the HTTP session.
-        """
-        self.close()
diff --git a/earth2studio/serve/client/e2client.py b/earth2studio/serve/client/e2client.py
deleted file mode 100644
index 59a17aebf..000000000
--- a/earth2studio/serve/client/e2client.py
+++ /dev/null
@@ -1,479 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from collections import OrderedDict
-from collections.abc import Iterator
-from typing import Any, Literal
-from urllib.parse import urljoin
-
-import numpy as np
-import torch
-
-try:
-    import aiohttp
-except ImportError:
-    aiohttp = None  # type: ignore[assignment]
-import xarray as xr
-
-from earth2studio.data import (  # type: ignore[import-untyped]
-    InferenceOutputSource,
-    fetch_data,
-)
-from earth2studio.models.auto import AutoModelMixin  # type: ignore[import-untyped]
-from earth2studio.models.px.utils import PrognosticMixin  # type: ignore[import-untyped]
-from earth2studio.serve.client import fsspec_utils
-from earth2studio.serve.client.client import Earth2StudioClient
-from earth2studio.serve.client.exceptions import Earth2StudioAPIError
-from earth2studio.serve.client.models import (
-    InferenceRequest,
-    InferenceRequestResults,
-    StorageType,
-)
-from earth2studio.utils.type import CoordSystem  # type: ignore[import-untyped]
-
-
-class RemoteEarth2Workflow:
-    """
-    Remote inference workflow client for Earth2Studio API.
-
-    Provides Earth2Studio-compatible interface for running inference on a remote
-    Earth2Studio API server. Supports both direct calls and iterator-based access.
-
-    Parameters
-    ----------
-    base_url : str
-        URL of the Earth2Studio API server.
-    workflow_name : str
-        Name of the workflow to execute on the server.
-    device : str or torch.device, optional
-        Device for tensor operations (e.g. "cuda", "cpu"). Default from CUDA availability.
-    xr_args : dict, optional
-        Additional arguments passed to xarray.open_dataset / xarray.open_zarr.
-    **client_kwargs : Any
-        Additional arguments passed to Earth2StudioClient (e.g. token for authentication).
-    """
-
-    def __init__(
-        self,
-        base_url: str,
-        workflow_name: str,
-        device: str | torch.device | None = None,
-        xr_args: dict[str, Any] | None = None,
-        **client_kwargs: Any,
-    ) -> None:
-        self.base_url = base_url
-        self.client = Earth2StudioClient(
-            base_url=base_url, workflow_name=workflow_name, **client_kwargs
-        )
-        self.workflow_name = workflow_name
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.xr_args = xr_args.copy() if xr_args else {}
-
-    def __call__(self, **kwargs: Any) -> "RemoteEarth2WorkflowResult":
-        """
-        Execute inference request and return result object for accessing outputs.
-
-        Parameters
-        ----------
-        **kwargs : Any
-            Workflow parameters for the inference request.
-
-        Returns
-        -------
-        RemoteEarth2WorkflowResult
-            Result object for accessing inference outputs.
-        """
-        request = InferenceRequest(parameters=kwargs.copy())
-        response = self.client.submit_inference_request(request)
-        return RemoteEarth2WorkflowResult(self, response.execution_id)
-
-    def to(self, device: torch.device | str) -> "RemoteEarth2Workflow":
-        """
-        Move workflow to specified device.
-
-        Parameters
-        ----------
-        device : torch.device or str
-            Target device ("cuda", "cpu", or torch.device).
-
-        Returns
-        -------
-        RemoteEarth2Workflow
-            self for method chaining.
-        """
-        self.device = device
-        return self
-
-
-class RemoteEarth2WorkflowResult:
-    """
-    Result object for a remote inference request.
-
-    Provides methods to access inference results as datasets, data sources, or iterators.
-    The result is fetched lazily; methods wait for completion if needed.
-
-    Parameters
-    ----------
-    workflow : RemoteEarth2Workflow
-        Parent RemoteEarth2Workflow instance.
-    execution_id : str
-        Unique identifier for the inference execution.
-    """
-
-    def __init__(self, workflow: RemoteEarth2Workflow, execution_id: str) -> None:
-        """
-        Store the parent workflow and execution id; result is fetched lazily.
-
-        Parameters
-        ----------
-        workflow : RemoteEarth2Workflow
-            Parent workflow instance.
-        execution_id : str
-            Unique identifier for the inference execution.
-        """
-        self.workflow = workflow
-        self.execution_id = execution_id
-        self._result: InferenceRequestResults | None = None
-
-    def _get_result(self) -> InferenceRequestResults:
-        """
-        Return cached result or wait for completion and cache it.
-
-        Returns
-        -------
-        InferenceRequestResults
-            Cached or newly fetched inference results.
-        """
-        if self._result is None:
-            self._result = self.workflow.client.wait_for_completion(self.execution_id)
-        return self._result
-
-    def as_dataset(self) -> xr.Dataset:
-        """
-        Wait for inference to complete and return output dataset.
-
-        Returns
-        -------
-        xr.Dataset
-            Xarray Dataset containing the inference result data.
-
-        Raises
-        ------
-        Earth2StudioAPIError
-            If the request did not return any outputs.
-        ValueError
-            If the result file format is not .zarr or .nc.
-        """
-        request_result = self._get_result()
-        result_paths = request_result.result_paths()
-        if not result_paths:
-            raise Earth2StudioAPIError("The request did not return any outputs.")
-        result_path = result_paths[0]
-
-        if result_path.endswith(".zarr"):
-            if request_result.storage_type == StorageType.S3:
-                # Extract zarr path without execution_id prefix (first path component)
-                zarr_path = "/".join(result_path.split("/")[1:])
-                mapper = fsspec_utils.get_mapper(request_result, zarr_path)
-                ds = xr.open_zarr(mapper, consolidated=True, **self.workflow.xr_args)
-            elif request_result.storage_type == StorageType.SERVER:
-                result_url = urljoin(
-                    self.workflow.base_url + "/",
-                    (
-                        self.workflow.client.result_root_path(request_result)
-                        + result_path
-                    ).lstrip("/"),
-                )
-                # Pass auth token and longer timeout for HTTP requests to zarr store
-                xr_kwargs = dict(self.workflow.xr_args)
-                storage_options = dict(xr_kwargs.pop("storage_options", {}))
-                if self.workflow.client.token:
-                    headers = dict(storage_options.get("headers", {}))
-                    headers["Authorization"] = f"Bearer {self.workflow.client.token}"
-                    storage_options["headers"] = headers
-                # Use at least 300s timeout for zarr reads (fsspec/aiohttp)
-                zarr_timeout = max(300.0, self.workflow.client.timeout)
-                client_kwargs = dict(storage_options.get("client_kwargs", {}))
-                if aiohttp is not None and "timeout" not in client_kwargs:
-                    client_kwargs["timeout"] = aiohttp.ClientTimeout(total=zarr_timeout)
-                storage_options["client_kwargs"] = client_kwargs
-                ds = xr.open_zarr(
-                    result_url,
-                    consolidated=True,
-                    storage_options=storage_options or None,
-                    **xr_kwargs,
-                )
-            else:
-                raise ValueError(
-                    f"Unsupported storage type: {request_result.storage_type}"
-                )
-        elif result_path.endswith(".nc"):
-            # TODO: support OpenDAP in the future for remote NetCDF4 access?
-            result_data = self.workflow.client.download_result(
-                request_result, result_path
-            )
-            ds = xr.open_dataset(result_data, engine="netcdf4", **self.workflow.xr_args)
-        else:
-            raise ValueError(
-                f"Unsupported result file format: {result_path!r}. "
-                "Only .zarr and .nc are supported for as_dataset()."
-            )
-
-        return ds
-
-    def as_data_source(self) -> InferenceOutputSource:
-        """
-        Wait for inference to complete and return as DataSource.
-
-        Returns
-        -------
-        DataSource
-            Data source wrapper for the inference results.
-        """
-        ds = self.as_dataset()
-        return InferenceOutputSource(ds)
-
-    def as_model(
-        self, iter_coord: Literal["time", "lead_time"] = "lead_time"
-    ) -> "InferenceOutputModel":
-        """
-        Create model wrapper over inference results for iteration by time step.
-
-        Parameters
-        ----------
-        iter_coord : {"time", "lead_time"}, optional
-            Coordinate to iterate over. Default is "lead_time".
-
-        Returns
-        -------
-        InferenceOutputModel
-            Model that yields (tensor, coordinate_system) per time step.
-        """
-        data_source = self.as_data_source()
-        return InferenceOutputModel(
-            data_source=data_source, iter_coord=iter_coord, device=self.workflow.device
-        )
-
-
-def _convert_time_to_lead_time(
-    x: torch.Tensor, coords: CoordSystem, start_time: np.datetime64
-) -> tuple[torch.Tensor, CoordSystem]:
-    """
-    Convert time coordinate to lead_time coordinate.
-
-    Transforms absolute time coordinates to lead times relative to start_time,
-    adding a time dimension with the start time.
-
-    Parameters
-    ----------
-    x : torch.Tensor
-        Input tensor.
-    coords : CoordSystem
-        Coordinate system containing time coordinates.
-    start_time : np.datetime64
-        Reference start time for lead time calculation.
-
-    Returns
-    -------
-    tuple[torch.Tensor, CoordSystem]
-        Transformed tensor and updated coordinate system.
-    """
-    coords = coords.copy()
-    time = coords["time"]
-    dims = list(coords.keys())
-    time_dim = dims.index("time")
-    if "lead_time" not in dims:
-        raise ValueError(
-            "Cannot convert to lead_time: 'lead_time' coordinate not found in "
-            f"fetch_data output. Available coords: {dims}"
-        )
-    lead_time_dim = dims.index("lead_time")
-    lead_time = time - start_time
-    coords["time"] = np.array([start_time])
-    coords["lead_time"] = lead_time
-    x = x.transpose(time_dim, lead_time_dim)
-    return (x, coords)
-
-
-class InferenceOutputModel(AutoModelMixin, PrognosticMixin):
-    """
-    Prognostic model wrapper for inference output data sources.
-
-    Wraps an InferenceOutputSource to provide a prognostic model interface that
-    can be used in Earth2Studio workflows. Pre-computed inference results are
-    consumed as if generated by a live model.
-
-    Parameters
-    ----------
-    data_source : InferenceOutputSource
-        Pre-computed inference data source.
-    iter_coord : {"time", "lead_time"}, optional
-        Coordinate to iterate over. Default is "lead_time".
-    variables : list[str], optional
-        Variable names to include. If None, uses all from the data source.
-    device : torch.device or str, optional
-        Device for tensor operations (e.g. "cuda", "cpu"). Default is "cpu".
-    """
-
-    def __init__(
-        self,
-        data_source: InferenceOutputSource,
-        iter_coord: Literal["time", "lead_time"] = "lead_time",
-        variables: list[str] | None = None,
-        device: torch.device | str = "cpu",
-    ):
-        self.data_source = data_source
-        self.iter_coord = iter_coord
-        if variables is None:
-            variables = self.data_source.da.coords["variable"]
-        self.variables = np.array(variables)
-        self.device = device
-
-    def input_coords(self) -> CoordSystem:
-        """
-        Return empty input coordinate system.
-
-        This model reads from a pre-computed data source and requires no input
-        coordinates.
-
-        Returns
-        -------
-        CoordSystem
-            Empty ordered dictionary of coordinates.
-        """
-        return OrderedDict(
-            {
-                "batch": np.empty(0),
-                "time": np.empty(0),
-                "lead_time": np.array([np.timedelta64(0, "h")]),
-                "variable": np.array([]),
-                "lat": np.empty(0),
-                "lon": np.empty(0),
-            }
-        )
-
-    def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
-        """
-        Generate output coordinate system based on data source coordinates.
-
-        Constructs the coordinate system for output tensors. Time step is inferred
-        from the first two time coordinates if available, otherwise defaults to 6 hours.
-
-        Parameters
-        ----------
-        input_coords : CoordSystem
-            Input coordinate system (unused; kept for interface compatibility).
-
-        Returns
-        -------
-        CoordSystem
-            Coordinate system with batch, time, lead_time, variable, lat, lon.
-        """
-        time_coord = self.data_source.da.coords["time"][:2].values
-        if len(time_coord) >= 2:
-            time_step = time_coord[1] - time_coord[0]
-        else:
-            # use a placeholder if we only have one time step of data
-            time_step = np.timedelta64(6, "h")
-        output_coords = OrderedDict(
-            {
-                "batch": np.empty(0),
-                "time": np.empty(0),
-                "lead_time": np.array([time_step]),
-                "variable": self.variables,
-                "lat": self.data_source.da.coords["lat"].values,
-                "lon": self.data_source.da.coords["lon"].values,
-            }
-        )
-        return output_coords
-
-    def to(self, device: torch.device | str) -> "InferenceOutputModel":
-        """
-        Move model to specified device.
-
-        Parameters
-        ----------
-        device : torch.device or str
-            Target device ("cuda", "cpu", or torch.device).
-
-        Returns
-        -------
-        InferenceOutputModel
-            self for method chaining.
-        """
-        self.device = device
-        return self
-
-    def __call__(
-        self, x: torch.Tensor | None = None, coords: CoordSystem | None = None
-    ) -> tuple[torch.Tensor, CoordSystem]:
-        """
-        Execute single time-step from the data source.
-
-        Returns the first available time step. Input parameters are ignored;
-        data is read from the pre-computed data source.
-
-        Parameters
-        ----------
-        x : torch.Tensor, optional
-            Input tensor (unused; kept for interface compatibility).
-        coords : CoordSystem, optional
-            Input coordinate system (unused; kept for interface compatibility).
-
-        Returns
-        -------
-        tuple[torch.Tensor, CoordSystem]
-            Output tensor and coordinate system for the first time step.
-        """
-        return next(self.create_iterator(x, coords))
-
-    def create_iterator(
-        self, x: torch.Tensor | None = None, coords: CoordSystem | None = None
-    ) -> Iterator[tuple[torch.Tensor, CoordSystem]]:
-        """
-        Create iterator over time steps from the data source.
-
-        Iterates through all available time steps, optionally converting time
-        coordinates to lead_time relative to the first time step.
-
-        Parameters
-        ----------
-        x : torch.Tensor, optional
-            Input tensor (unused; kept for interface compatibility).
-        coords : CoordSystem, optional
-            Input coordinate system (unused; kept for interface compatibility).
-
-        Yields
-        ------
-        tuple[torch.Tensor, CoordSystem]
-            (tensor, coordinate_system) for each time step in the data source.
-        """
-        times = self.data_source.da.coords["time"].values
-        start_time = times[0]
-        for time in times:
-            (x, coords) = fetch_data(
-                self.data_source,
-                time=np.array([time]),
-                variable=self.variables,
-                device=self.device,
-            )
-            if self.iter_coord == "lead_time":
-                (x, coords) = _convert_time_to_lead_time(x, coords, start_time)
-
-            yield (x, coords)
diff --git a/earth2studio/serve/client/exceptions.py b/earth2studio/serve/client/exceptions.py
deleted file mode 100644
index f3c2de5c4..000000000
--- a/earth2studio/serve/client/exceptions.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class Earth2StudioAPIError(Exception):
-    """Base exception for Earth2Studio API errors.
-
-    Parameters
-    ----------
-    message : str
-        Human-readable error message.
-    status_code : int, optional
-        HTTP status code if the error came from an API response.
-    details : str, optional
-        Additional error details.
-    """
-
-    def __init__(
-        self,
-        message: str,
-        status_code: int | None = None,
-        details: str | None = None,
-    ):
-        super().__init__(message)
-        self.message = message
-        self.status_code = status_code
-        self.details = details
-
-    def __str__(self) -> str:
-        """
-        Return a human-readable error string.
-
-        Returns
-        -------
-        str
-            Error string including message, status code (if set), and details (if set).
-        """
-        error_msg = f"Earth2Studio API Error: {self.message}"
-        if self.status_code:
-            error_msg += f" (HTTP {self.status_code})"
-        if self.details:
-            error_msg += f" - {self.details}"
-        return error_msg
-
-
-class BadRequestError(Earth2StudioAPIError):
-    """Exception raised for HTTP 400 Bad Request errors."""
-
-    def __init__(self, message: str, details: str | None = None):
-        """
-        Initialize the exception.
-
-        Parameters
-        ----------
-        message : str
-            Human-readable error message.
-        details : str, optional
-            Additional error details.
-        """
-        super().__init__(message, status_code=400, details=details)
-
-
-class InferenceRequestNotFoundError(Earth2StudioAPIError):
-    """Exception raised for HTTP 404 Not Found errors when inference request is not found."""
-
-    def __init__(self, message: str, details: str | None = None):
-        """
-        Initialize the exception.
-
-        Parameters
-        ----------
-        message : str
-            Human-readable error message.
-        details : str, optional
-            Additional error details.
-        """
-        super().__init__(message, status_code=404, details=details)
-
-
-class InternalServerError(Earth2StudioAPIError):
-    """Exception raised for HTTP 500 Internal Server errors."""
-
-    def __init__(self, message: str, details: str | None = None):
-        """
-        Initialize the exception.
-
-        Parameters
-        ----------
-        message : str
-            Human-readable error message.
-        details : str, optional
-            Additional error details.
-        """
-        super().__init__(message, status_code=500, details=details)
-
-
-class RequestTimeoutError(Earth2StudioAPIError):
-    """Exception raised when a request times out."""
-
-    def __init__(self, message: str = "Request timed out"):
-        """
-        Initialize the exception.
-
-        Parameters
-        ----------
-        message : str, optional
-            Human-readable error message. Default is "Request timed out".
-        """
-        super().__init__(message)
-
-
-class APIConnectionError(Earth2StudioAPIError):
-    """Exception raised when connection to the API fails."""
-
-    def __init__(self, message: str = "Failed to connect to Earth2Studio API"):
-        """
-        Initialize the exception.
-
-        Parameters
-        ----------
-        message : str, optional
-            Human-readable error message. Default is "Failed to connect to Earth2Studio API".
-        """
-        super().__init__(message)
diff --git a/earth2studio/serve/client/fsspec_utils.py b/earth2studio/serve/client/fsspec_utils.py
deleted file mode 100644
index c121e7108..000000000
--- a/earth2studio/serve/client/fsspec_utils.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import Any, NoReturn
-from urllib.parse import parse_qs, urlencode, urlparse
-
-import fsspec  # type: ignore[import-untyped]
-
-from earth2studio.serve.client.models import InferenceRequestResults, StorageType
-
-
-class SignedURLFileSystem(fsspec.AbstractFileSystem):
-    """
-    FSSpec filesystem that appends signed params to requests.
-
-    Wraps a base HTTP filesystem and URL; converts 403 responses to
-    FileNotFoundError for compatibility with consumers like Zarr.
-    """
-
-    def __init__(
-        self, base_fs: Any, query_params: dict[str, str], base_url: str
-    ) -> None:
-        """
-        Wrap a base filesystem and URL with query params for signed requests.
-
-        Parameters
-        ----------
-        base_fs : Any
-            Base fsspec filesystem (e.g. HTTP).
-        query_params : dict[str, str]
-            Query parameters to append (e.g. Policy, Signature, Key-Pair-Id).
-        base_url : str
-            Base URL for the store.
-        """
-        super().__init__()
-        self._fs = base_fs
-        self._query_params = query_params
-        self._base_url = base_url
-        self._query_string = urlencode(query_params, safe="~")
-
-    def _make_signed_path(self, path: str) -> str:
-        """
-        Return the path with signed query parameters appended.
-
-        Parameters
-        ----------
-        path : str
-            Relative or absolute path/URL.
-
-        Returns
-        -------
-        str
-            Full URL with signed query string.
-        """
-        if path.startswith("http"):
-            full_url = path
-        else:
-            clean_path = path.lstrip("/")
-            full_url = (
-                f"{self._base_url}/{clean_path}" if clean_path else self._base_url
-            )
-        separator = "&" if "?" in full_url else "?"
-        return f"{full_url}{separator}{self._query_string}"
-
-    def _handle_403(self, e: BaseException, path: str) -> "NoReturn":
-        """
-        Convert 403 errors to FileNotFoundError; re-raise others.
-
-        Parameters
-        ----------
-        e : BaseException
-            Caught exception.
-        path : str
-            Path that was accessed (for error message).
-        """
-        error_str = str(e).lower()
-        if "403" in str(e) or "forbidden" in error_str:
-            raise FileNotFoundError(f"File not found: {path}") from None
-        raise e
-
-    def _open(self, path: str, mode: str = "rb", **kwargs: Any) -> Any:
-        """
-        Open a path with signed URL; 403 is converted to FileNotFoundError.
-
-        Parameters
-        ----------
-        path : str
-            Path or URL to open.
-        mode : str, optional
-            Open mode (e.g. "rb"). Default is "rb".
-        **kwargs : Any
-            Passed to the underlying filesystem.
-
-        Returns
-        -------
-        Any
-            File-like object from the base filesystem.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the server returns 403 (forbidden).
-        """
-        try:
-            return self._fs._open(self._make_signed_path(path), mode=mode, **kwargs)
-        except Exception as e:
-            self._handle_403(e, path)
-
-    def cat_file(
-        self, path: str, start: int | None = None, end: int | None = None, **kwargs: Any
-    ) -> Any:
-        """
-        Read file contents with signed URL; 403 is converted to FileNotFoundError.
-
-        Parameters
-        ----------
-        path : str
-            Path or URL to read.
-        start : int, optional
-            Start byte offset.
-        end : int, optional
-            End byte offset.
-        **kwargs : Any
-            Passed to the underlying filesystem.
-
-        Returns
-        -------
-        Any
-            File contents (typically bytes).
-
-        Raises
-        ------
-        FileNotFoundError
-            If the server returns 403.
-        """
-        try:
-            return self._fs.cat_file(
-                self._make_signed_path(path), start=start, end=end, **kwargs
-            )
-        except Exception as e:
-            self._handle_403(e, path)
-
-    def _cat_file(
-        self, path: str, start: int | None = None, end: int | None = None, **kwargs: Any
-    ) -> Any:
-        """
-        Async cat_file used by Zarr; delegates to cat_file.
-
-        Parameters
-        ----------
-        path : str
-            Path or URL to read.
-        start : int, optional
-            Start byte offset.
-        end : int, optional
-            End byte offset.
-        **kwargs : Any
-            Passed to cat_file.
-
-        Returns
-        -------
-        Any
-            File contents.
-        """
-        return self.cat_file(path, start=start, end=end, **kwargs)
-
-    def info(self, path: str, **kwargs: Any) -> Any:
-        """
-        Return metadata for path with signed URL; 403 becomes FileNotFoundError.
-
-        Parameters
-        ----------
-        path : str
-            Path or URL.
-        **kwargs : Any
-            Passed to the underlying filesystem.
-
-        Returns
-        -------
-        Any
-            Metadata dict for the path.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the server returns 403.
-        """
-        try:
-            return self._fs.info(self._make_signed_path(path), **kwargs)
-        except Exception as e:
-            self._handle_403(e, path)
-
-    def exists(self, path: str, **kwargs: Any) -> bool:
-        """
-        Return True if path exists; 403 is treated as not found.
-
-        Parameters
-        ----------
-        path : str
-            Path or URL to check.
-        **kwargs : Any
-            Passed to the underlying filesystem.
-
-        Returns
-        -------
-        bool
-            True if path exists, False if not found or 403.
-        """
-        try:
-            return self._fs.exists(self._make_signed_path(path), **kwargs)
-        except Exception as e:
-            try:
-                self._handle_403(e, path)
-            except FileNotFoundError:
-                return False
-
-
-def create_cloudfront_mapper(signed_url: str, zarr_path: str = "") -> Any:
-    """
-    Create an fsspec mapper for a CloudFront signed Zarr URL.
-
-    Parameters
-    ----------
-    signed_url : str
-        CloudFront signed URL with Policy, Signature, and Key-Pair-Id query params.
-    zarr_path : str, optional
-        Subpath within the signed URL to the Zarr store. Default is "".
-
-    Returns
-    -------
-    fsspec.mapping.FSMap
-        Mapper suitable for use with xarray.open_zarr().
-    """
-    # Parse the URL
-    parsed = urlparse(signed_url)
-
-    # Extract query parameters
-    query_params = {k: v[0] for k, v in parse_qs(parsed.query).items()}
-
-    # Get base path (remove trailing /* if present)
-    base_path = parsed.path.rstrip("/*").rstrip("*")
-
-    # Reconstruct base URL
-    if zarr_path != "":
-        base_url = f"{parsed.scheme}://{parsed.netloc}{base_path}/{zarr_path}"
-    else:
-        base_url = f"{parsed.scheme}://{parsed.netloc}{base_path}"
-
-    # Create HTTP filesystem
-    fs = fsspec.filesystem("https")
-
-    signed_fs = SignedURLFileSystem(fs, query_params, base_url)
-    mapper = fsspec.mapping.FSMap(root="", fs=signed_fs, check=False, create=False)
-
-    return mapper
-
-
-def get_mapper(
-    request_result: InferenceRequestResults, zarr_path: str = ""
-) -> Any | None:
-    """
-    Return an fsspec mapper for the result, or None for SERVER storage.
-
-    Parameters
-    ----------
-    request_result : InferenceRequestResults
-        Inference result containing storage type and optional signed_url.
-    zarr_path : str, optional
-        Subpath to the Zarr store within the result. Default is "".
-
-    Returns
-    -------
-    fsspec.mapping.FSMap or None
-        Mapper for S3/signed URL storage, or None for SERVER storage.
-
-    Raises
-    ------
-    ValueError
-        If storage type is S3 but signed_url is missing, or storage type is unsupported.
-    """
-    if request_result.storage_type == StorageType.S3:
-        if request_result.signed_url is None:
-            raise ValueError("S3 storage type requires a signed URL")
-        return create_cloudfront_mapper(request_result.signed_url, zarr_path)
-    elif request_result.storage_type == StorageType.SERVER:
-        return None
-    else:
-        raise ValueError(f"Unsupported storage type: {request_result.storage_type}")
diff --git a/earth2studio/serve/client/models.py b/earth2studio/serve/client/models.py
deleted file mode 100644
index 60e3f9234..000000000
--- a/earth2studio/serve/client/models.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import json
-from dataclasses import dataclass
-from datetime import date, datetime, timedelta
-from enum import Enum
-from typing import Any
-
-import numpy as np
-
-
-class StorageType(str, Enum):
-    """Storage type for inference results."""
-
-    SERVER = "server"
-    S3 = "s3"
-
-
-class RequestStatus(Enum):
-    """Status values for inference requests."""
-
-    # Initial statuses
-    ACCEPTED = "accepted"
-    QUEUED = "queued"
-
-    # Processing statuses
-    RUNNING = "running"
-
-    # Final statuses
-    COMPLETED = "completed"
-    FAILED = "failed"
-    CANCELLED = "cancelled"
-    PENDING_RESULTS = "pending_results"
-
-
-@dataclass
-class InferenceRequest:
-    """Main inference request payload."""
-
-    parameters: dict[str, Any] | None = None
-
-    def to_dict(self) -> dict[str, Any]:
-        """
-        Convert to dictionary for JSON serialization.
-
-        Returns
-        -------
-        dict[str, Any]
-            Dictionary suitable for JSON request body (e.g. {"parameters": ...}).
-        """
-        # use custom JSON encoder first to convert objects that are normally not serializable
-        parameters = json.loads(
-            json.dumps(self.parameters, default=InferenceRequest.json_serial)
-        )
-        return {"parameters": parameters}
-
-    @staticmethod
-    def json_serial(obj: Any) -> Any:
-        """
-        JSON serializer for objects not serializable by default json code.
-
-        Parameters
-        ----------
-        obj : Any
-            Object to serialize (numpy array, datetime, timedelta, etc.).
-
-        Returns
-        -------
-        Any
-            JSON-serializable representation of the object.
-        """
-        if isinstance(obj, np.ndarray):
-            return list(obj)
-        elif isinstance(obj, (datetime, date)):
-            return obj.isoformat()
-        elif isinstance(obj, timedelta):
-            return obj.total_seconds()
-        elif isinstance(obj, np.integer):
-            return int(obj)
-        elif isinstance(obj, np.floating):
-            return float(obj)
-        elif isinstance(obj, np.datetime64):
-            return str(obj)
-        elif isinstance(obj, np.timedelta64):
-            return obj / np.timedelta64(1, "s")  # seconds as float
-        else:
-            raise TypeError(
-                f"Object of type {type(obj).__name__} is not JSON serializable"
-            )
-
-
-@dataclass
-class InferenceRequestResponse:
-    """Response when submitting an inference request."""
-
-    execution_id: str
-    status: RequestStatus
-    message: str
-    timestamp: datetime
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestResponse":
-        """
-        Build an InferenceRequestResponse from an API response dict.
-
-        Parameters
-        ----------
-        data : dict[str, Any]
-            API response dictionary containing execution_id, status, message, timestamp.
-
-        Returns
-        -------
-        InferenceRequestResponse
-            Constructed response instance.
-        """
-        return cls(
-            execution_id=data["execution_id"],
-            status=RequestStatus(data["status"]),
-            message=data["message"],
-            timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
-        )
-
-
-@dataclass
-class ProgressInfo:
-    """Progress information for inference requests."""
-
-    progress: str
-    current_step: int
-    total_steps: int
-
-
-@dataclass
-class InferenceRequestStatus:
-    """Status information for an inference request."""
-
-    execution_id: str
-    status: RequestStatus
-    progress: ProgressInfo | None
-    error_message: str | None
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestStatus":
-        """
-        Build an InferenceRequestStatus from an API response dict.
-
-        Parameters
-        ----------
-        data : dict[str, Any]
-            API response dictionary containing execution_id, status, progress, error_message.
-
-        Returns
-        -------
-        InferenceRequestStatus
-            Constructed status instance.
-        """
-        progress_data = data.get("progress")
-        progress = (
-            ProgressInfo(
-                progress=progress_data["progress"],
-                current_step=progress_data["current_step"],
-                total_steps=progress_data["total_steps"],
-            )
-            if progress_data is not None
-            else None
-        )
-
-        return cls(
-            execution_id=data["execution_id"],
-            status=RequestStatus(data["status"]),
-            progress=progress,
-            error_message=data.get("error_message"),
-        )
-
-
-@dataclass
-class OutputFile:
-    """Information about an output file."""
-
-    path: str
-    size: int
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "OutputFile":
-        """
-        Build an OutputFile from an API response dict.
-
-        Parameters
-        ----------
-        data : dict[str, Any]
-            API response dictionary containing path and size.
-
-        Returns
-        -------
-        OutputFile
-            Constructed output file instance.
-        """
-        return cls(path=data["path"], size=data["size"])
-
-
-@dataclass
-class InferenceRequestResults:
-    """Results of a completed inference request."""
-
-    request_id: str
-    status: RequestStatus
-    output_files: list[OutputFile]
-    completion_time: datetime | None
-    execution_time_seconds: float | None = None
-    storage_type: StorageType = StorageType.SERVER
-    signed_url: str | None = None
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "InferenceRequestResults":
-        """
-        Build InferenceRequestResults from an API response dict.
-
-        Parameters
-        ----------
-        data : dict[str, Any]
-            API response dictionary containing request_id, status, output_files,
-            completion_time, and optional execution_time_seconds, storage_type, signed_url.
-
-        Returns
-        -------
-        InferenceRequestResults
-            Constructed results instance.
-        """
-        output_files = [
-            OutputFile.from_dict(file_data) for file_data in data["output_files"]
-        ]
-        completion_timestamp = data.get("completion_time")
-        completion_time = (
-            datetime.fromisoformat(completion_timestamp.replace("Z", "+00:00"))
-            if completion_timestamp is not None
-            else None
-        )
-
-        return cls(
-            request_id=data["request_id"],
-            status=RequestStatus(data["status"]),
-            output_files=output_files,
-            completion_time=completion_time,
-            execution_time_seconds=data.get("execution_time_seconds"),
-            storage_type=StorageType(
-                data.get("storage_type", StorageType.SERVER.value)
-            ),
-            signed_url=data.get("signed_url"),
-        )
-
-    def result_paths(self) -> list[str]:
-        """
-        Get paths to result data files found in the request response.
-
-        Returns
-        -------
-        list[str]
-            List of result paths (e.g. zarr roots or .nc files). Append to the
-            server results URL to download or open.
-        """
-        # locate the root of zarr store
-        zarr_paths = {
-            f.path
-            for f in self.output_files
-            if (".zarr/" in f.path) or f.path.endswith(".zarr")
-        }
-        zarr_paths_sorted = sorted(
-            {path[: path.find(".zarr") + len(".zarr")] for path in zarr_paths}
-        )
-        netcdf_paths = [f.path for f in self.output_files if f.path.endswith(".nc")]
-        return zarr_paths_sorted + netcdf_paths
-
-
-@dataclass
-class HealthStatus:
-    """Health status response."""
-
-    status: str
-    timestamp: datetime
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "HealthStatus":
-        """
-        Build a HealthStatus from an API response dict.
-
-        Parameters
-        ----------
-        data : dict[str, Any]
-            API response dictionary containing status and timestamp.
-
-        Returns
-        -------
-        HealthStatus
-            Constructed health status instance.
-        """
-        return cls(
-            status=data["status"],
-            timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
-        )
diff --git a/earth2studio/utils/coords.py b/earth2studio/utils/coords.py
index c8259f6c2..62f1d85ae 100644
--- a/earth2studio/utils/coords.py
+++ b/earth2studio/utils/coords.py
@@ -14,22 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from collections import OrderedDict
-from copy import deepcopy
 from typing import Literal
 
 import numpy as np
 import torch
-import xarray as xr
 
 from earth2studio.utils.type import CoordSystem
 
-try:
-    import cupy as cp
-except ImportError:
-    cp = None
-
 
 def handshake_dim(
     input_coords: CoordSystem,
@@ -83,7 +75,7 @@ def handshake_dim(
 def handshake_coords(
     input_coords: CoordSystem,
     target_coords: CoordSystem,
-    required_dim: str | list[str],
+    required_dim: str,
 ) -> None:
     """Simple check to see if the required dimensions have the same coordinate system
 
@@ -93,8 +85,8 @@ def handshake_coords(
         Input coordinate system to validate
     target_coords : CoordSystem
         Target coordinate system
-    required_dim : str | list[str]
-        Required dimension(s) (name of coordinate)
+    required_dim : str
+        Required dimension (name of coordinate)
     Raises
     ------
     KeyError
@@ -102,31 +94,27 @@ def handshake_coords(
     ValueError
         If coordinates of required dimensions don't match
     """
-    if isinstance(required_dim, str):
-        required_dim = [required_dim]
-
-    for _required_dim in required_dim:
-        if _required_dim not in input_coords:
-            raise KeyError(
-                f"Required dimension {_required_dim} not found in input coordinates"
-            )
+    if required_dim not in input_coords:
+        raise KeyError(
+            f"Required dimension {required_dim} not found in input coordinates"
+        )
 
-        if _required_dim not in target_coords:
-            raise KeyError(
-                f"Required dimension {_required_dim} not found in target coordinates"
-            )
+    if required_dim not in target_coords:
+        raise KeyError(
+            f"Required dimension {required_dim} not found in target coordinates"
+        )
 
-        if input_coords[_required_dim].shape != target_coords[_required_dim].shape:
-            raise ValueError(
-                f"Coordinate systems for required dim {_required_dim} are not the same"
-            )
+    if input_coords[required_dim].shape != target_coords[required_dim].shape:
+        raise ValueError(
+            f"Coordinate systems for required dim {required_dim} are not the same"
+        )
 
-        if not np.all(
-            (input_coords[_required_dim] == target_coords[_required_dim]).flatten()
-        ):
-            raise ValueError(
-                f"Coordinate systems for required dim {_required_dim} are not the same"
-            )
+    if not np.all(
+        (input_coords[required_dim] == target_coords[required_dim]).flatten()
+    ):
+        raise ValueError(
+            f"Coordinate systems for required dim {required_dim} are not the same"
+        )
 
 
 def handshake_size(
@@ -318,198 +306,6 @@ def map_coords(
     return x, mapped_coords
 
 
-def map_coords_xr(
-    x: xr.DataArray,
-    output_coords: CoordSystem,
-    method: Literal["nearest"] = "nearest",
-) -> xr.DataArray:
-    """Map xarray DataArray to target coordinate system using selection or interpolation.
-
-    Maps an input DataArray to match the coordinates specified in output_coords by
-    selecting or interpolating along dimensions. Supports both numpy and cupy-backed
-    DataArrays. Empty coordinate arrays are ignored, and warnings are issued for
-    missing coordinate keys.
-
-    Parameters
-    ----------
-    x : xr.DataArray
-        Input DataArray to map. May be backed by numpy or cupy arrays.
-    output_coords : CoordSystem
-        Target coordinate system containing a subset of coordinates present in x.
-        Dimensions not in output_coords are preserved from the input.
-    method : Literal["nearest"], optional
-        Interpolation method for numeric coordinates, by default "nearest"
-
-    Returns
-    -------
-    xr.DataArray
-        Mapped DataArray with coordinates matching output_coords where specified.
-        Preserves all dimensions and coordinates not specified in output_coords.
-
-    Raises
-    ------
-    KeyError
-        If output coordinate dimension is not found in input DataArray
-    ValueError
-        If non-numeric coordinate values are not present in input coordinates
-        If interpolation method is not supported
-    """
-    result = x.copy()
-
-    # Build selection/interpolation dictionary
-    sel_dict = {}
-    interp_dict = {}
-
-    for key, value in output_coords.items():
-        # Ignore batch dimension
-        if key == "batch":
-            continue
-
-        # Skip empty arrays (free coordinate system)
-        if len(value) == 0:
-            continue
-
-        # Check if dimension exists in input
-        if key not in result.dims and key not in result.coords:
-            warnings.warn(
-                f"Coordinate key '{key}' not found in input DataArray. "
-                f"Available dims: {list(result.dims)}, "
-                f"Available coords: {list(result.coords.keys())}"
-            )
-            continue
-
-        # Get coordinate values from input DataArray
-        if key in result.coords:
-            coord_values = result.coords[key]
-        elif key in result.dims:
-            coord_values = result[key]
-        else:
-            continue  # Should not happen due to check above
-
-        coord_array = (
-            coord_values.values if hasattr(coord_values, "values") else coord_values
-        )
-
-        # Check if coordinate types are compatible
-        # Check for datetime/timedelta first (these are not numeric for comparison purposes)
-        is_datetime = np.issubdtype(value.dtype, np.datetime64) or np.issubdtype(
-            coord_array.dtype, np.datetime64
-        )
-        is_timedelta = np.issubdtype(value.dtype, np.timedelta64) or np.issubdtype(
-            coord_array.dtype, np.timedelta64
-        )
-        # Only treat as numeric if both are numeric AND neither is datetime/timedelta
-        is_numeric = (
-            not is_datetime
-            and not is_timedelta
-            and np.issubdtype(value.dtype, np.number)
-            and np.issubdtype(coord_array.dtype, np.number)
-        )
-
-        # Check if all output values are in input (exact match)
-        if is_numeric:
-            # Numeric coordinate: check if values match exactly
-            if len(value) == len(coord_array) and np.allclose(
-                value, coord_array, equal_nan=True
-            ):
-                continue  # No change needed, exact match
-
-            # Check if all values are present in input (can use selection)
-            if np.all(np.isin(value, coord_array)):
-                sel_dict[key] = value
-            else:
-                # Need interpolation for values not in input
-                # xarray's interp uses coordinate names and handles dimension mapping
-                interp_dict[key] = xr.DataArray(value, dims=[key])
-        elif is_datetime or is_timedelta:
-            # Datetime/timedelta coordinate: use direct equality comparison
-            if len(value) == len(coord_array) and np.array_equal(value, coord_array):
-                continue  # No change needed, exact match
-
-            # Check if all values are present in input (can use selection)
-            if np.all(np.isin(value, coord_array)):
-                sel_dict[key] = value
-            else:
-                # Need interpolation for datetime/timedelta values not in input
-                # xarray's interp uses coordinate names and handles dimension mapping
-                interp_dict[key] = xr.DataArray(value, dims=[key])
-        else:
-            # Non-numeric coordinate: must use selection, all values must be present
-            if not np.all(np.isin(value, coord_array)):
-                raise ValueError(
-                    f"For non-numeric coordinate '{key}', all values of output coords "
-                    f"must be in the input coordinates. Some elements of {value} are "
-                    f"not in {coord_array}."
-                )
-            sel_dict[key] = value
-
-    # Apply selection first (exact matches)
-    if sel_dict:
-        result = result.sel(sel_dict)
-
-    # Apply nearest-neighbor interpolation per dimension using torch
-    if interp_dict:
-        if method != "nearest":
-            raise ValueError(f"Interpolation method '{method}' not supported")
-
-        data = result.data
-        is_cupy = cp is not None and isinstance(data, cp.ndarray)
-        dims = list(result.dims)
-        new_coords = dict(result.coords)
-
-        for key, target_da in interp_dict.items():
-            dim_idx = dims.index(key)
-            src_raw = np.asarray(result.coords[key].values)
-            tgt_raw = np.asarray(target_da.values)
-
-            sort_order = np.argsort(src_raw)
-            src_sorted = src_raw[sort_order]
-            idx_sorted = np.searchsorted(src_sorted, tgt_raw)
-            idx_sorted = np.clip(idx_sorted, 1, len(src_sorted) - 1)
-            left = np.abs(tgt_raw - src_sorted[idx_sorted - 1])
-            right = np.abs(tgt_raw - src_sorted[idx_sorted])
-            idx_sorted = np.where(left <= right, idx_sorted - 1, idx_sorted)
-
-            # Map back to original unsorted indices
-            idx = sort_order[idx_sorted]
-
-            # Index into the data array along this dimension
-            if is_cupy:
-                idx_arr = cp.asarray(idx)
-                data = cp.take(data, idx_arr, axis=dim_idx)
-            else:
-                data = np.take(data, idx, axis=dim_idx)
-
-            # Update the interpolated dimension coordinate
-            new_coords[key] = (key, tgt_raw)
-
-            # Re-index any non-dimension coordinates that depend on this dim
-            for cname, cval in list(new_coords.items()):
-                if cname == key:
-                    continue
-                if isinstance(cval, xr.Variable):
-                    c_dims = cval.dims
-                    c_data = cval.values
-                elif isinstance(cval, xr.DataArray):
-                    c_dims = cval.dims
-                    c_data = cval.values
-                elif isinstance(cval, tuple) and len(cval) == 2:
-                    c_dims, c_data = cval
-                    if isinstance(c_dims, str):
-                        c_dims = (c_dims,)
-                else:
-                    continue
-
-                if key in c_dims:
-                    ax = list(c_dims).index(key)
-                    c_data = np.take(np.asarray(c_data), idx, axis=ax)
-                    new_coords[cname] = (c_dims, c_data)
-
-        result = xr.DataArray(data=data, dims=dims, coords=new_coords)
-
-    return result
-
-
 def split_coords(
     x: torch.Tensor, coords: CoordSystem, dim: str = "variable"
 ) -> tuple[list[torch.Tensor], CoordSystem, np.ndarray]:
@@ -633,166 +429,3 @@ def convert_multidim_to_singledim(
             i += j + 1
 
     return CoordSystem(adjusted_coords), mapping
-
-
-def tile_coords(
-    x: torch.Tensor, coords: CoordSystem, target_coords: CoordSystem
-) -> tuple[torch.Tensor, CoordSystem]:
-    """Tile tensor x to match dimensions in target_coords that don't exist in coords.
-
-    This function tiles the input tensor to match leading dimensions from target_coords
-    that are not present in coords. Dimensions that exist in both coords and target_coords
-    are ignored in target_coords and use the values from coords instead.
-
-    Parameters
-    ----------
-    x : torch.Tensor
-        Source tensor to be tiled
-    coords : CoordSystem
-        Coordinate system for x tensor
-    target_coords : CoordSystem
-        Target coordinate system. Dimensions that exist in coords are ignored.
-
-    Returns
-    -------
-    tuple[torch.Tensor, CoordSystem]
-        Tuple containing the tiled tensor and updated coordinate system
-
-    Examples
-    --------
-    Tiling a tensor to match additional dimensions from target_coords
-
-    >>> from earth2studio.utils.coords import tile_coords
-    >>> from collections import OrderedDict
-    >>> import torch
-    >>> import numpy as np
-    >>>
-    >>> x = torch.randn(3, 4)
-    >>> coords = OrderedDict({
-    ...     "variable": np.array(["a", "b", "c"]),
-    ...     "time": np.array([0, 1, 2, 3])
-    ... })
-    >>>
-    >>> target_coords = OrderedDict({
-    ...     "batch": np.array([0, 1]),
-    ...     "ensemble": np.array([0, 1, 2]),
-    ...     "variable": np.array(["x", "y"]),  # Ignored, uses coords value
-    ...     "time": np.array([10, 20, 30, 40])  # Ignored, uses coords value
-    ... })
-    >>>
-    >>> # Tile x to match batch and ensemble dimensions
-    >>> x_tiled, out_coords = tile_coords(x, coords, target_coords)
-    >>> x_tiled.shape
-    torch.Size([2, 3, 3, 4])
-    >>> list(out_coords.keys())
-    ['batch', 'ensemble', 'variable', 'time']
-    """
-    coords_keys = set(coords.keys())
-    leading_dims = OrderedDict()
-    common_dims = []
-
-    for key, val in target_coords.items():
-        if key not in coords_keys:
-            leading_dims[key] = val
-        else:
-            common_dims.append(key)
-
-    # Validate that all common dims are at the end of target_coords
-    if common_dims:
-        target_keys = list(target_coords.keys())
-        coords_keys_list = list(coords.keys())
-        if target_keys[-len(common_dims) :] != coords_keys_list:
-            raise ValueError(
-                f"All common dimensions must appear at the end of target_coords. "
-                f"Common dimensions: {coords_keys_list}, "
-                f"target_coords trailing keys: {target_keys[-len(common_dims):]}, "
-                f"target_coords order: {target_keys}"
-            )
-
-    n_lead = len(leading_dims)
-
-    out_coords = deepcopy(leading_dims)
-    for key, val in coords.items():
-        out_coords[key] = val
-
-    # add leading size-1 dims so tile has one rep per dimension, then tile to match target
-    reps = [len(dim) for dim in leading_dims.values()] + [1] * len(x.shape)
-    x_tiled = x.view(*([1] * n_lead), *x.shape).tile(reps)
-
-    return x_tiled, out_coords
-
-
-def cat_coords(
-    tensors: tuple[torch.Tensor, ...],
-    coords: tuple[CoordSystem, ...],
-    dim: str = "variable",
-) -> tuple[torch.Tensor, CoordSystem]:
-    """
-    concatenate data along coordinate dimension.
-
-    Parameters
-    ----------
-    tensors : tuple[torch.Tensor, ...]
-        Tuple of input tensors to concatenate
-    coords : tuple[CoordSystem, ...]
-        Tuple of OrderedDicts representing coordinate systems for each tensor
-    dim : str
-        name of dimension along which to concatenate
-
-    Returns
-    -------
-    tuple[torch.Tensor, CoordSystem]
-        Tuple containing output tensor and coordinate OrderedDict from
-        concatenated data.
-
-    Raises
-    ------
-    ValueError
-        If tensors and coords have different lengths
-        If input tensors have different dimension names
-        If non-concatenation dimensions don't match across inputs
-    KeyError
-        If concatenation dimension is not found in all coordinate systems
-    """
-    if len(tensors) != len(coords):
-        raise ValueError(
-            f"tensors and coords must have the same length, got {len(tensors)} tensors and {len(coords)} coords"
-        )
-
-    if len(tensors) == 0:
-        raise ValueError("at least one tensor and coord must be provided")
-
-    if len(tensors) == 1:
-        return tensors[0], deepcopy(coords[0])
-
-    # Use first coord as reference
-    ref_coord = coords[0]
-
-    # make sure cat dim is present in all tensors
-    handshake_dim(ref_coord, dim)
-
-    # make sure all tensors have the same dimension names
-    ref_keys = list(ref_coord.keys())
-    for i, coord in enumerate(coords[1:], start=1):
-        handshake_dim(coord, dim)
-        if not list(coord.keys()) == ref_keys:
-            raise ValueError(
-                f"all input tensors must have the same dimension names. "
-                f"Tensor 0 has {ref_keys}, tensor {i} has {list(coord.keys())}"
-            )
-
-    # make sure all the other dimensions are of equal length
-    other_dims = list(ref_keys)
-    other_dims.remove(dim)
-    for i, coord in enumerate(coords[1:], start=1):
-        handshake_coords(ref_coord, coord, other_dims)
-
-    # assemble output coords
-    coz = deepcopy(ref_coord)
-    coz[dim] = np.concatenate([coord[dim] for coord in coords])
-
-    # concatenate tensors
-    dim_index = list(coz).index(dim)
-    zz = torch.cat(tensors, dim=dim_index)
-
-    return zz, coz
diff --git a/earth2studio/utils/time.py b/earth2studio/utils/time.py
index e58b70cc0..e2b823892 100644
--- a/earth2studio/utils/time.py
+++ b/earth2studio/utils/time.py
@@ -17,7 +17,7 @@
 
 import numpy as np
 
-from earth2studio.utils.type import LeadTimeArray, TimeArray, TimeTolerance
+from earth2studio.utils.type import LeadTimeArray, TimeArray
 
 
 def timearray_to_datetime(time: TimeArray) -> list[datetime]:
@@ -95,64 +95,3 @@ def to_time_array(time: list[str] | list[datetime] | TimeArray) -> TimeArray:
             )
 
     return np.array(output).astype("datetime64[ns]")
-
-
-def normalize_time_tolerance(
-    tolerance: TimeTolerance,
-) -> tuple[np.timedelta64, np.timedelta64]:
-    """Normalize a TimeTolerance to a tuple of (lower_bound, upper_bound) as np.timedelta64.
-
-    Parameters
-    ----------
-    tolerance : TimeTolerance
-        Time tolerance value. Can be:
-        - A single timedelta or np.timedelta64 (interpreted as symmetric +/- bounds)
-        - A tuple of (timedelta, timedelta) or (np.timedelta64, np.timedelta64)
-          (interpreted as (lower_bound, upper_bound))
-
-    Returns
-    -------
-    tuple[np.timedelta64, np.timedelta64]
-        Tuple of (lower_bound, upper_bound) as np.timedelta64 values.
-        For a single tolerance value, returns (-tolerance, +tolerance).
-        For a tuple, returns the normalized bounds.
-
-    Examples
-    --------
-    >>> normalize_time_tolerance(np.timedelta64(1, "h"))
-    (numpy.timedelta64(-3600,'s'), numpy.timedelta64(3600,'s'))
-
-    >>> normalize_time_tolerance((np.timedelta64(-2, "h"), np.timedelta64(3, "h")))
-    (numpy.timedelta64(-7200,'s'), numpy.timedelta64(10800,'s'))
-    """
-    # Handle single value (symmetric tolerance)
-    if isinstance(tolerance, (timedelta, np.timedelta64)):
-        tol = np.timedelta64(tolerance)
-        return (-tol, tol)
-
-    # Handle tuple
-    if isinstance(tolerance, tuple):
-        if len(tolerance) != 2:
-            raise ValueError(
-                f"TimeTolerance tuple must have 2 elements, got {len(tolerance)}"
-            )
-
-        lower, upper = tolerance
-
-        # Convert both to np.timedelta64
-        lower_td = np.timedelta64(lower)
-        upper_td = np.timedelta64(upper)
-
-        # Ensure lower <= upper
-        if lower_td > upper_td:
-            raise ValueError(
-                f"TimeTolerance tuple must have lower_bound <= upper_bound, "
-                f"got ({lower_td}, {upper_td})"
-            )
-
-        return (lower_td, upper_td)
-
-    raise TypeError(
-        f"TimeTolerance must be timedelta, np.timedelta64, or tuple of 2, "
-        f"got {type(tolerance)}"
-    )
diff --git a/earth2studio/utils/type.py b/earth2studio/utils/type.py
index 2384d33d3..345f87078 100644
--- a/earth2studio/utils/type.py
+++ b/earth2studio/utils/type.py
@@ -15,11 +15,9 @@
 # limitations under the License.
 
 from collections import OrderedDict
-from datetime import timedelta
 
 import numpy as np
 
-FrameSchema = OrderedDict[str, np.ndarray]
 CoordSystem = OrderedDict[str, np.ndarray]
 
 # Specific dimension custom types
@@ -27,10 +25,3 @@
 LeadTimeArray = np.ndarray[np.timedelta64]
 TimeArray = np.ndarray[np.datetime64]
 FieldArray = np.ndarray[str]
-
-TimeTolerance = (
-    timedelta
-    | np.timedelta64
-    | tuple[timedelta, timedelta]
-    | tuple[np.timedelta64, np.timedelta64]
-)
diff --git a/examples/16_cbottle_super_resolution.py b/examples/16_cbottle_super_resolution.py
index fa11adc19..60209aa64 100644
--- a/examples/16_cbottle_super_resolution.py
+++ b/examples/16_cbottle_super_resolution.py
@@ -92,7 +92,6 @@
     package,
     output_resolution=(1024, 1024),
     super_resolution_window=super_resolution_window,
-    distilled_model=True,
 )
 
 # Load the infill model
diff --git a/examples/21_stormcast_sda.py b/examples/21_stormcast_sda.py
deleted file mode 100644
index 610038dd6..000000000
--- a/examples/21_stormcast_sda.py
+++ /dev/null
@@ -1,488 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# %%
-"""
-StormCast Score-Based Data Assimilation
-=======================================
-
-Running StormCast with guided diffusion posterior sampling to assimilate observations.
-
-This example demonstrates how to use the StormCast score-based data assimilation (SDA)
-model for convection-allowing regional forecasts that incorporate sparse in-situ
-observations using diffusion posterior sampling (DPS).
-Two forecasts are run—one without observations and one with ISD surface station data
-from central United States region to illustrate the impact of data assimilation.
-
-In this example you will learn:
-
-- How to load and initialise the StormCast score-based data assimilation (SDA) model
-- Fetching HRRR initial conditions and ISD surface observations
-- Running the model iteratively with and without observation assimilation
-- Comparing assimilated and non-assimilated forecasts
-"""
-# /// script
-# dependencies = [
-#   "earth2studio[da-stormcast] @ git+https://github.com/NVIDIA/earth2studio.git",
-#   "cartopy",
-# ]
-# ///
-
-# %%
-# Set Up
-# ------
-# This example requires the following components:
-#
-# - Assimilation Model: StormCast SDA :py:class:`earth2studio.models.da.StormCastSDA`.
-# - Datasource (state): HRRR analysis :py:class:`earth2studio.data.HRRR`.
-# - Datasource (obs): NOAA ISD surface stations :py:class:`earth2studio.data.ISD`.
-# - Datasource (conditioning): GFS forecasts :py:class:`earth2studio.data.GFS_FX`
-#   (loaded automatically by the model).
-#
-# StormCast score-based data assimilation (SDA) extends StormCast with diffusion
-# posterior sampling (DPS) guidance,
-# allowing sparse point observations to steer the generative diffusion process.
-
-# %%
-import os
-
-os.makedirs("outputs", exist_ok=True)
-from dotenv import load_dotenv
-
-load_dotenv()  # TODO: make common example prep function
-
-from collections import OrderedDict
-from datetime import timedelta
-
-import numpy as np
-import torch
-import xarray as xr
-from loguru import logger
-from tqdm import tqdm
-
-logger.remove()
-logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
-
-from earth2studio.data import HRRR, ISD, fetch_data
-from earth2studio.models.da import StormCastSDA
-from earth2studio.utils.coords import map_coords_xr
-
-package = StormCastSDA.load_default_package()
-# Load the model onto the GPU and configure SDA
-# sda_std_obs: assumed (normalized) observation noise std (lower = trust obs more)
-# sda_gamma: DPS guidance scaling factor (lower = stronger assimilation)
-model = StormCastSDA.load_model(package, sda_std_obs=0.1, sda_gamma=1e-4)
-model = model.to("cuda:0")
-
-hrrr = HRRR()
-
-# %%
-# Fetch Initial Conditions
-# ------------------------
-# Pull HRRR analysis data for April 3rd 2025, a date that saw a major tornado
-# outbreak across the central United States, and select the sub-grid that
-# StormCast expects. The model's :py:meth:`init_coords` describes the required
-# coordinate system.
-
-# %%
-init_time = np.array([np.datetime64("2025-04-03T18:00")])
-ic = model.init_coords()[0]
-
-x = fetch_data(
-    hrrr,
-    time=init_time,
-    variable=ic["variable"],
-    lead_time=np.array([np.timedelta64(0, "h")]),
-    device="cuda:0",
-    legacy=False,
-)
-x = map_coords_xr(x, ic)
-
-# %%
-# Run Without Observations
-# ------------------------
-# Step the model forward 6 hours without any observations. This is equivalent to using
-# the StormCast prognostic model as it will just use EDM diffusion sampling under the
-# hood.  Each call to ``model.send(None)`` advances the state by one hour.
-# We store only the surface variables used for comparison (u10m, v10m, t2m).
-
-# %%
-nsteps = 6
-plot_vars = ["u10m", "v10m", "t2m"]
-
-np.random.seed(42)
-torch.manual_seed(42)
-if torch.cuda.is_available():
-    torch.cuda.manual_seed_all(42)
-
-no_obs_frames = []
-gen = model.create_generator(x.copy())
-x_state = next(gen)  # Prime the generator, yields initial state
-
-for step in tqdm(range(nsteps), desc="No-obs forecast"):
-    logger.info(f"Running no-obs forecast step {step}")
-    x_state = gen.send(None)  # Advance one hour without observations
-    no_obs_frames.append(x_state.sel(variable=plot_vars).copy())
-
-gen.close()
-no_obs_da = xr.concat(no_obs_frames, dim="lead_time")
-
-# Save to Zarr (convert to numpy for storage)
-no_obs_np = no_obs_da.copy(data=no_obs_da.data.get())
-no_obs_np.to_dataset(name="prediction").to_zarr("outputs/21_no_obs.zarr", mode="w")
-
-# %%
-# Fetch Observations and Run With Assimilation
-# ---------------------------------------------
-# Fetch NOAA Integrated Surface Database (ISD) surface observations from the central
-# United States and assimilate them at each forecast step. The observations are fetched
-# for the valid time (initialisation time + lead time) so the model assimilates
-# temporally relevant data.
-
-# %%
-stations = ISD.get_stations_bbox((32.0, -105.0, 45.0, -90.0))
-isd = ISD(stations=stations, tolerance=timedelta(minutes=15), verbose=False)
-
-# %%
-# Plot ISD Station Locations
-# --------------------------
-# Visualise the ISD stations that will provide observations for assimilation.
-
-# %%
-import cartopy
-import cartopy.crs as ccrs
-import matplotlib.pyplot as plt
-
-# Fetch a sample to get station locations
-sample_df = isd(init_time, ["t2m", "u10m", "v10m"])
-station_lats = sample_df["lat"].values
-station_lons = sample_df["lon"].values
-
-plt.close("all")
-fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}, figsize=(8, 6))
-ax.set_extent([-110, -85, 30, 47], crs=ccrs.PlateCarree())
-ax.add_feature(
-    cartopy.feature.STATES.with_scale("50m"), linewidth=0.5, edgecolor="black"
-)
-ax.add_feature(cartopy.feature.LAND, facecolor="lightyellow")
-ax.gridlines(draw_labels=True, linewidth=0.3, alpha=0.5)
-ax.scatter(
-    station_lons,
-    station_lats,
-    s=20,
-    marker="x",
-    transform=ccrs.PlateCarree(),
-    zorder=3,
-)
-ax.set_title("ISD Station Locations - Central United States")
-plt.savefig("outputs/21_isd_stations.jpg", dpi=150, bbox_inches="tight")
-
-# %%
-# Run Inference With Streaming Observations
-# ------------------------------------------
-# At each forecast step, fetch NOAA ISD observations for the current valid time
-# and send them to the model generator. The observations will be used in the SDA
-# guidance term when sampling the diffusion model effectively steering the generated
-# result to align with the stations data from the ISD data based.
-
-# %%
-np.random.seed(42)
-torch.manual_seed(42)
-if torch.cuda.is_available():
-    torch.cuda.manual_seed_all(42)
-
-obs_frames = []
-gen = model.create_generator(x)
-x_state = next(gen)  # Prime the generator, yields initial state
-
-for step in tqdm(range(nsteps), desc="Obs forecast"):
-    # Fetch observations for the next forecast step's valid time using model coords
-    x_coords = OrderedDict({d: x_state.coords[d].values for d in x_state.dims})
-    oc = model.output_coords((x_coords,))[0]
-    valid_time = oc["time"] + oc["lead_time"]
-    obs_df = isd(valid_time, plot_vars)
-    logger.info(f"Running obs forecast step {step}, {len(obs_df)} obs")
-    x_state = gen.send(obs_df)  # Advance one hour with observations
-    obs_frames.append(x_state.sel(variable=plot_vars).copy())
-
-gen.close()
-obs_da = xr.concat(obs_frames, dim="lead_time")
-
-# Save to Zarr
-obs_np = obs_da.copy(data=obs_da.data.get())
-obs_np.to_dataset(name="prediction").to_zarr("outputs/21_with_obs.zarr", mode="w")
-
-# %%
-# Post Processing
-# ---------------
-# Compare the two forecasts.  The top row shows the baseline forecast (no
-# observations), the middle row shows the assimilated forecast with observation
-# station locations overlaid as unfilled circles, and the bottom row shows the
-# difference (assimilated minus baseline).
-
-# %%
-plt.close("all")
-variable = "u10m"
-
-# Load saved forecasts from Zarr stores
-no_obs_ds = xr.open_zarr("outputs/21_no_obs.zarr")
-obs_ds = xr.open_zarr("outputs/21_with_obs.zarr")
-
-no_obs_vals = no_obs_ds["prediction"].sel(variable=variable).values
-obs_vals = obs_ds["prediction"].sel(variable=variable).values
-
-# Lambert Conformal projection matching HRRR
-projection = ccrs.LambertConformal(
-    central_longitude=262.5,
-    central_latitude=38.5,
-    standard_parallels=(38.5, 38.5),
-    globe=ccrs.Globe(semimajor_axis=6371229, semiminor_axis=6371229),
-)
-
-fig, axes = plt.subplots(
-    3,
-    nsteps,
-    subplot_kw={"projection": projection},
-    figsize=(4 * nsteps, 8),
-)
-fig.subplots_adjust(wspace=0.02, hspace=0.08, left=0.1, right=0.9)
-
-for step in range(nsteps):
-    lead_hr = step + 1
-    no_obs_field = no_obs_vals[0, step]
-    obs_field = obs_vals[0, step]
-    diff_field = obs_field - no_obs_field
-
-    vmin = -10
-    vmax = 10
-    # Row 0: No-obs forecast
-    ax = axes[0, step]
-    im0 = ax.pcolormesh(
-        model.lon,
-        model.lat,
-        no_obs_field,
-        transform=ccrs.PlateCarree(),
-        cmap="PRGn",
-        vmin=vmin,
-        vmax=vmax,
-    )
-    ax.add_feature(
-        cartopy.feature.STATES.with_scale("50m"),
-        linewidth=0.5,
-        edgecolor="black",
-        zorder=2,
-    )
-    ax.set_title(f"+{lead_hr}h")
-
-    # Row 1: With-obs forecast + station locations
-    ax = axes[1, step]
-    im1 = ax.pcolormesh(
-        model.lon,
-        model.lat,
-        obs_field,
-        transform=ccrs.PlateCarree(),
-        cmap="PRGn",
-        vmin=vmin,
-        vmax=vmax,
-    )
-    ax.scatter(
-        station_lons,
-        station_lats,
-        s=8,
-        facecolors="none",
-        edgecolors="black",
-        linewidths=0.8,
-        transform=ccrs.PlateCarree(),
-        zorder=3,
-        label="Stations",
-    )
-    ax.add_feature(
-        cartopy.feature.STATES.with_scale("50m"),
-        linewidth=0.5,
-        edgecolor="black",
-        zorder=2,
-    )
-
-    # Row 2: Difference (assimilated - baseline)
-    ax = axes[2, step]
-    im2 = ax.pcolormesh(
-        model.lon,
-        model.lat,
-        diff_field,
-        transform=ccrs.PlateCarree(),
-        cmap="RdBu_r",
-        vmin=-3,
-        vmax=3,
-    )
-    ax.add_feature(
-        cartopy.feature.STATES.with_scale("50m"),
-        linewidth=0.5,
-        edgecolor="black",
-        zorder=2,
-    )
-
-axes[0, 0].text(
-    -0.07,
-    0.5,
-    "No Obs",
-    va="bottom",
-    ha="center",
-    rotation="vertical",
-    rotation_mode="anchor",
-    fontsize=12,
-    transform=axes[0, 0].transAxes,
-)
-axes[1, 0].text(
-    -0.07,
-    0.5,
-    "Obs",
-    va="bottom",
-    ha="center",
-    rotation="vertical",
-    rotation_mode="anchor",
-    fontsize=12,
-    transform=axes[1, 0].transAxes,
-)
-axes[2, 0].text(
-    -0.07,
-    0.5,
-    "Difference",
-    va="bottom",
-    ha="center",
-    rotation="vertical",
-    rotation_mode="anchor",
-    fontsize=12,
-    transform=axes[2, 0].transAxes,
-)
-
-# Add colour bars
-plt.colorbar(im0, ax=axes[0, -1], shrink=0.6, label=f"{variable} (m/s)")
-plt.colorbar(im1, ax=axes[1, -1], shrink=0.6, label=f"{variable} (m/s)")
-plt.colorbar(im2, ax=axes[2, -1], shrink=0.6, label=f"{variable} (m/s)")
-
-plt.tight_layout()
-plt.savefig("outputs/21_stormcast_sda_comparison.jpg", dpi=150)
-
-# %%
-# Ground Truth Comparison
-# -----------------------
-# Fetch HRRR analysis (ground truth) at each valid forecast time and compute
-# the absolute error of both the no-obs and obs forecasts. This shows whether
-# assimilation improves accuracy relative to the actual analysis.
-
-# %%
-truth = fetch_data(
-    hrrr,
-    time=init_time,
-    variable=np.array(plot_vars),
-    lead_time=np.array([np.timedelta64(h + 1, "h") for h in range(nsteps)]),
-    device="cpu",
-    legacy=False,
-)
-ic["variable"] = np.array(plot_vars)
-
-truth = map_coords_xr(truth, {"hrrr_y": ic["hrrr_y"], "hrrr_x": ic["hrrr_x"]})
-truth_vals = truth.sel(variable=variable).values  # [nsteps, hrrr_y, hrrr_x]
-no_obs_vals = no_obs_ds["prediction"].sel(variable=variable).values
-obs_vals = obs_ds["prediction"].sel(variable=variable).values
-
-# Compute absolute errors against ground truth
-no_obs_err = np.abs(no_obs_vals[0] - truth_vals[0])
-obs_err = np.abs(obs_vals[0] - truth_vals[0])
-
-# %%
-# Plot absolute errors between the StormCast predictions and HRRR analysis ground truth.
-# In later time-steps it is clear that StormCast with SDA sampling using ISD station
-# observations has improved accuracy over the vanilla stormcast prediction.
-
-# %%
-plt.close("all")
-fig, axes = plt.subplots(
-    2,
-    nsteps,
-    subplot_kw={"projection": projection},
-    figsize=(4 * nsteps, 6),
-)
-fig.subplots_adjust(wspace=0.02, hspace=0.08, left=0.1, right=0.9)
-
-err_max = 5
-for step in range(nsteps):
-    lead_hr = step + 1
-
-    # Row 0: No-obs absolute error
-    ax = axes[0, step]
-    im0 = ax.pcolormesh(
-        model.lon,
-        model.lat,
-        no_obs_err[step],
-        transform=ccrs.PlateCarree(),
-        cmap="viridis",
-        vmin=0,
-        vmax=err_max,
-    )
-    ax.add_feature(
-        cartopy.feature.STATES.with_scale("50m"),
-        linewidth=0.5,
-        edgecolor="grey",
-        zorder=2,
-    )
-    ax.set_title(f"+{lead_hr}h")
-
-    # Row 1: Obs absolute error
-    ax = axes[1, step]
-    im1 = ax.pcolormesh(
-        model.lon,
-        model.lat,
-        obs_err[step],
-        transform=ccrs.PlateCarree(),
-        cmap="viridis",
-        vmin=0,
-        vmax=err_max,
-    )
-    ax.add_feature(
-        cartopy.feature.STATES.with_scale("50m"),
-        linewidth=0.5,
-        edgecolor="grey",
-        zorder=2,
-    )
-
-axes[0, 0].text(
-    -0.07,
-    0.5,
-    "|No Obs - Truth|",
-    va="bottom",
-    ha="center",
-    rotation="vertical",
-    rotation_mode="anchor",
-    fontsize=12,
-    transform=axes[0, 0].transAxes,
-)
-axes[1, 0].text(
-    -0.07,
-    0.5,
-    "|Obs - Truth|",
-    va="bottom",
-    ha="center",
-    rotation="vertical",
-    rotation_mode="anchor",
-    fontsize=12,
-    transform=axes[1, 0].transAxes,
-)
-
-plt.colorbar(im0, ax=axes[0, -1], shrink=0.6, label=f"|Δ{variable}| (m/s)")
-plt.colorbar(im1, ax=axes[1, -1], shrink=0.6, label=f"|Δ{variable}| (m/s)")
-plt.tight_layout()
-plt.savefig("outputs/21_stormcast_sda_gt_comparison.jpg")
diff --git a/pyproject.toml b/pyproject.toml
index 40e8d7db1..e9d82c3fd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,8 +60,7 @@ Changelog = "https://github.com/NVIDIA/earth2studio/blob/main/CHANGELOG.md"
 data = [
     "cdsapi>=0.7.5",
     "cfgrib >= 0.9.10.3",
-    "eccodes>=2.39.2",
-    "eccodeslib>=2.39.2",
+    "eccodes>=2.38.0",
     "ecmwf-opendata>=0.3.3",
     "intake-esgf>=2025.9.26",
     "pandas<3.0", # https://github.com/esgf2-us/intake-esgf/issues/167
@@ -80,7 +79,8 @@ perturbation = [
     "scipy>=1.15.2",
 ]
 statistics = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 utils = [
     "scipy>=1.15.2",
@@ -93,19 +93,16 @@ serve = [
     "pydantic>=2.0.0",
     # Redis dependencies
     "redis>=5.0.0",
-    "types-redis",
     "hiredis>=2.0.0",
     "rq>=1.15.0",
     # Metrics
     "prometheus_client>=0.17.0",
     # Additional utilities
+    "python-multipart>=0.0.6",
+    "python-dotenv>=1.0.0",
     "aiofiles>=23.0.0",
     "httpx>=0.25.0",
     "hydra-core>=1.3.0",
-    "python-dotenv>=1.0.0",
-    "python-multipart>=0.0.6",
-    "requests>=2.25.0",
-    "urllib3>=1.26.0",
     # Object storage related
     "cryptography>=41.0.0",
     "multi-storage-client>=0.44.0",
@@ -115,6 +112,7 @@ serve = [
 # PX Models
 ace2 = [
     "fme",
+    "h5py<=3.14.0",
     "pandas>=2.3.2",
     "scipy>=1.15.2",
 ]
@@ -135,23 +133,27 @@ atlas = [
     "timm",
     "torch-harmonics",
     "einops",
-    "nvidia-physicsnemo>=1.3.0",
+    "nvidia-physicsnemo>=1.3.0,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 aurora = [
     "microsoft-aurora>=1.6.0",
 ]
 dlesym = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
     "earth2grid",
     "omegaconf>=2.3.0",
     "hydra-core>=1.3.2",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 dlwp = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
     "importlib_metadata",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 fcn = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 fcn3 = [
     "makani",
@@ -159,6 +161,7 @@ fcn3 = [
     "scipy>=1.10.0",
     "torch-harmonics>=0.8.0",
     "moviepy",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 fengwu = [
     "onnx",
@@ -178,7 +181,8 @@ graphcast = [
     "dm-tree>=0.1.9",
 ]
 interp-modafno = [
-    "nvidia-physicsnemo>=2.0",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 pangu = [
     "onnx",
@@ -195,17 +199,19 @@ sfno = [
 ]
 stormcast = [
     "einops>=0.8.1",
-    "nvidia-physicsnemo>=2.0",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
     "nvtx>=0.2.11",
     "omegaconf>=2.3.0",
     "scipy>=1.15.2",
     "pyproj>=3.7.1",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 stormscope = [
     "natten",
-    "nvidia-physicsnemo>=2.0",
+    "nvidia-physicsnemo==1.3.0",
     "nvtx>=0.2.11",
     "earth2studio[utils]",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 # DX Models
 cbottle = [
@@ -215,9 +221,10 @@ cbottle = [
 climatenet = []
 corrdiff = [
     "einops>=0.8.1",
-    "nvidia-physicsnemo>=2.0",
+    "nvidia-physicsnemo>=1.2.0,<2.0",
     "nvtx>=0.2.11",
     "scipy>=1.15.2",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 cyclone = [
     "cucim-cu12>=25.4.0",
@@ -226,55 +233,36 @@ cyclone = [
 ]
 derived = []
 precip-afno = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 precip-afno-v2 = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 solarradiation-afno = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 windgust-afno = [
-    "nvidia-physicsnemo>=1.0.1",
+    "nvidia-physicsnemo>=1.0.1,<2.0",
+    "pytz", # Remove on physicsnemo 2.0
 ]
 # DA Models
 da-interp = [
     "cupy-cuda12x<14.0.0",
     "cudf-cu12==26.2.*",
 ]
-da-stormcast = [
-    "earth2studio[stormcast]",
-    "cupy-cuda12x<14.0.0",
-    "cudf-cu12==26.2.*",
-]
 # All, must not have conflicts
 all = [
     "earth2studio[data,perturbation,statistics,utils]",
-    "earth2studio[aifs,aifsens,atlas,aurora,dlesym,dlwp,fcn,fcn3,fengwu,interp-modafno,pangu,stormcast,sfno,stormscope,graphcast]",
+    "earth2studio[ace2,aifs,aifsens,atlas,aurora,dlesym,dlwp,fcn,fcn3,fengwu,interp-modafno,pangu,sfno,stormcast,stormscope,graphcast]",
     "earth2studio[cbottle,climatenet,corrdiff,precip-afno,cyclone,precip-afno-v2,solarradiation-afno,windgust-afno]",
-    "earth2studio[da-interp,da-stormcast]",
 ]
 
 # ==== UV configuration ====
 [tool.uv]
-conflicts = [
-  [
-    { extra = "ace2" },
-    { extra = "atlas" },
-  ],
-  [
-    { extra = "ace2" },
-    { extra = "fcn3" },
-  ],
-  [
-    { extra = "ace2" },
-    { extra = "perturbation" },
-  ],
-  [
-    { extra = "ace2" },
-    { extra = "sfno" },
-  ]
-]
+conflicts = []
 default-groups = ["dev", "build"]
 # Needed for some packages that build with PEP 518
 no-build-isolation-package = ["earth2grid", "flash-attn", "natten", "torch-harmonics"]
@@ -287,19 +275,12 @@ natten = ["setuptools", "cmake", "torch"]
 
 [tool.uv.sources]
 cfgrib = { git = "https://github.com/NickGeneva/cfgrib.git", rev = "6a9af3173d7050b5fa5d221755f97e6ecb8c3433" } # https://github.com/ecmwf/cfgrib/issues/433
-cbottle = { git = "https://github.com/NVlabs/cBottle.git", rev = "8b8b358466e6b2f50d1779009790002ceb596e72" }
+cbottle = { git = "https://github.com/NVlabs/cBottle.git", rev = "1d4435abe730d6b966e983633b466b345d60ac78" }
 earth2grid = { git = "https://github.com/NVlabs/earth2grid.git", rev = "11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3" }
-makani = { git = "https://github.com/NVIDIA/makani.git", rev = "b38fcb2799d7dbc146fa60459f3f9823394a8bf1"}
+makani = { git = "https://github.com/NVIDIA/makani.git", rev = "fd1a62b9f27aebbc793f0cbafd838a3811de43fd"}
 graphcast = { git = "https://github.com/google-deepmind/graphcast.git", rev = "7077d40a36db6541e3ed72ccaed1c0d202fa6014" }
 pytest-testmon = { git = "https://github.com/NickGeneva/pytest-testmon.git", rev = "48cd5ed1b67bf2f2bd9f63335389f262db472806" }
 fme = { git = "https://github.com/ai2cm/ace.git", rev = "e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" }
-# Specific source for each of the current torch-harmonics models, avoiding ace2 conflicts until they update torch-harmonics
-torch-harmonics = [
-  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "perturbation"},
-  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "atlas"},
-  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "fcn3"},
-  { git = "https://github.com/NVIDIA/torch-harmonics.git", rev = "a632ca748a12bd9f74dbc1e00653317810991f74", extra = "sfno"},
-]
 
 [[tool.uv.index]]
 name = "pytorch-cpu"
@@ -333,7 +314,6 @@ dev = [
     "pytest-skip-slow>=0.0.5",
     "pytest-testmon>=2.1.3",
     "pyyaml>=6.0",
-    "types-redis",
     "types-requests",
     "ruff",
     "pytest-asyncio>=0.26.0",
@@ -352,7 +332,7 @@ docs = [
     "sphinx-togglebutton>=0.3.0",
     "myst-parser>=0.18.0",
     "sphinx-favicon>=1.0.1",
-    "nvidia-sphinx-theme"
+    "pydata-sphinx-theme==0.16.1"
 ]
 
 [tool.hatch.version]
@@ -382,9 +362,10 @@ exclude = []
 "docs/*.py" = ["F401"]
 "examples/*.py" = ["E402"]
 "recipes/**/*_notebook.py" = ["E402"]
+
 # Ignore `S101` (assertions) in all `test` files.
 "test/*.py" = ["S101"]
-"serve/server/test/*.py" = ["S101"]
+"serve/test/*.py" = ["S101"]
 
 # ==== Pytest configuration ====
 [tool.pytest.ini_options]
diff --git a/recipes/hens/cfg/storm_bernd.yaml b/recipes/hens/cfg/storm_bernd.yaml
index 22dc67eaf..678103470 100644
--- a/recipes/hens/cfg/storm_bernd.yaml
+++ b/recipes/hens/cfg/storm_bernd.yaml
@@ -29,7 +29,7 @@ data_source:
     _target_: earth2studio.data.GFS  # data source class
 
 file_output:
-    path: '/outputs'       # directory to which outfiles are written
+    path: '/output/'       # directory to which outfiles are written
     output_vars: ['t2m','u10m','r500','tp']
     thread_io: False      # write out in seperate thread (needs further testing)
     format:               # io backend class
diff --git a/recipes/hens/src/hens_ensemble.py b/recipes/hens/src/hens_ensemble.py
index d11e0622a..021ca2766 100644
--- a/recipes/hens/src/hens_ensemble.py
+++ b/recipes/hens/src/hens_ensemble.py
@@ -33,11 +33,12 @@
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, cat_coords, map_coords, split_coords
+from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
 from earth2studio.utils.time import to_time_array
 
 from .hens_utilities import (
     TCTracking,
+    cat_coords,
     get_batchid_from_ensid,
     save_corrdiff_output,
 )
@@ -388,14 +389,8 @@ def __call__(self) -> dict[str, IOBackend]:
                         yy, codia = map_coords(xx, coords, self.dx_ic_dict[dx_name])
                         yy, codib = dx_model(yy, codia)
 
-                        # map lat/lon of diagnostic model to forecast model (often there is difference in lat)
-                        _codib = OrderedDict(
-                            {_dim: coords[_dim] for _dim in ["lat", "lon"]}
-                        )
-                        yy, codib = map_coords(yy, codib, _codib)
-
-                        # concatenate tensors along variable dimension
-                        xx, coords = cat_coords((xx, yy), (coords, codib), "variable")
+                        # concatenate diagnostic variable to forecast vars
+                        xx, coords = cat_coords(xx, coords, yy, codib, "variable")
 
                     # --- CorrDiff models (run after each step, separately) ---
                     # For each CorrDiff model, run the downscaling, collect outputs and coordinates,
diff --git a/recipes/hens/src/hens_utilities.py b/recipes/hens/src/hens_utilities.py
index a3e26d6a8..8e5a108eb 100644
--- a/recipes/hens/src/hens_utilities.py
+++ b/recipes/hens/src/hens_utilities.py
@@ -34,6 +34,7 @@
 from earth2studio.models.dx import CorrDiff
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
+from earth2studio.utils.coords import CoordSystem, map_coords
 from earth2studio.utils.time import to_time_array
 
 from .hens_utilities_reproduce import (
@@ -1023,6 +1024,55 @@ def get_batchid_from_ensid(
     return batch_id
 
 
+def cat_coords(
+    xx: torch.Tensor,
+    cox: CoordSystem,
+    yy: torch.Tensor,
+    coy: CoordSystem,
+    dim: str = "variable",
+) -> tuple[torch.Tensor, CoordSystem]:
+    """
+    concatenate data along coordinate dimension.
+
+    Parameters
+    ----------
+    xx : torch.Tensor
+        First input tensor which to concatenate
+    cox : CoordSystem
+        Ordered dict representing coordinate system that describes xx
+    yy : torch.Tensor
+        Second input tensor which to concatenate
+    coy : CoordSystem
+        Ordered dict representing coordinate system that describes yy
+    dim : str
+        name of dimension along which to concatenate
+
+    Returns
+    -------
+    tuple[torch.Tensor, CoordSystem]
+        Tuple containing output tensor and coordinate OrderedDict from
+        concatenated data.
+    """
+
+    if dim not in cox:
+        raise ValueError(f"dim {dim} is not in coords: {list(cox)}.")
+    if dim not in coy:
+        raise ValueError(f"dim {dim} is not in coords: {list(coy)}.")
+
+    # fix difference in latitude
+    _cox = cox.copy()
+    _cox["lat"] = coy["lat"]
+    xx, cox = map_coords(xx, cox, _cox)
+
+    coords = cox.copy()
+    dim_index = list(coords).index(dim)
+
+    zz = torch.cat((xx, yy), dim=dim_index)
+    coords[dim] = np.append(cox[dim], coy[dim])
+
+    return zz, coords
+
+
 def save_corrdiff_output(cd_output_dict: dict, save_path: str) -> None:
     """
     Save CorrDiff model output to a NetCDF file as a Dataset with one variable per CorrDiff output.
diff --git a/recipes/s2s/src/s2s_ensemble.py b/recipes/s2s/src/s2s_ensemble.py
index 9dcb70ab4..7d2beaed7 100644
--- a/recipes/s2s/src/s2s_ensemble.py
+++ b/recipes/s2s/src/s2s_ensemble.py
@@ -18,7 +18,6 @@
 from concurrent.futures import Future, ThreadPoolExecutor
 from datetime import datetime
 from math import ceil
-from typing import cast
 
 import numpy as np
 import torch
@@ -30,11 +29,12 @@
 from earth2studio.models.dx import DiagnosticModel
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import CoordSystem, cat_coords, map_coords, split_coords
+from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
 from earth2studio.utils.time import to_time_array
 
 from .s2s_utilities import (
     calculate_torch_seed,
+    cat_coords,
     get_batchid_from_ensid,
     run_with_rank_ordered_execution,
 )
@@ -184,16 +184,12 @@ def fetch_ics(
             IC times
         """
         self.time = to_time_array(time)
-        self.x0, self.coords0 = cast(
-            tuple[torch.Tensor, CoordSystem],
-            fetch_data(
-                source=data,
-                time=time,
-                variable=self.prognostic_ic["variable"],
-                lead_time=self.prognostic_ic["lead_time"],
-                device="cpu",
-                legacy=True,
-            ),
+        self.x0, self.coords0 = fetch_data(
+            source=data,
+            time=time,
+            variable=self.prognostic_ic["variable"],
+            lead_time=self.prognostic_ic["lead_time"],
+            device="cpu",
         )
         logger.success(f"Fetched data from {data.__class__.__name__}")
 
@@ -333,7 +329,7 @@ def write(self, xx_sub: torch.Tensor, coords_sub: CoordSystem) -> None:
         for k in self.io_dict.keys():
             if self.writer_executor is not None:
                 future = self.writer_executor.submit(
-                    self.io_dict[k].write, *split_coords(xx_sub, coords_sub)  # type: ignore[arg-type]
+                    self.io_dict[k].write, *split_coords(xx_sub, coords_sub)
                 )
                 self.writer_threads.append(future)
             else:
@@ -375,7 +371,7 @@ def __call__(self) -> dict[str, ZarrBackend]:
                         yy, codib = dx_model(yy, codia)
 
                         # concatenate diagnostic variable to forecast vars
-                        xx, coords = cat_coords((xx, yy), (coords, codib), "variable")
+                        xx, coords = cat_coords(xx, coords, yy, codib, "variable")
 
                     # pass output variables to io backend
                     for k in self.io_dict.keys():
diff --git a/recipes/s2s/src/s2s_utilities.py b/recipes/s2s/src/s2s_utilities.py
index c27655c78..227a5e688 100644
--- a/recipes/s2s/src/s2s_utilities.py
+++ b/recipes/s2s/src/s2s_utilities.py
@@ -38,7 +38,7 @@
 from earth2studio.models.auto import Package
 from earth2studio.models.px import PrognosticModel
 from earth2studio.perturbation import Perturbation
-from earth2studio.utils.coords import handshake_coords
+from earth2studio.utils.coords import CoordSystem, handshake_coords, map_coords
 from earth2studio.utils.time import to_time_array
 
 
@@ -943,6 +943,55 @@ def get_batchid_from_ensid(
     return batch_id
 
 
+def cat_coords(
+    xx: torch.Tensor,
+    cox: CoordSystem,
+    yy: torch.Tensor,
+    coy: CoordSystem,
+    dim: str = "variable",
+) -> tuple[torch.Tensor, CoordSystem]:
+    """
+    concatenate data along coordinate dimension.
+
+    Parameters
+    ----------
+    xx : torch.Tensor
+        First input tensor which to concatenate
+    cox : CoordSystem
+        Ordered dict representing coordinate system that describes xx
+    yy : torch.Tensor
+        Second input tensor which to concatenate
+    coy : CoordSystem
+        Ordered dict representing coordinate system that describes yy
+    dim : str
+        name of dimension along which to concatenate
+
+    Returns
+    -------
+    tuple[torch.Tensor, CoordSystem]
+        Tuple containing output tensor and coordinate OrderedDict from
+        concatenated data.
+    """
+
+    if dim not in cox:
+        raise ValueError(f"dim {dim} is not in coords: {list(cox)}.")
+    if dim not in coy:
+        raise ValueError(f"dim {dim} is not in coords: {list(coy)}.")
+
+    # fix difference in latitude
+    _cox = cox.copy()
+    _cox["lat"] = coy["lat"]
+    xx, cox = map_coords(xx, cox, _cox)
+
+    coords = cox.copy()
+    dim_index = list(coords).index(dim)
+
+    zz = torch.cat((xx, yy), dim=dim_index)
+    coords[dim] = np.append(cox[dim], coy[dim])
+
+    return zz, coords
+
+
 def calculate_torch_seed(s: str) -> int:
     """Calculates torch seed based on a given string.
     String s is used as input to sha256 hash algorithm.
diff --git a/serve/Dockerfile b/serve/Dockerfile
index d0d368855..6c9cde587 100644
--- a/serve/Dockerfile
+++ b/serve/Dockerfile
@@ -11,6 +11,7 @@ ENV EARTH2STUDIO_PACKAGE_TIMEOUT=1200
 ENV FORCE_CUDA_EXTENSION=1
 ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 
+ARG MAKANI_HASH=28f38e3e929ed1303476518552c64673bbd6f722
 ARG E2G_HASH=11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3
 ARG E2S_HASH=8ca8771c6be6ca051e082d41b99378fa14b58d9f
 
@@ -18,31 +19,21 @@ WORKDIR /workspace
 
 COPY --from=ghcr.io/astral-sh/uv:0.7.3 /uv /uvx /bin/
 RUN uv pip install --system --break-system-packages --no-build-isolation torch-harmonics==0.8.0
+RUN uv pip install --system --break-system-packages "makani @ git+https://github.com/NVIDIA/makani.git@${MAKANI_HASH}"
 RUN uv pip install --system --break-system-packages --no-build-isolation "earth2grid @ git+https://github.com/NVlabs/earth2grid@${E2G_HASH}"
 RUN uv pip install --system --break-system-packages natten==0.21.5+torch2100cu130 -f https://whl.natten.org
+RUN uv pip install --system --break-system-packages "earth2studio[dlwp,dlesym,pangu,sfno,stormcast,fcn3] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
+RUN uv pip install --system --break-system-packages "earth2studio[cbottle,corrdiff,cyclone] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
+RUN uv pip install --system --break-system-packages "earth2studio[data,perturbation,statistics] @ git+https://github.com/NVIDIA/earth2studio.git@${E2S_HASH}"
 RUN uv pip install --system --break-system-packages cartopy xskillscore matplotlib
 
-# Install the requirements for stormcast.
-RUN pip uninstall -y torch-harmonics && \
-    FORCE_CUDA_EXTENSION=1 uv pip install --system --break-system-packages --no-build-isolation torch-harmonics==0.8.0
-
 # Install the requirements for the inference server.
 COPY serve /workspace/earth2studio-project/serve
-COPY examples /workspace/earth2studio-project/examples
-RUN ln -s /outputs /workspace/earth2studio-project/examples/outputs
-# Config and scripts live under serve/server/ after COPY serve
-ENV SCRIPT_DIR=/workspace/earth2studio-project/serve/server/scripts
-ENV CONFIG_DIR=/workspace/earth2studio-project/serve/server/conf
-ENV WORKFLOW_DIR=/workspace/earth2studio-project/serve/server/example_workflows
-
-# Install local earth2studio from repo, then various extras and serve requirements.
-# Copy project root files needed for pip install (pyproject.toml is at repo root, not inside earth2studio).
-COPY pyproject.toml README.md /workspace/earth2studio-project/
-COPY earth2studio /workspace/earth2studio-project/earth2studio
+RUN cd / && mkdir /workspace/earth2studio-project/examples && ln -s /outputs /workspace/earth2studio-project/examples/outputs
 RUN apt-get update && apt-get install -y redis-server && \
     cd /workspace/earth2studio-project && \
-    uv pip install --system --break-system-packages ".[dlwp,dlesym,pangu,sfno,stormcast,fcn3,serve,cbottle,corrdiff,cyclone,data,perturbation,statistics]" && \
-    uv pip install --system --break-system-packages -r serve/server/requirements.txt && \
-    rm -rf /workspace/earth2studio-project/earth2studio /workspace/earth2studio-project/pyproject.toml /workspace/earth2studio-project/README.md
+    uv pip install --system --break-system-packages -r serve/inferenceserver/requirements.txt
+
+CMD ["/workspace/earth2studio-project/serve/inferenceserver/scripts/startup.sh"]
 
-CMD ["/workspace/earth2studio-project/serve/server/scripts/startup.sh"]
+#RUN python -c "import earth2studio; print(f'Earth2Studio version: {earth2studio.__version__}')"
diff --git a/serve/client/README.md b/serve/client/README.md
deleted file mode 100644
index 5fff2298d..000000000
--- a/serve/client/README.md
+++ /dev/null
@@ -1,442 +0,0 @@
-# Earth2Studio Python Client SDK
-
-A Python client library for the Earth2Studio REST API.
-
-The client SDK provides two interfaces to remotely access the Earth2Studio API server:
-
-1. **`earth2studio.serve.client.e2client.RemoteEarth2Workflow`** - High-level interface for seamless
-   integration with Earth2Studio and Xarray. Recommended for most use cases.
-2. **`earth2studio.serve.client.client.Earth2StudioClient`** - Low-level interface for direct API access
-   and result file management. Useful for custom workflows and integrations.
-
-Please refer to the server's `README_workflows.md` and `README_earth2workflows.md` for
-information about the server-side interface.
-
-## Quick Start
-
-To use a remote workflow, first initialize the `RemoteEarth2Workflow` object and then
-call it using the workflow parameters. Here, we access the `DeterministicEarth2Workflow`
-included in the server examples and get the results as an Earth2Studio data source.
-
-```python
-from datetime import datetime, timedelta
-import os
-from earth2studio.serve.client.e2client import RemoteEarth2Workflow
-
-# Create client (configurable via environment variable)
-api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-workflow = RemoteEarth2Workflow(
-    api_url,
-    workflow_name="deterministic_earth2_workflow",
-    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
-)
-
-data_source = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_data_source()
-# get temperature forecast 24 h after start time
-t2m = data_source(datetime(2025, 8, 22, 6), "t2m")
-t2m_np = t2m.values  # get the results as a NumPy array
-```
-
-Data access is lazy if the workflow produces Zarr files: In the above example, only the
-temperature will be downloaded and the actual download happens on the last line of code.
-
-## Advanced Usage
-
-In the examples below, we initialize `workflow` as in the Quick Start above,
-unless otherwise mentioned.
-
-### Access the result dataset directly
-
-You can directly access the underlying Xarray Dataset:
-
-```python
-ds = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_dataset()
-# same result as getting data from `data_source` in the Quick Start example
-t2m = ds["t2m"].sel(lead_time=np.timedelta64(timedelta(hours=24)))
-t2m_np = t2m.values  # NumPy array
-```
-
-### Iterate over results
-
-Similar to Earth2Studio model iterators, we can iterate over the results as
-`(Tensor, CoordSystem)` tuples:
-
-```python
-model = workflow(start_time=[datetime(2025, 8, 21, 6)]).as_model()
-for (x, coords) in model.create_iterator():
-    # computes the mean on GPU if we used `device='cuda'` when creating the Workflow
-    print(x.mean().cpu().numpy())
-```
-
-This can be used to provide an input to a local workflow that expects a prognostic
-model. The example `diagnostic_analysis.py` shows how to use this to run a local
-diagnostic.
-
-### Save reference to remote results
-
-You can create a `RemoteEarth2WorkflowResult` manually in order to access the results later.
-This allows you to do other work and even close Python while waiting for results to complete.
-
-```python
-result = workflow(start_time=[datetime(2025, 8, 21, 6)])
-print(result.execution_id)  # write down the execution id
-exec_1766159252_5f779460
-
-# *** After restarting Python ***
-
-from datetime import datetime
-import os
-from earth2studio.serve.client.e2client import RemoteEarth2Workflow, RemoteEarth2WorkflowResult
-
-# re-initialize workflow
-api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-workflow = RemoteEarth2Workflow(
-    api_url,
-    workflow_name="deterministic_earth2_workflow",
-    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
-)
-
-# manually create result object with the execution_id from earlier
-execution_id = "exec_1766159252_5f779460"
-result = RemoteEarth2WorkflowResult(workflow, execution_id)
-
-# now we can access the results as before
-data_source = result.as_data_source()  # will block until results are available
-t2m = data_source(datetime(2025, 8, 22, 6), "t2m")
-```
-
-### Use remote workflow to condition a local model
-
-Use the results as a low-resolution conditioning for a StormCast model running locally:
-
-```python
-from earth2studio.data import HRRR
-from earth2studio.models.px import StormCast
-from earth2studio.io import NetCDF4Backend
-
-fcn3_workflow = RemoteEarth2Workflow(
-    api_url,
-    workflow_name="stormcast_fcn3_workflow",
-    device='cuda'  # use 'cpu' if you don't have a GPU on the client machine
-)
-
-hrrr_ic = HRRR()
-stormcast = StormCast.from_pretrained()
-io = NetCDF4Backend("stormcast_result.nc")
-conditioning_source = fcn3_workflow(
-    start_time=[datetime(2025, 8, 21, 6)],
-    num_hours=num_steps,
-    run_stormcast=False
-).as_data_source()
-stormcast.conditioning_data_source = conditioning_source
-num_hours=5
-
-run.deterministic(
-    [datetime(2025, 8, 21, 6)],
-    num_hours,
-    stormcast,
-    hrrr_ic,
-    io,
-    device='cuda',
-)
-```
-
-### Object Storage (S3) Support
-
-The client SDK seamlessly supports both local server storage and object storage
-(S3/CloudFront). When the server is configured to use object storage, all the examples
-in this README work without any code changes. The client automatically detects the
-storage type from the server response and handles downloads appropriately:
-
-- **Server storage**: Files are downloaded directly from the inference server
-- **S3 storage**: Files are downloaded from S3 using pre-signed CloudFront URLs
-
-This means you can use the same code regardless of how the server is configured:
-
-```python
-# Same whether server uses local or S3 storage
-result = workflow(start_time=[datetime(2025, 8, 21, 6)])
-ds = result.as_dataset()  # Fetches from S3 if configured
-```
-
-### Download result files directly
-
-`Earth2StudioClient` gives lower-level access than `RemoteEarth2Workflow`. Use it to
-download individual result files programmatically.
-
-Initialize the client:
-
-```python
-import os
-from datetime import datetime
-from earth2studio.serve.client.client import Earth2StudioClient, InferenceRequest
-
-# Create client (configurable via environment variable)
-api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-client = Earth2StudioClient(api_url, workflow_name="deterministic_earth2_workflow")
-
-# Check API health
-health = client.health_check()
-print(f"API Status: {health.status}")
-```
-
-Now, submit a workflow and download result files directly:
-
-```python
-parameters = {"start_time": [datetime(2025, 8, 21, 6)]}
-request = InferenceRequest(parameters=parameters)
-request_result = client.run_inference_sync(request)
-
-# Download individual output files (works with both local and S3 storage)
-for file in request_result.output_files[:5]:  # limit to first 5 files
-    print(f"Downloading: {file.path}")
-    content = client.download_result(request_result, file.path)
-    print(f"Downloaded {file.path}: {len(content.getvalue())} bytes")
-
-    # Optionally save to disk
-    # with open(file.path.split("/")[-1], "wb") as f:
-    #     f.write(content.getvalue())
-```
-
-To get the URL of a result file for use with external tools like `wget` (server storage only):
-
-```python
-from urllib.parse import urljoin
-
-result_root = client.result_root_path(request_result)
-result_path = request_result.result_paths()[0]
-
-result_url = urljoin(api_url, f"{result_root}{result_path}")
-print(result_url)
-# e.g. http://localhost:8000/v1/infer/.../results/.../results.zarr
-```
-
-### Asynchronous usage of `Earth2StudioClient`
-
-For long-running forecasts, you can submit requests and check status separately:
-
-```python
-import time
-from earth2studio.serve.client.models import RequestStatus
-
-# Submit request
-response = client.submit_inference_request(request)
-execution_id = response.execution_id
-print(f"Submitted request: {execution_id}")
-
-# Check status periodically
-while True:
-    status = client.get_request_status(execution_id)
-    print(f"Status: {status.status.value}, Step: {status.progress.current_step}")
-
-    if status.status in [RequestStatus.COMPLETED, RequestStatus.FAILED]:
-        break
-
-    time.sleep(10)
-
-# Get results when completed
-if status.status == RequestStatus.COMPLETED:
-    request_result = client.get_request_results(execution_id)
-    print(f"Forecast completed with {len(request_result.output_files)} output files")
-
-# Now we can use ``request_result`` as in the above example
-```
-
-### Authentication
-
-If your server requires authentication, you can pass a Bearer token to the client:
-
-```python
-# With authentication token
-client = Earth2StudioClient(
-    base_url="http://localhost:8000",
-    workflow_name="deterministic_earth2_workflow",
-    token="your-api-token"  # Bearer token for authenticated requests
-)
-
-workflow = RemoteEarth2Workflow(
-    base_url="http://localhost:8000",
-    workflow_name="deterministic_earth2_workflow",
-    token="your-api-token"
-)
-```
-
-The token is sent as a Bearer token in the `Authorization` header with all API requests.
-
-### Custom HTTP Settings
-
-You can modify the settings for HTTP access. The HTTP-related parameters are
-identical for `Earth2StudioClient` and `RemoteEarth2Workflow`.
-
-```python
-client = Earth2StudioClient(
-    base_url="http://localhost:8000",
-    timeout=60.0,           # Request timeout
-    max_retries=5,          # Maximum retries
-    retry_backoff_factor=1.0, # Retry backoff factor
-    token="your-api-token"  # Optional authentication token
-)
-
-workflow = RemoteEarth2Workflow(
-    base_url="http://localhost:8000",
-    timeout=60.0,           # Request timeout
-    max_retries=5,          # Maximum retries
-    retry_backoff_factor=1.0, # Retry backoff factor
-    token="your-api-token"  # Optional authentication token
-)
-```
-
-## API Reference
-
-### RemoteEarth2Workflow
-
-High-level client for Earth2Studio-compatible remote inference workflows.
-
-#### RemoteEarth2Workflow constructor
-
-```python
-RemoteEarth2Workflow(
-    base_url: str,
-    workflow_name: str,
-    device: str | torch.device | None = None,
-    xr_args: dict[str, Any] | None = None,
-    **client_kwargs
-)
-```
-
-**Args:**
-
-- `base_url`: URL of the Earth2Studio API server
-- `workflow_name`: Name of the workflow to execute on the server
-- `device`: Device for tensor operations (e.g., "cuda", "cpu").
-- `xr_args`: Additional keyword arguments passed to xarray.open_dataset/xarray.open_zarr
-- `**client_kwargs`: Additional arguments passed to Earth2StudioClient
-
-#### RemoteEarth2Workflow methods
-
-- `__call__(**kwargs)` → `RemoteEarth2WorkflowResult`: Submit inference with parameters
-- `to(device)` → `RemoteEarth2Workflow`: Move workflow to specified device
-
-### RemoteEarth2WorkflowResult
-
-Result object for accessing remote inference outputs.
-
-#### Attributes
-
-- `execution_id`: Unique identifier for the inference execution
-- `workflow`: Parent RemoteEarth2Workflow instance
-
-#### RemoteEarth2WorkflowResult methods
-
-- `as_dataset()` → `xr.Dataset`: Wait for completion and return result as xarray Dataset
-- `as_data_source()` → `InferenceOutputSource`: Wait for completion and return as Earth2Studio data source
-- `as_model(iter_coord="lead_time")` → `InferenceOutputModel`: Model iterable over time steps
-
-### Earth2StudioClient
-
-Low-level client class for direct API access and result file management.
-
-#### Earth2StudioClient constructor
-
-```python
-Earth2StudioClient(
-    base_url: str = "http://localhost:8000",
-    workflow_name: str = "deterministic_earth2_workflow",
-    timeout: float = 30.0,
-    max_retries: int = 3,
-    retry_backoff_factor: float = 0.3,
-    token: str | None = None
-)
-```
-
-**Args:**
-
-- `base_url`: URL of the Earth2Studio API server
-- `workflow_name`: Name of the workflow to execute on the server
-- `timeout`: Request timeout in seconds
-- `max_retries`: Maximum number of retries for failed requests
-- `retry_backoff_factor`: Backoff factor between retries
-- `token`: Optional Bearer token for authenticated requests
-
-#### Earth2StudioClient methods
-
-- `health_check()` → `HealthStatus`: Check API health
-- `submit_inference_request(request)` → `InferenceRequestResponse`: Submit request
-- `get_request_status(request_id)` → `InferenceRequestStatus`: Get status
-- `get_request_results(request_id, timeout=None)` → `InferenceRequestResults`: Get results
-- `wait_for_completion(request_id, poll_interval=5.0, timeout=None)` →
-  `InferenceRequestResults`: Wait for completion
-- `run_inference_sync(request, poll_interval=5.0, timeout=None)` → `InferenceRequestResults`:
-  Submit and wait for completion
-- `result_root_path(result)` → `str`: Get root URL path for result files
-- `download_result(result, path, timeout=None)` → `io.BytesIO`: Download a result file
-
-### Data Models
-
-- `InferenceRequest`: Inference request payload with workflow parameters
-- `InferenceRequestResponse`: Response when submitting a request (contains execution_id)
-- `InferenceRequestStatus`: Status information for an inference request
-- `InferenceRequestResults`: Results of a completed request with output file paths
-- `OutputFile`: Information about an output file (name, path, size)
-- `HealthStatus`: API health status response
-- `ProgressInfo`: Progress information (progress string, current_step, total_steps)
-
-### Enums
-
-- `RequestStatus`: Request status values
-  - Initial: `ACCEPTED`, `QUEUED`
-  - Processing: `RUNNING`
-  - Final: `COMPLETED`, `FAILED`, `CANCELLED`, `PENDING_RESULTS`
-
-## Examples
-
-The `examples/` directory contains complete, runnable example scripts:
-
-### `basic_forecast.py` - Basic Deterministic Forecast
-
-A comprehensive example showing how to run a simple deterministic forecast using
-`RemoteEarth2Workflow`.
-
-### `downscaled_forecast.py` - Downscaled Forecast
-
-Example demonstrating high-resolution downscaled forecasting using a remote global
-forecast model with a local downscaling model.
-
-### `diagnostic_analysis.py` - Diagnostic Analysis
-
-Specialized example for running local diagnostic models with remote inference results.
-
-### Running the Examples
-
-Each example can be run independently:
-
-```bash
-cd examples/
-
-# Run basic deterministic forecast
-python basic_forecast.py
-
-# Run downscaled forecast
-python downscaled_forecast.py
-
-# Run diagnostic analysis
-python diagnostic_analysis.py
-```
-
-**Prerequisites:**
-
-- Earth2Studio API server running
-- Client SDK installed with dependencies
-- Sufficient disk space for forecast outputs
-
-**Configuration:**
-
-All examples support configuring the API endpoint via environment variable:
-
-```bash
-# Set custom API endpoint (optional)
-export EARTH2STUDIO_API_URL="http://your-api-server:8000"
-
-# Or use default localhost:8000 if not set
-python basic_forecast.py
-```
diff --git a/serve/client/examples/basic_forecast.py b/serve/client/examples/basic_forecast.py
deleted file mode 100644
index a2cd9236f..000000000
--- a/serve/client/examples/basic_forecast.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Basic deterministic forecast example using Earth2Studio Client SDK.
-
-This example shows how to:
-1. Access a remote workflow
-2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
-3. Extract 2-meter temperature data at a specific location
-4. Create and save a matplotlib line plot of the forecast
-"""
-
-import os
-from datetime import datetime
-
-import matplotlib.pyplot as plt
-import torch
-
-from earth2studio.serve.client.e2client import RemoteEarth2Workflow
-
-# /// script
-# dependencies = [
-#   "torch",
-#   "earth2studio[serve]>=0.9.0",
-#   "matplotlib>=3.3.0",
-# ]
-# ///
-
-
-def main(
-    plot_file: str = "t2m_plot.png",
-    lat: float = 37.4,
-    lon: float = -122.0,
-    start_time: datetime = datetime(2025, 8, 21, 6),
-    num_steps: int = 10,
-) -> None:
-    """Run a basic deterministic forecast and save a t2m plot.
-
-    Args:
-        plot_file: Path to save the temperature plot (default: 't2m_plot.png')
-        lat: Latitude for temperature extraction (default: 37.4)
-        lon: Longitude for temperature extraction (default: -122.0)
-        start_time: Forecast initialisation time (default: 2025-08-21 06:00 UTC)
-        num_steps: Number of forecast time steps (default: 10)
-    """
-
-    # Create client (configurable via environment variable)
-    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
-    workflow = RemoteEarth2Workflow(
-        api_url,
-        workflow_name="deterministic_earth2_workflow",
-        device="cuda" if torch.cuda.is_available() else "cpu",
-        token=api_token,
-    )
-
-    # Check if API is healthy
-    try:
-        health = workflow.client.health_check()
-        print(f"✓ API Status: {health.status}")
-    except Exception as e:
-        print(f"✗ API not available: {e}")
-        return
-
-    # Call workflow and get result
-    try:
-        ds = workflow(start_time=[start_time], num_steps=num_steps).as_dataset()
-    except Exception as e:
-        print(f"\n❌ Forecast failed: {e}")
-        return
-
-    # Extract t2m data for the specified location
-    t2m = ds["t2m"].sel(lat=lat, lon=lon, method="nearest").values.ravel()
-
-    # Extract time coordinate
-    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
-
-    # Create line plot of temperature
-    print(f"   Creating plot: {plot_file}")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.plot(time_coord, t2m, marker="o", linewidth=2, markersize=6)
-    ax.set_xlabel("Lead Time (h)", fontsize=12)
-    ax.set_ylabel("2-meter Temperature (K)", fontsize=12)
-    ax.set_title(
-        f"Temperature Forecast starting {start_time} at {lat}°N, {lon}°E",
-        fontsize=14,
-        fontweight="bold",
-    )
-    ax.grid(True, alpha=0.3)
-
-    # Save the plot
-    fig.tight_layout()
-    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-
-    print("\n🎉 Forecast complete!")
-    print(f"   Plot saved to: {plot_file}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/serve/client/examples/diagnostic_analysis.py b/serve/client/examples/diagnostic_analysis.py
deleted file mode 100644
index d4040b0e8..000000000
--- a/serve/client/examples/diagnostic_analysis.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Forecast example with local diagnostic using Earth2Studio Client SDK.
-
-This example shows how to:
-1. Access a remote workflow
-2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
-3. Run precipitation diagnostic model locally to get precipitation
-4. Create and save a matplotlib line plot of the precipitation forecast
-"""
-
-import os
-from datetime import datetime, timedelta
-
-import matplotlib.pyplot as plt
-import torch
-import xarray as xr
-
-from earth2studio.io import XarrayBackend  # type: ignore[import-untyped]
-from earth2studio.models.dx import PrecipitationAFNO  # type: ignore[import-untyped]
-from earth2studio.run import diagnostic  # type: ignore[import-untyped]
-from earth2studio.serve.client.e2client import RemoteEarth2Workflow
-from earth2studio.utils.type import (  # type: ignore[import-untyped]
-    LeadTimeArray,
-    TimeArray,
-    VariableArray,
-)
-
-# /// script
-# dependencies = [
-#   "torch",
-#   "earth2studio[serve]>=0.9.0",
-#   "matplotlib>=3.3.0",
-# ]
-# ///
-
-
-def main(
-    plot_file: str = "tp_plot.png",
-    lat: float = 37.4,
-    lon: float = -122.0,
-    start_time: datetime = datetime(2025, 8, 21, 6),
-    num_steps: int = 10,
-) -> None:
-    """Run a basic deterministic forecast and save a t2m plot.
-
-    Args:
-        plot_file: Path to save the precipitation plot (default: 'tp_plot.png')
-        lat: Latitude for precipitation extraction (default: 37.4)
-        lon: Longitude for precipitation extraction (default: -122.0)
-        start_time: Start time for the forecast (default: 2025-08-21 06:00 UTC)
-        num_steps: Number of forecast time steps (default: 10)
-    """
-
-    # Create client (configurable via environment variable)
-    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
-    workflow = RemoteEarth2Workflow(
-        api_url,
-        workflow_name="deterministic_earth2_workflow",
-        device="cuda" if torch.cuda.is_available() else "cpu",
-        token=api_token,
-    )
-
-    # Check if API is healthy
-    try:
-        health = workflow.client.health_check()
-        print(f"✓ API Status: {health.status}")
-    except Exception as e:
-        print(f"✗ API not available: {e}")
-        return
-
-    # Call workflow and get result as an iterable model
-    try:
-        model = workflow(start_time=[start_time], num_steps=num_steps).as_model()
-    except Exception as e:
-        print(f"\n❌ Forecast failed: {e}")
-        return
-
-    precip_afno = PrecipitationAFNO.from_pretrained()
-    io = XarrayBackend()
-    data = NullDataSource()
-
-    diagnostic(
-        [start_time], num_steps, model, precip_afno, data, io, device=model.device
-    )
-
-    # Extract total precipitation for the specified location
-    ds = io.root
-    tp = ds["tp"].sel(lat=lat, lon=lon, method="nearest").values.ravel()
-
-    # Extract time coordinate
-    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
-
-    # Create line plot of total precipitation
-    print(f"   Creating plot: {plot_file}")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.plot(time_coord, tp, marker="o", linewidth=2, markersize=6)
-    ax.set_xlabel("Lead Time (h)", fontsize=12)
-    ax.set_ylabel("Total Precipitation (m)", fontsize=12)
-    ax.set_title(
-        f"Precipitation Forecast starting {start_time} at {lat}°N, {lon}°E",
-        fontsize=14,
-        fontweight="bold",
-    )
-    ax.grid(True, alpha=0.3)
-
-    # Save the plot
-    fig.tight_layout()
-    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-
-    print("\n🎉 Forecast complete!")
-    print(f"   Plot saved to: {plot_file}")
-
-
-class NullDataSource:
-    """Minimal data source that returns empty DataArray for diagnostic input."""
-
-    def __call__(
-        self,
-        time: datetime | list[datetime] | TimeArray,
-        lead_time: timedelta | list[timedelta] | LeadTimeArray,
-        variable: str | list[str] | VariableArray,
-    ) -> xr.DataArray:
-        """Return an empty DataArray (unused coords for local diagnostic)."""
-        return xr.DataArray()
-
-    async def fetch(
-        self,
-        time: datetime | list[datetime] | TimeArray,
-        lead_time: timedelta | list[timedelta] | LeadTimeArray,
-        variable: str | list[str] | VariableArray,
-    ) -> xr.DataArray:
-        return xr.DataArray()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/serve/client/examples/downscaled_forecast.py b/serve/client/examples/downscaled_forecast.py
deleted file mode 100644
index 190a155f1..000000000
--- a/serve/client/examples/downscaled_forecast.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Forecast example with local diagnostic using Earth2Studio Client SDK.
-
-This example shows how to:
-1. Access a remote workflow
-2. Submit a simple deterministic forecast request and retrieve results as xarray dataset
-3. Run precipitation diagnostic model locally to get precipitation
-4. Create and save a matplotlib line plot of the precipitation forecast
-"""
-
-import os
-from datetime import datetime
-
-import matplotlib.pyplot as plt
-import torch
-
-from earth2studio.data import HRRR  # type: ignore[import-untyped]
-from earth2studio.io import XarrayBackend  # type: ignore[import-untyped]
-from earth2studio.models.px import StormCast  # type: ignore[import-untyped]
-from earth2studio.run import deterministic  # type: ignore[import-untyped]
-from earth2studio.serve.client.e2client import RemoteEarth2Workflow
-
-# /// script
-# dependencies = [
-#   "torch",
-#   "earth2studio[serve]>=0.9.0",
-#   "matplotlib>=3.3.0",
-# ]
-# ///
-
-
-def main(
-    plot_file: str = "downscaled_t2m_plot.png",
-    y: float = 200.0,
-    x: float = 200.0,
-    start_time: datetime = datetime(2025, 8, 21, 6),
-    num_steps: int = 10,
-) -> None:
-    """Run a remote FourCastNet 3 forecast and run a StormCast downscaling locally.
-
-    Args:
-        plot_file: Path to save the temperature plot (default: 'downscaled_t2m_plot.png')
-        x: HRRR x coordinate for temperature extraction (default: 200.0)
-        y: HRRR y coordinate for temperature extraction (default: 200.0)
-    """
-
-    # Create client (configurable via environment variable)
-    api_url = os.getenv("EARTH2STUDIO_API_URL", "http://localhost:8000")
-    api_token = os.getenv("EARTH2STUDIO_API_TOKEN", "")
-    workflow = RemoteEarth2Workflow(
-        api_url,
-        workflow_name="stormcast_fcn3_workflow",
-        device="cuda" if torch.cuda.is_available() else "cpu",
-        token=api_token,
-    )
-
-    # Check if API is healthy
-    try:
-        health = workflow.client.health_check()
-        print(f"✓ API Status: {health.status}")
-    except Exception as e:
-        print(f"✗ API not available: {e}")
-        return
-
-    # Call workflow and get result as a data source
-    try:
-        conditioning_source = workflow(
-            start_time=start_time, num_hours=num_steps, run_stormcast=False
-        ).as_data_source()
-    except Exception as e:
-        print(f"\n❌ Forecast failed: {e}")
-        return
-
-    stormcast = StormCast.from_pretrained()
-    stormcast.conditioning_data_source = conditioning_source
-    io = XarrayBackend()
-    hrrr_ic = HRRR()
-
-    deterministic(
-        [start_time], num_steps, stormcast, hrrr_ic, io, device=workflow.device
-    )
-
-    # Extract t2m data for the specified location
-    ds = io.root
-    t2m = ds["t2m"].sel(hrrr_x=x, hrrr_y=y, method="nearest").values.ravel()
-
-    # Extract time coordinate
-    time_coord = ds["lead_time"].values.astype("timedelta64[h]")
-
-    # Create line plot of temperature
-    print(f"   Creating plot: {plot_file}")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.plot(time_coord, t2m, marker="o", linewidth=2, markersize=6)
-    ax.set_xlabel("Lead Time (h)", fontsize=12)
-    ax.set_ylabel("2-meter Temperature (K)", fontsize=12)
-    ax.set_title(
-        f"Temperature Forecast at x={x}, y={y}", fontsize=14, fontweight="bold"
-    )
-    ax.grid(True, alpha=0.3)
-
-    # Save the plot
-    fig.tight_layout()
-    fig.savefig(plot_file, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-
-    print("\n🎉 Forecast complete!")
-    print(f"   Plot saved to: {plot_file}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/serve/client/pyproject.toml b/serve/client/pyproject.toml
deleted file mode 100644
index 535eed801..000000000
--- a/serve/client/pyproject.toml
+++ /dev/null
@@ -1,88 +0,0 @@
-[build-system]
-requires = ["hatchling", "uv-dynamic-versioning"]
-build-backend = "hatchling.build"
-
-[project]
-name = "earth2studio.serve.client"
-dynamic = ["version"]
-description = "Python client SDK for Earth2Studio REST API"
-authors = [
-    {name = "NVIDIA Scicomp Team"}
-]
-readme = "README.md"
-license = {text = "Apache-2.0"}
-requires-python = ">=3.11"
-classifiers = [
-    "Development Status :: 4 - Beta",
-    "Intended Audience :: Science/Research",
-    "Intended Audience :: Developers",
-    "License :: OSI Approved :: Apache Software License",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-    "Topic :: Scientific/Engineering :: Atmospheric Science",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    "Topic :: Software Development :: Libraries :: Python Modules",
-]
-keywords = [
-    "earth2studio",
-    "weather",
-    "forecasting",
-    "climate",
-    "machine learning",
-    "api client",
-    "ensemble forecasting",
-    "diagnostic analysis"
-]
-dependencies = [
-    "aiohttp>=3.8.0",
-    "earth2studio>=0.9.0",
-    "fsspec>=2021.6.0",
-    "numpy>=1.21.0",
-    "requests>=2.25.0",
-    "urllib3>=1.26.0",
-    "xarray>=0.19.0",
-]
-
-[project.optional-dependencies]
-e2studio = [
-    "torch>=2.10.0",
-]
-examples = [
-    "matplotlib>=3.3.0",
-    "nvidia-physicsnemo>=1.0.1,<2.0",
-    "omegaconf>=2.3.0",
-    "pytz",
-]
-
-[tool.hatch.version]
-source = "uv-dynamic-versioning"
-
-[tool.hatch.build.targets.wheel]
-packages = ["earth2studio/serve/client"]
-
-[tool.hatch.build.targets.wheel.sources]
-"earth2studio/serve/client" = "earth2studio/serve/client"
-
-[tool.uv-dynamic-versioning]
-vcs = "git"
-style = "pep440"
-bump = true
-
-[project.urls]
-Homepage = "https://github.com/NVIDIA/earth2studio"
-Documentation = "https://nvidia.github.io/earth2studio/"
-Repository = "https://github.com/NVIDIA/earth2studio"
-"Bug Reports" = "https://github.com/NVIDIA/earth2studio/issues"
-
-[tool.uv]
-dev-dependencies = [
-    "pytest>=6.0",
-    "pytest-cov>=2.0",
-    "black>=21.0",
-    "isort>=5.0",
-    "flake8>=3.8",
-    "mypy>=0.910",
-    "pre-commit>=2.15.0",
-    "ruff>=0.7.2",
-]
diff --git a/serve/client/requirements.txt b/serve/client/requirements.txt
deleted file mode 100644
index 3f5a5881d..000000000
--- a/serve/client/requirements.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Core dependencies for Earth2Studio Python Client SDK
-requests>=2.25.0
-urllib3>=1.26.0
-aiohttp>=3.8.0   # For longer timeouts when reading remote zarr (fsspec HTTP)
-
-# Optional dependencies for enhanced functionality
-# Uncomment as needed
-# pandas>=1.3.0         # For data manipulation
-# numpy>=1.21.0         # For numerical operations
-# xarray>=0.19.0        # For working with multidimensional arrays
-# matplotlib>=3.3.0     # For plotting and visualization
-# omegaconf>=2.3.0      # For StormCast and other models that use OmegaConf
-# nvidia-physicsnemo>=1.0.1,<2.0  # For StormCast and other physicsnemo-based models
-# pytz                 # Required by nvidia-physicsnemo
diff --git a/serve/server/Makefile b/serve/inferenceserver/Makefile
similarity index 96%
rename from serve/server/Makefile
rename to serve/inferenceserver/Makefile
index 7b44e8857..73d96e3dc 100644
--- a/serve/server/Makefile
+++ b/serve/inferenceserver/Makefile
@@ -61,4 +61,4 @@ install:
 # Run tests
 # integration_test:
 #	@echo "Running API tests..."
-#	@python server/test/integration/test_api.py
+#	@python inferenceserver/test/integration/test_api.py
diff --git a/serve/server/README.md b/serve/inferenceserver/README.md
similarity index 99%
rename from serve/server/README.md
rename to serve/inferenceserver/README.md
index c58bb3af5..f55f55bd9 100644
--- a/serve/server/README.md
+++ b/serve/inferenceserver/README.md
@@ -40,7 +40,7 @@ For current API usage, please refer to the workflow documentation linked above.
 1. **Clone and navigate to the API directory:**
 
    ```bash
-   cd serve/server
+   cd service/inferenceserver
    ```
 
 2. **Make scripts executable and start services:**
@@ -522,7 +522,7 @@ InferenceRequest:
 ### Server Configuration (config.yaml)
 
 The API server can be configured via the `config.yaml` file located at
-`serve/server/conf/config.yaml`:
+`api_server/conf/config.yaml`:
 
 #### Results Management
 
diff --git a/serve/server/README_earth2workflows.md b/serve/inferenceserver/README_earth2workflows.md
similarity index 98%
rename from serve/server/README_earth2workflows.md
rename to serve/inferenceserver/README_earth2workflows.md
index 45ee1a059..9eb10f16a 100644
--- a/serve/server/README_earth2workflows.md
+++ b/serve/inferenceserver/README_earth2workflows.md
@@ -17,7 +17,7 @@ This is a simplified example demonstrating the key concepts. For complete workin
 `example_workflows` directory.
 
 ```python
-from earth2studio.serve.server import workflow_registry, Earth2Workflow
+from api_server.workflow import workflow_registry, Earth2Workflow
 from earth2studio.io import IOBackend
 from datetime import datetime
 from earth2studio.data import GFS
diff --git a/serve/server/README_object_storage.md b/serve/inferenceserver/README_object_storage.md
similarity index 100%
rename from serve/server/README_object_storage.md
rename to serve/inferenceserver/README_object_storage.md
diff --git a/serve/server/README_workflows.md b/serve/inferenceserver/README_workflows.md
similarity index 98%
rename from serve/server/README_workflows.md
rename to serve/inferenceserver/README_workflows.md
index e5dbe64ae..008cb8863 100644
--- a/serve/server/README_workflows.md
+++ b/serve/inferenceserver/README_workflows.md
@@ -75,7 +75,7 @@ updates.
 Create a **Pydantic model** that defines your workflow's input parameters:
 
 ```python
-from earth2studio.serve.server.workflow import WorkflowParameters
+from api_server.workflow import WorkflowParameters
 from pydantic import Field
 from typing import List
 
@@ -101,7 +101,7 @@ Implement the **`Workflow`** base class and assign the parameter class to its `P
 attribute:
 
 ```python
-from earth2studio.serve.server.workflow import Workflow, WorkflowProgress, workflow_registry
+from api_server.workflow import Workflow, WorkflowProgress, workflow_registry
 from typing import Any, Dict, Union
 import json
 
@@ -314,7 +314,7 @@ class DeterministicWorkflowParameters(WorkflowParameters):
 ### Workflow Implementation
 
 ```python
-from earth2studio.serve.server.workflow import WorkflowProgress
+from api_server.workflow import WorkflowProgress
 
 @workflow_registry.register()
 class DeterministicWorkflow(Workflow):
@@ -763,7 +763,7 @@ The `WorkflowProgress` class provides four standard fields:
 ### Basic Usage
 
 ```python
-from earth2studio.serve.server.workflow import WorkflowProgress
+from api_server.workflow import WorkflowProgress
 
 # Simple progress update
 progress = WorkflowProgress(
@@ -790,7 +790,7 @@ except Exception as e:
 You can extend `WorkflowProgress` with workflow-specific fields:
 
 ```python
-from earth2studio.serve.server.workflow import WorkflowProgress
+from api_server.workflow import WorkflowProgress
 from pydantic import Field
 from typing import Optional
 
diff --git a/serve/client/Makefile b/serve/inferenceserver/api_server/__init__.py
similarity index 65%
rename from serve/client/Makefile
rename to serve/inferenceserver/api_server/__init__.py
index 99b518462..f07be226e 100644
--- a/serve/client/Makefile
+++ b/serve/inferenceserver/api_server/__init__.py
@@ -14,16 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-.PHONY: deps integration_test run_deterministic run_stormcast
+"""
+Earth2Studio API Server Package
 
-deps:
-	uv add -r requirements.txt
+This package contains the main API server components including:
+- main: FastAPI application and REST endpoints
+- workflow: Workflow framework and registry
+- worker: RQ worker functions for processing jobs
+- cpu_worker: CPU-intensive worker functions
+"""
 
-# integration_test:
-# TODO: Add integration tests here.
-
-run_deterministic:
-	python examples/basic_forecast.py
-
-run_stormcast:
-	python examples/downscaled_forecast.py
+__all__ = ["main", "workflow", "worker", "cpu_worker", "config", "object_storage"]
diff --git a/earth2studio/serve/server/cleanup_daemon.py b/serve/inferenceserver/api_server/cleanup_daemon.py
similarity index 82%
rename from earth2studio/serve/server/cleanup_daemon.py
rename to serve/inferenceserver/api_server/cleanup_daemon.py
index 4a0b29498..689f88d2f 100644
--- a/earth2studio/serve/server/cleanup_daemon.py
+++ b/serve/inferenceserver/api_server/cleanup_daemon.py
@@ -14,6 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Cleanup Daemon for API Server
+
+This daemon runs as a separate process and periodically cleans up expired
+inference results based on the configured TTL (time-to-live).
+"""
+
 import json
 import logging
 import shutil
@@ -27,21 +34,36 @@
 
 import redis  # type: ignore[import-untyped]
 
-from earth2studio.serve.server.config import get_config, get_config_manager
-from earth2studio.serve.server.workflow import WorkflowStatus
+from api_server.config import get_config, get_config_manager
+from api_server.workflow import WorkflowStatus
 
-# Configure logging (config is obtained in main())
+# Get configuration
+config = get_config()
 config_manager = get_config_manager()
+
+# Configure logging
 config_manager.setup_logging()
 logger = logging.getLogger(__name__)
 
+# Path configuration from config
+DEFAULT_OUTPUT_DIR = Path(config.paths.default_output_dir)
+RESULTS_ZIP_DIR = Path(config.paths.results_zip_dir)
+
+# Global flag for graceful shutdown
+shutdown_flag = False
+
+
+def signal_handler(signum: int, frame: Any) -> None:
+    """Handle shutdown signals gracefully"""
+    global shutdown_flag
+    logger.info(f"Received signal {signum}, initiating graceful shutdown...")
+    shutdown_flag = True
+
 
 def _delete_result_files(
     redis_client: redis.Redis,
     result_id: str,
     search_id: str,
-    default_output_dir: Path,
-    results_zip_dir: Path,
     workflow_name: str | None = None,
 ) -> None:
     """
@@ -51,21 +73,19 @@ def _delete_result_files(
         redis_client: Redis client
         result_id: ID to use for zip file lookup (request_id or combined_id)
         search_id: ID to search for in directory names
-        default_output_dir: Base directory for raw result outputs
-        results_zip_dir: Directory for result zip files and metadata
         workflow_name: Optional workflow name for workflow-specific subdirectory checking
     """
     # Delete zip file
     zip_key = f"inference_request_zips:{result_id}:zip_file"
     zip_filename = redis_client.get(zip_key)
     if zip_filename:
-        zip_path = results_zip_dir / zip_filename
+        zip_path = RESULTS_ZIP_DIR / zip_filename
         if zip_path.exists():
             zip_path.unlink()
             logger.info(f"Deleted zip file: {zip_path}")
 
     # Delete metadata file (metadata_{request_id}.json)
-    for item in results_zip_dir.iterdir():
+    for item in RESULTS_ZIP_DIR.iterdir():
         if (
             item.is_file()
             and item.name.startswith("metadata")
@@ -75,20 +95,20 @@ def _delete_result_files(
             logger.info(f"Deleted metadata file: {item}")
 
     # Delete raw results directory (search for directories containing search_id)
-    for item in default_output_dir.iterdir():
+    for item in DEFAULT_OUTPUT_DIR.iterdir():
         if item.is_dir() and search_id in item.name:
             shutil.rmtree(item)
             logger.info(f"Deleted raw results directory: {item}")
 
     # Also check subdirectories recursively for results
-    for item in results_zip_dir.iterdir():
+    for item in RESULTS_ZIP_DIR.iterdir():
         if item.is_dir() and search_id in item.name:
             shutil.rmtree(item)
             logger.info(f"Deleted results from zip dir: {item}")
 
     # Check in workflow-specific subdirectories if workflow_name is provided
     if workflow_name:
-        workflow_dir = default_output_dir / workflow_name
+        workflow_dir = DEFAULT_OUTPUT_DIR / workflow_name
         if workflow_dir.exists():
             for item in workflow_dir.iterdir():
                 if item.is_dir() and search_id in item.name:
@@ -101,7 +121,6 @@ def _process_expired_key(
     key: str,
     current_time: datetime,
     results_ttl_hours: float,
-    retention_ttl: int,
     expected_status: str,
     get_end_time_field: str,
     delete_files_func: Callable[[redis.Redis, str], None],
@@ -115,7 +134,6 @@ def _process_expired_key(
         key: Redis key to process
         current_time: Current timestamp for age calculation
         results_ttl_hours: TTL threshold in hours
-        retention_ttl: Redis TTL in seconds for the key after marking expired
         expected_status: Expected status value for completed items
         get_end_time_field: Primary field name for end time
         delete_files_func: Function to call for deleting files
@@ -162,7 +180,7 @@ def _process_expired_key(
         data["status"] = WorkflowStatus.EXPIRED
         redis_client.setex(
             key,
-            retention_ttl,
+            config.redis.retention_ttl,
             json.dumps(data),
         )
 
@@ -171,13 +189,7 @@ def _process_expired_key(
     return False
 
 
-def cleanup_expired_results(
-    redis_client: redis.Redis,
-    results_ttl_hours: float,
-    retention_ttl: int,
-    default_output_dir: Path,
-    results_zip_dir: Path,
-) -> None:
+def cleanup_expired_results(redis_client: redis.Redis) -> None:
     """
     Check Redis for expired results and clean them up.
 
@@ -186,6 +198,8 @@ def cleanup_expired_results(
     2. Deletes both raw result files and zip files
     3. Updates the status to "expired"
     """
+    results_ttl_hours = config.server.results_ttl_hours
+
     logger.info("Running cleanup watchdog check...")
     current_time = datetime.now(timezone.utc)
 
@@ -217,8 +231,6 @@ def delete_func(
                         rc,
                         result_id=_combined_id,
                         search_id=_execution_id,
-                        default_output_dir=default_output_dir,
-                        results_zip_dir=results_zip_dir,
                         workflow_name=_workflow_name,
                     )
 
@@ -227,7 +239,6 @@ def delete_func(
                     key=key,
                     current_time=current_time,
                     results_ttl_hours=results_ttl_hours,
-                    retention_ttl=retention_ttl,
                     expected_status=WorkflowStatus.COMPLETED,
                     get_end_time_field="end_time",
                     delete_files_func=delete_func,
@@ -246,16 +257,7 @@ def delete_func(
 
 def main() -> None:
     """Main daemon loop"""
-    config = get_config()
-    default_output_dir = Path(config.paths.default_output_dir)
-    results_zip_dir = Path(config.paths.results_zip_dir)
-
-    state: dict[str, bool] = {"shutdown": False}
-
-    def signal_handler(signum: int, frame: Any) -> None:
-        """Handle shutdown signals gracefully."""
-        logger.info(f"Received signal {signum}, initiating graceful shutdown...")
-        state["shutdown"] = True
+    global shutdown_flag
 
     # Set up signal handlers
     signal.signal(signal.SIGINT, signal_handler)
@@ -285,21 +287,15 @@ def signal_handler(signum: int, frame: Any) -> None:
 
     # Main daemon loop
     try:
-        while not state["shutdown"]:
+        while not shutdown_flag:
             try:
-                cleanup_expired_results(
-                    redis_client,
-                    results_ttl_hours=config.server.results_ttl_hours,
-                    retention_ttl=config.redis.retention_ttl,
-                    default_output_dir=default_output_dir,
-                    results_zip_dir=results_zip_dir,
-                )
+                cleanup_expired_results(redis_client)
             except Exception as e:
                 logger.exception(f"Error in cleanup cycle: {e}")
 
             # Sleep for the configured interval, checking shutdown flag periodically
             sleep_remaining = config.server.cleanup_watchdog_sec
-            while sleep_remaining > 0 and not state["shutdown"]:
+            while sleep_remaining > 0 and not shutdown_flag:
                 sleep_time = min(5, sleep_remaining)  # Check every 5 seconds
                 time.sleep(sleep_time)
                 sleep_remaining -= sleep_time
diff --git a/serve/server/conf/config.yaml b/serve/inferenceserver/api_server/conf/config.yaml
similarity index 100%
rename from serve/server/conf/config.yaml
rename to serve/inferenceserver/api_server/conf/config.yaml
diff --git a/serve/server/conf/profiles.json b/serve/inferenceserver/api_server/conf/profiles.json
similarity index 100%
rename from serve/server/conf/profiles.json
rename to serve/inferenceserver/api_server/conf/profiles.json
diff --git a/serve/server/conf/workflows.yaml b/serve/inferenceserver/api_server/conf/workflows.yaml
similarity index 100%
rename from serve/server/conf/workflows.yaml
rename to serve/inferenceserver/api_server/conf/workflows.yaml
diff --git a/earth2studio/serve/server/config.py b/serve/inferenceserver/api_server/config.py
similarity index 95%
rename from earth2studio/serve/server/config.py
rename to serve/inferenceserver/api_server/config.py
index 69bc6a353..fc1f86fee 100644
--- a/earth2studio/serve/server/config.py
+++ b/serve/inferenceserver/api_server/config.py
@@ -14,6 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Configuration Management for Earth2Studio API Server
+
+This module provides centralized configuration management using Hydra.
+All configuration values are defined here with sensible defaults and can be
+overridden via YAML files or command-line arguments.
+"""
+
 import logging
 import os
 from dataclasses import dataclass, field
@@ -198,13 +206,8 @@ def __init__(self) -> None:
     def _initialize_config(self) -> None:
         """Initialize configuration from Hydra"""
         try:
-            # Prefer CONFIG_DIR env var (required when package is installed without repo layout)
-            config_dir_env = os.environ.get("CONFIG_DIR")
-            if config_dir_env:
-                config_dir = Path(config_dir_env)
-            else:
-                _repo_root = Path(__file__).resolve().parent.parent.parent.parent
-                config_dir = _repo_root / "serve" / "server" / "conf"
+            # Get config directory
+            config_dir = Path(__file__).parent / "conf"
 
             # Clear any existing Hydra instance
             GlobalHydra.instance().clear()
@@ -514,6 +517,10 @@ def workflow_config(self) -> dict[str, Any]:
         return self._workflow_config
 
 
+# Global configuration instance
+_config_manager: ConfigManager | None = None
+
+
 def get_config() -> AppConfig:
     """
     Get the global configuration instance.
@@ -522,7 +529,7 @@ def get_config() -> AppConfig:
         AppConfig: The application configuration
 
     Example:
-        >>> from earth2studio.serve.server.config import get_config
+        >>> from api_server.config import get_config
         >>> config = get_config()
         >>> print(config.redis.host)
         'localhost'
@@ -532,23 +539,27 @@ def get_config() -> AppConfig:
 
 def get_config_manager() -> ConfigManager:
     """
-    Get the configuration manager instance (singleton).
+    Get the global configuration manager instance.
 
     Returns:
         ConfigManager: The configuration manager
 
     Example:
-        >>> from earth2studio.serve.server.config import get_config_manager
+        >>> from api_server.config import get_config_manager
         >>> manager = get_config_manager()
         >>> manager.setup_logging()
     """
-    return ConfigManager()
+    global _config_manager
+    if _config_manager is None:
+        _config_manager = ConfigManager()
+    return _config_manager
 
 
 # Convenience function to reset config (mainly for testing)
 def reset_config() -> None:
-    """Reset the configuration manager singleton (mainly for testing)."""
-    ConfigManager._instance = None
+    """Reset the global configuration instance (mainly for testing)"""
+    global _config_manager
+    _config_manager = None
     GlobalHydra.instance().clear()
 
 
@@ -560,7 +571,7 @@ def get_workflow_config(name: str) -> dict[str, Any]:
         dict[str, Any]: The workflow configuration
 
     Example:
-        >>> from earth2studio.serve.server.config import get_workflow_config
+        >>> from api_server.config import get_workflow_config
         >>> config = get_workflow_config("deterministic_earth2_workflow")
         >>> print(config["model_type"])
         'fcn'
diff --git a/earth2studio/serve/server/cpu_worker.py b/serve/inferenceserver/api_server/cpu_worker.py
similarity index 98%
rename from earth2studio/serve/server/cpu_worker.py
rename to serve/inferenceserver/api_server/cpu_worker.py
index 78248ad6f..99ac82099 100644
--- a/earth2studio/serve/server/cpu_worker.py
+++ b/serve/inferenceserver/api_server/cpu_worker.py
@@ -29,7 +29,6 @@
     "process_finalize_metadata",
 ]
 
-
 import json
 import logging
 import zipfile
@@ -41,8 +40,8 @@
 import redis  # type: ignore[import-untyped]
 
 # Import configuration
-from earth2studio.serve.server.config import get_config, get_config_manager
-from earth2studio.serve.server.utils import (
+from api_server.config import get_config, get_config_manager
+from api_server.utils import (
     get_inference_request_metadata_key,
     get_inference_request_output_path_key,
     get_inference_request_zip_key,
@@ -50,7 +49,7 @@
     get_signed_url_key,
     queue_next_stage,
 )
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     WorkflowResult,
     WorkflowStatus,
     workflow_registry,
@@ -77,7 +76,7 @@
 
 # Register custom workflows in the CPU worker process
 try:
-    from earth2studio.serve.server.workflow import register_all_workflows
+    from api_server.workflow import register_all_workflows
 
     register_all_workflows(redis_client)
     logger.info("Custom workflows registered successfully in CPU worker process")
@@ -328,14 +327,14 @@ def create_results_zip(
         file_manifest = build_file_manifest(output_path)
 
         if not output_path.exists():
-            log.warning(f"Output path {output_path} does not exist")
+            logger.warning(f"Output path {output_path} does not exist")
 
         # Create zip file only if requested
         if create_zip:
             if zip_filename is None:
                 zip_filename = f"{request_id}"
             zip_path = results_zip_dir / zip_filename
-            log.info(f"Creating results zip file: {zip_path}")
+            logger.info(f"Creating results zip file: {zip_path}")
 
             with zipfile.ZipFile(
                 zip_path, "w", zipfile.ZIP_DEFLATED, compresslevel=6
@@ -369,9 +368,9 @@ def create_results_zip(
                     FileManifestEntry(path=zip_filename, size=zip_size)
                 )
 
-                log.info(f"Created zip file {zip_filename} ({zip_size} bytes)")
+                logger.info(f"Created zip file {zip_filename} ({zip_size} bytes)")
         else:
-            log.info(
+            logger.info(
                 f"Skipping zip creation for {request_id}, manifest has {len(file_manifest)} files"
             )
             zip_filename = None  # No zip file created
@@ -414,7 +413,7 @@ def create_results_zip(
             config.redis.retention_ttl,
             str(results_zip_dir),
         )
-        log.info(f"Stored pending metadata in Redis for {request_id}")
+        logger.info(f"Stored pending metadata in Redis for {request_id}")
 
         # Store zip filename association in Redis (only if zip was created)
         if zip_filename:
@@ -560,7 +559,7 @@ def process_object_storage_upload(
             config.object_storage.bucket
             or config.object_storage.storage_type == "azure"
         ):
-            from earth2studio.serve.server.object_storage import (
+            from api_server.object_storage import (
                 MSCObjectStorage,
                 ObjectStorageError,
             )
@@ -574,7 +573,7 @@ def process_object_storage_upload(
                 )
 
             # Create S3 storage instance
-            storage_kwargs: dict[str, Any] = {
+            storage_kwargs = {
                 "bucket": config.object_storage.bucket
                 or config.object_storage.azure_container_name
                 or "",
@@ -1025,6 +1024,7 @@ def process_finalize_metadata(
     Returns:
         Dict containing result info, None on critical failure
     """
+    from api_server.workflow import WorkflowStatus, workflow_registry
 
     request_id = f"{workflow_name}:{execution_id}"
     logger.info(f"Processing finalize metadata for {request_id}")
@@ -1039,6 +1039,7 @@ def process_finalize_metadata(
     storage_info_json = redis_client.get(storage_info_key)
 
     if not pending_metadata_json or not results_zip_dir_str:
+        logger.error(f"Pending metadata not found in Redis for {request_id}")
         return fail_workflow(
             workflow_name,
             execution_id,
diff --git a/serve/server/example_workflows/__init__.py b/serve/inferenceserver/api_server/example_workflows/__init__.py
similarity index 100%
rename from serve/server/example_workflows/__init__.py
rename to serve/inferenceserver/api_server/example_workflows/__init__.py
diff --git a/serve/server/example_workflows/deterministic_earth2_workflow.py b/serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py
similarity index 96%
rename from serve/server/example_workflows/deterministic_earth2_workflow.py
rename to serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py
index d995fbc76..088bebcf4 100644
--- a/serve/server/example_workflows/deterministic_earth2_workflow.py
+++ b/serve/inferenceserver/api_server/example_workflows/deterministic_earth2_workflow.py
@@ -25,11 +25,11 @@
 from datetime import datetime
 from typing import Literal
 
+from api_server.workflow import Earth2Workflow, workflow_registry
 from earth2studio import run
 from earth2studio.data import GFS
 from earth2studio.io import IOBackend
 from earth2studio.models.px import DLWP, FCN
-from earth2studio.serve.server import Earth2Workflow, workflow_registry
 
 
 @workflow_registry.register
diff --git a/serve/server/example_workflows/deterministic_fcn.py b/serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py
similarity index 99%
rename from serve/server/example_workflows/deterministic_fcn.py
rename to serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py
index cdc63c250..6afc30bf1 100644
--- a/serve/server/example_workflows/deterministic_fcn.py
+++ b/serve/inferenceserver/api_server/example_workflows/deterministic_fcn.py
@@ -31,7 +31,7 @@
 import zarr
 from pydantic import Field
 
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/server/example_workflows/deterministic_workflow.py b/serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py
similarity index 99%
rename from serve/server/example_workflows/deterministic_workflow.py
rename to serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py
index ffa52e5e0..fa16ff285 100644
--- a/serve/server/example_workflows/deterministic_workflow.py
+++ b/serve/inferenceserver/api_server/example_workflows/deterministic_workflow.py
@@ -29,7 +29,7 @@
 import zarr
 from pydantic import Field
 
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/server/example_workflows/diagnostic_workflow.py b/serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py
similarity index 99%
rename from serve/server/example_workflows/diagnostic_workflow.py
rename to serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py
index 23ca1d0f0..b6b05c841 100644
--- a/serve/server/example_workflows/diagnostic_workflow.py
+++ b/serve/inferenceserver/api_server/example_workflows/diagnostic_workflow.py
@@ -28,7 +28,7 @@
 import zarr
 from pydantic import Field
 
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/server/example_workflows/example_user_workflow.py b/serve/inferenceserver/api_server/example_workflows/example_user_workflow.py
similarity index 99%
rename from serve/server/example_workflows/example_user_workflow.py
rename to serve/inferenceserver/api_server/example_workflows/example_user_workflow.py
index 3245d5726..69e390fdb 100644
--- a/serve/server/example_workflows/example_user_workflow.py
+++ b/serve/inferenceserver/api_server/example_workflows/example_user_workflow.py
@@ -38,7 +38,7 @@
 
 from pydantic import Field
 
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
diff --git a/serve/server/example_workflows/foundry_fcn3.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
similarity index 100%
rename from serve/server/example_workflows/foundry_fcn3.py
rename to serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
index 62e1334d8..192f0e190 100644
--- a/serve/server/example_workflows/foundry_fcn3.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3.py
@@ -5,8 +5,8 @@
 import numpy as np
 import torch
 import zarr
-from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 
+from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 from earth2studio.data import PlanetaryComputerECMWFOpenDataIFS, fetch_data
 from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
 from earth2studio.models.px import FCN3
diff --git a/serve/server/example_workflows/foundry_fcn3_stormscope_goes.py b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
similarity index 97%
rename from serve/server/example_workflows/foundry_fcn3_stormscope_goes.py
rename to serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
index 9bf5d304f..642b0ea89 100644
--- a/serve/server/example_workflows/foundry_fcn3_stormscope_goes.py
+++ b/serve/inferenceserver/api_server/example_workflows/foundry_fcn3_stormscope_goes.py
@@ -7,8 +7,8 @@
 import torch
 import xarray as xr
 import zarr
-from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 
+from api_server.workflow import Earth2Workflow, WorkflowProgress, workflow_registry
 from earth2studio.data import (
     GOES,
     InferenceOutputSource,
@@ -404,16 +404,14 @@ def __call__(
         y_ori, coords_y_ori = self.get_stormscope_input(start_time_stormscope)
 
         coords_out = self.stormscope.output_coords(self.stormscope.input_coords())
-        output_coords = OrderedDict(
-            {
-                "ensemble": np.arange(len(seeds_stormscope)),
-                # Planetary Computer does not like separate 'lead_time'
-                "time": to_time_array([start_time_stormscope]) + lead_times,
-                "variable": variables,
-                "y": coords_out["y"],
-                "x": coords_out["x"],
-            }
-        )
+        output_coords = {
+            "ensemble": np.arange(len(seeds_stormscope)),
+            # Planetary Computer does not like separate 'lead_time'
+            "time": to_time_array([start_time_stormscope]) + lead_times,
+            "variable": variables,
+            "y": coords_out["y"],
+            "x": coords_out["x"],
+        }
         self.setup_io(io, output_coords, seeds_fcn3, seeds_stormscope)
 
         total_samples = len(seeds_stormscope)
diff --git a/serve/server/example_workflows/stormcast_fcn3_workflow.py b/serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py
similarity index 98%
rename from serve/server/example_workflows/stormcast_fcn3_workflow.py
rename to serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py
index c369906e6..06ede58d1 100644
--- a/serve/server/example_workflows/stormcast_fcn3_workflow.py
+++ b/serve/inferenceserver/api_server/example_workflows/stormcast_fcn3_workflow.py
@@ -23,12 +23,12 @@
 import torch
 import xarray as xr
 
+from api_server.workflow import Earth2Workflow, workflow_registry
 from earth2studio import run
 from earth2studio.data import GFS, HRRR, InferenceOutputSource
 from earth2studio.io import IOBackend, NetCDF4Backend, XarrayBackend
 from earth2studio.models.dx import DerivedSurfacePressure
 from earth2studio.models.px import FCN3, DiagnosticWrapper, InterpModAFNO, StormCast
-from earth2studio.serve.server import Earth2Workflow, workflow_registry
 
 
 @workflow_registry.register
diff --git a/earth2studio/serve/server/main.py b/serve/inferenceserver/api_server/main.py
similarity index 83%
rename from earth2studio/serve/server/main.py
rename to serve/inferenceserver/api_server/main.py
index 3673c9c96..96905a618 100644
--- a/earth2studio/serve/server/main.py
+++ b/serve/inferenceserver/api_server/main.py
@@ -15,16 +15,15 @@
 # limitations under the License.
 
 """
-Earth2Studio REST API server.
+Earth2Studio REST API Server
 
-Provides FastAPI endpoints for workflow execution, status, and result retrieval,
-with Redis and RQ for job queuing and Prometheus metrics.
+This module provides a REST API interface for running Earth2Studio workflows
+including deterministic, diagnostic, ensemble, and custom inference workflows.
 """
 
 import asyncio
 import json
 import logging
-import os
 import time
 import uuid
 from collections.abc import AsyncGenerator
@@ -36,7 +35,6 @@
 import aiofiles  # type: ignore[import-untyped]
 import redis as redis_sync  # type: ignore[import-untyped]  # For RQ (synchronous)
 import redis.asyncio as redis  # type: ignore[import-untyped]
-import uvicorn
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import Response, StreamingResponse
@@ -45,8 +43,8 @@
 from rq import Queue
 
 # Import configuration
-from earth2studio.serve.server.config import get_config, get_config_manager
-from earth2studio.serve.server.utils import (
+from api_server.config import get_config, get_config_manager
+from api_server.utils import (
     create_file_stream,
     get_inference_request_output_path_key,
     get_inference_request_zip_key,
@@ -54,7 +52,7 @@
 )
 
 # Import workflow registry
-from earth2studio.serve.server.workflow import (
+from api_server.workflow import (
     WorkflowResult,
     WorkflowStatus,
     workflow_registry,
@@ -82,16 +80,13 @@ def check_admission_control() -> None:
     """
     Check if the inference request can be admitted based on queue sizes.
 
-    Checks all relevant queues (inference, result zip, object storage,
-    and finalize metadata) to ensure none are at capacity before allowing
+    This function checks all relevant queues (inference, result zip, object storage,
+    and finalize metadata) to ensure none of them are at capacity before allowing
     a new request to be enqueued.
 
-    Raises
-    ------
-    HTTPException
-        429 if any queue is full (service temporarily unavailable; retry later).
-        503 if Redis is not initialized.
-        500 if queue status cannot be determined.
+    Raises:
+        HTTPException: 429 status if any queue is full, indicating the service
+            is temporarily unavailable and the client should retry later.
     """
     queue_names = [
         config.queue.name,
@@ -129,18 +124,14 @@ def get_queue_position(job_id: str) -> int | None:
     """
     Get the position of a job in the queue.
 
-    Uses RQ's Queue.job_ids property, which correctly returns job IDs
+    Uses RQ's Queue.job_ids property which correctly returns the job IDs
     including custom job_id values set during enqueue.
 
-    Parameters
-    ----------
-    job_id : str
-        The ID of the job to find.
+    Args:
+        job_id: The ID of the job to find
 
-    Returns
-    -------
-    int or None
-        Position in queue (0-indexed), or None if not found (e.g. picked up by worker).
+    Returns:
+        Position in queue (0-indexed), or None if not found in queue
     """
     if not inference_queue:
         logger.warning("Inference queue not available for position lookup")
@@ -176,12 +167,7 @@ def get_queue_position(job_id: str) -> int | None:
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
-    """
-    Lifespan context manager for FastAPI application startup and shutdown.
-
-    On startup: connects to Redis (async and sync), initializes the RQ inference
-    queue, and registers custom workflows. On shutdown: closes Redis connections.
-    """
+    """Lifespan event handler for application startup and shutdown"""
     # Startup
     global redis_client, redis_sync_client, inference_queue
     try:
@@ -225,7 +211,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 
         # Register custom workflows
         try:
-            from earth2studio.serve.server.workflow import register_all_workflows
+            from api_server.workflow import register_all_workflows
 
             register_all_workflows(redis_sync_client)
             logger.info("Custom workflows registered successfully")
@@ -279,30 +265,10 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 @app.get("/health")
 @app.get("/readiness")
 async def health_check() -> dict[str, str]:
-    """
-    Health and readiness check endpoint.
-
-    Runs the status script and returns overall health based on its exit code.
-
-    Returns
-    -------
-    dict
-        Keys ``status`` (e.g. "healthy"/"unhealthy") and ``timestamp`` (ISO format).
-
-    Raises
-    ------
-    HTTPException
-        503 if status is unhealthy; 500 if the check fails.
-    """
+    """Health check endpoint using status script"""
     try:
         # Run the status script and check exit code
-        # Prefer SCRIPT_DIR env var (required when package is installed without repo layout)
-        script_dir_env = os.environ.get("SCRIPT_DIR")
-        if script_dir_env:
-            script_path = Path(script_dir_env) / "status.sh"
-        else:
-            _repo_root = Path(__file__).resolve().parent.parent.parent.parent
-            script_path = _repo_root / "serve" / "server" / "scripts" / "status.sh"
+        script_path = Path(__file__).parent.parent / "scripts" / "status.sh"
         process = await asyncio.create_subprocess_exec(
             str(script_path),
             stdout=asyncio.subprocess.PIPE,
@@ -336,32 +302,13 @@ async def health_check() -> dict[str, str]:
 # Azure ML uses this to see if the container is alive
 @app.get("/liveness")
 def liveness() -> dict[str, str]:
-    """
-    Liveness probe for Azure ML.
-
-    Returns
-    -------
-    dict
-        ``status`` key set to "alive" to indicate the container is running.
-    """
+    """Liveness probe for Azure ML; returns alive status to indicate container is running."""
     return {"status": "alive"}
 
 
 @app.get("/metrics")
 async def get_metrics() -> Response:
-    """
-    Expose Prometheus metrics.
-
-    Returns
-    -------
-    Response
-        Response with Prometheus text format and appropriate content type.
-
-    Raises
-    ------
-    HTTPException
-        500 if metrics generation fails.
-    """
+    """Get Prometheus metrics"""
     try:
         metrics_data = generate_latest()
         return Response(content=metrics_data, media_type=CONTENT_TYPE_LATEST)
@@ -380,14 +327,7 @@ async def get_metrics() -> Response:
 @app.get("/v1/workflows")
 @app.get("/v1/infer/workflows")
 async def list_workflows() -> dict[str, dict[str, str]]:
-    """
-    List all available workflows.
-
-    Returns
-    -------
-    dict
-        Single key ``workflows`` mapping workflow name to description.
-    """
+    """List all available workflows (only exposed workflows)"""
     workflows = workflow_registry.list_workflows(exposed_only=True)
     return {"workflows": workflows}
 
@@ -398,24 +338,15 @@ async def get_workflow_schema(workflow_name: str) -> dict[str, Any]:
     """
     Get the OpenAPI JSON schema for a workflow's parameters.
 
-    For Workflow subclasses the schema comes from WorkflowParameters; for
-    Earth2Workflow subclasses it is generated from the __call__ signature via
-    AutoParameters.
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the workflow.
+    For workflows derived from Workflow class, the schema is generated from
+    the WorkflowParameters class. For workflows derived from Earth2Workflow class,
+    the schema is auto-generated from the __call__ signature via AutoParameters.
 
-    Returns
-    -------
-    dict
-        JSON schema for the workflow parameters (OpenAPI-compatible).
+    Args:
+        workflow_name: Name of the workflow
 
-    Raises
-    ------
-    HTTPException
-        404 if workflow not found; 500 if schema generation fails.
+    Returns:
+        JSON schema for the workflow parameters
     """
     # Check if workflow exists and is exposed
     workflow_class = workflow_registry.get_workflow_class(workflow_name)
@@ -455,14 +386,7 @@ async def get_workflow_schema(workflow_name: str) -> dict[str, Any]:
 
 
 class WorkflowExecutionRequest(BaseModel):
-    """
-    Request body for workflow execution.
-
-    Attributes
-    ----------
-    parameters : dict
-        Workflow-specific parameters (validated by the workflow's parameter class).
-    """
+    """Request model for workflow execution"""
 
     model_config = {"extra": "forbid"}
 
@@ -472,24 +396,7 @@ class WorkflowExecutionRequest(BaseModel):
 
 
 class WorkflowExecutionResponse(BaseModel):
-    """
-    Response after submitting a workflow execution.
-
-    Attributes
-    ----------
-    workflow_name : str
-        Name of the workflow.
-    execution_id : str
-        Unique execution identifier.
-    status : str
-        Current status (e.g. QUEUED).
-    position : int or None
-        Queue position (0-indexed); set when status is QUEUED.
-    message : str
-        Human-readable message.
-    timestamp : str
-        ISO format timestamp.
-    """
+    """Response model for workflow execution"""
 
     workflow_name: str
     execution_id: str
@@ -505,31 +412,8 @@ class WorkflowExecutionResponse(BaseModel):
 async def execute_workflow(
     workflow_name: str, request: WorkflowExecutionRequest
 ) -> WorkflowExecutionResponse:
-    """
-    Enqueue a custom workflow for execution.
-
-    Validates parameters, performs admission control, persists execution data
-    in Redis, and enqueues the job to the RQ inference queue.
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the registered workflow.
-    request : WorkflowExecutionRequest
-        Request body containing workflow parameters.
-
-    Returns
-    -------
-    WorkflowExecutionResponse
-        Execution ID, status (QUEUED), queue position, and message.
-
-    Raises
-    ------
-    HTTPException
-        404 if workflow not found; 422 if parameters invalid; 429 if queues full;
-        503 if Redis/queue not initialized; 500 on enqueue failure.
-    """
-    # Check if workflow exists and get the workflow class for validation
+    """Execute a custom workflow"""
+    # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
     if not custom_workflow_class:
         raise HTTPException(
@@ -583,7 +467,7 @@ async def execute_workflow(
                 status_code=503, detail="Inference queue not initialized"
             )
         job = inference_queue.enqueue(
-            "earth2studio.serve.server.worker.run_custom_workflow",
+            "api_server.worker.run_custom_workflow",  # New worker function
             workflow_name,
             execution_id,
             validated_params_dict,
@@ -638,26 +522,7 @@ async def execute_workflow(
     "/v1/infer/{workflow_name}/{execution_id}/status", response_model=WorkflowResult
 )
 async def get_workflow_status(workflow_name: str, execution_id: str) -> WorkflowResult:
-    """
-    Get the status of a workflow execution.
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the workflow.
-    execution_id : str
-        Unique execution identifier.
-
-    Returns
-    -------
-    WorkflowResult
-        Current status, progress, timestamps, and optional queue position.
-
-    Raises
-    ------
-    HTTPException
-        404 if workflow or execution not found; 500 on server error.
-    """
+    """Get status of a workflow execution"""
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -709,27 +574,7 @@ async def get_workflow_status(workflow_name: str, execution_id: str) -> Workflow
 async def get_workflow_results(
     workflow_name: str, execution_id: str
 ) -> dict[str, Any] | StreamingResponse:
-    """
-    Get result metadata (e.g. metadata.json) for a completed workflow execution.
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the workflow.
-    execution_id : str
-        Unique execution identifier.
-
-    Returns
-    -------
-    dict
-        JSON metadata (e.g. from metadata_{workflow_name}:{execution_id}.json).
-
-    Raises
-    ------
-    HTTPException
-        202 if still queued/running/pending; 400 if expired or bad request;
-        404 if workflow, execution, or metadata file not found, or if execution failed/cancelled.
-    """
+    """Get metadata.json for a workflow execution"""
     # Create logger adapter with execution_id
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -842,29 +687,13 @@ async def get_workflow_result_file(
     """
     Stream a specific file from the workflow execution results.
 
-    If filepath equals the request ID (workflow_name:execution_id), streams the
-    results zip file. Otherwise serves a file from the output directory (path
-    normalized and checked to prevent directory traversal).
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the workflow.
-    execution_id : str
-        Execution identifier.
-    filepath : str
-        Relative path within the output directory, or the request ID for the zip.
-
-    Returns
-    -------
-    StreamingResponse
-        File or zip contents with appropriate headers.
-
-    Raises
-    ------
-    HTTPException
-        403 on path traversal attempt; 404 if workflow, execution, file, or zip
-        not found or results not completed; 503 if Redis not initialized; 500 on error.
+    Args:
+        workflow_name: Name of the workflow
+        execution_id: Execution ID
+        filepath: Relative path to the file within the output directory
+
+    Returns:
+        Streaming response with the file contents
     """
     # Check if workflow exists and is exposed
     custom_workflow_class = workflow_registry.get_workflow_class(workflow_name)
@@ -1080,6 +909,8 @@ async def get_workflow_result_file(
 
 
 if __name__ == "__main__":
+    import uvicorn
+
     uvicorn.run(
         app,
         host=config.server.host,  # noqa: S104
diff --git a/earth2studio/serve/server/object_storage.py b/serve/inferenceserver/api_server/object_storage.py
similarity index 90%
rename from earth2studio/serve/server/object_storage.py
rename to serve/inferenceserver/api_server/object_storage.py
index 42f8e07a9..7961df4ff 100644
--- a/earth2studio/serve/server/object_storage.py
+++ b/serve/inferenceserver/api_server/object_storage.py
@@ -14,6 +14,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Object Storage Module
+
+This module provides object storage functionality using NVIDIA Multi-Storage Client (MSC)
+with Rust backend for high-performance parallel file transfers.
+
+Example:
+    >>> from api_server.object_storage import MSCObjectStorage
+    >>> storage = MSCObjectStorage(bucket="my-bucket", region="us-east-1")
+    >>> storage.upload_directory("/path/to/local/dir", "remote/prefix")
+"""
+
 import base64
 import datetime
 import logging
@@ -29,22 +41,7 @@
 
 @dataclass
 class UploadResult:
-    """
-    Result of an upload operation.
-
-    Attributes
-    ----------
-    success : bool
-        Whether the upload completed without errors.
-    files_uploaded : int
-        Number of files uploaded.
-    total_bytes : int
-        Total bytes uploaded.
-    destination : str
-        Destination path or URI.
-    errors : list of str
-        List of error messages, if any.
-    """
+    """Result of an upload operation"""
 
     success: bool
     files_uploaded: int
@@ -64,9 +61,9 @@ class ObjectStorage(ABC):
     """
     Abstract base class for object storage operations.
 
-    Defines the interface that all object storage implementations must follow.
-    Subclasses implement the abstract methods for specific cloud providers
-    (AWS S3, GCS, Azure Blob Storage, etc.).
+    This class defines the interface that all object storage implementations must follow.
+    Subclasses should implement the abstract methods to provide functionality for
+    specific cloud providers (AWS S3, GCS, Azure Blob Storage, etc.).
     """
 
     @abstractmethod
@@ -80,31 +77,20 @@ def upload_directory(
         """
         Upload a local directory to the object store at the specified path.
 
-        Parameters
-        ----------
-        local_directory : str or Path
-            Path to the local directory to upload.
-        remote_prefix : str
-            Prefix/path in the bucket where files will be uploaded.
-            Files maintain their relative paths under this prefix.
-        recursive : bool, optional
-            If True, recursively upload all subdirectories. Default is True.
-        overwrite : bool, optional
-            If True, overwrite existing files in the destination. Default is True.
-
-        Returns
-        -------
-        UploadResult
-            Details about the upload operation.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the local directory does not exist.
-        PermissionError
-            If there are permission issues with the bucket.
-        ObjectStorageError
-            For other storage-related errors.
+        Args:
+            local_directory: Path to the local directory to upload.
+            remote_prefix: The prefix/path in the bucket where files will be uploaded.
+                          Files will maintain their relative paths under this prefix.
+            recursive: If True, recursively upload all subdirectories.
+            overwrite: If True, overwrite existing files in the destination.
+
+        Returns:
+            UploadResult containing details about the upload operation.
+
+        Raises:
+            FileNotFoundError: If the local directory does not exist.
+            PermissionError: If there are permission issues with the bucket.
+            ObjectStorageError: For other storage-related errors.
         """
         pass
 
@@ -118,18 +104,12 @@ def upload_file(
         """
         Upload a single file to the object store.
 
-        Parameters
-        ----------
-        local_file : str or Path
-            Path to the local file to upload.
-        remote_key : str
-            Full key/path in the bucket for the file.
-        overwrite : bool, optional
-            If True, overwrite if the file already exists. Default is True.
-
-        Returns
-        -------
-        bool
+        Args:
+            local_file: Path to the local file to upload.
+            remote_key: The full key/path in the bucket for the file.
+            overwrite: If True, overwrite if the file already exists.
+
+        Returns:
             True if upload was successful, False otherwise.
         """
         pass
@@ -139,14 +119,10 @@ def file_exists(self, remote_key: str) -> bool:
         """
         Check if a file exists in the object store.
 
-        Parameters
-        ----------
-        remote_key : str
-            Full key/path in the bucket.
+        Args:
+            remote_key: The full key/path in the bucket.
 
-        Returns
-        -------
-        bool
+        Returns:
             True if the file exists, False otherwise.
         """
         pass
@@ -156,14 +132,10 @@ def delete_file(self, remote_key: str) -> bool:
         """
         Delete a file from the object store.
 
-        Parameters
-        ----------
-        remote_key : str
-            Full key/path in the bucket.
+        Args:
+            remote_key: The full key/path in the bucket.
 
-        Returns
-        -------
-        bool
+        Returns:
             True if deletion was successful, False otherwise.
         """
         pass
@@ -173,25 +145,15 @@ def generate_signed_url(self, remote_key: str, expires_in: int = 86400) -> str:
         """
         Generate a signed URL for a file in the object store.
 
-        Parameters
-        ----------
-        remote_key : str
-            Full key/path in the bucket.
-        expires_in : int, optional
-            Number of seconds the URL will be valid for. Default is 86400.
-
-        Returns
-        -------
-        str
-            Signed URL string.
+        Args:
+            remote_key: The full key/path in the bucket.
+            expires_in: The number of seconds the URL will be valid for.
         """
         pass
 
 
 class ObjectStorageError(Exception):
-    """
-    Base exception for object storage errors.
-    """
+    """Base exception for object storage errors"""
 
     pass
 
@@ -670,9 +632,7 @@ def file_exists(self, remote_key: str) -> bool:
         Args:
             remote_key: The storage key to check.
 
-        Returns
-        -------
-        bool
+        Returns:
             True if the file exists, False otherwise.
         """
         try:
@@ -691,9 +651,7 @@ def delete_file(self, remote_key: str) -> bool:
         Args:
             remote_key: The storage key to delete.
 
-        Returns
-        -------
-        bool
+        Returns:
             True if successful, False otherwise.
         """
         try:
@@ -708,19 +666,7 @@ def delete_file(self, remote_key: str) -> bool:
             return False
 
     def _rsa_signer(self, message: bytes) -> bytes:
-        """
-        Sign a message using the CloudFront private key.
-
-        Parameters
-        ----------
-        message : bytes
-            Message to sign.
-
-        Returns
-        -------
-        bytes
-            RSA signature.
-        """
+        """Sign a message using the CloudFront private key."""
         from cryptography.hazmat.primitives import hashes
         from cryptography.hazmat.primitives.asymmetric import padding
         from cryptography.hazmat.primitives.serialization import load_pem_private_key
@@ -741,19 +687,7 @@ def _rsa_signer(self, message: bytes) -> bytes:
 
     @staticmethod
     def _url_safe_b64(data: bytes) -> str:
-        """
-        Convert bytes to URL-safe base64.
-
-        Parameters
-        ----------
-        data : bytes
-            Data to encode.
-
-        Returns
-        -------
-        str
-            URL-safe base64 string.
-        """
+        """Convert bytes to URL-safe base64."""
         return (
             base64.b64encode(data)
             .decode("utf-8")
@@ -892,3 +826,44 @@ def _generate_azure_sas_url(self, remote_key: str, expires_in: int) -> str:
             f"Generated Azure SAS URL for {remote_key}, expires in {expires_in}s"
         )
         return signed_url
+
+
+def get_object_storage(
+    provider: Literal["msc"] = "msc",
+    **kwargs: Any,
+) -> ObjectStorage:
+    """
+    Factory function to get an object storage instance.
+
+    This function provides a convenient way to instantiate the MSCObjectStorage
+    implementation which uses NVIDIA Multi-Storage Client with Rust backend.
+
+    Args:
+        provider: The storage provider to use. Currently only "msc" is supported.
+        **kwargs: Provider-specific configuration arguments.
+
+    Returns:
+        An ObjectStorage instance.
+
+    Raises:
+        ValueError: If an unsupported provider is specified.
+
+    Example:
+        >>> storage = get_object_storage(
+        ...     "msc",
+        ...     bucket="my-bucket",
+        ...     region="us-east-1",
+        ...     use_rust_client=True
+        ... )
+    """
+    providers = {
+        "msc": MSCObjectStorage,
+    }
+
+    if provider not in providers:
+        raise ValueError(
+            f"Unsupported storage provider: {provider}. "
+            f"Supported providers: {list(providers.keys())}"
+        )
+
+    return providers[provider](**kwargs)
diff --git a/serve/server/planetary_computer/pc_client.py b/serve/inferenceserver/api_server/planetary_computer/pc_client.py
similarity index 100%
rename from serve/server/planetary_computer/pc_client.py
rename to serve/inferenceserver/api_server/planetary_computer/pc_client.py
diff --git a/serve/server/planetary_computer/template-collection-fcn3-stormscope-goes.json b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
similarity index 100%
rename from serve/server/planetary_computer/template-collection-fcn3-stormscope-goes.json
rename to serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3-stormscope-goes.json
diff --git a/serve/server/planetary_computer/template-collection-fcn3.json b/serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
similarity index 100%
rename from serve/server/planetary_computer/template-collection-fcn3.json
rename to serve/inferenceserver/api_server/planetary_computer/template-collection-fcn3.json
diff --git a/serve/server/planetary_computer/template-feature-fcn3-stormscope-goes.json b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
similarity index 100%
rename from serve/server/planetary_computer/template-feature-fcn3-stormscope-goes.json
rename to serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3-stormscope-goes.json
diff --git a/serve/server/planetary_computer/template-feature-fcn3.json b/serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json
similarity index 100%
rename from serve/server/planetary_computer/template-feature-fcn3.json
rename to serve/inferenceserver/api_server/planetary_computer/template-feature-fcn3.json
diff --git a/earth2studio/serve/server/utils.py b/serve/inferenceserver/api_server/utils.py
similarity index 94%
rename from earth2studio/serve/server/utils.py
rename to serve/inferenceserver/api_server/utils.py
index 1cc044caa..2c6d725df 100644
--- a/earth2studio/serve/server/utils.py
+++ b/serve/inferenceserver/api_server/utils.py
@@ -34,7 +34,7 @@
 from fastapi import HTTPException
 from rq import Queue
 
-from earth2studio.serve.server.config import get_config
+from api_server.config import get_config
 
 logger = logging.getLogger(__name__)
 
@@ -201,9 +201,7 @@ async def create_file_stream(
 
 def queue_next_stage(
     redis_client: redis.Redis,
-    current_stage: Literal[
-        "inference", "result_zip", "object_storage", "geocatalog_ingestion"
-    ],
+    current_stage: Stage,
     workflow_name: str,
     execution_id: str,
     output_path_str: str,
@@ -234,7 +232,7 @@ def queue_next_stage(
     if current_stage == "inference":
         # Always go to result_zip stage (for manifest building), but control zip creation via config
         next_queue = "result_zip"
-        next_func = "earth2studio.serve.server.cpu_worker.process_result_zip"
+        next_func = "api_server.cpu_worker.process_result_zip"
         # Pass create_zip parameter based on config
         # Disable zip creation when object storage is enabled (files uploaded directly)
         create_zip = (
@@ -251,13 +249,11 @@ def queue_next_stage(
     elif current_stage == "result_zip":
         if config.object_storage.enabled:
             next_queue = "object_storage"
-            next_func = (
-                "earth2studio.serve.server.cpu_worker.process_object_storage_upload"
-            )
+            next_func = "api_server.cpu_worker.process_object_storage_upload"
             args = (workflow_name, execution_id, output_path_str)
         else:
             next_queue = "finalize_metadata"
-            next_func = "earth2studio.serve.server.cpu_worker.process_finalize_metadata"
+            next_func = "api_server.cpu_worker.process_finalize_metadata"
             args = (workflow_name, execution_id)
 
     elif current_stage == "object_storage":
@@ -272,7 +268,7 @@ def queue_next_stage(
 
     elif current_stage == "geocatalog_ingestion":
         next_queue = "finalize_metadata"
-        next_func = "earth2studio.serve.server.cpu_worker.process_finalize_metadata"
+        next_func = "api_server.cpu_worker.process_finalize_metadata"
         args = (workflow_name, execution_id)
 
     else:
diff --git a/earth2studio/serve/server/worker.py b/serve/inferenceserver/api_server/worker.py
similarity index 79%
rename from earth2studio/serve/server/worker.py
rename to serve/inferenceserver/api_server/worker.py
index 804c6d3e0..f8ff9c601 100644
--- a/earth2studio/serve/server/worker.py
+++ b/serve/inferenceserver/api_server/worker.py
@@ -14,6 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Earth2Studio RQ Worker
+
+This module contains the worker functions for processing inference requests
+using Redis Queue (RQ). Workers run the actual inference computations.
+
+"""
+
 import logging
 import time
 from datetime import datetime, timezone
@@ -23,13 +31,13 @@
 import redis  # type: ignore[import-untyped]
 
 # Import configuration
-from earth2studio.serve.server.config import get_config, get_config_manager
+from api_server.config import get_config, get_config_manager
 
 # Import queue_next_stage utility
-from earth2studio.serve.server.utils import queue_next_stage
+from api_server.utils import queue_next_stage
 
 # Import workflow registry
-from earth2studio.serve.server.workflow import WorkflowStatus, workflow_registry
+from api_server.workflow import WorkflowStatus, workflow_registry
 
 # Get configuration
 config = get_config()
@@ -59,7 +67,7 @@
 
 # Register custom workflows in the worker process
 try:
-    from earth2studio.serve.server.workflow import register_all_workflows
+    from api_server.workflow import register_all_workflows
 
     register_all_workflows(redis_client)
     logger.info("Custom workflows registered successfully in worker process")
@@ -78,25 +86,7 @@ def get_output_path(
     workflow_type: str,
     request_id: str,
 ) -> Path:
-    """
-    Generate output path for workflow results.
-
-    Parameters
-    ----------
-    io_config : dict or None
-        I/O configuration (used for backend_type); if None, backend_type defaults to "zarr".
-    timestamp : str
-        Timestamp string used in the output directory name.
-    workflow_type : str
-        Type of workflow (e.g. workflow name).
-    request_id : str
-        Request identifier.
-
-    Returns
-    -------
-    Path
-        Path to the forecast output file (e.g. forecast.zarr).
-    """
+    """Generate output path for workflow results"""
     # Create timestamp-based subdirectory
     timestamp_str = timestamp.replace(":", "").replace("Z", "").replace("+", "")
     output_dir = DEFAULT_OUTPUT_DIR / f"{workflow_type}_{timestamp_str}_{request_id}"
@@ -109,33 +99,7 @@ def get_output_path(
 def run_custom_workflow(
     workflow_name: str, execution_id: str, parameters: dict[str, Any]
 ) -> Any:
-    """
-    RQ worker function to run a custom workflow.
-
-    Executes the workflow, updates execution status in Redis, and queues the
-    next pipeline stage (e.g. result_zip or object_storage) as configured.
-
-    Parameters
-    ----------
-    workflow_name : str
-        Name of the registered workflow to run.
-    execution_id : str
-        Unique execution identifier.
-    parameters : dict
-        Workflow input parameters (validated by the workflow's parameter type).
-
-    Returns
-    -------
-    Any
-        Result returned by the workflow's run() method.
-
-    Raises
-    ------
-    ValueError
-        If the workflow is not found or cannot be instantiated.
-    RuntimeError
-        If queuing the next pipeline stage fails.
-    """
+    """RQ Worker function to run custom workflow"""
     # Create logger adapter with execution_id for automatic log prefixing
     log = logging.LoggerAdapter(logger, {"execution_id": execution_id})
 
@@ -148,13 +112,11 @@ def run_custom_workflow(
 
     # Create workflow instance for execution
     custom_workflow = workflow_registry.get(workflow_name, redis_client=redis_client)
-    if custom_workflow is None:
-        raise ValueError(f"Custom workflow '{workflow_name}' could not be instantiated")
 
     try:
         start_timestamp = time.time()
         start_time = datetime.now(timezone.utc).isoformat()
-        updates: dict[str, Any] = {
+        updates = {
             "status": WorkflowStatus.RUNNING,
             "start_time": start_time,
         }
diff --git a/earth2studio/serve/server/__init__.py b/serve/inferenceserver/api_server/workflow/__init__.py
similarity index 62%
rename from earth2studio/serve/server/__init__.py
rename to serve/inferenceserver/api_server/workflow/__init__.py
index 475e291bf..91d3cf418 100644
--- a/earth2studio/serve/server/__init__.py
+++ b/serve/inferenceserver/api_server/workflow/__init__.py
@@ -14,9 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Workflow package for custom workflows.
+
+This package provides the base classes and functionality for creating custom workflows
+that can be served as REST APIs.
+"""
+
 from typing import Any
 
-from earth2studio.serve.server.workflow import (
+from .workflow import (
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
@@ -30,9 +37,26 @@
 )
 
 
+# Lazy import for Earth2Workflow to avoid requiring torch/earth2studio dependencies
+# unless actually needed
 def __getattr__(name: str) -> Any:
     if name == "Earth2Workflow":
-        from earth2studio.serve.server import e2workflow
+        from .e2workflow import Earth2Workflow
 
-        return e2workflow.Earth2Workflow
+        return Earth2Workflow
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = [
+    "Workflow",
+    "WorkflowParameters",
+    "WorkflowProgress",
+    "WorkflowResult",
+    "WorkflowStatus",
+    "WorkflowRegistry",
+    "workflow_registry",
+    "get_workflow_config",
+    "register_all_workflows",
+    "parse_workflow_directories_from_env",
+    "Earth2Workflow",
+]
diff --git a/earth2studio/serve/server/e2workflow.py b/serve/inferenceserver/api_server/workflow/e2workflow.py
similarity index 97%
rename from earth2studio/serve/server/e2workflow.py
rename to serve/inferenceserver/api_server/workflow/e2workflow.py
index e71719de5..3e52df372 100644
--- a/earth2studio/serve/server/e2workflow.py
+++ b/serve/inferenceserver/api_server/workflow/e2workflow.py
@@ -24,16 +24,12 @@
 import zarr
 from pydantic import Field, create_model
 
+from api_server.config import get_config
 from earth2studio.io import IOBackend, NetCDF4Backend, ZarrBackend
-from earth2studio.serve.server.config import get_config
-from earth2studio.serve.server.workflow import (
-    Workflow,
-    WorkflowConfig,
-    WorkflowParameters,
-    WorkflowProgress,
-)
 from earth2studio.utils.type import CoordSystem
 
+from .workflow import Workflow, WorkflowConfig, WorkflowParameters, WorkflowProgress
+
 
 def _convert_param(param: inspect.Parameter, typeinfo: type) -> tuple[type, Field]:
     """Convert parameter to Pydantic Field declaration with validation constraints"""
@@ -232,7 +228,7 @@ class BackendProgress:
     def __init__(
         self,
         io: IOBackend,
-        workflow: "Earth2Workflow",
+        workflow: Workflow,
         execution_id: str,
         progress_dim: str = "lead_time",
     ) -> None:
diff --git a/earth2studio/serve/server/workflow.py b/serve/inferenceserver/api_server/workflow/workflow.py
similarity index 75%
rename from earth2studio/serve/server/workflow.py
rename to serve/inferenceserver/api_server/workflow/workflow.py
index c262bdefd..8e786c34b 100644
--- a/earth2studio/serve/server/workflow.py
+++ b/serve/inferenceserver/api_server/workflow/workflow.py
@@ -14,6 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Workflow module
+
+This module provides the base classes and functionality for custom workflows.
+"""
+
 import importlib.util
 import json
 import logging
@@ -25,10 +31,10 @@
 from typing import Any, Union
 
 import redis  # type: ignore[import-untyped]
-from pydantic import BaseModel, ConfigDict, Field, ValidationInfo, field_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 # Import configuration
-from earth2studio.serve.server.config import (
+from api_server.config import (
     get_config,
     get_config_manager,
     get_workflow_config,
@@ -44,7 +50,7 @@
 
 
 class WorkflowStatus:
-    """Workflow execution status constants."""
+    """Workflow execution status constants"""
 
     QUEUED = "queued"
     RUNNING = "running"
@@ -56,7 +62,7 @@ class WorkflowStatus:
 
 
 class WorkflowArgsBase(BaseModel):
-    """Base parameters for workflow execution."""
+    """Base parameters for workflow execution"""
 
     # Add strict validation - reject unknown fields
     model_config = ConfigDict(extra="forbid")
@@ -72,20 +78,14 @@ def validate(
         It ensures that all required fields are present, types are correct, and any
         custom validators defined in the parameter class are executed.
 
-        Parameters
-        ----------
-        data : dict or WorkflowArgsBase
-            Input data as dict or WorkflowArgsBase instance.
+        Args:
+            data: Input data as dict or WorkflowArgsBase instance
 
-        Returns
-        -------
-        WorkflowArgsBase
-            Validated WorkflowArgsBase instance.
+        Returns:
+            Validated WorkflowArgsBase instance
 
-        Raises
-        ------
-        ValueError
-            If validation fails.
+        Raises:
+            ValueError: If validation fails
         """
         if isinstance(data, cls):
             # Already the correct type, re-validate to ensure all constraints are met
@@ -109,34 +109,24 @@ class WorkflowParameters(WorkflowArgsBase):
 
     @field_validator("forecast_times", "start_time", mode="before", check_fields=False)
     @classmethod
-    def validate_datetime_list(
-        cls: type["WorkflowParameters"],
-        v: list[str] | list[datetime] | str | datetime | None,
-        info: ValidationInfo,
-    ) -> list[str] | list[datetime] | str | datetime | None:
+    def validate_datetime_list(cls, v: Any, info: Any) -> Any:
         """
         Validate that forecast_times and start_time are in valid ISO 8601 datetime format.
 
         This validator handles both string lists (forecast_times) and can validate
         datetime objects or ISO 8601 strings (start_time).
+
         Requires full datetime format with 'T' separator (not just date) for strings.
 
-        Parameters
-        ----------
-        v : list of str or datetime, or single value, or None
-            The value (can be list of strings/datetime objects, single value, or None).
-        info : ValidationInfo
-            Pydantic validation context (e.g. field name).
+        Args:
+            v: The value (can be list of strings/datetime objects, single value, or None)
+            info: Validation context info
 
-        Returns
-        -------
-        list of str or list of datetime or str or datetime or None
-            The validated value (same shape as input).
+        Returns:
+            The validated value
 
-        Raises
-        ------
-        ValueError
-            If any value is not in valid ISO 8601 datetime format.
+        Raises:
+            ValueError: If any value is not in valid ISO 8601 datetime format
         """
         from datetime import datetime
 
@@ -188,7 +178,7 @@ def validate_datetime_list(
 
 class WorkflowConfig(WorkflowArgsBase):
     """
-    Specialize in case we want to differentiate parameters and config handling later.
+    Specialize in case we want to differentiate parameters and config handling  later.
     """
 
     pass
@@ -201,46 +191,38 @@ class WorkflowProgress(BaseModel):
     This class provides standard progress tracking fields that can be extended
     by custom workflows to include additional tracking information.
 
-    Attributes
-    ----------
-    progress : str or None
-        Human-readable progress message.
-    current_step : int or None
-        Current step number in the workflow.
-    total_steps : int or None
-        Total number of steps in the workflow.
-    error_message : str or None
-        Error message to report specific errors when workflow fails.
-
-    Examples
-    --------
-    Basic usage:
-
-    >>> progress = WorkflowProgress(
-    ...     progress="Processing data...",
-    ...     current_step=3,
-    ...     total_steps=10
-    ... )
-
-    Error reporting:
-
-    >>> progress = WorkflowProgress(
-    ...     progress="Failed!",
-    ...     error_message="Connection timeout: Could not reach data source"
-    ... )
-
-    Extended for custom workflow:
-
-    >>> class MyCustomProgress(WorkflowProgress):
-    ...     data_processed_gb: float = 0.0
-    ...     error_count: int = 0
-    >>> progress = MyCustomProgress(
-    ...     progress="Processing batch 5",
-    ...     current_step=5,
-    ...     total_steps=20,
-    ...     data_processed_gb=150.5,
-    ...     error_count=2
-    ... )
+    Attributes:
+        progress: Human-readable progress message
+        current_step: Current step number in the workflow
+        total_steps: Total number of steps in the workflow
+        error_message: Error message to report specific errors when workflow fails
+
+    Example:
+        Basic usage:
+            progress = WorkflowProgress(
+                progress="Processing data...",
+                current_step=3,
+                total_steps=10
+            )
+
+        Error reporting:
+            progress = WorkflowProgress(
+                progress="Failed!",
+                error_message="Connection timeout: Could not reach data source"
+            )
+
+        Extended for custom workflow:
+            class MyCustomProgress(WorkflowProgress):
+                data_processed_gb: float = 0.0
+                error_count: int = 0
+
+            progress = MyCustomProgress(
+                progress="Processing batch 5",
+                current_step=5,
+                total_steps=20,
+                data_processed_gb=150.5,
+                error_count=2
+            )
     """
 
     progress: str | None = None
@@ -250,7 +232,7 @@ class WorkflowProgress(BaseModel):
 
 
 class WorkflowResult(BaseModel):
-    """Base result structure for workflow execution."""
+    """Base result structure for workflow execution"""
 
     workflow_name: str
     execution_id: str
@@ -294,16 +276,14 @@ def __init__(self) -> None:
         """
         Initialize workflow.
 
-        Notes
-        -----
-        name and description are set by the registry during workflow instantiation.
+        Note: name and description are set by the registry during workflow instantiation.
         """
         self.redis_client: redis.Redis | None = None
         self.output_dir = Path(config.paths.default_output_dir)
         self.output_dir.mkdir(parents=True, exist_ok=True)
 
     def set_redis_client(self, redis_client: redis.Redis) -> None:
-        """Set Redis client for state management."""
+        """Set Redis client for state management"""
         self.redis_client = redis_client
 
     @classmethod
@@ -318,26 +298,19 @@ def validate_parameters(
         Pydantic field validation and model validation. Subclasses should implement
         this method to specify their parameter type.
 
-        Parameters
-        ----------
-        parameters : dict or WorkflowParameters
-            Input parameters as dict or WorkflowParameters instance.
+        Args:
+            parameters: Input parameters as dict or WorkflowParameters instance
 
-        Returns
-        -------
-        WorkflowParameters
-            Validated WorkflowParameters instance of the workflow-specific type.
+        Returns:
+            Validated WorkflowParameters instance of the workflow-specific type
 
-        Raises
-        ------
-        ValueError
-            If validation fails.
+        Raises:
+            ValueError: If validation fails
 
-        Examples
-        --------
-        >>> @classmethod
-        ... def validate_parameters(cls, parameters):
-        ...     return MyWorkflowParameters.validate(parameters)
+        Example:
+            @classmethod
+            def validate_parameters(cls, parameters):
+                return MyWorkflowParameters.validate(parameters)
         """
         raise NotImplementedError(
             "Subclasses must implement validate_parameters() method"
@@ -353,17 +326,12 @@ def run(
         This method should implement the actual workflow logic.
         It will be called when a POST request is made to /v1/infer/{workflow_name}.
 
-        Parameters
-        ----------
-        parameters : dict or WorkflowParameters
-            Input parameters for the workflow.
-        execution_id : str
-            Unique execution identifier.
+        Args:
+            parameters: Input parameters for the workflow (dict or WorkflowParameters)
+            execution_id: Unique execution identifier
 
-        Returns
-        -------
-        dict
-            Dictionary containing workflow results.
+        Returns:
+            Dictionary containing workflow results
         """
         raise NotImplementedError("Subclasses must implement run() method")
 
@@ -376,49 +344,41 @@ def update_execution_data(
         This method can be called by workflow implementations to update specific
         fields in the execution data without replacing the entire record.
 
-        Parameters
-        ----------
-        execution_id : str
-            Unique execution identifier.
-        updates : dict or WorkflowProgress
-            Dictionary of fields to update, or WorkflowProgress instance.
-            If a WorkflowProgress object is provided, it will update the
-            'progress' field of WorkflowResult. Dict updates can include
-            any WorkflowResult fields including 'progress', 'metadata', etc.
-
-        Raises
-        ------
-        RuntimeError
-            If Redis client is not set.
-
-        Examples
-        --------
-        Using dict for general updates:
-
-        >>> self.update_execution_data(execution_id, {
-        ...     "metadata": {"custom_field": "value"}
-        ... })
-
-        Using WorkflowProgress for progress updates:
-
-        >>> progress = WorkflowProgress(
-        ...     progress="Processing data...",
-        ...     current_step=3,
-        ...     total_steps=10
-        ... )
-        >>> self.update_execution_data(execution_id, progress)
-
-        Using custom WorkflowProgress subclass:
-
-        >>> class MyProgress(WorkflowProgress):
-        ...     data_processed_gb: float = 0.0
-        >>> progress = MyProgress(
-        ...     progress="Batch processing",
-        ...     current_step=5,
-        ...     total_steps=20,
-        ...     data_processed_gb=150.5
-        ... )
-        >>> self.update_execution_data(execution_id, progress)
+        Args:
+            execution_id: Unique execution identifier
+            updates: Dictionary of fields to update, or WorkflowProgress instance.
+                    If a WorkflowProgress object is provided, it will update the
+                    'progress' field of WorkflowResult. Dict updates can include
+                    any WorkflowResult fields including 'progress', 'metadata', etc.
+
+        Raises:
+            RuntimeError: If Redis client is not set
+
+        Example:
+            # Using dict for general updates
+            self.update_execution_data(execution_id, {
+                "metadata": {"custom_field": "value"}
+            })
+
+            # Using WorkflowProgress for progress updates
+            progress = WorkflowProgress(
+                progress="Processing data...",
+                current_step=3,
+                total_steps=10
+            )
+            self.update_execution_data(execution_id, progress)
+
+            # Using custom WorkflowProgress subclass
+            class MyProgress(WorkflowProgress):
+                data_processed_gb: float = 0.0
+
+            progress = MyProgress(
+                progress="Batch processing",
+                current_step=5,
+                total_steps=20,
+                data_processed_gb=150.5
+            )
+            self.update_execution_data(execution_id, progress)
         """
         if not self.redis_client:
             raise RuntimeError("Redis client not set. Call set_redis_client() first.")
@@ -454,15 +414,11 @@ def get_output_path(self, execution_id: str) -> Path:
         output directory path for a specific execution. The directory is automatically
         created if it doesn't exist.
 
-        Parameters
-        ----------
-        execution_id : str
-            Unique execution identifier.
+        Args:
+            execution_id: Unique execution identifier
 
-        Returns
-        -------
-        Path
-            Path object pointing to the output directory.
+        Returns:
+            Path object pointing to the output directory
         """
         workflow_dir = self.output_dir / self.name / execution_id
         workflow_dir.mkdir(parents=True, exist_ok=True)
@@ -478,19 +434,13 @@ def _get_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Parameters
-        ----------
-        redis_client : redis.Redis
-            Redis client instance.
-        workflow_name : str
-            Name of the workflow.
-        execution_id : str
-            Unique execution identifier.
+        Args:
+            redis_client: Redis client instance
+            workflow_name: Name of the workflow
+            execution_id: Unique execution identifier
 
-        Returns
-        -------
-        WorkflowResult
-            Execution data.
+        Returns:
+            WorkflowResult with execution data
         """
         try:
             # Get execution data from Redis
@@ -527,16 +477,11 @@ def _save_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Parameters
-        ----------
-        redis_client : redis.Redis
-            Redis client instance.
-        workflow_name : str
-            Name of the workflow.
-        execution_id : str
-            Unique execution identifier.
-        data : WorkflowResult
-            WorkflowResult to save.
+        Args:
+            redis_client: Redis client instance
+            workflow_name: Name of the workflow
+            execution_id: Unique execution identifier
+            data: WorkflowResult to save
         """
         try:
             redis_client.setex(
@@ -563,16 +508,11 @@ def _update_execution_data(
 
         This method can only be called by the system, and should not be called by workflow implementations.
 
-        Parameters
-        ----------
-        redis_client : redis.Redis
-            Redis client instance.
-        workflow_name : str
-            Name of the workflow.
-        execution_id : str
-            Unique execution identifier.
-        updates : dict
-            Dictionary of fields to update.
+        Args:
+            redis_client: Redis client instance
+            workflow_name: Name of the workflow
+            execution_id: Unique execution identifier
+            updates: Dictionary of fields to update
         """
         try:
             # Get current data
@@ -595,7 +535,7 @@ def _update_execution_data(
 
 
 def json_serial(obj: Any) -> Any:
-    """JSON serializer for objects not serializable by default json code."""
+    """JSON serializer for objects not serializable by default json code"""
 
     if isinstance(obj, (datetime, date)):
         return obj.isoformat()
@@ -605,7 +545,7 @@ def json_serial(obj: Any) -> Any:
 
 
 class WorkflowRegistry:
-    """Registry for managing custom workflows."""
+    """Registry for managing custom workflows"""
 
     def __init__(self, **config: Any) -> None:
         self._workflows: dict[str, type[Workflow]] = {}
@@ -615,22 +555,15 @@ def register(self, workflow_class: type[Workflow]) -> type[Workflow]:
         """
         Register a workflow class.
 
-        Parameters
-        ----------
-        workflow_class : type[Workflow]
-            Workflow class (not instance) to register.
+        Args:
+            workflow_class: Workflow class (not instance) to register
 
-        Returns
-        -------
-        type[Workflow]
-            workflow_class (enabling the use of ``register`` as a decorator).
+        Returns:
+            workflow_class (enabling the use of ``register`` as a decorator)
 
-        Raises
-        ------
-        TypeError
-            If workflow_class is not a class or not a Workflow subclass.
-        ValueError
-            If workflow name is already registered.
+        Raises:
+            TypeError: If workflow_class is not a class or not a Workflow subclass
+            ValueError: If workflow name is already registered
         """
         # Skip registration in a non-API environment
         api_server_running = os.environ.get("EARTH2STUDIO_API_ACTIVE", "0") == "1"
@@ -665,7 +598,7 @@ def register(self, workflow_class: type[Workflow]) -> type[Workflow]:
         return workflow_class
 
     def unregister(self, name: str) -> None:
-        """Unregister a workflow."""
+        """Unregister a workflow"""
         if name not in self._workflows:
             raise ValueError(f"Workflow '{name}' is not registered")
 
@@ -679,15 +612,11 @@ def get_workflow_class(self, name: str) -> type[Workflow] | None:
         """
         Get a workflow class by name.
 
-        Parameters
-        ----------
-        name : str
-            Name of the workflow.
+        Args:
+            name: Name of the workflow
 
-        Returns
-        -------
-        type[Workflow] or None
-            Workflow class or None if not found.
+        Returns:
+            Workflow class or None if not found
         """
         return self._workflows.get(name)
 
@@ -700,17 +629,12 @@ def get(
         Creates a new instance on first call and caches it for subsequent calls.
         The instance is automatically initialized with the registered name and description.
 
-        Parameters
-        ----------
-        name : str
-            Name of the workflow.
-        redis_client : redis.Redis or None, optional
-            Optional Redis client to set on the instance.
+        Args:
+            name: Name of the workflow
+            redis_client: Optional Redis client to set on the instance
 
-        Returns
-        -------
-        Workflow or None
-            Cached workflow instance or None if not found.
+        Returns:
+            Cached workflow instance or None if not found
         """
         workflow_class = self.get_workflow_class(name)
         if workflow_class is None:
@@ -753,7 +677,7 @@ def is_workflow_exposed(self, workflow_name: str) -> bool:
         Returns:
             True if workflow should be exposed, False otherwise
         """
-        from earth2studio.serve.server.config import get_config
+        from api_server.config import get_config
 
         config = get_config()
         exposed_workflows = config.workflow_exposure.exposed_workflows
@@ -779,7 +703,7 @@ def list_workflows(self, exposed_only: bool = True) -> dict[str, str]:
             Dictionary mapping workflow names to descriptions
         """
         if exposed_only:
-            from earth2studio.serve.server.config import get_config
+            from api_server.config import get_config
 
             config = get_config()
             exposed_workflows = config.workflow_exposure.exposed_workflows
@@ -810,24 +734,16 @@ def discover_and_register_from_directories(
         """
         Discover and register workflows from specified directories.
 
-        Parameters
-        ----------
-        workflow_dirs : list
-            List of directory paths to search for workflow modules.
-        include_builtin : bool, optional
-            Whether to automatically include built-in example_workflows. Default is True.
+        Args:
+            workflow_dirs: List of directory paths to search for workflow modules
+            include_builtin: Whether to automatically include built-in example_workflows
 
-        Returns
-        -------
-        tuple
-            (successful_imports, failed_imports) counts.
+        Returns:
+            tuple: (successful_imports, failed_imports) counts
         """
-        # Always include built-in workflows if requested (serve/server/example_workflows)
+        # Always include built-in workflows if requested
         if include_builtin:
-            _repo_root = Path(__file__).resolve().parent.parent.parent.parent
-            builtin_workflows_dir = (
-                _repo_root / "serve" / "server" / "example_workflows"
-            )
+            builtin_workflows_dir = Path(__file__).parent.parent / "example_workflows"
             already_included = builtin_workflows_dir in {Path(d) for d in workflow_dirs}
             if builtin_workflows_dir.exists() and not already_included:
                 # Add to the beginning so built-in workflows are discovered first
@@ -913,10 +829,8 @@ def auto_register_workflows(self, redis_client: redis.Redis) -> None:
 
         This is the main entry point for workflow registration during server startup.
 
-        Parameters
-        ----------
-        redis_client : redis.Redis
-            Redis client instance to set on registered workflows.
+        Args:
+            redis_client: Redis client instance to set on registered workflows
         """
         try:
             logger.info("=" * 60)
@@ -964,10 +878,8 @@ def parse_workflow_directories_from_env() -> list[str]:
     - Comma-separated list of directories
     - Colon-separated list of directories
 
-    Returns
-    -------
-    list of str
-        List of directory paths to search for workflows.
+    Returns:
+        list: List of directory paths to search for workflows
     """
     workflow_dirs = []
 
@@ -1006,16 +918,13 @@ def register_all_workflows(redis_client: redis.Redis) -> None:
     1. Built-in example_workflows directory (always included)
     2. User directories specified via WORKFLOW_DIR environment variable
 
-    Parameters
-    ----------
-    redis_client : redis.Redis
-        Redis client instance to set on registered workflows.
-
-    Examples
-    --------
-    >>> import redis
-    >>> from earth2studio.serve.server.workflow import register_all_workflows
-    >>> redis_client = redis.Redis(host='localhost', port=6379)
-    >>> register_all_workflows(redis_client)
+    Args:
+        redis_client: Redis client instance to set on registered workflows
+
+    Example:
+        >>> import redis
+        >>> from api_server.workflow import register_all_workflows
+        >>> redis_client = redis.Redis(host='localhost', port=6379)
+        >>> register_all_workflows(redis_client)
     """
     workflow_registry.auto_register_workflows(redis_client)
diff --git a/serve/server/env.example b/serve/inferenceserver/env.example
similarity index 100%
rename from serve/server/env.example
rename to serve/inferenceserver/env.example
diff --git a/serve/server/openapi.yaml b/serve/inferenceserver/openapi.yaml
similarity index 100%
rename from serve/server/openapi.yaml
rename to serve/inferenceserver/openapi.yaml
diff --git a/serve/server/requirements.txt b/serve/inferenceserver/requirements.txt
similarity index 80%
rename from serve/server/requirements.txt
rename to serve/inferenceserver/requirements.txt
index 69b3a0404..30815a96e 100644
--- a/serve/server/requirements.txt
+++ b/serve/inferenceserver/requirements.txt
@@ -1,7 +1,3 @@
-# For local runs from serve/server: install the project first so earth2studio.serve.server
-# is importable:  pip install -e ../..
-# (Dockerfile installs the project from repo root with -e . instead.)
-
 # Core FastAPI dependencies
 fastapi>=0.104.0
 uvicorn[standard]>=0.24.0
diff --git a/serve/server/scripts/profile_workflow.sh b/serve/inferenceserver/scripts/profile_workflow.sh
similarity index 100%
rename from serve/server/scripts/profile_workflow.sh
rename to serve/inferenceserver/scripts/profile_workflow.sh
diff --git a/serve/server/scripts/redis.conf b/serve/inferenceserver/scripts/redis.conf
similarity index 100%
rename from serve/server/scripts/redis.conf
rename to serve/inferenceserver/scripts/redis.conf
diff --git a/serve/server/scripts/start_api_server.sh b/serve/inferenceserver/scripts/start_api_server.sh
similarity index 92%
rename from serve/server/scripts/start_api_server.sh
rename to serve/inferenceserver/scripts/start_api_server.sh
index 1c03c1b04..ca92db083 100755
--- a/serve/server/scripts/start_api_server.sh
+++ b/serve/inferenceserver/scripts/start_api_server.sh
@@ -18,13 +18,10 @@
 # Earth2Studio Multiple Workers Startup Script
 # This script starts multiple API workers, and RQ workers
 
-# Get the directory where this script is located (or use env from Docker/startup.sh).
-SCRIPT_DIR=${SCRIPT_DIR:-"$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"}
-CONFIG_DIR=${CONFIG_DIR:-"${SCRIPT_DIR}/../conf"}
-# Export so uvicorn/rq workers (Python) see them for CONFIG_DIR/SCRIPT_DIR in main.py and config.py
-export SCRIPT_DIR
-export CONFIG_DIR
-export WORKFLOW_DIR
+# Get the directory where this script is located.
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+# A way to override the config location while starting up the endpoint.
+CONFIG_DIR=${CONFIG_DIR:-"${SCRIPT_DIR}/../api_server/conf"}
 CONFIG_FILE="$CONFIG_DIR/config.yaml"
 
 # Function to read config values from YAML using Python
@@ -85,9 +82,9 @@ cleanup() {
     fi
 
     # Stop all API workers
-    if pgrep -f "uvicorn.*earth2studio.serve.server.main:app" > /dev/null; then
+    if pgrep -f "uvicorn.*api_server.main:app" > /dev/null; then
         echo "Stopping API workers..."
-        pkill -f "uvicorn.*earth2studio.serve.server.main:app"
+        pkill -f "uvicorn.*api_server.main:app"
     fi
 
     # Stop all RQ inference workers
@@ -130,7 +127,7 @@ trap cleanup SIGINT SIGTERM
 export EARTH2STUDIO_API_ACTIVE=1
 
 # Start multiple workers using uvicorn with extended timeouts for large file downloads
-CUDA_VISIBLE_DEVICES="" uvicorn earth2studio.serve.server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
+CUDA_VISIBLE_DEVICES="" uvicorn api_server.main:app --host 0.0.0.0 --port $API_PORT --workers $NUM_WORKERS --loop asyncio --timeout-keep-alive 300 --timeout-graceful-shutdown 30 &
 UVICORN_PID=$!
 
 # Start RQ workers
@@ -187,7 +184,7 @@ for i in $(seq 1 $NUM_FINALIZE_WORKERS); do
 done
 
 # Start cleanup daemon
-CUDA_VISIBLE_DEVICES="" python -m earth2studio.serve.server.cleanup_daemon &
+CUDA_VISIBLE_DEVICES="" python -m api_server.cleanup_daemon &
 CLEANUP_DAEMON_PID=$!
 echo "Started cleanup daemon (PID: $CLEANUP_DAEMON_PID)"
 
@@ -195,7 +192,7 @@ echo "Started cleanup daemon (PID: $CLEANUP_DAEMON_PID)"
 sleep 5
 
 # Check if API workers are running
-API_WORKER_COUNT=$(pgrep -f "uvicorn.*earth2studio.serve.server.main:app" | wc -l)
+API_WORKER_COUNT=$(pgrep -f "uvicorn.*api_server.main:app" | wc -l)
 if [ "$API_WORKER_COUNT" -eq 0 ]; then
     echo "Failed to start API workers..."
     exit 1
diff --git a/serve/server/scripts/start_redis.sh b/serve/inferenceserver/scripts/start_redis.sh
similarity index 100%
rename from serve/server/scripts/start_redis.sh
rename to serve/inferenceserver/scripts/start_redis.sh
diff --git a/serve/server/scripts/startup.sh b/serve/inferenceserver/scripts/startup.sh
similarity index 59%
rename from serve/server/scripts/startup.sh
rename to serve/inferenceserver/scripts/startup.sh
index b5739fe07..5ba90f71f 100755
--- a/serve/server/scripts/startup.sh
+++ b/serve/inferenceserver/scripts/startup.sh
@@ -17,18 +17,16 @@
 
 set -euo pipefail
 
-# Use CONFIG_DIR/SCRIPT_DIR from env if set (e.g. in Docker); else resolve from script location
-SCRIPT_DIR="${SCRIPT_DIR:-$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)}"
-SERVE_SERVER_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
-REPO_ROOT="$(cd "$SERVE_SERVER_DIR/../.." && pwd)"
-export SCRIPT_DIR
-export CONFIG_DIR="${CONFIG_DIR:-$SCRIPT_DIR/../conf}"
-export WORKFLOW_DIR="${WORKFLOW_DIR:-}"
+# Set EARTH2STUDIO_MODEL_CACHE to use AZUREML_MODEL_DIR if available
+if [ -n "${AZUREML_MODEL_DIR:-}" ]; then
+    echo "AZUREML_MODEL_DIR: $AZUREML_MODEL_DIR"
+    export EARTH2STUDIO_MODEL_CACHE="$AZUREML_MODEL_DIR/${EARTH2STUDIO_MODEL_SUBPATH:-e2s_fcn3_stormscope}"
+    echo "--------------------------------"
+    echo "EARTH2STUDIO_MODEL_CACHE: $EARTH2STUDIO_MODEL_CACHE"
+    ls -la $EARTH2STUDIO_MODEL_CACHE && echo "--------------------------------"
+fi
 
-# Ensure Python can find earth2studio (including earth2studio.serve.server)
-export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}"
-
-cd "$SERVE_SERVER_DIR"
+cd /workspace/earth2studio-project/serve/inferenceserver
 make start-redis
 make start-api-server
 sleep infinity
diff --git a/serve/server/scripts/status.sh b/serve/inferenceserver/scripts/status.sh
similarity index 98%
rename from serve/server/scripts/status.sh
rename to serve/inferenceserver/scripts/status.sh
index 2dcdaadf4..5fc56b48c 100755
--- a/serve/server/scripts/status.sh
+++ b/serve/inferenceserver/scripts/status.sh
@@ -52,7 +52,7 @@ echo ""
 
 # Check API workers status
 echo "API Workers:"
-API_WORKERS=$(pgrep -f "uvicorn.*earth2studio.serve.server.main:app")
+API_WORKERS=$(pgrep -f "uvicorn.*api_server.main:app")
 if [ -n "$API_WORKERS" ]; then
     WORKER_COUNT=$(echo "$API_WORKERS" | wc -l)
     echo "  Status: Running ($WORKER_COUNT workers)"
diff --git a/serve/server/scripts/stop_api_server.sh b/serve/inferenceserver/scripts/stop_api_server.sh
similarity index 96%
rename from serve/server/scripts/stop_api_server.sh
rename to serve/inferenceserver/scripts/stop_api_server.sh
index 6f00e0830..fc612450f 100755
--- a/serve/server/scripts/stop_api_server.sh
+++ b/serve/inferenceserver/scripts/stop_api_server.sh
@@ -19,7 +19,7 @@ echo "Stopping API Server..."
 
 # Stop API workers
 echo "Stopping API workers..."
-pkill -f "uvicorn.*earth2studio.serve.server.main:app"
+pkill -f "uvicorn.*api_server.main:app"
 
 # Stop RQ inference workers
 echo "Stopping RQ inference workers..."
diff --git a/serve/server/scripts/stop_redis.sh b/serve/inferenceserver/scripts/stop_redis.sh
similarity index 100%
rename from serve/server/scripts/stop_redis.sh
rename to serve/inferenceserver/scripts/stop_redis.sh
diff --git a/serve/server/test/unit/test_e2workflow.py b/serve/test/unit/test_e2workflow.py
similarity index 82%
rename from serve/server/test/unit/test_e2workflow.py
rename to serve/test/unit/test_e2workflow.py
index b275bd433..6a4e08d1f 100644
--- a/serve/server/test/unit/test_e2workflow.py
+++ b/serve/test/unit/test_e2workflow.py
@@ -14,21 +14,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections import OrderedDict
-from pathlib import Path
 from unittest.mock import MagicMock, Mock, patch
 
 import pytest
-import redis  # type: ignore[import-untyped]
+import redis
 import torch
-
-from earth2studio.io import IOBackend
-from earth2studio.serve.server import Earth2Workflow, WorkflowParameters
-from earth2studio.serve.server.e2workflow import (
+from api_server.workflow import (
+    Earth2Workflow,
+    WorkflowParameters,
+)
+from api_server.workflow.e2workflow import (
     AutoParameters,
     BackendProgress,
     func_to_model,
 )
+
+from earth2studio.io import IOBackend
 from earth2studio.utils.type import CoordSystem
 
 
@@ -36,12 +37,10 @@
 class TestFuncToModel:
     """Test func_to_model helper function"""
 
-    def test_function_to_model_conversion(self) -> None:
+    def test_function_to_model_conversion(self):
         """Test converting function signature to Pydantic model"""
 
-        def test_func(
-            required: int, optional: str = "default", exclude_me: int = 0
-        ) -> None:
+        def test_func(required: int, optional: str = "default", exclude_me: int = 0):
             pass
 
         model = func_to_model(test_func, exclude_params={"exclude_me"})
@@ -58,11 +57,11 @@ def test_func(
 class TestAutoParameters:
     """Test AutoParameters metaclass"""
 
-    def test_auto_generates_parameters_from_call_signature(self) -> None:
+    def test_auto_generates_parameters_from_call_signature(self):
         """Test that AutoParameters creates Parameters from __call__ signature"""
 
         class TestWorkflow(metaclass=AutoParameters):
-            def __call__(self, io: IOBackend, x: int, y: str = "default") -> None:
+            def __call__(self, io: IOBackend, x: int, y: str = "default"):
                 pass
 
         # Verify Parameters class is created with correct name
@@ -81,7 +80,7 @@ def __call__(self, io: IOBackend, x: int, y: str = "default") -> None:
 class TestEarth2WorkflowImpl(Earth2Workflow):
     """Simple test implementation of Earth2Workflow"""
 
-    def __call__(self, io: IOBackend, x: int = 10, y: str = "default") -> None:
+    def __call__(self, io: IOBackend, x: int = 10, y: str = "default"):
         """Test implementation with simple parameters"""
         # Store call info for testing
         self.called_with = {"io": io, "x": x, "y": y}
@@ -96,7 +95,7 @@ def __call__(
         required_param: int,
         optional_param: str = "default",
         num_hours: int = 24,
-    ) -> None:
+    ):
         """Test implementation with required and optional parameters"""
         self.called_with = {
             "io": io,
@@ -110,12 +109,12 @@ def __call__(
 class TestEarth2Workflow:
     """Test Earth2Workflow-specific functionality (base Workflow tests in test_workflow.py)"""
 
-    def setup_method(self) -> None:
+    def setup_method(self):
         """Set up test fixtures"""
         self.workflow = TestEarth2WorkflowImpl()
         self.workflow.name = "test_workflow"
 
-    def test_validate_parameters_uses_auto_generated_parameters(self) -> None:
+    def test_validate_parameters_uses_auto_generated_parameters(self):
         """Test that validate_parameters works with auto-generated Parameters class"""
         params = TestEarth2WorkflowImpl.validate_parameters({"x": 30, "y": "test"})
         assert isinstance(params, WorkflowParameters)
@@ -126,10 +125,10 @@ def test_validate_parameters_uses_auto_generated_parameters(self) -> None:
         with pytest.raises(ValueError):
             ComplexEarth2WorkflowImpl.validate_parameters({"optional_param": "test"})
 
-    @patch("earth2studio.serve.server.e2workflow.get_config")
+    @patch("api_server.e2workflow.get_config")
     def test_run_method_with_io_backend_and_progress_updates(
-        self, mock_get_config: MagicMock, tmp_path: Path
-    ) -> None:
+        self, mock_get_config, tmp_path
+    ):
         """Test Earth2Workflow.run creates IOBackend and updates progress"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
@@ -141,7 +140,7 @@ def test_run_method_with_io_backend_and_progress_updates(
         with (
             patch.object(self.workflow, "update_execution_data") as mock_update,
             patch.object(self.workflow, "get_output_path") as mock_get_path,
-            patch("earth2studio.serve.server.e2workflow.ZarrBackend") as mock_zarr,
+            patch("api_server.e2workflow.ZarrBackend") as mock_zarr,
         ):
             mock_get_path.return_value = tmp_path / "test_output"
             mock_io = Mock(spec=IOBackend)
@@ -157,17 +156,15 @@ def test_run_method_with_io_backend_and_progress_updates(
             # Verify progress updates were made
             assert mock_update.call_count >= 2
 
-    @patch("earth2studio.serve.server.e2workflow.get_config")
-    def test_run_method_handles_workflow_exceptions(
-        self, mock_get_config: MagicMock
-    ) -> None:
+    @patch("api_server.e2workflow.get_config")
+    def test_run_method_handles_workflow_exceptions(self, mock_get_config):
         """Test that run method properly handles exceptions from workflow execution"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
         mock_get_config.return_value = mock_config
 
         class FailingWorkflow(Earth2Workflow):
-            def __call__(self, io: IOBackend, x: int = 10) -> None:
+            def __call__(self, io: IOBackend, x: int = 10):
                 raise RuntimeError("Workflow execution failed")
 
         failing_workflow = FailingWorkflow()
@@ -177,7 +174,7 @@ def __call__(self, io: IOBackend, x: int = 10) -> None:
         with (
             patch.object(failing_workflow, "update_execution_data") as mock_update,
             patch.object(failing_workflow, "get_output_path"),
-            patch("earth2studio.serve.server.e2workflow.ZarrBackend"),
+            patch("api_server.e2workflow.ZarrBackend"),
         ):
             with pytest.raises(RuntimeError, match="Workflow execution failed"):
                 failing_workflow.run({}, "exec_fail")
@@ -195,7 +192,7 @@ def __call__(self, io: IOBackend, x: int = 10) -> None:
 class TestBackendProgress:
     """Test BackendProgress wrapper for progress tracking"""
 
-    def setup_method(self) -> None:
+    def setup_method(self):
         """Set up test fixtures"""
         self.mock_io = Mock(spec=IOBackend)
         self.mock_workflow = Mock(spec=Earth2Workflow)
@@ -204,9 +201,9 @@ def setup_method(self) -> None:
             self.mock_io, self.mock_workflow, self.execution_id
         )
 
-    def test_add_array_initializes_progress_tracking(self) -> None:
+    def test_add_array_initializes_progress_tracking(self):
         """Test that add_array initializes progress tracking when progress_dim present"""
-        coords = OrderedDict({"lead_time": [0, 1, 2, 3], "lat": [0, 1]})
+        coords = {"lead_time": [0, 1, 2, 3], "lat": [0, 1]}
 
         self.backend.add_array(coords, "temperature")
 
@@ -221,23 +218,23 @@ def test_add_array_initializes_progress_tracking(self) -> None:
         self.backend.add_array(coords, "pressure")
         assert self.backend.progress_array == "temperature"
 
-    def test_write_updates_progress(self) -> None:
+    def test_write_updates_progress(self):
         """Test that write updates progress for tracked arrays"""
         # Initialize progress tracking
-        init_coords: CoordSystem = OrderedDict({"lead_time": [0, 1, 2], "lat": [0, 1]})
+        init_coords: CoordSystem = {"lead_time": [0, 1, 2], "lat": [0, 1]}
         self.backend.add_array(init_coords, "temperature")
         self.mock_workflow.update_execution_data.reset_mock()
 
         # Write data at different time steps
         data = torch.randn(1, 2)
 
-        write_coords1: CoordSystem = OrderedDict({"lead_time": [0], "lat": [0, 1]})
+        write_coords1: CoordSystem = {"lead_time": [0], "lat": [0, 1]}
         self.backend.write(data, write_coords1, "temperature")
         self.mock_workflow.update_execution_data.assert_called_with(
             self.execution_id, updates={"current_step": 1}
         )
 
-        write_coords2: CoordSystem = OrderedDict({"lead_time": [2], "lat": [0, 1]})
+        write_coords2: CoordSystem = {"lead_time": [2], "lat": [0, 1]}
         self.backend.write(data, write_coords2, "temperature")
         self.mock_workflow.update_execution_data.assert_called_with(
             self.execution_id, updates={"current_step": 3}
@@ -258,29 +255,23 @@ def test_passthrough_to_underlying_io(self) -> None:
 class TestEarth2WorkflowIntegration:
     """Integration tests for Earth2Workflow with BackendProgress"""
 
-    @patch("earth2studio.serve.server.e2workflow.get_config")
-    def test_workflow_with_backend_progress_tracking(
-        self, mock_get_config: MagicMock
-    ) -> None:
+    @patch("api_server.e2workflow.get_config")
+    def test_workflow_with_backend_progress_tracking(self, mock_get_config):
         """Test complete workflow execution with automatic progress tracking"""
         mock_config = MagicMock()
         mock_config.paths.output_format = "zarr"
         mock_get_config.return_value = mock_config
 
         class TestWorkflow(Earth2Workflow):
-            def __call__(self, io: IOBackend, num_steps: int = 3) -> None:
-                coords: CoordSystem = OrderedDict(
-                    {
-                        "lead_time": list(range(num_steps)),
-                        "lat": [0, 1],
-                    }
-                )
+            def __call__(self, io: IOBackend, num_steps: int = 3):
+                coords: CoordSystem = {
+                    "lead_time": list(range(num_steps)),
+                    "lat": [0, 1],
+                }
                 io.add_array(coords, "forecast")
                 for i in range(num_steps):
                     data = torch.randn(1, 2)
-                    write_coords: CoordSystem = OrderedDict(
-                        {"lead_time": [i], "lat": [0, 1]}
-                    )
+                    write_coords: CoordSystem = {"lead_time": [i], "lat": [0, 1]}
                     io.write(data, write_coords, "forecast")
 
         workflow = TestWorkflow()
@@ -290,7 +281,7 @@ def __call__(self, io: IOBackend, num_steps: int = 3) -> None:
         with (
             patch.object(workflow, "update_execution_data") as mock_update,
             patch.object(workflow, "get_output_path"),
-            patch("earth2studio.serve.server.e2workflow.ZarrBackend"),
+            patch("api_server.e2workflow.ZarrBackend"),
         ):
             result = workflow.run({"num_steps": 3}, "exec_test")
 
diff --git a/serve/server/test/unit/test_e2workflow_params.py b/serve/test/unit/test_e2workflow_params.py
similarity index 86%
rename from serve/server/test/unit/test_e2workflow_params.py
rename to serve/test/unit/test_e2workflow_params.py
index a62984613..691bb5f7c 100644
--- a/serve/server/test/unit/test_e2workflow_params.py
+++ b/serve/test/unit/test_e2workflow_params.py
@@ -21,26 +21,41 @@
 import os
 from datetime import datetime
 from typing import Literal
+from unittest.mock import Mock
 
 import pytest
 from pydantic import ValidationError
 
-from earth2studio.io import IOBackend
-from earth2studio.serve.server.e2workflow import Earth2Workflow, func_to_model
-
 # Set environment variable before importing workflows
 os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
 
+# Try to import earth2studio, skip tests if not available
+try:
+    from earth2studio.io import IOBackend
+
+    EARTH2STUDIO_AVAILABLE = True
+except ImportError:
+    EARTH2STUDIO_AVAILABLE = False
+    IOBackend = Mock  # Mock for type hints
+
+# ruff: noqa: E402
+from api_server.workflow.e2workflow import Earth2Workflow, func_to_model
+
+# Skip all tests in this module if earth2studio is not available
+pytestmark = pytest.mark.skipif(
+    not EARTH2STUDIO_AVAILABLE, reason="earth2studio not available in test environment"
+)
+
 
 class TestAutoParametersValidation:
     """Test validation of auto-generated parameters from Earth2Workflow"""
 
-    def test_auto_parameters_with_num_steps_validation(self) -> None:
+    def test_auto_parameters_with_num_steps_validation(self):
         """Test that num_steps parameter gets automatic range validation"""
 
         # Create a mock Earth2Workflow with num_steps parameter
         class TestWorkflow(Earth2Workflow):
-            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn") -> None:
+            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn"):
                 pass
 
             def __call__(
@@ -48,7 +63,7 @@ def __call__(
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
-            ) -> None:
+            ):
                 pass
 
         # Test valid num_steps
@@ -77,18 +92,18 @@ def __call__(
                 }
             )
 
-    def test_auto_parameters_with_start_time_validation(self) -> None:
+    def test_auto_parameters_with_start_time_validation(self):
         """Test that start_time parameter gets ISO 8601 validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self) -> None:
+            def __init__(self):
                 pass
 
             def __call__(
                 self,
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
-            ) -> None:
+            ):
                 pass
 
         # Test valid ISO 8601 formats
@@ -116,14 +131,14 @@ def __call__(
             ):
                 TestWorkflow.validate_parameters({"start_time": times})
 
-    def test_auto_parameters_with_config_validation(self) -> None:
+    def test_auto_parameters_with_config_validation(self):
         """Test that __init__ parameters get proper validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn") -> None:
+            def __init__(self, model_type: Literal["fcn", "dlwp"] = "fcn"):
                 self.model_type = model_type
 
-            def __call__(self, io: IOBackend) -> None:
+            def __call__(self, io: IOBackend):
                 pass
 
         # Valid model_type
@@ -140,14 +155,14 @@ def __call__(self, io: IOBackend) -> None:
         with pytest.raises(ValidationError):
             TestWorkflow.Config.model_validate({"model_type": "invalid"})
 
-    def test_auto_parameters_nsteps_variant(self) -> None:
+    def test_auto_parameters_nsteps_variant(self):
         """Test that nsteps (alternate spelling) also gets range validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self) -> None:
+            def __init__(self):
                 pass
 
-            def __call__(self, io: IOBackend, nsteps: int = 10) -> None:
+            def __call__(self, io: IOBackend, nsteps: int = 10):
                 pass
 
         # Valid nsteps
@@ -158,7 +173,7 @@ def __call__(self, io: IOBackend, nsteps: int = 10) -> None:
         with pytest.raises(ValidationError, match="greater than or equal to 1"):
             TestWorkflow.validate_parameters({"nsteps": 0})
 
-    def test_func_to_model_with_validation(self) -> None:
+    def test_func_to_model_with_validation(self):
         """Test func_to_model creates models with proper validation"""
 
         def test_func(
@@ -184,11 +199,11 @@ def test_func(
         with pytest.raises(ValidationError, match="is not a valid ISO 8601"):
             TestModel.model_validate({"start_time": ["2024-01-01"]})  # Missing time
 
-    def test_auto_parameters_with_defaults(self) -> None:
+    def test_auto_parameters_with_defaults(self):
         """Test that default values work correctly with validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self) -> None:
+            def __init__(self):
                 pass
 
             def __call__(
@@ -196,7 +211,7 @@ def __call__(
                 io: IOBackend,
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
-            ) -> None:
+            ):
                 pass
 
         # Use defaults
@@ -214,7 +229,7 @@ def __call__(
 class TestEarth2WorkflowIntegration:
     """Integration tests for Earth2Workflow validation"""
 
-    def test_complete_workflow_validation(self) -> None:
+    def test_complete_workflow_validation(self):
         """Test complete validation workflow"""
 
         class CompleteWorkflow(Earth2Workflow):
@@ -222,7 +237,7 @@ def __init__(
                 self,
                 model_type: Literal["fcn", "dlwp"] = "fcn",
                 device: Literal["cpu", "cuda"] = "cpu",
-            ) -> None:
+            ):
                 self.model_type = model_type
                 self.device = device
 
@@ -232,7 +247,7 @@ def __call__(
                 start_time: list[datetime] = [datetime(2024, 1, 1, 0)],
                 num_steps: int = 20,
                 output_frequency: int = 1,
-            ) -> None:
+            ):
                 pass
 
         # Test valid complete parameters
@@ -259,14 +274,14 @@ def __call__(
         with pytest.raises(ValidationError):
             CompleteWorkflow.validate_parameters({"num_steps": -1})
 
-    def test_workflow_with_extra_fields_rejected(self) -> None:
+    def test_workflow_with_extra_fields_rejected(self):
         """Test that extra fields are rejected due to strict validation"""
 
         class TestWorkflow(Earth2Workflow):
-            def __init__(self) -> None:
+            def __init__(self):
                 pass
 
-            def __call__(self, io: IOBackend, num_steps: int = 20) -> None:
+            def __call__(self, io: IOBackend, num_steps: int = 20):
                 pass
 
         # Extra field should be rejected
diff --git a/test/Dockerfile b/test/Dockerfile
index f7547d178..413fa7d24 100644
--- a/test/Dockerfile
+++ b/test/Dockerfile
@@ -1,6 +1,6 @@
 FROM nvcr.io/nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04
 # Install uv
-COPY --from=ghcr.io/astral-sh/uv:0.10.9 /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.9.7 /uv /uvx /bin/
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
     python3-dev \
diff --git a/test/data/test_data_utils.py b/test/data/test_data_utils.py
index f9c3eaf5b..3eeeea098 100644
--- a/test/data/test_data_utils.py
+++ b/test/data/test_data_utils.py
@@ -20,7 +20,6 @@
 from collections import OrderedDict
 
 import numpy as np
-import pandas as pd
 import pytest
 import torch
 import xarray as xr
@@ -29,17 +28,11 @@
 from earth2studio.data import (
     DataArrayFile,
     Random,
-    RandomDataFrame,
     datasource_to_file,
     fetch_data,
-    fetch_dataframe,
     prep_data_array,
 )
-from earth2studio.data.utils import (
-    AsyncCachingFileSystem,
-    datasource_cache_root,
-    prep_forecast_inputs,
-)
+from earth2studio.data.utils import AsyncCachingFileSystem, datasource_cache_root
 
 
 @pytest.fixture
@@ -120,7 +113,6 @@ def test_prep_dataarray(foo_data_array, dims, device):
 
 
 def test_prep_data_array_curvilinear(equilinear_data_array, curvilinear_data_array):
-    pytest.importorskip("scipy", reason="scipy not installed")
     # Create another curvilinear grid
     y, x = np.mgrid[0:20, 0:24]
     target_lat = 30 + y * 1 + np.cos(x * 0.3) * 0.3
@@ -192,118 +184,6 @@ def test_fetch_data(time, lead_time, device):
     assert not torch.isnan(x).any()
 
 
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_fetch_data_legacy_false(device):
-
-    if device == "cuda:0" and torch.cuda.is_available():
-        try:
-            import cupy as cp
-        except ImportError:
-            pytest.skip("cupy not available for CUDA device")
-
-    time = np.array([np.datetime64("1993-04-05T00:00")])
-    lead_time = np.array([np.timedelta64(0, "h")])
-    variable = np.array(["a", "b", "c"])
-    domain = OrderedDict({"lat": np.random.randn(720), "lon": np.random.randn(1440)})
-    r = Random(domain)
-
-    da = fetch_data(r, time, variable, lead_time, device=device, legacy=False)
-
-    assert isinstance(da, xr.DataArray)
-    assert da.dims == ("time", "lead_time", "variable", "lat", "lon")
-    assert np.all(da.coords["time"].values == time)
-    assert np.all(da.coords["lead_time"].values == lead_time)
-    assert np.all(da.coords["variable"].values == variable)
-
-    if device == "cuda:0" and torch.cuda.is_available():
-        assert isinstance(da.data, cp.ndarray)
-        assert not cp.all(cp.isnan(da.data))
-    else:
-        assert isinstance(da.data, np.ndarray)
-        assert not np.all(np.isnan(da.data))
-
-
-@pytest.mark.parametrize(
-    "time",
-    [
-        np.array([np.datetime64("1993-04-05T00:00")]),
-        np.array(
-            [
-                np.datetime64("1999-10-11T12:00"),
-                np.datetime64("2001-06-04T00:00"),
-            ]
-        ),
-    ],
-)
-@pytest.mark.parametrize(
-    "lead_time",
-    [
-        np.array([np.timedelta64(0, "h")]),
-        np.array([np.timedelta64(-6, "h"), np.timedelta64(0, "h")]),
-    ],
-)
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_fetch_dataframe(time, lead_time, device):
-    variable = np.array(["t2m", "u10m"])
-    rdf = RandomDataFrame(n_obs=5)
-
-    # For CUDA, check if cudf is available first
-    if device != "cpu":
-        try:
-            import cudf
-        except ImportError:
-            pytest.skip("cudf not available for CUDA device")
-
-    result = fetch_dataframe(rdf, time, variable, lead_time=lead_time, device=device)
-
-    # Check return type based on device
-    if device == "cpu":
-        assert isinstance(result, pd.DataFrame)
-        df = result
-    else:
-        # CUDA device - should return cudf.DataFrame
-        import cudf
-
-        assert isinstance(result, cudf.DataFrame)
-        df = result.to_pandas()
-
-    # Check that DataFrame has expected columns
-    assert "time" in df.columns
-    assert "lat" in df.columns
-    assert "lon" in df.columns
-    assert "observation" in df.columns
-    assert "variable" in df.columns
-
-    # Check that variables match
-    assert set(df["variable"].unique()).issubset(set(variable))
-
-    # Check that we have data
-    assert len(df) > 0
-    assert not df.isnull().any().any()
-
-
 @pytest.mark.parametrize(
     "time",
     [
@@ -325,7 +205,6 @@ def test_fetch_dataframe(time, lead_time, device):
 )
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
 def test_fetch_data_interp(time, lead_time, device):
-    pytest.importorskip("scipy", reason="scipy not installed")
     # Original (source) domain
     variable = np.array(["a", "b", "c"])
     domain = OrderedDict(
@@ -562,64 +441,6 @@ def test_clear_cache(tmp_path):
     assert not os.path.exists(dummy_file)
 
 
-@pytest.mark.parametrize(
-    "time, lead_time, variable",
-    [
-        (datetime.datetime(2020, 1, 1, 12, 0), datetime.timedelta(hours=6), "t2m"),
-        (
-            [
-                datetime.datetime(2020, 1, 1, 12, 0),
-                datetime.datetime(2020, 1, 2, 12, 0),
-            ],
-            [datetime.timedelta(hours=6), datetime.timedelta(hours=12)],
-            ["t2m", "u10m"],
-        ),
-        (
-            np.array(
-                [np.datetime64("2020-01-01T12:00"), np.datetime64("2020-01-02T12:00")]
-            ),
-            np.array([np.timedelta64(6, "h"), np.timedelta64(12, "h")]),
-            np.array(["t2m", "u10m", "v10m"]),
-        ),
-    ],
-)
-def test_prep_forecast_inputs(time, lead_time, variable):
-    time_list, lead_time_list, variable_list = prep_forecast_inputs(
-        time, lead_time, variable
-    )
-
-    assert isinstance(time_list, list)
-    assert all(isinstance(t, datetime.datetime) for t in time_list)
-
-    assert isinstance(lead_time_list, list)
-    assert all(isinstance(lt, datetime.timedelta) for lt in lead_time_list)
-
-    assert isinstance(variable_list, list)
-    assert all(isinstance(v, str) for v in variable_list)
-
-    # Verify correct lengths
-    if isinstance(time, datetime.datetime):
-        assert len(time_list) == 1
-    elif isinstance(time, list):
-        assert len(time_list) == len(time)
-    else:  # np.ndarray
-        assert len(time_list) == len(time)
-
-    if isinstance(lead_time, datetime.timedelta):
-        assert len(lead_time_list) == 1
-    elif isinstance(lead_time, list):
-        assert len(lead_time_list) == len(lead_time)
-    else:  # np.ndarray
-        assert len(lead_time_list) == len(lead_time)
-
-    if isinstance(variable, str):
-        assert len(variable_list) == 1
-    elif isinstance(variable, list):
-        assert len(variable_list) == len(variable)
-    else:  # np.ndarray
-        assert len(variable_list) == len(variable)
-
-
 @pytest.mark.asyncio
 async def test_async_cache_fs_storage_handling(tmp_path):
     fs = HTTPFileSystem()
diff --git a/test/data/test_goes.py b/test/data/test_goes.py
index d6543705f..d3b03dab7 100644
--- a/test/data/test_goes.py
+++ b/test/data/test_goes.py
@@ -219,7 +219,6 @@ def test_goes_sources(satellite, scan_mode, time, variable, valid):
     assert shape[3] == expected_dims[1]
 
 
-@pytest.mark.xfail
 @pytest.mark.timeout(15)
 @pytest.mark.parametrize(
     "time",
diff --git a/test/models/da/test_da_interp.py b/test/models/da/test_da_interp.py
deleted file mode 100644
index 0fa10aa2a..000000000
--- a/test/models/da/test_da_interp.py
+++ /dev/null
@@ -1,359 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import numpy as np
-import pandas as pd
-import pytest
-import torch
-import xarray as xr
-
-try:
-    import cudf
-except ImportError:
-    cudf = None
-
-try:
-    import cupy as cp
-except ImportError:
-    cp = None
-
-from earth2studio.models.da.interp import InterpEquirectangular
-
-
-@pytest.fixture
-def sample_observations_pandas():
-    """Create sample pandas DataFrame observations for testing with multiple times and variables."""
-    time1 = np.datetime64("2024-01-01T12:00:00")
-    time2 = np.datetime64("2024-01-01T13:00:00")
-    return pd.DataFrame(
-        {
-            "time": [
-                time1,
-                time1,
-                time1,
-                time1,
-                time2,
-                time2,
-                time2,
-                time2,
-            ],
-            "lat": [30.0, 30.0, 40.0, 40.0, 30.0, 30.0, 40.0, 40.0],
-            "lon": [240.0, 250.0, 240.0, 250.0, 240.0, 250.0, 240.0, 250.0],
-            "observation": [10.0, 20.0, 25.0, 35.0, 30.0, 40.0, 35.0, 45.0],
-            "variable": ["t2m", "t2m", "u10m", "u10m", "t2m", "t2m", "u10m", "u10m"],
-        }
-    )
-
-
-@pytest.fixture
-def sample_observations_cudf():
-    """Create sample cudf DataFrame observations for testing with multiple times and variables."""
-    if cudf is None:
-        pytest.skip("cudf not available")
-    time1 = np.datetime64("2024-01-01T12:00:00")
-    time2 = np.datetime64("2024-01-01T13:00:00")
-    return cudf.DataFrame(
-        {
-            "time": [
-                time1,
-                time1,
-                time1,
-                time1,
-                time2,
-                time2,
-                time2,
-                time2,
-            ],
-            "lat": [30.0, 30.0, 40.0, 40.0, 30.0, 30.0, 40.0, 40.0],
-            "lon": [240.0, 250.0, 240.0, 250.0, 240.0, 250.0, 240.0, 250.0],
-            "observation": [10.0, 20.0, 30.0, 40.0, 30.0, 40.0, 50.0, 60.0],
-            "variable": ["t2m", "t2m", "u10m", "u10m", "t2m", "t2m", "u10m", "u10m"],
-        }
-    )
-
-
-@pytest.fixture
-def small_grid():
-    """Create a small lat/lon grid for testing."""
-    lat = np.linspace(25.0, 50.0, 11, dtype=np.float32)
-    lon = np.linspace(235.0, 295.0, 13, dtype=np.float32)
-    return lat, lon
-
-
-@pytest.mark.parametrize(
-    "interp_method",
-    ["nearest", "smolyak"],
-)
-def test_interp_init(interp_method, small_grid):
-    lat, lon = small_grid
-    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method)
-    assert model.interp_method == interp_method
-    assert np.array_equal(model._lat, lat)
-    assert np.array_equal(model._lon, lon)
-
-    with pytest.raises(ValueError, match="interp_method must be one of"):
-        InterpEquirectangular(interp_method="invalid")
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-@pytest.mark.parametrize(
-    "interp_method",
-    ["nearest", "smolyak"],
-)
-def test_interp_call_pandas(
-    sample_observations_pandas, small_grid, device, interp_method
-):
-    lat, lon = small_grid
-    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method).to(
-        device
-    )
-
-    # Set up request metadata
-    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
-    sample_observations_pandas.attrs = {
-        "request_time": request_time,
-        "request_lead_time": np.array([np.timedelta64(0, "h")]),
-    }
-
-    da = model(sample_observations_pandas)
-
-    assert isinstance(da, xr.DataArray)
-    assert da.dims == ("time", "variable", "lat", "lon")
-    assert da.shape == (1, 4, len(lat), len(lon))  # 4 times, 2 variables
-    assert da.coords["time"].values[0] == request_time[0]
-    assert len(da.coords["variable"]) == 4
-    assert "t2m" in da.coords["variable"].values
-    assert "u10m" in da.coords["variable"].values
-    assert np.array_equal(da.coords["lat"].values, lat)
-    assert np.array_equal(da.coords["lon"].values, lon)
-
-    # Check device-specific return type
-    if device == "cuda:0" and torch.cuda.is_available():
-        if cp is not None:
-            assert isinstance(da.data, cp.ndarray)
-            assert not cp.all(cp.isnan(da.data))
-        else:
-            # If cupy not available, should fall back to numpy
-            assert isinstance(da.data, np.ndarray)
-    else:
-        assert isinstance(da.data, np.ndarray)
-        assert not np.all(np.isnan(da.values))
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-@pytest.mark.parametrize(
-    "interp_method",
-    ["nearest", "smolyak"],
-)
-def test_interp_call_cudf(sample_observations_cudf, small_grid, device, interp_method):
-    if cudf is None:
-        pytest.skip("cudf not available")
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA not available")
-
-    lat, lon = small_grid
-    model = InterpEquirectangular(lat=lat, lon=lon, interp_method=interp_method).to(
-        device
-    )
-
-    # Set up request metadata
-    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
-    sample_observations_cudf.attrs = {
-        "request_time": request_time,
-        "request_lead_time": np.array([np.timedelta64(0, "h")]),
-    }
-
-    da = model(sample_observations_cudf)
-
-    assert isinstance(da, xr.DataArray)
-    assert da.dims == ("time", "variable", "lat", "lon")
-    assert da.shape == (1, 4, len(lat), len(lon))  # 1 time, 4 variables
-    assert da.coords["time"].values[0] == request_time[0]
-    assert len(da.coords["variable"]) == 4
-    assert "t2m" in da.coords["variable"].values
-    assert "u10m" in da.coords["variable"].values
-    assert np.array_equal(da.coords["lat"].values, lat)
-    assert np.array_equal(da.coords["lon"].values, lon)
-
-    # Check device-specific return type
-    if cp is not None:
-        assert isinstance(da.data, cp.ndarray)
-        assert not cp.all(cp.isnan(da.data))
-        t2m_data = da.sel(variable="t2m").data
-        u10m_data = da.sel(variable="u10m").data
-        assert not cp.any(cp.isnan(t2m_data))
-        assert not cp.any(cp.isnan(u10m_data))
-    else:
-        assert isinstance(da.data, np.ndarray)
-        assert not np.all(np.isnan(da.values))
-        t2m_data = da.sel(variable="t2m").values
-        u10m_data = da.sel(variable="u10m").values
-        assert not np.any(np.isnan(t2m_data))
-        assert not np.any(np.isnan(u10m_data))
-
-
-def test_interp_multiple_times(sample_observations_pandas, small_grid):
-    lat, lon = small_grid
-    model = InterpEquirectangular(lat=lat, lon=lon, interp_method="nearest")
-    model.VARIABLES = ["t2m"]
-
-    time1 = np.datetime64("2024-01-01T12:00:00")
-    time2 = np.datetime64("2024-01-01T13:00:00")
-    request_time = np.array([time1, time2])
-    sample_observations_pandas.attrs = {
-        "request_time": request_time,
-        "request_lead_time": np.array([np.timedelta64(0, "h"), np.timedelta64(0, "h")]),
-    }
-
-    da = model(sample_observations_pandas)
-
-    assert da.shape == (2, 1, len(lat), len(lon))  # 2 times, 1 variable
-    assert len(da.coords["time"]) == 2
-    assert da.coords["time"].values[0] == time1
-    assert da.coords["time"].values[1] == time2
-
-    # Verify interpolated values for each time step
-    # time1 t2m observations: (30.0, 240.0) = 10.0, (30.0, 250.0) = 20.0
-    # time2 t2m observations: (30.0, 240.0) = 15.0, (30.0, 250.0) = 25.0
-    time1_data = da.sel(time=time1).values
-    time2_data = da.sel(time=time2).values
-
-    assert not np.all(np.isnan(time1_data))
-    assert not np.all(np.isnan(time2_data))
-
-    # Values should be different between time steps
-    assert not np.allclose(time1_data, time2_data, equal_nan=True)
-
-    if len(time1_data) > 0:
-        assert np.min(time1_data) >= 0  # Should be positive based on observations
-        assert np.max(time1_data) <= 25  # Should be close to max observation (20.0)
-
-    if len(time2_data) > 0:
-        assert np.min(time2_data) >= 30  # Should be close to min observation (15.0)
-        assert np.max(time2_data) <= 40  # Should be close to max observation (25.0)
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_interp_tolerance(sample_observations_pandas, small_grid, device):
-    lat, lon = small_grid
-    # Use a larger tolerance to capture observations
-    time_tolerance = np.timedelta64(2, "h")
-    model = InterpEquirectangular(lat=lat, lon=lon, time_tolerance=time_tolerance).to(
-        device
-    )
-
-    # Create observations with times spread out
-    base_time = np.datetime64("2024-01-01T12:00:00")
-    time1 = base_time - np.timedelta64(1, "h")
-    time2 = base_time
-    time3 = base_time + np.timedelta64(1, "h")
-    time4 = base_time + np.timedelta64(3, "h")  # Outside tolerance
-
-    tolerance_df = pd.DataFrame(
-        {
-            "time": [time1, time2, time3, time4],
-            "lat": [30.0, 30.0, 40.0, 40.0],
-            "lon": [240.0, 250.0, 240.0, 250.0],
-            "observation": [10.0, 20.0, 30.0, 40.0],
-            "variable": ["t2m", "t2m", "t2m", "t2m"],
-        }
-    )
-
-    request_time = np.array([base_time])
-    tolerance_df.attrs = {
-        "request_time": request_time,
-        "request_lead_time": np.array([np.timedelta64(0, "h")]),
-    }
-
-    da = model(tolerance_df)
-
-    # Should interpolate using observations within tolerance (time1, time2, time3)
-    assert da.shape == (1, 4, len(lat), len(lon))
-    if cp is not None and isinstance(da.data, cp.ndarray):
-        assert not cp.all(cp.isnan(da.data))
-    else:
-        assert not np.all(np.isnan(da.values))
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_interp_generator(sample_observations_pandas, small_grid, device):
-    lat, lon = small_grid
-    model = InterpEquirectangular(lat=lat, lon=lon).to(device)
-
-    request_time = np.array([np.datetime64("2024-01-01T12:00:00")])
-    sample_observations_pandas.attrs = {
-        "request_time": request_time,
-        "request_lead_time": np.array([np.timedelta64(0, "h")]),
-    }
-
-    generator = model.create_generator()
-    # Prime the generator
-    result = generator.send(None)
-    assert result is None
-
-    # Send observations
-    da = generator.send(sample_observations_pandas)
-    assert isinstance(da, xr.DataArray)
-    assert da.shape == (1, 4, len(lat), len(lon))
-
-    # Send another set of observations
-    da2 = generator.send(sample_observations_pandas)
-    assert isinstance(da2, xr.DataArray)
-    assert da2.shape == (1, 4, len(lat), len(lon))
-
-    generator.close()
diff --git a/test/models/da/test_da_sda_stormcast.py b/test/models/da/test_da_sda_stormcast.py
deleted file mode 100644
index b6b54b9f2..000000000
--- a/test/models/da/test_da_sda_stormcast.py
+++ /dev/null
@@ -1,537 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from collections import OrderedDict
-from unittest.mock import patch
-
-import numpy as np
-import pandas as pd
-import pytest
-import torch
-import xarray as xr
-
-from earth2studio.data import Random, RandomDataFrame, fetch_data, fetch_dataframe
-from earth2studio.models.da.sda_stormcast import StormCastSDA
-
-try:
-    import cupy as cp
-except ImportError:
-    cp = None
-
-
-# ---------- Mock neural networks ----------
-
-
-class PhooRegressionModel(torch.nn.Module):
-    def __init__(self, out_vars=3):
-        super().__init__()
-        self.out_vars = out_vars
-
-    def forward(self, x):
-        return x[:, : self.out_vars, :, :]
-
-
-class PhooSDADiffusionModel(torch.nn.Module):
-    def forward(self, x, t, condition=None):
-        return x
-
-
-# ---------- Constants ----------
-
-Y_START, Y_END = 32, 128
-X_START, X_END = 32, 64
-NVAR = 3
-NVAR_COND = 5
-SAMPLER_ARGS = {
-    "num_steps": 2,
-    "sigma_min": 0.002,
-    "sigma_max": 88.0,
-    "rho": 7.0,
-    "S_churn": 0.0,
-    "S_min": 0.0,
-    "S_max": float("inf"),
-    "S_noise": 1.0,
-}
-
-
-# ---------- Helpers ----------
-
-
-def _build_model(device="cpu"):
-    regression = PhooRegressionModel(out_vars=NVAR)
-    diffusion = PhooSDADiffusionModel()
-
-    r_condition = Random(
-        OrderedDict(
-            [
-                ("lat", np.linspace(90, -90, num=181, endpoint=True)),
-                ("lon", np.linspace(0, 360, num=360)),
-            ]
-        )
-    )
-
-    ny = Y_END - Y_START
-    nx = X_END - X_START
-    variables = np.array(["u%02d" % i for i in range(NVAR)])
-    means = torch.zeros(1, NVAR, 1, 1)
-    stds = torch.ones(1, NVAR, 1, 1)
-    invariants = torch.randn(1, 2, ny, nx)
-    conditioning_means = torch.randn(1, NVAR_COND, 1, 1)
-    conditioning_stds = torch.randn(1, NVAR_COND, 1, 1).abs() + 0.1
-    conditioning_variables = np.array(["c%02d" % i for i in range(NVAR_COND)])
-
-    return StormCastSDA(
-        regression,
-        diffusion,
-        means,
-        stds,
-        invariants,
-        hrrr_lat_lim=(Y_START, Y_END),
-        hrrr_lon_lim=(X_START, X_END),
-        variables=variables,
-        conditioning_means=conditioning_means,
-        conditioning_stds=conditioning_stds,
-        conditioning_variables=conditioning_variables,
-        conditioning_data_source=r_condition,
-        sampler_args=SAMPLER_ARGS,
-    ).to(device)
-
-
-def _build_input_da(model, time, device="cpu"):
-    dc = OrderedDict([("hrrr_y", model.hrrr_y), ("hrrr_x", model.hrrr_x)])
-    r = Random(dc)
-    x = fetch_data(
-        r,
-        time,
-        model.variables,
-        lead_time=np.array([np.timedelta64(0, "h")]),
-        device=device,
-        legacy=False,
-    )
-    return x.assign_coords(
-        lat=(["hrrr_y", "hrrr_x"], model.lat),
-        lon=(["hrrr_y", "hrrr_x"], model.lon),
-    )
-
-
-def _build_obs_source(model, n_obs=10):
-    grid_lat, grid_lon = model.lat, model.lon
-    _state: dict = {}
-
-    def lat_gen():
-        y = np.random.randint(5, grid_lat.shape[0] - 5)
-        x = np.random.randint(5, grid_lat.shape[1] - 5)
-        _state["y"], _state["x"] = y, x
-        return float(grid_lat[y, x])
-
-    def lon_gen():
-        return float(grid_lon[_state["y"], _state["x"]])
-
-    return RandomDataFrame(
-        n_obs=n_obs,
-        field_generators={"lat": lat_gen, "lon": lon_gen},
-    )
-
-
-def _mock_forward(x, conditioning, y_obs, mask):
-    return torch.zeros_like(x)
-
-
-# ---------- Unit tests: _points_in_polygon ----------
-
-
-def test_points_in_polygon_square():
-    polygon = np.array([[0, 0], [0, 1], [1, 1], [1, 0]], dtype=np.float64)
-    inside = np.array([[0.5, 0.5], [0.1, 0.1], [0.9, 0.9]], dtype=np.float64)
-    outside = np.array([[-1, -1], [2, 2], [0.5, 1.5]], dtype=np.float64)
-    points = np.vstack([inside, outside])
-
-    result = StormCastSDA._points_in_polygon(points, polygon)
-
-    assert result[:3].all()
-    assert not result[3:].any()
-
-
-def test_points_in_polygon_triangle():
-    polygon = np.array([[0, 0], [2, 0], [1, 2]], dtype=np.float64)
-    inside = np.array([[1, 0.5]], dtype=np.float64)
-    outside = np.array([[3, 3], [-1, 0]], dtype=np.float64)
-    points = np.vstack([inside, outside])
-
-    result = StormCastSDA._points_in_polygon(points, polygon)
-
-    assert result[0]
-    assert not result[1:].any()
-
-
-# ---------- Unit tests: _build_obs_tensors ----------
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_build_obs_tensors(device):
-    model = _build_model(device=device)
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    obs_source = _build_obs_source(model, n_obs=5)
-    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
-    ny, nx = model.lat.shape
-
-    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
-
-    assert y_obs.shape == (1, NVAR, ny, nx)
-    assert mask.shape == (1, NVAR, ny, nx)
-    assert mask.sum() > 0
-    assert y_obs.device == model.device
-    assert mask.device == model.device
-
-
-def test_build_obs_tensors_none():
-    model = _build_model()
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    ny, nx = model.lat.shape
-
-    y_obs, mask = model._build_obs_tensors(None, time[0], model.device)
-
-    assert y_obs.shape == (1, NVAR, ny, nx)
-    assert (mask == 0).all()
-    assert (y_obs == 0).all()
-
-
-def test_build_obs_tensors_outside_grid():
-    model = _build_model()
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-
-    # RandomDataFrame with lat/lon fixed far outside the HRRR grid
-    obs_source = RandomDataFrame(
-        n_obs=5,
-        field_generators={
-            "lat": lambda: 0.0,
-            "lon": lambda: 10.0,
-        },
-    )
-    obs_df = fetch_dataframe(obs_source, time, [str(model.variables[0])])
-
-    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
-
-    assert (mask == 0).all()
-
-
-def test_build_obs_tensors_averages_duplicates():
-    model = _build_model()
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    ny, nx = model.lat.shape
-
-    mid_y, mid_x = ny // 2, nx // 2
-    pt_lat = float(model.lat[mid_y, mid_x])
-    pt_lon = float(model.lon[mid_y, mid_x])
-    var_name = str(model.variables[0])
-
-    # Three observations at the exact same location and variable
-    obs_df = pd.DataFrame(
-        {
-            "time": pd.to_datetime([time[0]] * 3),
-            "lat": [pt_lat] * 3,
-            "lon": [pt_lon] * 3,
-            "variable": [var_name] * 3,
-            "observation": [3.0, 9.0, 30.0],
-        }
-    )
-
-    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
-
-    assert mask.sum() == 1
-    assert torch.isclose(y_obs[mask == 1], torch.tensor(14.0)).all()
-
-    obs_df = pd.DataFrame(
-        {
-            "time": pd.to_datetime([time[0]] * 3),
-            "lat": [0, pt_lat, pt_lat],
-            "lon": [0, pt_lon, pt_lon],
-            "variable": [var_name] * 3,
-            "observation": [3.0, 9.0, 30.0],
-        }
-    )
-
-    y_obs, mask = model._build_obs_tensors(obs_df, time[0], model.device)
-    assert mask.sum() == 1
-    assert torch.isclose(y_obs[mask == 1], torch.tensor(19.5)).all()
-
-
-# ---------- Unit test: _fetch_and_interp_conditioning ----------
-
-
-def test_fetch_and_interp_conditioning():
-    model = _build_model(device="cpu")
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    x = _build_input_da(model, time, device="cpu")
-    ny, nx = model.lat.shape
-
-    c = model._fetch_and_interp_conditioning(x)
-
-    assert c.shape == (1, 1, NVAR_COND, ny, nx)
-    assert "hrrr_y" in c.dims
-    assert "hrrr_x" in c.dims
-    assert "variable" in c.dims
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda missing")
-def test_fetch_and_interp_conditioning_gpu():
-    from scipy.interpolate import RegularGridInterpolator
-
-    from earth2studio.data.utils import prep_data_inputs
-
-    # Descending latitude (typical weather data convention: 90 → -90)
-    src_lat = np.linspace(90, -90, num=181)
-    src_lon = np.linspace(0, 360, num=361)
-    field = np.random.randn(src_lat.shape[0], src_lon.shape[0])
-
-    class _AnalyticSource:
-        def __init__(self):
-            self.domain_coords = OrderedDict([("lat", src_lat), ("lon", src_lon)])
-
-        def __call__(self, time, variable):
-            time, variable = prep_data_inputs(time, variable)
-            data = np.broadcast_to(
-                field, (len(time), len(variable), len(src_lat), len(src_lon))
-            ).copy()
-            return xr.DataArray(
-                data=data,
-                dims=["time", "variable", "lat", "lon"],
-                coords={
-                    "time": time,
-                    "variable": variable,
-                    "lat": src_lat,
-                    "lon": src_lon,
-                },
-            )
-
-    ny = Y_END - Y_START
-    nx = X_END - X_START
-    model = StormCastSDA(
-        PhooRegressionModel(out_vars=NVAR),
-        PhooSDADiffusionModel(),
-        torch.zeros(1, NVAR, 1, 1),
-        torch.ones(1, NVAR, 1, 1),
-        torch.randn(1, 2, ny, nx),
-        hrrr_lat_lim=(Y_START, Y_END),
-        hrrr_lon_lim=(X_START, X_END),
-        variables=np.array(["u%02d" % i for i in range(NVAR)]),
-        conditioning_means=torch.zeros(1, NVAR_COND, 1, 1),
-        conditioning_stds=torch.ones(1, NVAR_COND, 1, 1),
-        conditioning_variables=np.array(["c%02d" % i for i in range(NVAR_COND)]),
-        conditioning_data_source=_AnalyticSource(),
-        sampler_args=SAMPLER_ARGS,
-    ).to("cuda:0")
-
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    x = _build_input_da(model, time, device="cuda:0")
-    c = model._fetch_and_interp_conditioning(x)
-
-    # Scipy reference bilinear interpolation
-    ref = RegularGridInterpolator((src_lat, src_lon), field, method="linear")
-    target_pts = np.column_stack([model.lat.ravel(), model.lon.ravel()])
-    expected = ref(target_pts).reshape(ny, nx)
-
-    c_np = c.data.get()
-    for v in range(NVAR_COND):
-        np.testing.assert_allclose(c_np[0, 0, v], expected, atol=1e-6, rtol=0)
-
-
-# ---------- Test: __call__ ----------
-
-
-@pytest.mark.parametrize(
-    "time",
-    [
-        np.array([np.datetime64("2020-04-05T00:00")]),
-        np.array(
-            [np.datetime64("2020-10-11T12:00"), np.datetime64("2020-06-04T00:00")]
-        ),
-    ],
-)
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_stormcast_sda_call(time, device):
-    model = _build_model(device=device)
-    x = _build_input_da(model, time, device=device)
-    obs_source = _build_obs_source(model, n_obs=10)
-    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
-    ny, nx = model.lat.shape
-
-    with patch.object(model, "_forward", _mock_forward):
-        out = model(x, obs_df)
-
-    assert out.shape == (len(time), 1, NVAR, ny, nx)
-    assert set(out.dims) == {"time", "lead_time", "variable", "hrrr_y", "hrrr_x"}
-    assert np.all(out.coords["time"].values == time)
-    assert out.coords["lead_time"].values[0] == np.timedelta64(1, "h")
-
-    # Without observations
-    with patch.object(model, "_forward", _mock_forward):
-        out_none = model(x, None)
-
-    assert out_none.shape == out.shape
-
-
-# ---------- Test: create_generator ----------
-
-
-@pytest.mark.parametrize(
-    "device",
-    [
-        "cpu",
-        pytest.param(
-            "cuda:0",
-            marks=pytest.mark.skipif(
-                not torch.cuda.is_available(), reason="cuda missing"
-            ),
-        ),
-    ],
-)
-def test_stormcast_sda_generator(device):
-    model = _build_model(device=device)
-    time = np.array([np.datetime64("2020-04-05T00:00")])
-    x = _build_input_da(model, time, device=device)
-    obs_source = _build_obs_source(model, n_obs=10)
-    obs_df = fetch_dataframe(obs_source, time, model.variables[:2])
-    ny, nx = model.lat.shape
-
-    with patch.object(model, "_forward", _mock_forward):
-        gen = model.create_generator(x)
-
-        # First yield returns initial state
-        state = next(gen)
-        assert state.shape == x.shape
-
-        # Send observations, receive next forecast
-        state = gen.send(obs_df)
-        assert state.shape == (len(time), 1, NVAR, ny, nx)
-        assert state.coords["lead_time"].values[0] == np.timedelta64(1, "h")
-
-        # Send None (no observations), receive next forecast
-        state = gen.send(None)
-        assert state.shape == (len(time), 1, NVAR, ny, nx)
-        assert state.coords["lead_time"].values[0] == np.timedelta64(2, "h")
-
-        gen.close()
-
-
-# ---------- Test: exceptions ----------
-
-
-def test_stormcast_sda_exceptions():
-    ny = Y_END - Y_START
-    nx = X_END - X_START
-    regression = PhooRegressionModel(out_vars=NVAR)
-    diffusion = PhooSDADiffusionModel()
-    means = torch.zeros(1, NVAR, 1, 1)
-    stds = torch.ones(1, NVAR, 1, 1)
-    invariants = torch.randn(1, 2, ny, nx)
-
-    # No conditioning_data_source
-    model = StormCastSDA(
-        regression,
-        diffusion,
-        means,
-        stds,
-        invariants,
-        hrrr_lat_lim=(Y_START, Y_END),
-        hrrr_lon_lim=(X_START, X_END),
-    )
-
-    time = np.array([np.datetime64("2020-01-01T00:00")])
-    x = _build_input_da(model, time)
-
-    with pytest.raises(RuntimeError):
-        model(x, None)
-
-    gen = model.create_generator(x)
-    with pytest.raises(RuntimeError):
-        next(gen)
-
-
-# ---------- Test: package loading ----------
-
-
-@pytest.fixture(scope="function")
-def sda_model() -> StormCastSDA:
-    package = StormCastSDA.load_default_package()
-    return StormCastSDA.load_model(package)
-
-
-@pytest.mark.package
-@pytest.mark.parametrize("device", ["cuda:0"])
-def test_stormcast_sda_package(device, sda_model):
-    torch.cuda.empty_cache()
-    time = np.array([np.datetime64("2020-04-05T00:00")])
-
-    model = sda_model.to(device)
-
-    # Set up Random conditioning source to avoid external data fetches
-    r_condition = Random(
-        OrderedDict(
-            [
-                ("lat", np.linspace(90, -90, num=721, endpoint=True)),
-                ("lon", np.linspace(0, 360, num=1440)),
-            ]
-        )
-    )
-    model.conditioning_data_source = r_condition
-    model.sampler_args = SAMPLER_ARGS
-
-    # Build input from Random source matching model init_coords
-    ic = model.init_coords()[0]
-    dc = OrderedDict([("hrrr_y", ic["hrrr_y"]), ("hrrr_x", ic["hrrr_x"])])
-    r = Random(dc)
-    x = fetch_data(
-        r,
-        time,
-        ic["variable"],
-        lead_time=np.array([np.timedelta64(0, "h")]),
-        device=device,
-        legacy=False,
-    )
-    x = x.assign_coords(
-        lat=(["hrrr_y", "hrrr_x"], model.lat),
-        lon=(["hrrr_y", "hrrr_x"], model.lon),
-    )
-
-    out = model(x, None)
-
-    assert out.shape == (1, 1, 99, 512, 640)
-    assert set(out.dims) == {"time", "lead_time", "variable", "hrrr_y", "hrrr_x"}
-    assert np.all(out.coords["time"].values == time)
-    assert out.coords["lead_time"].values[0] == np.timedelta64(1, "h")
diff --git a/test/models/da/test_utils.py b/test/models/da/test_utils.py
deleted file mode 100644
index 10cd95d57..000000000
--- a/test/models/da/test_utils.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from datetime import datetime
-
-import numpy as np
-import pandas as pd
-import pytest
-import torch
-
-try:
-    import cudf
-except ImportError:
-    cudf = None
-
-from earth2studio.models.da.utils import (
-    dfseries_to_torch,
-    filter_time_range,
-    validate_observation_fields,
-)
-
-
-@pytest.fixture
-def sample_pandas_df():
-    """Create a sample pandas DataFrame for testing."""
-    return pd.DataFrame(
-        {
-            "time": pd.to_datetime(
-                [
-                    "2024-01-01T10:00:00",
-                    "2024-01-01T11:00:00",
-                    "2024-01-01T12:00:00",
-                    "2024-01-01T13:00:00",
-                ]
-            ),
-            "lat": [30.0, 31.0, 32.0, 33.0],
-            "lon": [240.0, 241.0, 242.0, 243.0],
-            "observation": [10.0, 20.0, 30.0, 40.0],
-            "variable": ["t2m", "t2m", "u10m", "u10m"],
-        }
-    )
-
-
-@pytest.fixture
-def sample_cudf_df():
-    """Create a sample cudf DataFrame for testing."""
-    if cudf is None:
-        pytest.skip("cudf not available")
-    return cudf.DataFrame(
-        {
-            "time": pd.to_datetime(
-                [
-                    "2024-01-01T10:00:00",
-                    "2024-01-01T11:00:00",
-                    "2024-01-01T12:00:00",
-                    "2024-01-01T13:00:00",
-                ]
-            ),
-            "lat": [30.0, 31.0, 32.0, 33.0],
-            "lon": [240.0, 241.0, 242.0, 243.0],
-            "observation": [10.0, 20.0, 30.0, 40.0],
-            "variable": ["t2m", "t2m", "u10m", "u10m"],
-        }
-    )
-
-
-def test_validate_observation_fields(sample_pandas_df):
-    validate_observation_fields(sample_pandas_df, ["time", "lat", "lon"])
-    validate_observation_fields(
-        sample_pandas_df, ["time", "lat", "lon", "observation", "variable"]
-    )
-    validate_observation_fields(sample_pandas_df, ["time"])
-
-    with pytest.raises(ValueError, match="DataFrame missing required fields"):
-        validate_observation_fields(sample_pandas_df, ["time", "missing_field"])
-
-    with pytest.raises(ValueError, match="DataFrame missing required fields"):
-        validate_observation_fields(
-            sample_pandas_df, ["time", "missing_field1", "missing_field2"]
-        )
-
-
-class TestFilterTimeRange:
-    @pytest.mark.parametrize(
-        "request_time",
-        [
-            np.datetime64("2024-01-01T12:00:00"),
-            datetime(2024, 1, 1, 12, 0, 0),
-            "2024-01-01T12:00:00",
-        ],
-    )
-    def test_filter_time_range_pandas(self, sample_pandas_df, request_time):
-        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
-        result = filter_time_range(sample_pandas_df, request_time, tolerance)
-
-        assert isinstance(result, pd.DataFrame)
-        assert len(result) == 1
-        assert result.iloc[0]["time"] == pd.Timestamp(request_time)
-
-    @pytest.mark.parametrize(
-        "request_time",
-        [
-            np.datetime64("2024-01-01T12:00:00"),
-            datetime(2024, 1, 1, 12, 0, 0),
-            "2024-01-01T12:00:00",
-        ],
-    )
-    def test_filter_time_range_cudf(self, sample_cudf_df, request_time):
-        if cudf is None:
-            pytest.skip("cudf not available")
-        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
-        result = filter_time_range(sample_cudf_df, request_time, tolerance)
-
-        assert isinstance(result, cudf.DataFrame)
-        assert len(result) == 1
-        assert (result.iloc[0]["time"] == pd.Timestamp(request_time)).all()
-
-    def test_filter_time_range_tolerance_window(self, sample_pandas_df):
-        request_time = np.datetime64("2024-01-01T11:30:00")
-        tolerance = (np.timedelta64(-30, "m"), np.timedelta64(30, "m"))
-        result = filter_time_range(sample_pandas_df, request_time, tolerance)
-        assert len(result) == 2
-
-        tolerance = (np.timedelta64(-2, "h"), np.timedelta64(2, "h"))
-        result = filter_time_range(sample_pandas_df, request_time, tolerance)
-        assert len(result) >= 2
-
-        request_time = np.datetime64("2024-01-01T20:00:00")
-        tolerance = (np.timedelta64(-1, "h"), np.timedelta64(1, "h"))
-        result = filter_time_range(sample_pandas_df, request_time, tolerance)
-        assert len(result) == 0
-
-    def test_filter_time_range_custom_column(self, sample_pandas_df):
-        df = sample_pandas_df.rename(columns={"time": "timestamp"})
-        request_time = np.datetime64("2024-01-01T12:00:00")
-        tolerance = (np.timedelta64(-1, "h"), np.timedelta64(1, "h"))
-        result = filter_time_range(df, request_time, tolerance, time_column="timestamp")
-        assert len(result) == 3
-
-
-class TestDfseriesToTorch:
-    @pytest.mark.parametrize(
-        "device",
-        [
-            "cpu",
-            pytest.param(
-                "cuda:0",
-                marks=pytest.mark.skipif(
-                    not torch.cuda.is_available(), reason="cuda missing"
-                ),
-            ),
-        ],
-    )
-    def test_dfseries_to_torch_pandas_cpu(self, sample_pandas_df, device):
-        if device != "cpu" and not torch.cuda.is_available():
-            pytest.skip("CUDA not available")
-        series = sample_pandas_df["lat"]
-        tensor = dfseries_to_torch(series, dtype=torch.float32, device=device)
-
-        assert isinstance(tensor, torch.Tensor)
-        assert tensor.dtype == torch.float32
-        assert tensor.device.type == device.split(":")[0] if ":" in device else device
-        assert len(tensor) == len(series)
-        assert torch.allclose(
-            tensor, torch.tensor(series.values, dtype=torch.float32, device=device)
-        )
-
-    @pytest.mark.parametrize(
-        "device",
-        [
-            "cpu",
-            pytest.param(
-                "cuda:0",
-                marks=pytest.mark.skipif(
-                    not torch.cuda.is_available(), reason="cuda missing"
-                ),
-            ),
-        ],
-    )
-    def test_dfseries_to_torch_cudf(self, sample_cudf_df, device):
-        if cudf is None:
-            pytest.skip("cudf not available")
-        if device != "cpu" and not torch.cuda.is_available():
-            pytest.skip("CUDA not available")
-        series = sample_cudf_df["lat"]
-        tensor = dfseries_to_torch(series, dtype=torch.float32, device=device)
-
-        assert isinstance(tensor, torch.Tensor)
-        assert tensor.dtype == torch.float32
-        assert tensor.device.type == device.split(":")[0] if ":" in device else device
-        assert len(tensor) == len(series)
diff --git a/test/models/px/test_ace2.py b/test/models/px/test_ace2.py
index 67509420b..ac1c6325a 100644
--- a/test/models/px/test_ace2.py
+++ b/test/models/px/test_ace2.py
@@ -29,8 +29,6 @@
 )
 from earth2studio.utils import handshake_dim
 
-pytest.importorskip("fme")
-
 
 class PhooOutput:
     prediction: dict = {}
diff --git a/test/models/px/test_aurora.py b/test/models/px/test_aurora.py
index ae90f7829..748c45e8c 100644
--- a/test/models/px/test_aurora.py
+++ b/test/models/px/test_aurora.py
@@ -20,11 +20,7 @@
 import numpy as np
 import pytest
 import torch
-
-try:
-    from aurora import Batch, Metadata
-except ImportError:
-    pytest.importorskip("aurora")
+from aurora import Batch, Metadata
 
 from earth2studio.data import Random, fetch_data
 from earth2studio.models.px import Aurora
diff --git a/test/models/px/test_dxwrapper.py b/test/models/px/test_dxwrapper.py
index 5152e93ab..75c080ecf 100644
--- a/test/models/px/test_dxwrapper.py
+++ b/test/models/px/test_dxwrapper.py
@@ -117,7 +117,6 @@ def round_sigma(self, sigma):
         return torch.as_tensor(sigma)
 
 
-@pytest.mark.skip(reason="FCN3 test for dx wrapper is slow")
 @pytest.mark.parametrize("device", ["cuda:0"])  # Removing CPU here too slow atm "cpu",
 @pytest.mark.parametrize("model_type", ["precip", "solar"])
 @pytest.mark.parametrize(
diff --git a/test/models/px/test_fcn.py b/test/models/px/test_fcn.py
index 10bf64f12..eb9e375fa 100644
--- a/test/models/px/test_fcn.py
+++ b/test/models/px/test_fcn.py
@@ -165,6 +165,7 @@ def model() -> FCN:
     return p
 
 
+@pytest.mark.xfail(reason="package missing on ngc")
 @pytest.mark.package
 @pytest.mark.parametrize("device", ["cuda:0"])
 def test_fcn_package(model, device):
diff --git a/test/models/px/test_graphcast.py b/test/models/px/test_graphcast.py
index f174767af..98a9a07ea 100644
--- a/test/models/px/test_graphcast.py
+++ b/test/models/px/test_graphcast.py
@@ -22,11 +22,7 @@
 import pytest
 import torch
 import xarray as xr
-
-try:
-    from graphcast import graphcast
-except ImportError:
-    pytest.importorskip("graphcast")
+from graphcast import graphcast
 
 from earth2studio.data import Random, fetch_data
 from earth2studio.models.px.graphcast_operational import GraphCastOperational
@@ -131,8 +127,8 @@ def __init__(self, model_config, task_config):
     [
         np.array([np.datetime64("1993-04-05T00:00")]),
         np.array(
-            [np.datetime64("1993-04-05T00:00"), np.datetime64("2001-06-04T00:00")]
-        ),
+            [np.datetime64("2001-06-04T00:00")]
+        ),  # Only len 1 time array is supported by GraphCastSmall model
     ],
 )
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
@@ -156,6 +152,9 @@ def test_graphcast_small_call(time, device, mock_GraphCastSmall_model):
 
     out, out_coords = p(x, coords)
 
+    if not isinstance(time, Iterable):
+        time = [time]
+
     assert out.shape == torch.Size([len(time), 1, 83, 181, 360])
     assert (out_coords["variable"] == p.output_coords(coords)["variable"]).all()
     assert (out_coords["time"] == time).all()
@@ -347,18 +346,9 @@ def __init__(self, model_config, task_config):
     return p
 
 
-@pytest.mark.parametrize(
-    "time",
-    [
-        np.array([np.datetime64("2010-01-01T00:00")]),
-        np.array(
-            [np.datetime64("2010-01-01T00:00"), np.datetime64("2010-01-02T00:00")]
-        ),
-    ],
-)
 @pytest.mark.parametrize("device", ["cpu", "cuda:0"])
 @mock.patch("graphcast.rollout.chunked_prediction", mocked_chunked_prediction)
-def test_graphcast_operational_call(time, device, mock_GraphCastOperational_model):
+def test_graphcast_operational_call(device, mock_GraphCastOperational_model):
 
     p = mock_GraphCastOperational_model.to(device)
 
@@ -372,11 +362,12 @@ def test_graphcast_operational_call(time, device, mock_GraphCastOperational_mode
     r = Random(dc)
 
     # Get Data and convert to tensor, coords
+    time = np.array([np.datetime64("2010-01-01T00:00")])
     lead_time = p.input_coords()["lead_time"]
     variable = p.input_coords()["variable"]
     x, coords = fetch_data(r, time, variable, lead_time, device=device)
     out, out_coords = p(x, coords)
-    assert out.shape == torch.Size([len(time), 1, 83, 721, 1440])
+    assert out.shape == (1, 1, 83, 721, 1440)
     assert (out_coords["variable"] == p.output_coords(coords)["variable"]).all()
     assert (out_coords["time"] == time).all()
     handshake_dim(out_coords, "lon", 4)
diff --git a/test/serve/client/conftest.py b/test/serve/client/conftest.py
deleted file mode 100644
index 2bd76cb79..000000000
--- a/test/serve/client/conftest.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-from collections.abc import Iterator
-from typing import Any
-
-import pytest
-
-# earth2studio.serve.client is importable because uv adds the repository
-# root to sys.path when running: uv run -m pytest test/serve/client
-
-# =============================================================================
-# Pytest Configuration
-# =============================================================================
-
-
-def pytest_configure(config: Any) -> None:
-    """Configure pytest with custom markers."""
-    config.addinivalue_line(
-        "markers",
-        "integration: mark test as an integration test requiring API server",
-    )
-
-
-# =============================================================================
-# Integration Test Fixtures
-# =============================================================================
-
-
-@pytest.fixture(scope="session")
-def api_url() -> str | None:
-    """
-    Get API URL from environment for integration tests.
-
-    Returns None if not set (integration tests will be skipped).
-    """
-    return os.getenv("EARTH2STUDIO_API_URL")
-
-
-@pytest.fixture(scope="session")
-def check_api_availability(api_url: str | None) -> Iterator[str]:
-    """
-    Check if API is available before running integration tests.
-
-    This fixture should be used by integration tests to ensure
-    the API server is running and accessible.
-    """
-    if not api_url:
-        pytest.skip(
-            "EARTH2STUDIO_API_URL environment variable not set. "
-            "Set it to run integration tests: export EARTH2STUDIO_API_URL=http://localhost:8000"
-        )
-
-    # Verify API is reachable
-    try:
-        import requests  # type: ignore[import-untyped]
-
-        response = requests.get(f"{api_url}/health", timeout=10)
-        if response.status_code != 200:
-            pytest.skip(f"API health check failed: {response.status_code}")
-    except Exception as e:
-        pytest.skip(f"Cannot connect to API at {api_url}: {e}")
-
-    assert api_url is not None  # narrow type for mypy after skip
-    yield api_url
-
-
-# =============================================================================
-# Unit Test Mock Fixtures
-# =============================================================================
-
-
-@pytest.fixture
-def mock_health_response() -> dict[str, Any]:
-    """Fixture providing a mock health check response"""
-    return {
-        "status": "healthy",
-        "timestamp": "2024-01-01T00:00:00+00:00",
-    }
-
-
-@pytest.fixture
-def mock_inference_response() -> dict[str, Any]:
-    """Fixture providing a mock inference request response"""
-    return {
-        "execution_id": "exec_test_123",
-        "status": "accepted",
-        "message": "Request accepted for processing",
-        "timestamp": "2024-01-01T00:00:00+00:00",
-    }
-
-
-@pytest.fixture
-def mock_status_response() -> dict[str, Any]:
-    """Fixture providing a mock status response"""
-    return {
-        "execution_id": "exec_test_123",
-        "status": "running",
-        "progress": {
-            "progress": "Processing step 5 of 10",
-            "current_step": 5,
-            "total_steps": 10,
-        },
-        "error_message": None,
-    }
-
-
-@pytest.fixture
-def mock_results_response() -> dict[str, Any]:
-    """Fixture providing a mock results response"""
-    return {
-        "request_id": "exec_test_123",
-        "status": "completed",
-        "output_files": [
-            {"path": "output.zarr", "size": 1024000},
-            {"path": "metadata.json", "size": 512},
-        ],
-        "completion_time": "2024-01-01T00:10:00+00:00",
-        "execution_time_seconds": 600.0,
-    }
diff --git a/test/serve/client/test_client.py b/test/serve/client/test_client.py
deleted file mode 100644
index e55ed30fa..000000000
--- a/test/serve/client/test_client.py
+++ /dev/null
@@ -1,444 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import io
-import json
-from datetime import datetime
-from unittest.mock import Mock, patch
-
-import pytest
-import requests  # type: ignore[import-untyped]
-
-from earth2studio.serve.client.client import Earth2StudioClient
-from earth2studio.serve.client.exceptions import (
-    APIConnectionError as ClientConnectionError,
-)
-from earth2studio.serve.client.exceptions import (
-    BadRequestError,
-    Earth2StudioAPIError,
-    InferenceRequestNotFoundError,
-    InternalServerError,
-    RequestTimeoutError,
-)
-from earth2studio.serve.client.models import (
-    HealthStatus,
-    InferenceRequest,
-    InferenceRequestResponse,
-    InferenceRequestResults,
-    InferenceRequestStatus,
-    OutputFile,
-    RequestStatus,
-)
-
-
-class TestEarth2StudioClientInitialization:
-    """Test Earth2StudioClient initialization"""
-
-    def test_client_initialization(self) -> None:
-        """Test client initialization with default and custom parameters"""
-        # Test defaults
-        client = Earth2StudioClient()
-        assert client.base_url == "http://localhost:8000"
-        assert client.workflow_name == "deterministic_earth2_workflow"
-        assert client.timeout == 30.0
-        assert client.session is not None
-
-        # Test custom params and trailing slash stripping
-        client = Earth2StudioClient(
-            base_url="https://api.example.com:8080/",
-            workflow_name="custom_workflow",
-            timeout=60.0,
-        )
-        assert client.base_url == "https://api.example.com:8080"
-        assert client.workflow_name == "custom_workflow"
-        assert client.timeout == 60.0
-
-
-class TestEarth2StudioClientHealthCheck:
-    """Test health_check method"""
-
-    def test_health_check(self) -> None:
-        """Test health check success and error handling"""
-        client = Earth2StudioClient()
-
-        # Test success
-        mock_response = {
-            "status": "healthy",
-            "timestamp": "2024-01-01T00:00:00+00:00",
-        }
-        with patch.object(client, "_make_request", return_value=mock_response):
-            health = client.health_check()
-            assert isinstance(health, HealthStatus)
-            assert health.status == "healthy"
-
-        # Test error
-        with patch.object(
-            client, "_make_request", side_effect=Earth2StudioAPIError("API error")
-        ):
-            with pytest.raises(Earth2StudioAPIError, match="API error"):
-                client.health_check()
-
-
-class TestEarth2StudioClientInferenceRequest:
-    """Test submit_inference_request method"""
-
-    def test_submit_inference_request(self) -> None:
-        """Test inference request submission with success and error cases"""
-        client = Earth2StudioClient()
-        request = InferenceRequest(
-            parameters={"forecast_times": ["2024-01-01T00:00:00"]}
-        )
-
-        # Test success
-        mock_response = {
-            "execution_id": "exec_123",
-            "status": "accepted",
-            "message": "Request accepted",
-            "timestamp": "2024-01-01T00:00:00+00:00",
-        }
-        with patch.object(client, "_make_request", return_value=mock_response):
-            response = client.submit_inference_request(request)
-            assert isinstance(response, InferenceRequestResponse)
-            assert response.execution_id == "exec_123"
-            assert response.status == RequestStatus.ACCEPTED
-
-        # Test bad request error
-        with patch.object(
-            client,
-            "_make_request",
-            side_effect=BadRequestError("Invalid parameters"),
-        ):
-            with pytest.raises(BadRequestError, match="Invalid parameters"):
-                client.submit_inference_request(request)
-
-
-class TestEarth2StudioClientGetRequestStatus:
-    """Test get_request_status method"""
-
-    def test_get_request_status(self) -> None:
-        """Test status retrieval for various states"""
-        client = Earth2StudioClient()
-
-        # Test running status with progress
-        mock_response = {
-            "execution_id": "exec_123",
-            "status": "running",
-            "progress": {
-                "progress": "Processing step 5 of 10",
-                "current_step": 5,
-                "total_steps": 10,
-            },
-            "error_message": None,
-        }
-        with patch.object(client, "_make_request", return_value=mock_response):
-            status = client.get_request_status("exec_123")
-            assert isinstance(status, InferenceRequestStatus)
-            assert status.status == RequestStatus.RUNNING
-            assert status.progress.current_step == 5
-
-        # Test failed status
-        mock_response["status"] = "failed"
-        mock_response["error_message"] = "Data source unavailable"
-        mock_response["progress"] = None
-        with patch.object(client, "_make_request", return_value=mock_response):
-            status = client.get_request_status("exec_123")
-            assert status.status == RequestStatus.FAILED
-            assert status.error_message == "Data source unavailable"
-
-        # Test not found
-        with patch.object(
-            client,
-            "_make_request",
-            side_effect=InferenceRequestNotFoundError("Request not found"),
-        ):
-            with pytest.raises(InferenceRequestNotFoundError):
-                client.get_request_status("nonexistent_id")
-
-
-class TestEarth2StudioClientGetRequestResults:
-    """Test get_request_results method"""
-
-    def test_get_request_results(self) -> None:
-        """Test results retrieval in various scenarios"""
-        client = Earth2StudioClient()
-
-        # Test successful results
-        mock_response_data = {
-            "request_id": "exec_123",
-            "status": "completed",
-            "output_files": [
-                {"path": "output.zarr", "size": 1024},
-                {"path": "metadata.json", "size": 256},
-            ],
-            "completion_time": "2024-01-01T00:10:00+00:00",
-            "execution_time_seconds": 600.0,
-        }
-        mock_http_response = Mock()
-        mock_http_response.status_code = 200
-        mock_http_response.json.return_value = mock_response_data
-
-        with patch.object(client, "_make_request", return_value=mock_http_response):
-            results = client.get_request_results("exec_123")
-            assert isinstance(results, InferenceRequestResults)
-            assert results.status == RequestStatus.COMPLETED
-            assert len(results.output_files) == 2
-
-        # Test still processing (202)
-        mock_http_response.status_code = 202
-        mock_http_response.json.return_value = {"message": "Still processing"}
-        with patch.object(client, "_make_request", return_value=mock_http_response):
-            with pytest.raises(Earth2StudioAPIError, match="[Ss]till processing"):
-                client.get_request_results("exec_123")
-
-        # Test custom timeout
-        mock_http_response.status_code = 200
-        mock_http_response.json.return_value = mock_response_data
-        with patch.object(
-            client, "_make_request", return_value=mock_http_response
-        ) as mock_req:
-            client.get_request_results("exec_123", timeout=120.0)
-            assert mock_req.call_args[1]["timeout"] == 120.0
-
-
-class TestEarth2StudioClientWaitForCompletion:
-    """Test wait_for_completion method"""
-
-    def test_wait_for_completion(self) -> None:
-        """Test waiting for completion with various outcomes"""
-        client = Earth2StudioClient()
-
-        # Test success after polling
-        status_responses = [
-            InferenceRequestStatus(
-                execution_id="exec_123",
-                status=RequestStatus.RUNNING,
-                progress=None,
-                error_message=None,
-            ),
-            InferenceRequestStatus(
-                execution_id="exec_123",
-                status=RequestStatus.COMPLETED,
-                progress=None,
-                error_message=None,
-            ),
-        ]
-        mock_results = InferenceRequestResults(
-            request_id="exec_123",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-        )
-        with (
-            patch.object(client, "get_request_status", side_effect=status_responses),
-            patch.object(client, "get_request_results", return_value=mock_results),
-            patch("time.sleep"),
-        ):
-            results = client.wait_for_completion("exec_123", poll_interval=1.0)
-            assert results.status == RequestStatus.COMPLETED
-
-        # Test timeout
-        status_response = InferenceRequestStatus(
-            execution_id="exec_123",
-            status=RequestStatus.RUNNING,
-            progress=None,
-            error_message=None,
-        )
-        with (
-            patch.object(client, "get_request_status", return_value=status_response),
-            patch("time.sleep"),
-            patch("time.time", side_effect=[0, 0, 5, 11]),
-        ):
-            with pytest.raises(RequestTimeoutError, match="did not complete within"):
-                client.wait_for_completion("exec_123", poll_interval=1.0, timeout=10.0)
-
-        # Test failed request
-        status_response.status = RequestStatus.FAILED
-        status_response.error_message = "Data source error"
-        with patch.object(client, "get_request_status", return_value=status_response):
-            with pytest.raises(Earth2StudioAPIError, match="failed"):
-                client.wait_for_completion("exec_123")
-
-        # Test cancelled request
-        status_response.status = RequestStatus.CANCELLED
-        with patch.object(client, "get_request_status", return_value=status_response):
-            with pytest.raises(Earth2StudioAPIError, match="was cancelled"):
-                client.wait_for_completion("exec_123")
-
-
-class TestEarth2StudioClientDownloadResult:
-    """Test download_result and related methods"""
-
-    def test_download_result(self) -> None:
-        """Test downloading result files with custom timeout"""
-        client = Earth2StudioClient(workflow_name="test_workflow")
-
-        result = InferenceRequestResults(
-            request_id="exec_123",
-            status=RequestStatus.COMPLETED,
-            output_files=[OutputFile(path="output.zarr", size=1024)],
-            completion_time=datetime.now(),
-        )
-
-        # Test result_root_path
-        path = client.result_root_path(result)
-        assert path == "/v1/infer/test_workflow/exec_123/results/"
-
-        # Test download
-        mock_http_response = Mock()
-        mock_http_response.content = b"mock file content"
-
-        with patch.object(
-            client, "_make_request", return_value=mock_http_response
-        ) as mock_req:
-            data = client.download_result(result, "output.zarr", timeout=300.0)
-            assert isinstance(data, io.BytesIO)
-            assert data.read() == b"mock file content"
-            assert mock_req.call_args[1]["timeout"] == 300.0
-
-
-class TestEarth2StudioClientMakeRequest:
-    """Test _make_request method"""
-
-    def test_make_request_success_and_errors(self) -> None:
-        """Test HTTP request handling for various scenarios"""
-        client = Earth2StudioClient()
-
-        # Test successful request
-        mock_response = Mock()
-        mock_response.ok = True
-        mock_response.status_code = 200
-        mock_response.json.return_value = {"result": "success"}
-        with patch.object(client.session, "request", return_value=mock_response):
-            result = client._make_request("GET", "/test")
-            assert result == {"result": "success"}
-
-        # Test timeout
-        with patch.object(
-            client.session, "request", side_effect=requests.exceptions.Timeout()
-        ):
-            with pytest.raises(RequestTimeoutError, match="timed out"):
-                client._make_request("GET", "/test")
-
-        # Test connection error
-        with patch.object(
-            client.session,
-            "request",
-            side_effect=requests.exceptions.ConnectionError("Connection failed"),
-        ):
-            with pytest.raises(ClientConnectionError, match="Failed to connect"):
-                client._make_request("GET", "/test")
-
-    def test_make_request_http_errors(self) -> None:
-        """Test HTTP error status code handling"""
-        client = Earth2StudioClient()
-
-        # Test 400 Bad Request
-        mock_response = Mock()
-        mock_response.ok = False
-        mock_response.status_code = 400
-        mock_response.json.return_value = {"error": "Invalid parameters"}
-        with patch.object(client.session, "request", return_value=mock_response):
-            with pytest.raises(BadRequestError, match="Invalid parameters"):
-                client._make_request("POST", "/test")
-
-        # Test 404 Not Found
-        mock_response.status_code = 404
-        mock_response.json.return_value = {"error": "Not found"}
-        with patch.object(client.session, "request", return_value=mock_response):
-            with pytest.raises(InferenceRequestNotFoundError, match="Not found"):
-                client._make_request("GET", "/test")
-
-        # Test 500 Internal Server Error
-        mock_response.status_code = 500
-        mock_response.json.return_value = {"error": "Internal server error"}
-        with patch.object(client.session, "request", return_value=mock_response):
-            with pytest.raises(InternalServerError, match="Internal server error"):
-                client._make_request("GET", "/test")
-
-        # Test invalid JSON response
-        mock_response.ok = True
-        mock_response.status_code = 200
-        mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "", 0)
-        with patch.object(client.session, "request", return_value=mock_response):
-            with pytest.raises(Earth2StudioAPIError, match="Invalid JSON response"):
-                client._make_request("GET", "/test")
-
-    def test_make_request_return_response_and_stream(self) -> None:
-        """Test returning raw response and streaming"""
-        client = Earth2StudioClient()
-
-        mock_response = Mock()
-        mock_response.ok = True
-        mock_response.status_code = 200
-        mock_response.content = b"streaming content"
-
-        # Test return_response
-        with patch.object(client.session, "request", return_value=mock_response):
-            result = client._make_request("GET", "/test", return_response=True)
-            assert result is mock_response
-
-        # Test stream with custom timeout
-        with patch.object(
-            client.session, "request", return_value=mock_response
-        ) as mock_req:
-            client._make_request(
-                "GET", "/test", return_response=True, stream=True, timeout=120.0
-            )
-            assert mock_req.call_args[1]["stream"] is True
-            assert mock_req.call_args[1]["timeout"] == 120.0
-
-
-class TestEarth2StudioClientContextManager:
-    """Test context manager protocol"""
-
-    def test_context_manager(self) -> None:
-        """Test using client as context manager"""
-        with Earth2StudioClient() as client:
-            assert isinstance(client, Earth2StudioClient)
-
-        # Test that session is closed on exit
-        client = Earth2StudioClient()
-        with patch.object(client.session, "close") as mock_close:
-            with client:
-                pass
-            mock_close.assert_called_once()
-
-
-class TestEarth2StudioClientParseErrorResponse:
-    """Test _parse_error_response method"""
-
-    def test_parse_error_response(self) -> None:
-        """Test parsing error responses with valid and invalid JSON"""
-        client = Earth2StudioClient()
-
-        # Test valid JSON
-        mock_response = Mock()
-        mock_response.json.return_value = {"error": "Test error", "details": "Details"}
-        result = client._parse_error_response(mock_response)
-        assert result == {"error": "Test error", "details": "Details"}
-
-        # Test invalid JSON with text
-        mock_response.status_code = 500
-        mock_response.text = "Plain text error"
-        mock_response.json.side_effect = json.JSONDecodeError("Invalid", "", 0)
-        result = client._parse_error_response(mock_response)
-        assert result == {"error": "Plain text error"}
-
-        # Test invalid JSON with empty text
-        mock_response.text = ""
-        result = client._parse_error_response(mock_response)
-        assert "HTTP 500 error" in result["error"]
diff --git a/test/serve/client/test_e2client.py b/test/serve/client/test_e2client.py
deleted file mode 100644
index b1a2fc368..000000000
--- a/test/serve/client/test_e2client.py
+++ /dev/null
@@ -1,663 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from collections import OrderedDict
-from datetime import datetime
-from typing import Any
-from unittest.mock import MagicMock, Mock, patch
-
-import numpy as np
-import pytest
-import torch
-import xarray as xr
-
-from earth2studio.serve.client.e2client import (
-    InferenceOutputModel,
-    RemoteEarth2Workflow,
-    RemoteEarth2WorkflowResult,
-    _convert_time_to_lead_time,
-)
-from earth2studio.serve.client.exceptions import Earth2StudioAPIError
-from earth2studio.serve.client.models import (
-    InferenceRequestResponse,
-    InferenceRequestResults,
-    OutputFile,
-    RequestStatus,
-)
-
-
-class TestRemoteEarth2WorkflowInitialization:
-    """Test RemoteEarth2Workflow initialization"""
-
-    def test_initialization(self) -> None:
-        """Test workflow initialization with various device configurations"""
-        with patch("earth2studio.serve.client.e2client.Earth2StudioClient"):
-            # Test with CUDA available
-            with patch("torch.cuda.is_available", return_value=True):
-                workflow = RemoteEarth2Workflow(
-                    base_url="http://localhost:8000",
-                    workflow_name="test_workflow",
-                )
-                assert workflow.device == "cuda"
-
-            # Test with CUDA unavailable
-            with patch("torch.cuda.is_available", return_value=False):
-                workflow = RemoteEarth2Workflow(
-                    base_url="http://localhost:8000",
-                    workflow_name="test_workflow",
-                )
-                assert workflow.device == "cpu"
-
-            # Test with custom device and xr_args
-            xr_args = {"chunks": {"time": 1}}
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-                device="cpu",
-                xr_args=xr_args,
-            )
-            assert workflow.device == "cpu"
-            assert workflow.xr_args == xr_args
-            assert workflow.xr_args is not xr_args  # Verify it's a copy
-
-    def test_initialization_with_client_kwargs(self) -> None:
-        """Test workflow initialization passes kwargs to client"""
-        with patch(
-            "earth2studio.serve.client.e2client.Earth2StudioClient"
-        ) as mock_client_class:
-            RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-                timeout=60.0,
-                max_retries=5,
-            )
-            mock_client_class.assert_called_once_with(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-                timeout=60.0,
-                max_retries=5,
-            )
-
-
-class TestRemoteEarth2WorkflowCall:
-    """Test RemoteEarth2Workflow __call__ method"""
-
-    def test_call_submits_request(self) -> None:
-        """Test that calling workflow submits inference request with parameters"""
-        with patch(
-            "earth2studio.serve.client.e2client.Earth2StudioClient"
-        ) as mock_client_class:
-            mock_client = Mock()
-            mock_client_class.return_value = mock_client
-
-            mock_response = InferenceRequestResponse(
-                execution_id="exec_123",
-                status=RequestStatus.ACCEPTED,
-                message="Request accepted",
-                timestamp=datetime.now(),
-            )
-            mock_client.submit_inference_request.return_value = mock_response
-
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-            )
-
-            # Call with parameters
-            result = workflow(
-                forecast_times=["2024-01-01T00:00:00"],
-                nsteps=20,
-                model="fcn",
-            )
-
-            assert isinstance(result, RemoteEarth2WorkflowResult)
-            assert result.execution_id == "exec_123"
-
-            # Verify parameters were passed correctly
-            call_args = mock_client.submit_inference_request.call_args
-            request = call_args[0][0]
-            assert request.parameters["nsteps"] == 20
-            assert request.parameters["model"] == "fcn"
-
-
-class TestRemoteEarth2WorkflowTo:
-    """Test RemoteEarth2Workflow to() method"""
-
-    def test_to_device(self) -> None:
-        """Test moving workflow to different devices"""
-        with patch("earth2studio.serve.client.e2client.Earth2StudioClient"):
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-                device="cpu",
-            )
-
-            # Test with string
-            result = workflow.to("cuda")
-            assert workflow.device == "cuda"
-            assert result is workflow
-
-            # Test with torch.device
-            device = torch.device("cpu")
-            result = workflow.to(device)
-            assert workflow.device == device
-
-
-class TestRemoteEarth2WorkflowResult:
-    """Test RemoteEarth2WorkflowResult class"""
-
-    def test_result_caching(self) -> None:
-        """Test that result is cached after first retrieval"""
-        with patch(
-            "earth2studio.serve.client.e2client.Earth2StudioClient"
-        ) as mock_client_class:
-            mock_client = Mock()
-            mock_client_class.return_value = mock_client
-
-            mock_inference_result = InferenceRequestResults(
-                request_id="exec_123",
-                status=RequestStatus.COMPLETED,
-                output_files=[],
-                completion_time=datetime.now(),
-            )
-            mock_client.wait_for_completion.return_value = mock_inference_result
-
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-            )
-            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
-
-            # Multiple calls should use cache
-            result1 = result._get_result()
-            result2 = result._get_result()
-            assert result1 is result2
-            mock_client.wait_for_completion.assert_called_once()
-
-    def test_as_dataset_formats(self) -> None:
-        """Test as_dataset with different output formats"""
-        with patch(
-            "earth2studio.serve.client.e2client.Earth2StudioClient"
-        ) as mock_client_class:
-            mock_client = Mock()
-            mock_client.timeout = 30.0  # used by as_dataset for zarr_timeout
-            mock_client_class.return_value = mock_client
-
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-                xr_args={"decode_coords": False},
-            )
-
-            # Test Zarr format
-            with patch("xarray.open_zarr") as mock_open_zarr:
-                mock_inference_result = InferenceRequestResults(
-                    request_id="exec_123",
-                    status=RequestStatus.COMPLETED,
-                    output_files=[OutputFile(path="output.zarr", size=1024)],
-                    completion_time=datetime.now(),
-                )
-                mock_client.wait_for_completion.return_value = mock_inference_result
-                mock_client.result_root_path.return_value = (
-                    "/v1/infer/test/exec_123/results/"
-                )
-
-                mock_ds = xr.Dataset()
-                mock_open_zarr.return_value = mock_ds
-
-                result = RemoteEarth2WorkflowResult(workflow, "exec_123")
-                ds = result.as_dataset()
-
-                assert ds is mock_ds
-                # Verify xr_args were passed
-                assert mock_open_zarr.call_args[1]["decode_coords"] is False
-
-            # Test NetCDF format
-            with patch("xarray.open_dataset") as mock_open_dataset:
-                mock_inference_result = InferenceRequestResults(
-                    request_id="exec_123",
-                    status=RequestStatus.COMPLETED,
-                    output_files=[OutputFile(path="output.nc", size=512)],
-                    completion_time=datetime.now(),
-                )
-                mock_client.wait_for_completion.return_value = mock_inference_result
-                mock_client.download_result.return_value = MagicMock()
-
-                result = RemoteEarth2WorkflowResult(workflow, "exec_123")
-                result._result = None  # Reset cache
-                ds = result.as_dataset()
-
-                mock_client.download_result.assert_called_once()
-                mock_open_dataset.assert_called_once()
-
-    def test_as_dataset_no_outputs(self) -> None:
-        """Test as_dataset when no output files are available"""
-        with patch(
-            "earth2studio.serve.client.e2client.Earth2StudioClient"
-        ) as mock_client_class:
-            mock_client = Mock()
-            mock_client_class.return_value = mock_client
-
-            mock_inference_result = InferenceRequestResults(
-                request_id="exec_123",
-                status=RequestStatus.COMPLETED,
-                output_files=[],
-                completion_time=datetime.now(),
-            )
-            mock_client.wait_for_completion.return_value = mock_inference_result
-
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-            )
-            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
-
-            with pytest.raises(
-                Earth2StudioAPIError, match="did not return any outputs"
-            ):
-                result.as_dataset()
-
-    def test_as_data_source_and_model(self) -> None:
-        """Test as_data_source and as_model methods"""
-        with (
-            patch(
-                "earth2studio.serve.client.e2client.Earth2StudioClient"
-            ) as mock_client_class,
-            patch("xarray.open_zarr"),
-            patch(
-                "earth2studio.serve.client.e2client.InferenceOutputSource"
-            ) as mock_ios_class,
-        ):
-            mock_client = Mock()
-            mock_client.timeout = 30.0  # used by as_dataset for zarr_timeout
-            mock_client_class.return_value = mock_client
-
-            mock_inference_result = InferenceRequestResults(
-                request_id="exec_123",
-                status=RequestStatus.COMPLETED,
-                output_files=[OutputFile(path="output.zarr", size=1024)],
-                completion_time=datetime.now(),
-            )
-            mock_client.wait_for_completion.return_value = mock_inference_result
-            mock_client.result_root_path.return_value = (
-                "/v1/infer/test/exec_123/results/"
-            )
-
-            workflow = RemoteEarth2Workflow(
-                base_url="http://localhost:8000",
-                workflow_name="test_workflow",
-            )
-            result = RemoteEarth2WorkflowResult(workflow, "exec_123")
-
-            # Test as_data_source
-            result.as_data_source()
-            mock_ios_class.assert_called_once()
-
-            # Test as_model with different iteration coordinates
-            model = result.as_model(iter_coord="lead_time")
-            assert isinstance(model, InferenceOutputModel)
-            assert model.iter_coord == "lead_time"
-
-            model = result.as_model(iter_coord="time")
-            assert model.iter_coord == "time"
-
-
-class TestConvertTimeToLeadTime:
-    """Test _convert_time_to_lead_time function"""
-
-    def test_convert_time_to_lead_time(self) -> None:
-        """Test converting time coordinate to lead_time"""
-        # Create test data - shape should match coord order: (time, lead_time, variable, lat, lon)
-        x = torch.randn(3, 1, 4, 5, 6)
-        start_time = np.datetime64("2024-01-01T00:00:00")
-        times = np.array(
-            [
-                np.datetime64("2024-01-01T00:00:00"),
-                np.datetime64("2024-01-01T06:00:00"),
-                np.datetime64("2024-01-01T12:00:00"),
-            ]
-        )
-
-        coords = OrderedDict(
-            {
-                "time": times,
-                "lead_time": np.array([np.timedelta64(0, "h")]),
-                "variable": np.array(["u10m", "v10m", "t2m", "tp"]),
-                "lat": np.arange(5),
-                "lon": np.arange(6),
-            }
-        )
-
-        x_new, coords_new = _convert_time_to_lead_time(x, coords, start_time)
-
-        # Verify transformation
-        assert coords_new["time"].shape == (1,)
-        assert coords_new["time"][0] == start_time
-        assert coords_new["lead_time"].shape == (3,)
-        # Verify lead times are correct
-        expected_lead_times = times - start_time
-        np.testing.assert_array_equal(coords_new["lead_time"], expected_lead_times)
-
-    def test_convert_time_to_lead_time_preserves_other_coords(self) -> None:
-        """Test that other coordinates are preserved"""
-        # Shape: (time, lead_time, variable, lat, lon)
-        x = torch.randn(2, 1, 3, 4, 5)
-        start_time = np.datetime64("2024-01-01T00:00:00")
-        times = np.array(
-            [
-                np.datetime64("2024-01-01T00:00:00"),
-                np.datetime64("2024-01-01T06:00:00"),
-            ]
-        )
-
-        coords = OrderedDict(
-            {
-                "time": times,
-                "lead_time": np.array([np.timedelta64(0, "h")]),
-                "variable": np.array(["u10m", "v10m", "t2m"]),
-                "lat": np.arange(4),
-                "lon": np.arange(5),
-            }
-        )
-
-        _, coords_new = _convert_time_to_lead_time(x, coords, start_time)
-
-        # Verify other coordinates are unchanged
-        np.testing.assert_array_equal(coords_new["variable"], coords["variable"])
-        np.testing.assert_array_equal(coords_new["lat"], coords["lat"])
-        np.testing.assert_array_equal(coords_new["lon"], coords["lon"])
-
-
-class TestInferenceOutputModel:
-    """Test InferenceOutputModel class"""
-
-    @pytest.fixture
-    def mock_data_source(self) -> Any:
-        """Create a mock InferenceOutputSource"""
-        mock_ds = Mock()
-
-        # Create mock DataArray with coordinates
-        times = np.array(
-            [
-                np.datetime64("2024-01-01T00:00:00"),
-                np.datetime64("2024-01-01T06:00:00"),
-            ]
-        )
-        variables = np.array(["u10m", "v10m", "t2m"])
-        lats = np.arange(10)
-        lons = np.arange(20)
-
-        # Create mock coordinate objects that support slicing
-        mock_time_coord = Mock()
-        mock_time_coord.values = times
-        mock_time_coord.__getitem__ = Mock(
-            side_effect=lambda key: Mock(values=times[key])
-        )
-
-        mock_lat_coord = Mock()
-        mock_lat_coord.values = lats
-
-        mock_lon_coord = Mock()
-        mock_lon_coord.values = lons
-
-        mock_da = Mock()
-        mock_da.coords = {
-            "time": mock_time_coord,
-            "variable": variables,
-            "lat": mock_lat_coord,
-            "lon": mock_lon_coord,
-        }
-
-        mock_ds.da = mock_da
-        return mock_ds
-
-    def test_initialization(self, mock_data_source: Any) -> None:
-        """Test InferenceOutputModel initialization with default and custom variables"""
-        # Test with default variables
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-        assert model.iter_coord == "lead_time"
-        assert model.device == "cpu"
-        np.testing.assert_array_equal(
-            model.variables, np.array(["u10m", "v10m", "t2m"])
-        )
-
-        # Test with custom variables
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            variables=["u10m", "v10m"],
-            device="cpu",
-        )
-        np.testing.assert_array_equal(model.variables, np.array(["u10m", "v10m"]))
-
-    def test_coords(self, mock_data_source: Any) -> None:
-        """Test input_coords and output_coords methods"""
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        # Test input_coords
-        input_coords = model.input_coords()
-        assert isinstance(input_coords, OrderedDict)
-        assert len(input_coords["time"]) == 0
-        assert len(input_coords["variable"]) == 0
-
-        # Test output_coords
-        output_coords = model.output_coords(OrderedDict())
-        assert isinstance(output_coords, OrderedDict)
-        np.testing.assert_array_equal(
-            output_coords["variable"], np.array(["u10m", "v10m", "t2m"])
-        )
-        assert len(output_coords["lat"]) == 10
-        assert len(output_coords["lon"]) == 20
-        # Time step should be inferred as 6 hours
-        assert output_coords["lead_time"][0] == np.timedelta64(6, "h")
-
-    def test_output_coords_single_time_step(self) -> None:
-        """Test output_coords with only one time step"""
-        mock_ds = Mock()
-
-        # Single time step
-        times = np.array([np.datetime64("2024-01-01T00:00:00")])
-
-        # Create mock coordinate that supports slicing
-        mock_time_coord = Mock()
-        mock_time_coord.values = times
-        mock_time_coord.__getitem__ = Mock(
-            side_effect=lambda key: Mock(values=times[key])
-        )
-
-        mock_lat_coord = Mock()
-        mock_lat_coord.values = np.arange(10)
-
-        mock_lon_coord = Mock()
-        mock_lon_coord.values = np.arange(20)
-
-        mock_da = Mock()
-        mock_da.coords = {
-            "time": mock_time_coord,
-            "variable": np.array(["u10m"]),
-            "lat": mock_lat_coord,
-            "lon": mock_lon_coord,
-        }
-        mock_ds.da = mock_da
-
-        model = InferenceOutputModel(
-            data_source=mock_ds,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        output_coords = model.output_coords(OrderedDict())
-
-        # Should use default placeholder of 6 hours
-        assert output_coords["lead_time"][0] == np.timedelta64(6, "h")
-
-    def test_to_device(self, mock_data_source: Any) -> None:
-        """Test to() method"""
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        result = model.to("cuda")
-        assert model.device == "cuda"
-        assert result is model
-
-    def test_call_method(self, mock_data_source: Any) -> None:
-        """Test __call__ method returns first item from iterator"""
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        mock_tensor = torch.randn(1, 1, 3, 10, 20)
-        mock_coords = OrderedDict({"time": np.array([np.datetime64("2024-01-01")])})
-
-        with patch.object(
-            model, "create_iterator", return_value=iter([(mock_tensor, mock_coords)])
-        ):
-            x, coords = model()
-            assert torch.is_tensor(x)
-            assert isinstance(coords, OrderedDict)
-
-    def test_create_iterator(self, mock_data_source: Any) -> None:
-        """Test create_iterator with and without lead_time conversion"""
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="time",
-            device="cpu",
-        )
-
-        # Mock fetch_data to return different data for each time
-        def mock_fetch_data(
-            source: Any, time: Any, variable: Any, device: Any
-        ) -> tuple[Any, Any]:
-            tensor = torch.randn(1, 3, 10, 20)
-            coords = OrderedDict(
-                {
-                    "time": time,
-                    "variable": variable,
-                    "lat": np.arange(10),
-                    "lon": np.arange(20),
-                }
-            )
-            return (tensor, coords)
-
-        with patch(
-            "earth2studio.serve.client.e2client.fetch_data",
-            side_effect=mock_fetch_data,
-        ):
-            iterator = model.create_iterator()
-            results = list(iterator)
-
-            # Should yield data for both time steps
-            assert len(results) == 2
-            for x, coords in results:
-                assert torch.is_tensor(x)
-                assert isinstance(coords, OrderedDict)
-
-    def test_create_iterator_with_lead_time_conversion(
-        self, mock_data_source: Any
-    ) -> None:
-        """Test create_iterator with lead_time coordinate conversion"""
-        model = InferenceOutputModel(
-            data_source=mock_data_source,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        mock_tensor = torch.randn(1, 3, 10, 20)
-        mock_coords = OrderedDict(
-            {
-                "time": np.array([np.datetime64("2024-01-01T00:00:00")]),
-                "variable": np.array(["u10m", "v10m", "t2m"]),
-                "lat": np.arange(10),
-                "lon": np.arange(20),
-                "lead_time": np.array([np.timedelta64(0, "h")]),
-            }
-        )
-
-        with (
-            patch(
-                "earth2studio.serve.client.e2client.fetch_data",
-                return_value=(mock_tensor, mock_coords),
-            ),
-            patch(
-                "earth2studio.serve.client.e2client._convert_time_to_lead_time",
-                return_value=(mock_tensor, mock_coords),
-            ) as mock_convert,
-        ):
-            iterator = model.create_iterator()
-            next(iterator)
-
-            # Verify conversion was called
-            mock_convert.assert_called_once()
-
-
-class TestInferenceOutputModelIntegration:
-    """Integration tests for InferenceOutputModel"""
-
-    def test_model_mimics_prognostic_interface(self) -> None:
-        """Test that InferenceOutputModel provides prognostic model interface"""
-        mock_ds = Mock()
-
-        times = np.array([np.datetime64("2024-01-01T00:00:00")])
-
-        # Create mock coordinate that supports slicing
-        mock_time_coord = Mock()
-        mock_time_coord.values = times
-        mock_time_coord.__getitem__ = Mock(
-            side_effect=lambda key: Mock(values=times[key])
-        )
-
-        mock_lat_coord = Mock()
-        mock_lat_coord.values = np.arange(10)
-
-        mock_lon_coord = Mock()
-        mock_lon_coord.values = np.arange(20)
-
-        mock_da = Mock()
-        mock_da.coords = {
-            "time": mock_time_coord,
-            "variable": np.array(["u10m"]),
-            "lat": mock_lat_coord,
-            "lon": mock_lon_coord,
-        }
-        mock_ds.da = mock_da
-
-        model = InferenceOutputModel(
-            data_source=mock_ds,
-            iter_coord="lead_time",
-            device="cpu",
-        )
-
-        # Verify interface methods exist and return correct types
-        assert hasattr(model, "input_coords")
-        assert hasattr(model, "output_coords")
-        assert hasattr(model, "to")
-        assert hasattr(model, "create_iterator")
-        assert callable(model)
-        assert isinstance(model.input_coords(), OrderedDict)
-        assert isinstance(model.output_coords(OrderedDict()), OrderedDict)
diff --git a/test/serve/client/test_fsspec_utils.py b/test/serve/client/test_fsspec_utils.py
deleted file mode 100644
index ec0936d86..000000000
--- a/test/serve/client/test_fsspec_utils.py
+++ /dev/null
@@ -1,378 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from datetime import datetime
-from unittest.mock import MagicMock, Mock, patch
-
-import pytest
-
-from earth2studio.serve.client.fsspec_utils import (
-    SignedURLFileSystem,
-    create_cloudfront_mapper,
-    get_mapper,
-)
-from earth2studio.serve.client.models import (
-    InferenceRequestResults,
-    RequestStatus,
-    StorageType,
-)
-
-
-class TestSignedURLFileSystemInit:
-    """Test SignedURLFileSystem initialization."""
-
-    def test_init_stores_params(self) -> None:
-        """Init stores base_fs, query_params, base_url and builds query_string."""
-        base_fs = Mock()
-        query_params = {"Policy": "p", "Signature": "s", "Key-Pair-Id": "k"}
-        base_url = "https://example.com/store"
-        fs = SignedURLFileSystem(base_fs, query_params, base_url)
-        assert fs._fs is base_fs
-        assert fs._query_params == query_params
-        assert fs._base_url == base_url
-        assert "Policy=p" in fs._query_string
-        assert "Signature=s" in fs._query_string
-        assert "Key-Pair-Id=k" in fs._query_string
-
-
-class TestSignedURLFileSystemMakeSignedPath:
-    """Test _make_signed_path."""
-
-    def test_relative_path_appends_to_base_url(self) -> None:
-        """Relative path is joined with base_url and query string appended."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(
-            base_fs, {"Policy": "p", "Signature": "s"}, "https://example.com/zarr"
-        )
-        out = fs._make_signed_path("subdir/.zmetadata")
-        assert out.startswith("https://example.com/zarr/subdir/.zmetadata")
-        assert "Policy=p" in out
-        assert "Signature=s" in out
-        assert "?" in out
-
-    def test_relative_path_empty_uses_base_url_only(self) -> None:
-        """Empty relative path uses base_url only."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com/")
-        out = fs._make_signed_path("")
-        assert out in ("https://example.com/?Policy=p", "https://example.com?Policy=p")
-
-    def test_path_starting_with_http_unchanged_with_query_appended(self) -> None:
-        """Path starting with http is used as full URL; query params appended."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
-        out = fs._make_signed_path("https://cdn.example.com/path")
-        assert out.startswith("https://cdn.example.com/path")
-        assert "Policy=p" in out
-
-    def test_url_with_existing_query_uses_ampersand(self) -> None:
-        """If URL already has ?, separator is &."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com?foo=1")
-        out = fs._make_signed_path("x")
-        assert "&Policy=" in out or "&policy=" in out.lower()
-
-
-class TestSignedURLFileSystemHandle403:
-    """Test _handle_403."""
-
-    def test_403_raises_file_not_found(self) -> None:
-        """Exception containing 403 raises FileNotFoundError."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(FileNotFoundError, match="File not found: /path"):
-            fs._handle_403(Exception("HTTP 403 Forbidden"), "/path")
-
-    def test_forbidden_text_raises_file_not_found(self) -> None:
-        """Exception containing 'forbidden' raises FileNotFoundError."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(FileNotFoundError, match="File not found: /x"):
-            fs._handle_403(Exception("Access forbidden"), "/x")
-
-    def test_other_exception_reraises(self) -> None:
-        """Non-403 exception is reraised."""
-        base_fs = Mock()
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(ValueError, match="other"):
-            fs._handle_403(ValueError("other"), "/path")
-
-
-class TestSignedURLFileSystemOpen:
-    """Test _open."""
-
-    def test_open_success_returns_file_like(self) -> None:
-        """Successful _open returns result from base fs _open."""
-        base_fs = Mock()
-        base_fs._open.return_value = MagicMock()
-        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
-        result = fs._open("sub/file", mode="rb")
-        assert result is base_fs._open.return_value
-        base_fs._open.assert_called_once()
-        call_url = base_fs._open.call_args[0][0]
-        assert (
-            "sub/file" in call_url
-            or "sub%2Ffile" in call_url
-            or call_url.endswith("sub/file")
-        )
-        assert base_fs._open.call_args[1]["mode"] == "rb"
-
-    def test_open_403_raises_file_not_found(self) -> None:
-        """Base _open raising 403 leads to FileNotFoundError."""
-        base_fs = Mock()
-        base_fs._open.side_effect = Exception("403 Forbidden")
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(FileNotFoundError, match="File not found"):
-            fs._open("x")
-
-
-class TestSignedURLFileSystemCatFile:
-    """Test cat_file."""
-
-    def test_cat_file_success(self) -> None:
-        """cat_file success returns content from base fs."""
-        base_fs = Mock()
-        base_fs.cat_file.return_value = b"content"
-        fs = SignedURLFileSystem(base_fs, {"Policy": "p"}, "https://example.com")
-        result = fs.cat_file("path/to/file")
-        assert result == b"content"
-        base_fs.cat_file.assert_called_once()
-        assert "path/to/file" in base_fs.cat_file.call_args[0][0] or "path" in str(
-            base_fs.cat_file.call_args
-        )
-
-    def test_cat_file_with_start_end(self) -> None:
-        """cat_file passes start/end to base fs."""
-        base_fs = Mock()
-        base_fs.cat_file.return_value = b"xx"
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        fs.cat_file("f", start=0, end=10)
-        base_fs.cat_file.assert_called_once()
-        assert base_fs.cat_file.call_args[1]["start"] == 0
-        assert base_fs.cat_file.call_args[1]["end"] == 10
-
-    def test_cat_file_403_raises_file_not_found(self) -> None:
-        """cat_file 403 raises FileNotFoundError."""
-        base_fs = Mock()
-        base_fs.cat_file.side_effect = Exception("403")
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(FileNotFoundError):
-            fs.cat_file("x")
-
-
-class TestSignedURLFileSystemCatFileAsync:
-    """Test _cat_file (delegates to cat_file)."""
-
-    def test_cat_file_delegates_to_cat_file(self) -> None:
-        """_cat_file calls cat_file with same args."""
-        base_fs = Mock()
-        base_fs.cat_file.return_value = b"data"
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        result = fs._cat_file("a", start=1, end=2)
-        assert result == b"data"
-        base_fs.cat_file.assert_called_once()
-        assert base_fs.cat_file.call_args[1]["start"] == 1
-        assert base_fs.cat_file.call_args[1]["end"] == 2
-
-
-class TestSignedURLFileSystemInfo:
-    """Test info."""
-
-    def test_info_success(self) -> None:
-        """info returns metadata from base fs."""
-        base_fs = Mock()
-        base_fs.info.return_value = {"size": 100, "type": "file"}
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        result = fs.info("path")
-        assert result == {"size": 100, "type": "file"}
-
-    def test_info_403_raises_file_not_found(self) -> None:
-        """info 403 raises FileNotFoundError."""
-        base_fs = Mock()
-        base_fs.info.side_effect = Exception("Forbidden")
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(FileNotFoundError):
-            fs.info("x")
-
-
-class TestSignedURLFileSystemExists:
-    """Test exists."""
-
-    def test_exists_true(self) -> None:
-        """exists returns True when base fs returns True."""
-        base_fs = Mock()
-        base_fs.exists.return_value = True
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        assert fs.exists("path") is True
-
-    def test_exists_403_returns_false(self) -> None:
-        """exists returns False when base raises 403."""
-        base_fs = Mock()
-        base_fs.exists.side_effect = Exception("403 Forbidden")
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        assert fs.exists("path") is False
-
-    def test_exists_false_when_base_returns_false(self) -> None:
-        """exists returns False when base fs returns False."""
-        base_fs = Mock()
-        base_fs.exists.return_value = False
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        assert fs.exists("path") is False
-
-    def test_exists_other_exception_reraises(self) -> None:
-        """exists re-raises non-403 exceptions."""
-        base_fs = Mock()
-        base_fs.exists.side_effect = RuntimeError("network error")
-        fs = SignedURLFileSystem(base_fs, {}, "https://example.com")
-        with pytest.raises(RuntimeError, match="network error"):
-            fs.exists("path")
-
-
-class TestCreateCloudfrontMapper:
-    """Test create_cloudfront_mapper."""
-
-    def test_create_cloudfront_mapper_builds_mapper(self) -> None:
-        """create_cloudfront_mapper parses URL and returns FSMap with SignedURLFileSystem."""
-        signed_url = "https://cdn.example.com/bucket/path?Policy=pol&Signature=sig&Key-Pair-Id=kid"
-        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
-            mock_fs = Mock()
-            mock_fsspec.filesystem.return_value = mock_fs
-            mock_map = Mock()
-            mock_fsspec.mapping.FSMap = mock_map
-            result = create_cloudfront_mapper(signed_url)
-        mock_fsspec.filesystem.assert_called_once_with("https")
-        mock_map.assert_called_once()
-        call_kwargs = mock_map.call_args[1]
-        assert call_kwargs["root"] == ""
-        assert call_kwargs["check"] is False
-        assert call_kwargs["create"] is False
-        assert isinstance(call_kwargs["fs"], SignedURLFileSystem)
-        assert result is mock_map.return_value
-
-    def test_create_cloudfront_mapper_with_zarr_path(self) -> None:
-        """create_cloudfront_mapper with zarr_path appends to base URL."""
-        signed_url = "https://cdn.example.com/bucket?Policy=p&Signature=s&Key-Pair-Id=k"
-        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
-            mock_fs = Mock()
-            mock_fsspec.filesystem.return_value = mock_fs
-            mock_map = Mock()
-            mock_fsspec.mapping.FSMap = mock_map
-            create_cloudfront_mapper(signed_url, zarr_path="results.zarr")
-        fs_arg = mock_map.call_args[1]["fs"]
-        assert isinstance(fs_arg, SignedURLFileSystem)
-        assert "results.zarr" in fs_arg._base_url
-
-    def test_create_cloudfront_mapper_strips_trailing_wildcard(self) -> None:
-        """URL path with trailing /* or * is stripped."""
-        signed_url = (
-            "https://cdn.example.com/bucket/*?Policy=p&Signature=s&Key-Pair-Id=k"
-        )
-        with patch("earth2studio.serve.client.fsspec_utils.fsspec") as mock_fsspec:
-            mock_fs = Mock()
-            mock_fsspec.filesystem.return_value = mock_fs
-            mock_fsspec.mapping.FSMap = Mock()
-            create_cloudfront_mapper(signed_url)
-        fs_arg = mock_fsspec.mapping.FSMap.call_args[1]["fs"]
-        assert "*" not in fs_arg._base_url
-        assert fs_arg._base_url.rstrip("/").endswith("bucket")
-
-
-class TestGetMapper:
-    """Test get_mapper."""
-
-    def test_get_mapper_server_returns_none(self) -> None:
-        """get_mapper returns None for SERVER storage."""
-        result = InferenceRequestResults(
-            request_id="r1",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-            storage_type=StorageType.SERVER,
-        )
-        assert get_mapper(result) is None
-
-    def test_get_mapper_s3_with_signed_url_returns_mapper(self) -> None:
-        """get_mapper with S3 and signed_url returns mapper from create_cloudfront_mapper."""
-        result = InferenceRequestResults(
-            request_id="r1",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-            storage_type=StorageType.S3,
-            signed_url="https://cdn.example.com/path?Policy=p&Signature=s&Key-Pair-Id=k",
-        )
-        with patch(
-            "earth2studio.serve.client.fsspec_utils.create_cloudfront_mapper"
-        ) as mock_create:
-            mock_create.return_value = Mock()
-            out = get_mapper(result)
-            assert out is mock_create.return_value
-            mock_create.assert_called_once_with(result.signed_url, "")
-
-    def test_get_mapper_s3_with_zarr_path_passes_through(self) -> None:
-        """get_mapper passes zarr_path to create_cloudfront_mapper."""
-        result = InferenceRequestResults(
-            request_id="r1",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-            storage_type=StorageType.S3,
-            signed_url="https://cdn.example.com?Policy=p&Signature=s&Key-Pair-Id=k",
-        )
-        with patch(
-            "earth2studio.serve.client.fsspec_utils.create_cloudfront_mapper"
-        ) as mock_create:
-            mock_create.return_value = Mock()
-            get_mapper(result, zarr_path="results.zarr")
-            mock_create.assert_called_once_with(result.signed_url, "results.zarr")
-
-    def test_get_mapper_s3_without_signed_url_raises(self) -> None:
-        """get_mapper with S3 and no signed_url raises ValueError."""
-        result = InferenceRequestResults(
-            request_id="r1",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-            storage_type=StorageType.S3,
-            signed_url=None,
-        )
-        with pytest.raises(ValueError, match="S3 storage type requires a signed URL"):
-            get_mapper(result)
-
-    def test_get_mapper_unsupported_storage_raises(self) -> None:
-        """get_mapper with unsupported storage type raises ValueError."""
-        result = InferenceRequestResults(
-            request_id="r1",
-            status=RequestStatus.COMPLETED,
-            output_files=[],
-            completion_time=datetime.now(),
-            storage_type=StorageType.SERVER,  # will override via monkeypatch for unsupported
-        )
-        # Use a storage type that doesn't exist in enum by creating a mock with invalid type
-        with patch.object(result, "storage_type", "invalid"):
-            # StorageType.SERVER is enum; "invalid" would be if we had another enum value.
-            # Instead simulate by patching the comparison: get_mapper checks
-            # request_result.storage_type == StorageType.S3 and == StorageType.SERVER.
-            # So unsupported = something that is neither. We need a value that is not
-            # StorageType.S3 and not StorageType.SERVER. StorageType only has SERVER and S3.
-            # So we need to pass something that fails both. E.g. a mock with storage_type
-            # that returns False for both. Or add a fake enum value. Easiest: patch
-            # StorageType to add a third value temporarily, or pass a mock object.
-            mock_result = Mock()
-            mock_result.storage_type = "unsupported_type"
-            with pytest.raises(ValueError, match="Unsupported storage type"):
-                get_mapper(mock_result)
diff --git a/test/serve/conftest.py b/test/serve/conftest.py
new file mode 100644
index 000000000..8eacca441
--- /dev/null
+++ b/test/serve/conftest.py
@@ -0,0 +1,112 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Shared pytest configuration for service tests.
+
+This conftest.py ensures that the service/inferenceserver directory is in the
+Python path so that api_server modules can be imported.
+"""
+
+import sys
+from pathlib import Path
+
+# Add service/inferenceserver to Python path for imports
+_inferenceserver_path = (
+    Path(__file__).parent.parent.parent / "service" / "inferenceserver"
+)
+if str(_inferenceserver_path) not in sys.path:
+    sys.path.insert(0, str(_inferenceserver_path))
+
+# Store original api_server.config module to restore it if it gets mocked
+_original_config_module = None
+
+
+def pytest_configure(config):
+    """Store the original api_server.config module before any tests run"""
+    global _original_config_module
+    if "api_server.config" in sys.modules:
+        _original_config_module = sys.modules["api_server.config"]
+
+
+def pytest_runtest_setup(item):
+    """Restore the real api_server.config module before test_config.py tests"""
+    # Only restore for test_config.py tests to avoid interfering with other tests
+    if "test_config" in str(item.fspath):
+        import importlib
+
+        # Remove any mock that might have been set by other test files
+        if "api_server.config" in sys.modules:
+            # Check if it's a mock (Mock objects have _mock_name attribute or are Mock instances)
+            config_module = sys.modules["api_server.config"]
+            is_mock = (
+                hasattr(config_module, "_mock_name")
+                or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
+                or (
+                    hasattr(config_module, "__class__")
+                    and "Mock" in str(config_module.__class__)
+                )
+            )
+            if is_mock:
+                # It's a mock, restore the real module
+                # First, clear any references in parent modules
+                if "api_server" in sys.modules:
+                    api_server_module = sys.modules["api_server"]
+                    if (
+                        hasattr(api_server_module, "config")
+                        and api_server_module.config is config_module
+                    ):
+                        delattr(api_server_module, "config")
+                # Remove the mock from sys.modules
+                del sys.modules["api_server.config"]
+                # Also clear any submodules that might reference the mock
+                modules_to_remove = [
+                    key
+                    for key in sys.modules.keys()
+                    if key.startswith("api_server.config.")
+                ]
+                for key in modules_to_remove:
+                    del sys.modules[key]
+        # Force reload the real module to ensure fresh state
+        if "api_server.config" not in sys.modules:
+            importlib.import_module("api_server.config")
+        else:
+            # Only reload if it was a mock, otherwise just ensure state is correct
+            config_module = sys.modules["api_server.config"]
+            is_mock = (
+                hasattr(config_module, "_mock_name")
+                or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
+            )
+            if is_mock:
+                importlib.reload(sys.modules["api_server.config"])
+        # Ensure _config_manager and ConfigManager._instance are properly initialized
+        real_config = sys.modules["api_server.config"]
+        # Check if _config_manager is a mock and reset if needed
+        cm = getattr(real_config, "_config_manager", None)
+        if cm is not None and (
+            hasattr(cm, "_mock_name") or str(type(cm)) == "<class 'unittest.mock.Mock'>"
+        ):
+            real_config._config_manager = None
+        # Check if ConfigManager._instance is a mock and reset if needed
+        if hasattr(real_config, "ConfigManager") and hasattr(
+            real_config.ConfigManager, "_instance"
+        ):
+            inst = getattr(real_config.ConfigManager, "_instance", None)
+            if inst is not None and (
+                hasattr(inst, "_mock_name")
+                or str(type(inst)) == "<class 'unittest.mock.Mock'>"
+            ):
+                real_config.ConfigManager._instance = None
diff --git a/test/serve/server/conftest.py b/test/serve/server/conftest.py
deleted file mode 100644
index 197a365e5..000000000
--- a/test/serve/server/conftest.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-from pathlib import Path
-
-# Repo root is parent of test/serve/server; ensure project is importable
-_repo_root = Path(__file__).resolve().parent.parent.parent
-if str(_repo_root) not in sys.path:
-    sys.path.insert(0, str(_repo_root))
-
-# Store original earth2studio.serve.server.config module to restore if mocked
-_original_config_module = None
-
-_CONFIG_MODULE = "earth2studio.serve.server.config"
-
-
-def pytest_configure(config):
-    """Store the original config module before any tests run"""
-    global _original_config_module
-    if _CONFIG_MODULE in sys.modules:
-        _original_config_module = sys.modules[_CONFIG_MODULE]
-
-
-def pytest_runtest_setup(item):
-    """Restore the real config module before test_config.py tests"""
-    if "test_config" not in str(item.fspath):
-        return
-    import importlib
-
-    if _CONFIG_MODULE in sys.modules:
-        config_module = sys.modules[_CONFIG_MODULE]
-        is_mock = (
-            hasattr(config_module, "_mock_name")
-            or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
-            or (
-                hasattr(config_module, "__class__")
-                and "Mock" in str(config_module.__class__)
-            )
-        )
-        if is_mock:
-            if "earth2studio.serve.server" in sys.modules:
-                server_module = sys.modules["earth2studio.serve.server"]
-                if (
-                    hasattr(server_module, "config")
-                    and server_module.config is config_module
-                ):
-                    delattr(server_module, "config")
-            del sys.modules[_CONFIG_MODULE]
-            modules_to_remove = [
-                k for k in sys.modules if k.startswith(_CONFIG_MODULE + ".")
-            ]
-            for k in modules_to_remove:
-                del sys.modules[k]
-    if _CONFIG_MODULE not in sys.modules:
-        importlib.import_module(_CONFIG_MODULE)
-    else:
-        config_module = sys.modules[_CONFIG_MODULE]
-        is_mock = (
-            hasattr(config_module, "_mock_name")
-            or str(type(config_module)) == "<class 'unittest.mock.Mock'>"
-        )
-        if is_mock:
-            importlib.reload(sys.modules[_CONFIG_MODULE])
-    real_config = sys.modules[_CONFIG_MODULE]
-    # Check if ConfigManager._instance is a mock and reset if needed
-    if hasattr(real_config, "ConfigManager") and hasattr(
-        real_config.ConfigManager, "_instance"
-    ):
-        inst = getattr(real_config.ConfigManager, "_instance", None)
-        if inst is not None and (
-            hasattr(inst, "_mock_name")
-            or str(type(inst)) == "<class 'unittest.mock.Mock'>"
-        ):
-            real_config.ConfigManager._instance = None
diff --git a/test/serve/server/test_cleanup_daemon.py b/test/serve/server/test_cleanup_daemon.py
deleted file mode 100644
index fb4800639..000000000
--- a/test/serve/server/test_cleanup_daemon.py
+++ /dev/null
@@ -1,775 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-from datetime import datetime, timedelta, timezone
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from earth2studio.serve.server.cleanup_daemon import (
-    _delete_result_files,
-    _process_expired_key,
-    cleanup_expired_results,
-    main,
-)
-from earth2studio.serve.server.config import get_config
-from earth2studio.serve.server.workflow import WorkflowStatus
-
-
-class TestDeleteResultFiles:
-    """Test suite for _delete_result_files function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    def test_delete_workflow_files_with_workflow_name(self, mock_redis):
-        """Test deletion of workflow execution files including workflow-specific directories and metadata"""
-        # Setup
-        workflow_name = "custom_workflow"
-        execution_id = "exec_456"
-        combined_id = f"{workflow_name}:{execution_id}"
-        zip_filename = f"workflow_{combined_id}.zip"
-
-        mock_redis.get.return_value = zip_filename
-
-        # Mock zip file
-        mock_zip_file = MagicMock()
-        mock_zip_file.exists.return_value = True
-        mock_zip_file.unlink = MagicMock()
-
-        # Mock metadata file for workflow
-        mock_metadata_file = MagicMock()
-        mock_metadata_file.is_file.return_value = True
-        mock_metadata_file.is_dir.return_value = False
-        mock_metadata_file.name = f"metadata_{combined_id}.json"
-        mock_metadata_file.unlink = MagicMock()
-
-        # Mock workflow directory
-        mock_workflow_dir = MagicMock()
-        mock_workflow_dir.exists.return_value = True
-
-        mock_workflow_subdir = MagicMock()
-        mock_workflow_subdir.is_dir.return_value = True
-        mock_workflow_subdir.is_file.return_value = False
-        mock_workflow_subdir.name = f"output_{execution_id}"
-
-        mock_workflow_dir.iterdir.return_value = [mock_workflow_subdir]
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_file
-        mock_zip_dir.iterdir.return_value = [mock_metadata_file]
-
-        with patch(
-            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
-        ) as mock_rmtree:
-            # Execute
-            _delete_result_files(
-                mock_redis,
-                combined_id,
-                execution_id,
-                mock_output_dir,
-                mock_zip_dir,
-                workflow_name=workflow_name,
-            )
-
-            # Verify zip file deleted
-            mock_zip_file.unlink.assert_called_once()
-
-            # Verify metadata file deleted
-            mock_metadata_file.unlink.assert_called_once()
-
-            # Verify workflow-specific directory deleted
-            mock_rmtree.assert_called_once_with(mock_workflow_subdir)
-
-    def test_delete_files_no_zip_in_redis(self, mock_redis):
-        """Test deletion when no zip file is found in Redis"""
-        # Setup
-        result_id = "result_789"
-        mock_redis.get.return_value = None
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        # Execute - should not raise exception
-        _delete_result_files(
-            mock_redis, result_id, result_id, mock_output_dir, mock_zip_dir
-        )
-
-        # Verify Redis was queried
-        mock_redis.get.assert_called_once()
-
-    def test_delete_result_files_zip_file_missing(self, mock_redis):
-        """When zip filename is in Redis but file does not exist, no unlink is called."""
-        result_id = "req_123"
-        mock_redis.get.return_value = "missing.zip"
-
-        mock_zip_path = MagicMock()
-        mock_zip_path.exists.return_value = False
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_path
-
-        _delete_result_files(
-            mock_redis, result_id, result_id, mock_output_dir, mock_zip_dir
-        )
-
-        mock_zip_path.unlink.assert_not_called()
-
-    def test_delete_result_files_legacy_path_deletes_metadata_and_dirs(
-        self, mock_redis
-    ):
-        """Without workflow_name, still deletes metadata files and raw/zip dirs matching search_id."""
-        search_id = "exec_789"
-        result_id = search_id
-        mock_redis.get.return_value = None
-
-        mock_metadata_file = MagicMock()
-        mock_metadata_file.is_file.return_value = True
-        mock_metadata_file.is_dir.return_value = False
-        mock_metadata_file.name = f"metadata_{search_id}.json"
-        mock_metadata_file.unlink = MagicMock()
-
-        mock_raw_dir = MagicMock()
-        mock_raw_dir.is_dir.return_value = True
-        mock_raw_dir.name = f"output_{search_id}"
-        mock_raw_dir.__str__ = lambda self: f"output_{search_id}"
-
-        mock_zip_subdir = MagicMock()
-        mock_zip_subdir.is_dir.return_value = True
-        mock_zip_subdir.name = f"results_{search_id}"
-        mock_zip_subdir.__str__ = lambda self: f"results_{search_id}"
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = [mock_raw_dir]
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = [mock_metadata_file, mock_zip_subdir]
-
-        with patch(
-            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
-        ) as mock_rmtree:
-            _delete_result_files(
-                mock_redis,
-                result_id,
-                search_id,
-                mock_output_dir,
-                mock_zip_dir,
-            )
-
-        mock_metadata_file.unlink.assert_called_once()
-        assert mock_rmtree.call_count == 2  # raw dir + zip_dir subdir
-        rmtree_calls = {str(c[0][0]) for c in mock_rmtree.call_args_list}
-        assert f"output_{search_id}" in rmtree_calls or mock_rmtree.call_count == 2
-
-    def test_delete_result_files_workflow_dir_not_exists(self, mock_redis):
-        """When workflow_name is set but workflow subdirectory does not exist, no rmtree on it."""
-        workflow_name = "my_wf"
-        execution_id = "exec_1"
-        combined_id = f"{workflow_name}:{execution_id}"
-        mock_redis.get.return_value = None
-
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_workflow_dir = MagicMock()
-        mock_workflow_dir.exists.return_value = False  # workflow dir not present
-        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
-
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        with patch(
-            "earth2studio.serve.server.cleanup_daemon.shutil.rmtree"
-        ) as mock_rmtree:
-            _delete_result_files(
-                mock_redis,
-                combined_id,
-                execution_id,
-                mock_output_dir,
-                mock_zip_dir,
-                workflow_name=workflow_name,
-            )
-
-        mock_workflow_dir.iterdir.assert_not_called()
-        mock_rmtree.assert_not_called()
-
-
-class TestProcessExpiredKey:
-    """Test suite for _process_expired_key function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    @pytest.fixture
-    def mock_delete_func(self):
-        """Create a mock delete function"""
-        return MagicMock()
-
-    def test_process_expired_key_success(self, mock_redis, mock_delete_func):
-        """Test successful processing of an expired key"""
-        # Setup
-        key = "workflow_execution:wf1:exec_123"
-        current_time = datetime.now(timezone.utc)
-        old_time = current_time - timedelta(hours=25)  # 25 hours ago
-        results_ttl_hours = 24
-
-        request_data = {
-            "status": WorkflowStatus.COMPLETED,
-            "end_time": old_time.isoformat(),
-            "execution_id": "exec_123",
-        }
-
-        mock_redis.get.return_value = json.dumps(request_data)
-        config = get_config()
-
-        # Execute
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key=key,
-            current_time=current_time,
-            results_ttl_hours=results_ttl_hours,
-            retention_ttl=config.redis.retention_ttl,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        # Verify
-        assert result is True
-        mock_delete_func.assert_called_once_with(mock_redis, key)
-        mock_redis.setex.assert_called_once()
-        args = mock_redis.setex.call_args[0]
-        assert args[0] == key
-        assert args[1] == config.redis.retention_ttl
-        updated_data = json.loads(args[2])
-        assert updated_data["status"] == WorkflowStatus.EXPIRED
-
-    def test_process_expired_key_not_expired(self, mock_redis, mock_delete_func):
-        """Test processing of a key that is not yet expired"""
-        # Setup
-        key = "workflow_execution:wf1:exec_456"
-        current_time = datetime.now(timezone.utc)
-        recent_time = current_time - timedelta(hours=12)  # 12 hours ago
-        results_ttl_hours = 24
-
-        request_data = {
-            "status": WorkflowStatus.COMPLETED,
-            "end_time": recent_time.isoformat(),
-            "execution_id": "exec_456",
-        }
-
-        mock_redis.get.return_value = json.dumps(request_data)
-
-        # Execute
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key=key,
-            current_time=current_time,
-            results_ttl_hours=results_ttl_hours,
-            retention_ttl=604800,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        # Verify - should not clean up
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-    def test_process_expired_key_key_missing(self, mock_redis, mock_delete_func):
-        """When Redis returns None for the key, return False without calling delete."""
-        mock_redis.get.return_value = None
-
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key="workflow_execution:wf1:exec_123",
-            current_time=datetime.now(timezone.utc),
-            results_ttl_hours=24,
-            retention_ttl=604800,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-    def test_process_expired_key_wrong_status(self, mock_redis, mock_delete_func):
-        """When status is not expected (e.g. RUNNING), return False."""
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=25)).isoformat()
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.RUNNING, "end_time": old_time}
-        )
-
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key="workflow_execution:wf1:exec_123",
-            current_time=current_time,
-            results_ttl_hours=24,
-            retention_ttl=604800,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-    def test_process_expired_key_no_end_time(self, mock_redis, mock_delete_func):
-        """When end_time and completion_time are missing, return False."""
-        mock_redis.get.return_value = json.dumps({"status": WorkflowStatus.COMPLETED})
-
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key="workflow_execution:wf1:exec_123",
-            current_time=datetime.now(timezone.utc),
-            results_ttl_hours=24,
-            retention_ttl=604800,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-    def test_process_expired_key_uses_completion_time_fallback(
-        self, mock_redis, mock_delete_func
-    ):
-        """When end_time is missing but completion_time is present, use it and expire."""
-        config = get_config()
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-        mock_redis.get.return_value = json.dumps(
-            {
-                "status": WorkflowStatus.COMPLETED,
-                "completion_time": old_time,
-            }
-        )
-
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key="workflow_execution:wf1:exec_old",
-            current_time=current_time,
-            results_ttl_hours=24,
-            retention_ttl=config.redis.retention_ttl,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        assert result is True
-        mock_delete_func.assert_called_once()
-        mock_redis.setex.assert_called_once()
-        updated = json.loads(mock_redis.setex.call_args[0][2])
-        assert updated["status"] == WorkflowStatus.EXPIRED
-
-    def test_process_expired_key_invalid_timestamp(self, mock_redis, mock_delete_func):
-        """When timestamp is invalid, log warning and return False."""
-        mock_redis.get.return_value = json.dumps(
-            {
-                "status": WorkflowStatus.COMPLETED,
-                "end_time": "not-a-valid-timestamp",
-            }
-        )
-
-        result = _process_expired_key(
-            redis_client=mock_redis,
-            key="workflow_execution:wf1:exec_123",
-            current_time=datetime.now(timezone.utc),
-            results_ttl_hours=24,
-            retention_ttl=604800,
-            expected_status=WorkflowStatus.COMPLETED,
-            get_end_time_field="end_time",
-            delete_files_func=mock_delete_func,
-            log_prefix="workflow",
-        )
-
-        assert result is False
-        mock_delete_func.assert_not_called()
-        mock_redis.setex.assert_not_called()
-
-
-class TestCleanupExpiredResults:
-    """Test suite for cleanup_expired_results function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create a mock Redis client"""
-        return MagicMock()
-
-    @pytest.fixture
-    def cleanup_args(self):
-        """Default args for cleanup_expired_results (no module-level config)."""
-        config = get_config()
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-            exists=lambda: False
-        )
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-        return {
-            "results_ttl_hours": config.server.results_ttl_hours,
-            "retention_ttl": config.redis.retention_ttl,
-            "default_output_dir": mock_output_dir,
-            "results_zip_dir": mock_zip_dir,
-        }
-
-    def test_cleanup_expired_results_workflow_only(self, mock_redis, cleanup_args):
-        """Test cleanup with workflow executions only"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-        recent_time = (current_time - timedelta(hours=12)).isoformat()
-
-        # Mock Redis keys - only workflow keys
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf1:exec_old",
-            "workflow_execution:wf2:exec_recent",
-        ]
-
-        # Mock Redis get responses
-        def get_side_effect(key):
-            if key == "workflow_execution:wf1:exec_old":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-                )
-            elif key == "workflow_execution:wf2:exec_recent":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
-            # Execute
-            cleanup_expired_results(mock_redis, **cleanup_args)
-
-            # Verify - should process 1 workflow (exec_old), skip exec_recent
-            assert mock_redis.setex.call_count == 1
-
-            # Verify the expired key was updated
-            setex_calls = mock_redis.setex.call_args_list
-            expired_keys = [call[0][0] for call in setex_calls]
-            assert "workflow_execution:wf1:exec_old" in expired_keys
-
-    def test_cleanup_expired_results_no_keys(self, mock_redis, cleanup_args):
-        """Test cleanup when there are no keys in Redis"""
-        # Setup
-        mock_redis.keys.return_value = []
-
-        # Execute
-        cleanup_expired_results(mock_redis, **cleanup_args)
-
-        # Verify - no cleanup operations
-        mock_redis.setex.assert_not_called()
-
-    def test_cleanup_expired_results_all_recent(self, mock_redis, cleanup_args):
-        """Test cleanup when all workflow keys are recent (not expired)"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        recent_time = (current_time - timedelta(hours=5)).isoformat()
-
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf1:exec_1",
-            "workflow_execution:wf2:exec_2",
-        ]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
-        )
-
-        # Execute
-        cleanup_expired_results(mock_redis, **cleanup_args)
-
-        # Verify - no cleanup operations (not expired)
-        mock_redis.setex.assert_not_called()
-
-    def test_cleanup_expired_results_redis_get_failure(self, mock_redis, cleanup_args):
-        """Test cleanup handles Redis get failures gracefully"""
-        # Setup
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf_fail:exec_1",
-            "workflow_execution:wf_ok:exec_2",
-        ]
-
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        # First key causes exception, second succeeds
-        def get_side_effect(key):
-            if key == "workflow_execution:wf_fail:exec_1":
-                raise Exception("Redis connection error")
-            elif key == "workflow_execution:wf_ok:exec_2":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        with (patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"),):
-            # Execute - should not raise exception
-            cleanup_expired_results(mock_redis, **cleanup_args)
-
-            # Verify - exec_2 should still be processed despite exec_1 error
-            assert mock_redis.setex.call_count == 1
-            assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf_ok:exec_2"
-
-    def test_cleanup_expired_results_delete_files_failure(
-        self, mock_redis, cleanup_args
-    ):
-        """Test cleanup handles file deletion failures gracefully and does not mark as expired"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        mock_redis.keys.return_value = ["workflow_execution:wf_fail:exec_fail"]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-        )
-
-        with patch(
-            "earth2studio.serve.server.cleanup_daemon._delete_result_files"
-        ) as mock_delete:
-            mock_delete.side_effect = Exception("File deletion error")
-
-            # Execute - should handle exception and continue
-            cleanup_expired_results(mock_redis, **cleanup_args)
-
-            # Verify delete was attempted
-            mock_delete.assert_called_once()
-
-            # Status should NOT be updated if file deletion fails
-            # (we want to retry cleanup on next run)
-            assert mock_redis.setex.call_count == 0
-
-    def test_cleanup_expired_results_workflow_execution(self, mock_redis, cleanup_args):
-        """Test cleanup of workflow execution keys"""
-        # Setup
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        mock_redis.keys.return_value = ["workflow_execution:my_workflow:exec_123"]
-
-        mock_redis.get.return_value = json.dumps(
-            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-        )
-
-        with patch(
-            "earth2studio.serve.server.cleanup_daemon._delete_result_files"
-        ) as mock_delete:
-            # Execute
-            cleanup_expired_results(mock_redis, **cleanup_args)
-
-            # Verify workflow was cleaned up
-            assert mock_redis.setex.call_count == 1
-            assert (
-                mock_redis.setex.call_args[0][0]
-                == "workflow_execution:my_workflow:exec_123"
-            )
-
-            # Verify delete was called with correct parameters
-            mock_delete.assert_called_once()
-            call_args = mock_delete.call_args
-            assert call_args[1]["result_id"] == "my_workflow:exec_123"
-            assert call_args[1]["search_id"] == "exec_123"
-            assert call_args[1]["workflow_name"] == "my_workflow"
-
-    def test_cleanup_expired_results_custom_ttl_config(self, mock_redis):
-        """Test cleanup with custom TTL configuration"""
-        # Custom 48 hour TTL (pass as args; no module-level config)
-        config = get_config()
-        mock_output_dir = MagicMock()
-        mock_output_dir.iterdir.return_value = []
-        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
-            exists=lambda: False
-        )
-        mock_zip_dir = MagicMock()
-        mock_zip_dir.iterdir.return_value = []
-
-        current_time = datetime.now(timezone.utc)
-        # 30 hours ago - should NOT be expired with 48 hour TTL
-        time_30h_ago = (current_time - timedelta(hours=30)).isoformat()
-        # 50 hours ago - should be expired with 48 hour TTL
-        time_50h_ago = (current_time - timedelta(hours=50)).isoformat()
-
-        mock_redis.keys.return_value = [
-            "workflow_execution:wf_30h:exec_30h",
-            "workflow_execution:wf_50h:exec_50h",
-        ]
-
-        def get_side_effect(key):
-            if key == "workflow_execution:wf_30h:exec_30h":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": time_30h_ago}
-                )
-            elif key == "workflow_execution:wf_50h:exec_50h":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": time_50h_ago}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
-            # Execute with 48 hour TTL
-            cleanup_expired_results(
-                mock_redis,
-                results_ttl_hours=48,
-                retention_ttl=config.redis.retention_ttl,
-                default_output_dir=mock_output_dir,
-                results_zip_dir=mock_zip_dir,
-            )
-
-            # Verify - only exec_50h should be cleaned up
-            assert mock_redis.setex.call_count == 1
-            assert (
-                mock_redis.setex.call_args[0][0] == "workflow_execution:wf_50h:exec_50h"
-            )
-
-    def test_cleanup_expired_results_malformed_key_skipped(
-        self, mock_redis, cleanup_args
-    ):
-        """Keys with fewer than 3 parts (e.g. 'workflow_execution:incomplete') are skipped."""
-        current_time = datetime.now(timezone.utc)
-        old_time = (current_time - timedelta(hours=30)).isoformat()
-
-        mock_redis.keys.return_value = [
-            "workflow_execution:incomplete",  # only 2 parts
-            "workflow_execution:wf1:exec_old",
-        ]
-
-        def get_side_effect(key):
-            if key == "workflow_execution:wf1:exec_old":
-                return json.dumps(
-                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
-                )
-            return None
-
-        mock_redis.get.side_effect = get_side_effect
-
-        with patch("earth2studio.serve.server.cleanup_daemon._delete_result_files"):
-            cleanup_expired_results(mock_redis, **cleanup_args)
-
-            # Only the valid key (exec_old) should be processed; malformed key is skipped
-        assert mock_redis.setex.call_count == 1
-        assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf1:exec_old"
-
-
-class TestCleanupDaemonMain:
-    """Tests for main() daemon entrypoint."""
-
-    def test_main_redis_connection_failure_exits_with_one(self, tmp_path):
-        """When Redis connection or ping fails, main() calls sys.exit(1)."""
-        mock_config = MagicMock()
-        mock_config.paths.default_output_dir = str(tmp_path / "out")
-        mock_config.paths.results_zip_dir = str(tmp_path / "zip")
-        mock_config.server.results_ttl_hours = 24
-        mock_config.server.cleanup_watchdog_sec = 60
-        mock_config.redis.retention_ttl = 604800
-        mock_config.redis.host = "localhost"
-        mock_config.redis.port = 6379
-        mock_config.redis.db = 0
-        mock_config.redis.password = None
-        mock_config.redis.decode_responses = True
-        mock_config.redis.socket_connect_timeout = 5
-        mock_config.redis.socket_timeout = 5
-
-        mock_redis_instance = MagicMock()
-        mock_redis_instance.ping.side_effect = Exception("Connection refused")
-
-        with (
-            patch(
-                "earth2studio.serve.server.cleanup_daemon.get_config",
-                return_value=mock_config,
-            ),
-            patch(
-                "earth2studio.serve.server.cleanup_daemon.redis.Redis",
-                return_value=mock_redis_instance,
-            ),
-            patch("earth2studio.serve.server.cleanup_daemon.sys.exit") as mock_exit,
-        ):
-            mock_exit.side_effect = SystemExit(1)
-            with pytest.raises(SystemExit):
-                main()
-            mock_exit.assert_called_once_with(1)
-
-    def test_main_runs_one_cycle_then_handles_keyboard_interrupt(self, tmp_path):
-        """When time.sleep raises KeyboardInterrupt (e.g. SIGINT), main() exits gracefully and closes Redis."""
-        mock_config = MagicMock()
-        mock_config.paths.default_output_dir = str(tmp_path / "out")
-        mock_config.paths.results_zip_dir = str(tmp_path / "zip")
-        mock_config.server.results_ttl_hours = 24
-        mock_config.server.cleanup_watchdog_sec = 60
-        mock_config.redis.retention_ttl = 604800
-        mock_config.redis.host = "localhost"
-        mock_config.redis.port = 6379
-        mock_config.redis.db = 0
-        mock_config.redis.password = None
-        mock_config.redis.decode_responses = True
-        mock_config.redis.socket_connect_timeout = 5
-        mock_config.redis.socket_timeout = 5
-
-        mock_redis_instance = MagicMock()
-        mock_redis_instance.ping.return_value = True
-        mock_redis_instance.close = MagicMock()
-
-        with (
-            patch(
-                "earth2studio.serve.server.cleanup_daemon.get_config",
-                return_value=mock_config,
-            ),
-            patch(
-                "earth2studio.serve.server.cleanup_daemon.redis.Redis",
-                return_value=mock_redis_instance,
-            ),
-            patch("earth2studio.serve.server.cleanup_daemon.cleanup_expired_results"),
-            patch("earth2studio.serve.server.cleanup_daemon.signal.signal"),
-            patch(
-                "earth2studio.serve.server.cleanup_daemon.time.sleep",
-                side_effect=KeyboardInterrupt,
-            ),
-        ):
-            main()
-            mock_redis_instance.close.assert_called_once()
diff --git a/test/serve/server/test_cpu_worker.py b/test/serve/server/test_cpu_worker.py
deleted file mode 100644
index 45e2c9553..000000000
--- a/test/serve/server/test_cpu_worker.py
+++ /dev/null
@@ -1,1403 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import sys
-import tempfile
-import zipfile
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Literal
-from unittest.mock import Mock, patch
-
-import pytest
-import redis
-
-
-# Create mock config classes before importing cpu_worker
-@dataclass
-class MockRedisConfig:
-    """Mock Redis configuration"""
-
-    host: str = "localhost"
-    port: int = 6379
-    db: int = 0
-    password: str | None = None
-    socket_connect_timeout: int = 5
-    socket_timeout: int = 5
-    decode_responses: bool = True
-    retention_ttl: int = 604800  # Redis key retention TTL in seconds (7 days)
-
-
-@dataclass
-class MockQueueConfig:
-    """Mock queue configuration"""
-
-    name: str = "inference"
-    result_zip_queue_name: str = "result_zip"
-    object_storage_queue_name: str = "object_storage"
-    finalize_metadata_queue_name: str = "finalize_metadata"
-    max_size: int = 10
-    default_timeout: str = "1h"
-    job_timeout: str = "2h"
-
-
-# Secure temp dir for mock path defaults (S108)
-_secure_test_dir = tempfile.mkdtemp(prefix="e2s_testing_")
-
-
-@dataclass
-class MockPathsConfig:
-    """Mock paths configuration"""
-
-    default_output_dir: str = field(default_factory=lambda: _secure_test_dir)
-    results_zip_dir: str = field(default_factory=lambda: _secure_test_dir)
-    output_format: Literal["zarr", "netcdf4"] = "zarr"
-    result_zip_enabled: bool = False
-
-
-@dataclass
-class MockLoggingConfig:
-    """Mock logging configuration"""
-
-    level: str = "INFO"
-    format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-
-
-@dataclass
-class MockServerConfig:
-    """Mock server configuration"""
-
-    host: str = "0.0.0.0"  # noqa: S104
-    port: int = 8000
-    workers: int = 1
-    title: str = "REST API"
-    description: str = "REST API for running workflows"
-    version: str = "1.0.0"
-    docs_url: str = "/docs"
-    redoc_url: str = "/redoc"
-
-
-@dataclass
-class MockCORSConfig:
-    """Mock CORS configuration"""
-
-    allow_origins: list = field(default_factory=lambda: ["*"])
-    allow_credentials: bool = True
-    allow_methods: list = field(default_factory=lambda: ["*"])
-    allow_headers: list = field(default_factory=lambda: ["*"])
-
-
-@dataclass
-class MockObjectStorageConfig:
-    """Mock object storage configuration"""
-
-    enabled: bool = False
-    bucket: str | None = None
-    region: str = "us-east-1"
-    prefix: str = "outputs"
-    access_key_id: str | None = None
-    secret_access_key: str | None = None
-    session_token: str | None = None
-    cloudfront_domain: str | None = None
-    cloudfront_key_pair_id: str | None = None
-    cloudfront_private_key_path: str | None = None
-    signed_url_expires_in: int = 3600
-
-
-@dataclass
-class MockAppConfig:
-    """Mock root configuration"""
-
-    redis: MockRedisConfig = field(default_factory=MockRedisConfig)
-    queue: MockQueueConfig = field(default_factory=MockQueueConfig)
-    paths: MockPathsConfig = field(default_factory=MockPathsConfig)
-    logging: MockLoggingConfig = field(default_factory=MockLoggingConfig)
-    server: MockServerConfig = field(default_factory=MockServerConfig)
-    cors: MockCORSConfig = field(default_factory=MockCORSConfig)
-    object_storage: MockObjectStorageConfig = field(
-        default_factory=MockObjectStorageConfig
-    )
-
-
-# Create a mock config module
-mock_config_module = Mock()
-mock_config_module.AppConfig = MockAppConfig
-mock_config_module.get_config = Mock(return_value=MockAppConfig())
-mock_config_module.get_config_manager = Mock()
-
-pytest.importorskip("earth2studio.serve.server")
-
-# Inject the mock before importing cpu_worker
-sys.modules["earth2studio.serve.server.config"] = mock_config_module
-
-# Now import the module under test
-from earth2studio.serve.server.cpu_worker import (  # noqa: E402  # noqa: E402
-    FileManifestEntry,
-    ResultMetadata,
-    build_file_manifest,
-    create_results_zip,
-    fail_workflow,
-    process_finalize_metadata,
-    process_object_storage_upload,
-    process_result_zip,
-)
-from earth2studio.serve.server.workflow import WorkflowResult  # noqa: E402
-
-
-class TestCreateResultsZip:
-    """Test suite for create_results_zip function"""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Mock Redis client"""
-        return Mock(spec=redis.Redis)
-
-    @pytest.fixture
-    def inference_request(self):
-        """Sample inference request data"""
-        return {
-            "id": "test_req_123",
-            "type": "deterministic",
-            "status": "completed",
-            "completion_time": "2024-01-01T12:00:00Z",
-            "execution_time_seconds": 45.5,
-            "created_at": "2024-01-01T11:00:00Z",
-            "peak_memory_usage": "2.5GB",
-            "device": "cuda:0",
-            "request": {
-                "workflow_type": "deterministic",
-                "time": "2024-01-01T00:00:00Z",
-                "nsteps": 10,
-                "prognostic": {"model_type": "fcn"},
-                "data": {"source_type": "gfs"},
-                "io": {"backend_type": "zarr"},
-            },
-        }
-
-    def test_create_results_zip_with_single_file(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip with a single output file"""
-        # Create a temporary output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data content")
-        file_size = output_file.stat().st_size
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_123",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip file was created - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_123"
-
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify Redis was updated (4 times: metadata, results_zip_dir, zip, output_path)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-
-        # First call: metadata (stored in Redis for finalize worker)
-        assert calls[0][0][0] == "inference_request:test_req_123:pending_metadata"
-
-        # Second call: results_zip_dir path
-        assert calls[1][0][0] == "inference_request:test_req_123:results_zip_dir"
-        assert calls[1][0][2] == str(results_zip_dir)
-
-        # Third call: zip filename
-        assert calls[2][0][0] == "inference_request_zips:test_req_123:zip_file"
-        assert calls[2][0][2] == zip_filename
-
-        # Fourth call: output path
-        assert calls[3][0][0] == "inference_request:test_req_123:output_path"
-        assert calls[3][0][1] == 86400  # 24 hours
-        assert calls[3][0][2] == str(output_file)
-
-        # Verify zip contents - metadata.json is NOT in the zip
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert "metadata.json" not in file_list  # metadata is now separate
-            assert "output.nc" in file_list
-
-        # Verify metadata is stored in Redis (not as file - file is created by finalize worker)
-        # Get the metadata from the first setex call
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify metadata structure
-        assert metadata["request_id"] == "test_req_123"
-        assert metadata["status"] == "completed"
-        assert metadata["workflow_type"] == "deterministic"
-        assert metadata["execution_time_seconds"] == 45.5
-        assert metadata["device"] == "cuda:0"
-        assert "zip_created_at" in metadata
-
-        # Verify parameters field
-        assert "parameters" in metadata
-        assert metadata["parameters"] == inference_request["request"]
-        assert metadata["parameters"]["workflow_type"] == "deterministic"
-        assert metadata["parameters"]["nsteps"] == 10
-
-        # Verify output_files manifest includes both the file AND the zip
-        assert "output_files" in metadata
-        assert isinstance(metadata["output_files"], list)
-        assert len(metadata["output_files"]) == 2  # output.nc + zip file entry
-
-        # Convert to dict for easier checking
-        files_dict = {f["path"]: f["size"] for f in metadata["output_files"]}
-
-        # Check the output file entry
-        assert "output.nc" in files_dict
-        assert files_dict["output.nc"] == file_size
-
-        # Check the zip file entry (path = request_id)
-        assert "test_req_123" in files_dict
-        assert files_dict["test_req_123"] == zip_path.stat().st_size
-
-    def test_create_results_zip_with_directory(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip with a directory containing multiple files"""
-        # Create a temporary output directory with files
-        output_dir = tmp_path / "output_data"
-        output_dir.mkdir()
-
-        file1 = output_dir / "data1.nc"
-        file1.write_text("data 1 content")
-        file1_size = file1.stat().st_size
-
-        file2 = output_dir / "data2.nc"
-        file2.write_text("data 2 content with more text")
-        file2_size = file2.stat().st_size
-
-        # Create subdirectory
-        subdir = output_dir / "plots"
-        subdir.mkdir()
-        file3 = subdir / "plot.png"
-        file3.write_bytes(b"fake png data")
-        file3_size = file3.stat().st_size
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_456",
-            output_path=output_dir,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip file was created - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_456"
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify zip contents - metadata.json is NOT in the zip
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert "metadata.json" not in file_list
-
-            # Verify files exist in zip
-            assert "output_data/data1.nc" in file_list
-            assert "output_data/data2.nc" in file_list
-            assert "output_data/plots/plot.png" in file_list
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify output_files manifest (3 files + 1 zip entry)
-        assert "output_files" in metadata
-        output_files = metadata["output_files"]
-        assert len(output_files) == 4  # 3 files + zip entry
-
-        # Convert to dict for easier checking
-        files_dict = {f["path"]: f["size"] for f in output_files}
-
-        # Verify all files are in manifest with correct sizes
-        assert "output_data/data1.nc" in files_dict
-        assert files_dict["output_data/data1.nc"] == file1_size
-
-        assert "output_data/data2.nc" in files_dict
-        assert files_dict["output_data/data2.nc"] == file2_size
-
-        assert "output_data/plots/plot.png" in files_dict
-        assert files_dict["output_data/plots/plot.png"] == file3_size
-
-        # Verify zip file entry (path = request_id)
-        assert "test_req_456" in files_dict
-        assert files_dict["test_req_456"] == zip_path.stat().st_size
-
-    def test_create_results_zip_with_nonexistent_output(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test creating zip when output path doesn't exist"""
-        # Use nonexistent path
-        output_path = tmp_path / "nonexistent"
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_789",
-            output_path=output_path,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should still create zip - filename is now just request_id
-        assert zip_filename is not None
-        assert zip_filename == "test_req_789"
-        zip_path = results_zip_dir / zip_filename
-        assert zip_path.exists()
-
-        # Verify zip contents - should be empty (no output files)
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            file_list = zf.namelist()
-            assert len(file_list) == 0  # No files in zip
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # output_files should only contain the zip entry
-        assert "output_files" in metadata
-        assert len(metadata["output_files"]) == 1  # Only zip entry
-        assert metadata["output_files"][0]["path"] == "test_req_789"
-
-    def test_create_results_zip_parameters_validation(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that parameters are correctly stored in metadata"""
-        # Create a temporary output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Modify inference request to have specific parameters
-        inference_request["request"] = {
-            "workflow_type": "ensemble",
-            "time": "2024-01-15T00:00:00Z",
-            "nsteps": 20,
-            "nensemble": 10,
-            "prognostic": {"model_type": "graphcast"},
-            "data": {"source_type": "era5"},
-            "io": {"backend_type": "netcdf"},
-            "perturbation": {"method": "spherical_gaussian"},
-        }
-
-        # Call create_results_zip
-        result = create_results_zip(
-            request_id="test_req_params",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-        assert result is not None
-
-        # Verify metadata is stored in Redis (not as file)
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Verify parameters field exists and matches
-        assert "parameters" in metadata
-        params = metadata["parameters"]
-
-        assert params["workflow_type"] == "ensemble"
-        assert params["time"] == "2024-01-15T00:00:00Z"
-        assert params["nsteps"] == 20
-        assert params["nensemble"] == 10
-        assert params["prognostic"]["model_type"] == "graphcast"
-        assert params["data"]["source_type"] == "era5"
-        assert params["io"]["backend_type"] == "netcdf"
-        assert params["perturbation"]["method"] == "spherical_gaussian"
-
-    def test_create_results_zip_file_manifest_structure(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that file manifest has correct structure"""
-        # Create output directory with multiple files
-        output_dir = tmp_path / "outputs"
-        output_dir.mkdir()
-
-        for i in range(5):
-            file = output_dir / f"file_{i}.dat"
-            file.write_text(f"content {i}" * 100)
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_manifest",
-            output_path=output_dir,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify metadata is stored in Redis
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        manifest = metadata["output_files"]
-
-        # Verify manifest structure (5 files + 1 zip entry)
-        assert isinstance(manifest, list)
-        assert len(manifest) == 6  # 5 files + zip entry
-
-        # Find the zip entry (path = request_id)
-        zip_entry = None
-        file_entries = []
-        for entry in manifest:
-            if entry["path"] == "test_manifest":
-                zip_entry = entry
-            else:
-                file_entries.append(entry)
-
-        assert zip_entry is not None
-        assert len(file_entries) == 5
-
-        # Verify file entries structure
-        zip_path = results_zip_dir / zip_filename
-        with zipfile.ZipFile(zip_path, "r") as zf:
-            for entry in file_entries:
-                assert isinstance(entry, dict)
-                assert "path" in entry
-                assert "size" in entry
-                assert isinstance(entry["path"], str)
-                assert isinstance(entry["size"], int)
-                assert entry["size"] > 0
-
-                # Verify path starts with output directory name
-                assert entry["path"].startswith("outputs/")
-
-                # Verify size matches actual file in zip
-                actual_size = zf.getinfo(entry["path"]).file_size
-                assert entry["size"] == actual_size
-
-    def test_create_results_zip_missing_request_parameters(self, mock_redis, tmp_path):
-        """Test handling when request doesn't have parameters field"""
-        # Create inference request without 'request' field
-        inference_request = {
-            "id": "test_req_no_params",
-            "type": "deterministic",
-            "status": "completed",
-        }
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_req_no_params",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Verify zip was created
-        assert zip_filename is not None
-
-        # Verify metadata stored in Redis - parameters should be None
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Parameters should be None or not present
-        assert metadata.get("parameters") is None
-
-    def test_create_results_zip_redis_failure(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test handling when Redis operation fails"""
-        # Make Redis setex raise an exception
-        mock_redis.setex.side_effect = Exception("Redis connection failed")
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip - should handle exception gracefully
-        zip_filename = create_results_zip(
-            request_id="test_redis_fail",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should return None since storing in Redis failed
-        assert zip_filename is None
-
-    def test_create_results_zip_exception_handling(self, mock_redis, tmp_path):
-        """Test general exception handling"""
-        # Use invalid inference request that will cause issues
-        invalid_request = None
-
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip with invalid data
-        zip_filename = create_results_zip(
-            request_id="test_exception",
-            output_path=output_file,
-            inference_request=invalid_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        # Should return None on exception
-        assert zip_filename is None
-
-    def test_create_results_zip_metadata_completeness(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that all expected metadata fields are present"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_metadata",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        # Verify metadata stored in Redis
-        assert mock_redis.setex.call_count == 4
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Check all expected fields
-        required_fields = [
-            "request_id",
-            "status",
-            "completion_time",
-            "execution_time_seconds",
-            "workflow_type",
-            "created_at",
-            "peak_memory_usage",
-            "device",
-            "zip_created_at",
-            "parameters",
-            "output_files",
-        ]
-
-        for f in required_fields:
-            assert f in metadata, f"Missing required field: {f}"
-
-        # Verify zip_created_at is a valid timestamp
-        zip_created_at = metadata["zip_created_at"]
-        assert zip_created_at.endswith("Z")
-        # Should be parseable as ISO format
-        datetime.fromisoformat(zip_created_at.replace("Z", "+00:00"))
-
-    def test_create_results_zip_manifest_includes_zip_entry(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that the manifest includes an entry for the zip file itself"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data content")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        request_id = "test_zip_entry"
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id=request_id,
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        zip_path = results_zip_dir / zip_filename
-        zip_size = zip_path.stat().st_size
-
-        # Verify metadata stored in Redis
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-
-        # Find the zip entry in manifest
-        zip_entry = None
-        for entry in metadata["output_files"]:
-            if entry["path"] == request_id:
-                zip_entry = entry
-                break
-
-        # Verify zip entry exists and has correct size
-        assert zip_entry is not None, "Zip file entry not found in manifest"
-        assert zip_entry["path"] == request_id
-        assert zip_entry["size"] == zip_size
-
-    def test_create_results_zip_output_path_stored_in_redis(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test that output path is stored in Redis"""
-        # Create output file
-        output_file = tmp_path / "output.nc"
-        output_file.write_text("test data")
-
-        # Create results directory
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        # Call create_results_zip
-        zip_filename = create_results_zip(
-            request_id="test_output_path",
-            output_path=output_file,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-
-        # Verify Redis setex was called 4 times (metadata, results_zip_dir, zip, output_path)
-        assert mock_redis.setex.call_count == 4
-
-        # Check the output path call (4th call)
-        calls = mock_redis.setex.call_args_list
-        output_path_call = calls[3]
-
-        assert (
-            output_path_call[0][0] == "inference_request:test_output_path:output_path"
-        )
-        assert output_path_call[0][1] == 86400  # 24 hours
-        assert output_path_call[0][2] == str(output_file)
-
-    def test_create_results_zip_manifest_only_no_zip(
-        self, mock_redis, inference_request, tmp_path
-    ):
-        """Test create_results_zip with create_zip=False: no zip file, manifest only."""
-        output_dir = tmp_path / "out"
-        output_dir.mkdir()
-        (output_dir / "a.nc").write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        result = create_results_zip(
-            request_id="req_manifest_only",
-            output_path=output_dir,
-            inference_request=inference_request,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-            create_zip=False,
-        )
-
-        assert result is None  # No zip filename when create_zip=False
-        assert not (results_zip_dir / "req_manifest_only").exists()
-        # Metadata should still be stored (manifest only)
-        assert mock_redis.setex.call_count >= 3
-        calls = mock_redis.setex.call_args_list
-        metadata_json = calls[0][0][2]
-        metadata = json.loads(metadata_json)
-        assert len(metadata["output_files"]) == 1  # Just the one file, no zip entry
-
-    def test_create_results_zip_with_workflow_result(self, mock_redis, tmp_path):
-        """Test create_results_zip with WorkflowResult (custom workflow) path."""
-        output_file = tmp_path / "out.nc"
-        output_file.write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        workflow_result = WorkflowResult(
-            workflow_name="my_workflow",
-            execution_id="exec_1",
-            status="completed",
-            start_time="2024-01-01T10:00:00Z",
-            end_time="2024-01-01T11:00:00Z",
-            execution_time_seconds=60.0,
-            metadata={
-                "peak_memory_usage": "1GB",
-                "device": "cuda:0",
-                "parameters": {"nsteps": 5},
-            },
-        )
-
-        zip_filename = create_results_zip(
-            request_id="my_workflow:exec_1",
-            output_path=output_file,
-            inference_request=workflow_result,
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert zip_filename is not None
-        metadata_json = mock_redis.setex.call_args_list[0][0][2]
-        metadata = json.loads(metadata_json)
-        assert metadata.get("workflow_name") == "my_workflow"
-        # from_workflow_result uses workflow_result.execution_id for request_id when set
-        assert metadata.get("request_id") == "exec_1"
-        assert metadata.get("peak_memory_usage") == "1GB"
-        assert metadata.get("parameters", {}).get("nsteps") == 5
-
-    def test_create_results_zip_invalid_type_returns_none(self, mock_redis, tmp_path):
-        """Test that invalid inference_request type is handled (exception caught, returns None)."""
-        output_file = tmp_path / "out.nc"
-        output_file.write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        result = create_results_zip(
-            request_id="req_invalid",
-            output_path=output_file,
-            inference_request=[],  # list is not dict or WorkflowResult
-            results_zip_dir=results_zip_dir,
-            redis_client=mock_redis,
-        )
-
-        assert result is None
-
-
-class TestFailWorkflow:
-    """Tests for fail_workflow."""
-
-    def test_fail_workflow_returns_error_dict(self):
-        """fail_workflow returns success=False and error message."""
-        out = fail_workflow("w", "e1", "Something failed")
-        assert out["success"] is False
-        assert out["error"] == "Something failed"
-
-    def test_fail_workflow_updates_redis_when_workflow_registered(self):
-        """When workflow is in registry, _update_execution_data is called."""
-        mock_workflow_class = Mock()
-        mock_workflow_class._update_execution_data = Mock()
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            mock_registry.get_workflow_class.return_value = mock_workflow_class
-            with patch("earth2studio.serve.server.cpu_worker.redis_client"):
-                out = fail_workflow("my_wf", "exec_1", "Failed")
-
-        assert out["success"] is False
-        mock_workflow_class._update_execution_data.assert_called_once()
-        call_args = mock_workflow_class._update_execution_data.call_args
-        assert call_args[0][1] == "my_wf"
-        assert call_args[0][2] == "exec_1"
-        assert call_args[0][3]["status"] == "failed"
-        assert call_args[0][3]["error_message"] == "Failed"
-
-    def test_fail_workflow_exception_still_returns_dict(self):
-        """When _update_execution_data raises, we still return error dict."""
-        mock_workflow_class = Mock()
-        mock_workflow_class._update_execution_data.side_effect = Exception("Redis down")
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            mock_registry.get_workflow_class.return_value = mock_workflow_class
-            out = fail_workflow("w", "e1", "Err")
-
-        assert out["success"] is False
-        assert out["error"] == "Err"
-
-
-class TestBuildFileManifest:
-    """Tests for build_file_manifest."""
-
-    def test_build_file_manifest_single_file(self, tmp_path):
-        """Single file returns one entry."""
-        f = tmp_path / "a.nc"
-        f.write_text("x")
-        entries = build_file_manifest(f)
-        assert len(entries) == 1
-        assert entries[0].path == "a.nc"
-        assert entries[0].size == f.stat().st_size
-
-    def test_build_file_manifest_directory_with_subdirs(self, tmp_path):
-        """Directory with subdirs is scanned recursively."""
-        d = tmp_path / "out"
-        d.mkdir()
-        (d / "a.nc").write_text("a")
-        sub = d / "sub"
-        sub.mkdir()
-        (sub / "b.nc").write_text("bb")
-        entries = build_file_manifest(d)
-        assert len(entries) == 2
-        paths = {e.path for e in entries}
-        assert "out/a.nc" in paths
-        assert "out/sub/b.nc" in paths
-
-    def test_build_file_manifest_with_arc_prefix(self, tmp_path):
-        """arc_prefix is applied to paths."""
-        f = tmp_path / "f.nc"
-        f.write_text("x")
-        entries = build_file_manifest(f, arc_prefix="prefix")
-        assert len(entries) == 1
-        assert entries[0].path == "prefix/f.nc"
-
-    def test_build_file_manifest_nonexistent_returns_empty(self, tmp_path):
-        """Nonexistent path returns empty list."""
-        entries = build_file_manifest(tmp_path / "nonexistent")
-        assert entries == []
-
-
-class TestResultMetadata:
-    """Tests for ResultMetadata factory methods and to_dict."""
-
-    def test_from_workflow_result(self):
-        """ResultMetadata.from_workflow_result sets workflow_name and metadata."""
-        wr = WorkflowResult(
-            workflow_name="custom_wf",
-            execution_id="exec_1",
-            status="completed",
-            start_time="2024-01-01T10:00:00Z",
-            end_time="2024-01-01T11:00:00Z",
-            execution_time_seconds=30.0,
-            metadata={"peak_memory_usage": "2GB", "parameters": {"n": 1}},
-        )
-        manifest = [FileManifestEntry(path="out.nc", size=100)]
-        meta = ResultMetadata.from_workflow_result(
-            wr, "exec_1", manifest, "2024-01-01T12:00:00Z"
-        )
-        assert meta.request_id == "exec_1"
-        assert meta.workflow_name == "custom_wf"
-        assert meta.status == "completed"
-        assert meta.parameters == {"n": 1}
-        assert meta.output_files == manifest
-
-    def test_from_legacy_dict(self):
-        """ResultMetadata.from_legacy_dict sets workflow_type from dict."""
-        req = {
-            "status": "completed",
-            "completion_time": "2024-01-01T11:00:00Z",
-            "execution_time_seconds": 10.0,
-            "type": "deterministic",
-            "created_at": "2024-01-01T10:00:00Z",
-            "request": {"nsteps": 5},
-        }
-        manifest = [FileManifestEntry(path="out.zarr", size=200)]
-        meta = ResultMetadata.from_legacy_dict(
-            req, "req_123", manifest, "2024-01-01T12:00:00Z"
-        )
-        assert meta.request_id == "req_123"
-        assert meta.workflow_type == "deterministic"
-        assert meta.parameters == {"nsteps": 5}
-
-    def test_to_dict_includes_workflow_name_and_type(self):
-        """to_dict includes workflow_name or workflow_type when set."""
-        meta = ResultMetadata(
-            request_id="r1",
-            status="completed",
-            completion_time=None,
-            execution_time_seconds=None,
-            workflow_name="my_wf",
-            workflow_type=None,
-            output_files=[],
-        )
-        d = meta.to_dict()
-        assert d["workflow_name"] == "my_wf"
-        assert "workflow_type" not in d
-
-        meta2 = ResultMetadata(
-            request_id="r2",
-            status="completed",
-            completion_time=None,
-            execution_time_seconds=None,
-            workflow_type="ensemble",
-            output_files=[],
-        )
-        d2 = meta2.to_dict()
-        assert d2["workflow_type"] == "ensemble"
-        assert "workflow_name" not in d2
-
-    def test_to_dict_includes_signed_url_and_remote_path(self):
-        """to_dict includes signed_url and remote_path when set."""
-        meta = ResultMetadata(
-            request_id="r1",
-            status="completed",
-            completion_time=None,
-            execution_time_seconds=None,
-            signed_url="https://s3.example.com/signed",
-            remote_path="outputs/r1/out.nc",
-            output_files=[],
-        )
-        d = meta.to_dict()
-        assert d["signed_url"] == "https://s3.example.com/signed"
-        assert d["remote_path"] == "outputs/r1/out.nc"
-
-
-class TestProcessResultZip:
-    """Tests for process_result_zip RQ worker function."""
-
-    def test_process_result_zip_success_returns_zip_filename(self, tmp_path):
-        """process_result_zip with valid workflow and create_zip returns zip filename."""
-        output_file = tmp_path / "out.nc"
-        output_file.write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        mock_workflow_class = Mock()
-        mock_workflow_class._get_execution_data.return_value = {
-            "id": "wf:exec_1",
-            "type": "deterministic",
-            "status": "completed",
-        }
-        mock_workflow_class._update_execution_data = Mock()
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.redis_client"
-            ) as mock_redis:
-                with patch(
-                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
-                ) as mock_queue_next:
-                    mock_registry.get_workflow_class.return_value = mock_workflow_class
-                    mock_redis.setex = Mock()
-                    mock_queue_next.return_value = "job_123"
-
-                    result = process_result_zip(
-                        workflow_name="test_wf",
-                        execution_id="exec_1",
-                        output_path_str=str(output_file),
-                        results_zip_dir_str=str(results_zip_dir),
-                        create_zip=True,
-                    )
-
-                    assert result is not None
-                    assert result == "test_wf:exec_1"  # zip filename is request_id
-                    mock_queue_next.assert_called_once_with(
-                        redis_client=mock_redis,
-                        current_stage="result_zip",
-                        workflow_name="test_wf",
-                        execution_id="exec_1",
-                        output_path_str=str(output_file),
-                        results_zip_dir_str=str(results_zip_dir),
-                    )
-
-    def test_process_result_zip_workflow_not_found_raises(self, tmp_path):
-        """process_result_zip when workflow not in registry raises after fail_workflow."""
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.fail_workflow"
-            ) as mock_fail:
-                mock_registry.get_workflow_class.return_value = None
-
-                with pytest.raises(ValueError, match="not found in registry"):
-                    process_result_zip(
-                        workflow_name="nonexistent",
-                        execution_id="e1",
-                        output_path_str=str(tmp_path / "out"),
-                        results_zip_dir_str=str(tmp_path / "zip"),
-                    )
-                mock_fail.assert_called_once()
-
-    def test_process_result_zip_queue_next_stage_returns_none(self, tmp_path):
-        """When queue_next_stage returns None, fail_workflow is called and function returns None."""
-        output_file = tmp_path / "out.nc"
-        output_file.write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        mock_workflow_class = Mock()
-        mock_workflow_class._get_execution_data.return_value = {
-            "type": "deterministic",
-            "status": "completed",
-        }
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.redis_client"
-            ) as mock_redis:
-                with patch(
-                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
-                ) as mock_queue_next:
-                    with patch(
-                        "earth2studio.serve.server.cpu_worker.fail_workflow"
-                    ) as mock_fail:
-                        mock_registry.get_workflow_class.return_value = (
-                            mock_workflow_class
-                        )
-                        mock_redis.setex = Mock()
-                        mock_queue_next.return_value = None
-
-                        result = process_result_zip(
-                            workflow_name="test_wf",
-                            execution_id="exec_1",
-                            output_path_str=str(output_file),
-                            results_zip_dir_str=str(results_zip_dir),
-                            create_zip=True,
-                        )
-
-        assert result is None
-        mock_fail.assert_called_once()
-        assert "queue next pipeline stage" in mock_fail.call_args[0][2].lower()
-
-    def test_process_result_zip_manifest_only_returns_manifest_only(self, tmp_path):
-        """When create_zip=False and flow succeeds, returns 'manifest_only'."""
-        output_file = tmp_path / "out.nc"
-        output_file.write_text("data")
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-
-        mock_workflow_class = Mock()
-        mock_workflow_class._get_execution_data.return_value = {
-            "type": "deterministic",
-            "status": "completed",
-        }
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.redis_client"
-            ) as mock_redis:
-                with patch(
-                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
-                ) as mock_queue_next:
-                    mock_registry.get_workflow_class.return_value = mock_workflow_class
-                    mock_redis.setex = Mock()
-                    mock_queue_next.return_value = "job_123"
-
-                    result = process_result_zip(
-                        workflow_name="test_wf",
-                        execution_id="exec_1",
-                        output_path_str=str(output_file),
-                        results_zip_dir_str=str(results_zip_dir),
-                        create_zip=False,
-                    )
-
-        assert result == "manifest_only"
-
-    def test_process_result_zip_create_fails_returns_none(self, tmp_path):
-        """When create_results_zip returns None (create_zip=True), fail_workflow and return None."""
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-        # Nonexistent output path so create_results_zip may still build manifest but we can mock return
-        output_path_str = str(tmp_path / "nonexistent")
-
-        mock_workflow_class = Mock()
-        mock_workflow_class._get_execution_data.return_value = {
-            "type": "deterministic",
-            "status": "completed",
-        }
-
-        with patch(
-            "earth2studio.serve.server.cpu_worker.workflow_registry"
-        ) as mock_registry:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.redis_client"
-            ) as mock_redis:
-                with patch("earth2studio.serve.server.cpu_worker.queue_next_stage"):
-                    with patch(
-                        "earth2studio.serve.server.cpu_worker.create_results_zip"
-                    ) as mock_create:
-                        with patch(
-                            "earth2studio.serve.server.cpu_worker.fail_workflow"
-                        ) as mock_fail:
-                            mock_registry.get_workflow_class.return_value = (
-                                mock_workflow_class
-                            )
-                            mock_redis.setex = Mock()
-                            mock_create.return_value = None  # zip creation failed
-
-                            result = process_result_zip(
-                                workflow_name="test_wf",
-                                execution_id="exec_1",
-                                output_path_str=output_path_str,
-                                results_zip_dir_str=str(results_zip_dir),
-                                create_zip=True,
-                            )
-
-        assert result is None
-        mock_fail.assert_called_once()
-        assert "create results zip" in mock_fail.call_args[0][2].lower()
-
-
-class TestProcessFinalizeMetadata:
-    """Tests for process_finalize_metadata RQ worker function."""
-
-    def test_process_finalize_metadata_success(self, tmp_path):
-        """process_finalize_metadata writes metadata file and updates workflow status."""
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-        request_id = "my_wf:exec_1"
-
-        metadata_key = f"inference_request:{request_id}:pending_metadata"
-        results_zip_dir_key = f"inference_request:{request_id}:results_zip_dir"
-        storage_info_key = f"inference_request:{request_id}:storage_info"
-
-        pending_metadata = {
-            "request_id": request_id,
-            "status": "completed",
-            "output_files": [],
-            "storage_type": "server",
-        }
-        mock_workflow_class = Mock()
-        mock_workflow_class._update_execution_data = Mock()
-
-        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.workflow_registry"
-            ) as mock_registry:
-                mock_redis.get.side_effect = lambda k: {
-                    metadata_key: json.dumps(pending_metadata),
-                    results_zip_dir_key: str(results_zip_dir),
-                    storage_info_key: None,
-                }.get(k)
-                mock_registry.get_workflow_class.return_value = mock_workflow_class
-
-                result = process_finalize_metadata(
-                    workflow_name="my_wf",
-                    execution_id="exec_1",
-                )
-
-        assert result is not None
-        assert result["success"] is True
-        assert "metadata_path" in result
-        metadata_path = tmp_path / "results" / f"metadata_{request_id}.json"
-        assert metadata_path.exists()
-        mock_workflow_class._update_execution_data.assert_called_once()
-        mock_redis.delete.assert_any_call(metadata_key)
-        mock_redis.delete.assert_any_call(results_zip_dir_key)
-
-    def test_process_finalize_metadata_missing_pending_metadata_returns_failure(self):
-        """When pending metadata or results_zip_dir is missing in Redis, returns fail_workflow dict."""
-        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.fail_workflow"
-            ) as mock_fail:
-                mock_redis.get.return_value = None
-                mock_fail.return_value = {
-                    "success": False,
-                    "error": "Pending metadata not found",
-                }
-
-                result = process_finalize_metadata(
-                    workflow_name="my_wf",
-                    execution_id="exec_1",
-                )
-
-        assert result is not None and result.get("success") is False
-        mock_fail.assert_called_once()
-        assert "not found" in mock_fail.call_args[0][2].lower()
-
-    def test_process_finalize_metadata_workflow_not_found_returns_failure(
-        self, tmp_path
-    ):
-        """When workflow class is not in registry, returns fail_workflow dict."""
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-        request_id = "my_wf:exec_1"
-        metadata_key = f"inference_request:{request_id}:pending_metadata"
-        results_zip_dir_key = f"inference_request:{request_id}:results_zip_dir"
-
-        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.workflow_registry"
-            ) as mock_registry:
-                with patch(
-                    "earth2studio.serve.server.cpu_worker.fail_workflow"
-                ) as mock_fail:
-                    mock_redis.get.side_effect = lambda k: {
-                        metadata_key: json.dumps({"request_id": request_id}),
-                        results_zip_dir_key: str(results_zip_dir),
-                    }.get(k)
-                    mock_registry.get_workflow_class.return_value = None
-                    mock_fail.return_value = {
-                        "success": False,
-                        "error": "Workflow class not found",
-                    }
-
-                    result = process_finalize_metadata(
-                        workflow_name="my_wf",
-                        execution_id="exec_1",
-                    )
-
-        assert result is not None and result.get("success") is False
-        mock_fail.assert_called_once()
-        assert "not found" in mock_fail.call_args[0][2].lower()
-
-    def test_process_finalize_metadata_with_storage_info(self, tmp_path):
-        """When storage_info is in Redis, it is applied to metadata and key is deleted."""
-        results_zip_dir = tmp_path / "results"
-        results_zip_dir.mkdir()
-        request_id = "my_wf:exec_1"
-        storage_info_key = f"inference_request:{request_id}:storage_info"
-        pending_metadata = {"request_id": request_id, "status": "completed"}
-        storage_info = {
-            "storage_type": "s3",
-            "remote_path": "s3://bucket/prefix",
-            "signed_url": "https://signed.example.com",
-        }
-
-        mock_workflow_class = Mock()
-        mock_workflow_class._update_execution_data = Mock()
-
-        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.workflow_registry"
-            ) as mock_registry:
-                mock_redis.get.side_effect = lambda k: {
-                    f"inference_request:{request_id}:pending_metadata": json.dumps(
-                        pending_metadata
-                    ),
-                    f"inference_request:{request_id}:results_zip_dir": str(
-                        results_zip_dir
-                    ),
-                    storage_info_key: json.dumps(storage_info),
-                }.get(k)
-                mock_registry.get_workflow_class.return_value = mock_workflow_class
-
-                result = process_finalize_metadata(
-                    workflow_name="my_wf",
-                    execution_id="exec_1",
-                )
-
-        assert result is not None
-        assert result["storage_type"] == "s3"
-        mock_redis.delete.assert_any_call(storage_info_key)
-        metadata_path = tmp_path / "results" / f"metadata_{request_id}.json"
-        data = json.loads(metadata_path.read_text())
-        assert data.get("signed_url") == "https://signed.example.com"
-        assert data.get("remote_path") == "s3://bucket/prefix"
-
-
-class TestProcessObjectStorageUpload:
-    """Tests for process_object_storage_upload RQ worker function."""
-
-    def test_process_object_storage_upload_disabled_queues_finalize(self, tmp_path):
-        """When object storage is disabled, skips upload and queues finalize_metadata."""
-        output_dir = tmp_path / "out"
-        output_dir.mkdir()
-        (output_dir / "f.nc").write_text("data")
-
-        with patch("earth2studio.serve.server.cpu_worker.redis_client") as mock_redis:
-            with patch("earth2studio.serve.server.cpu_worker.config") as mock_config:
-                with patch(
-                    "earth2studio.serve.server.cpu_worker.queue_next_stage"
-                ) as mock_queue_next:
-                    mock_config.object_storage.enabled = False
-                    mock_config.object_storage.bucket = None
-                    mock_redis.setex = Mock()
-                    mock_queue_next.return_value = "job_1"
-
-                    result = process_object_storage_upload(
-                        workflow_name="wf",
-                        execution_id="exec_1",
-                        output_path_str=str(output_dir),
-                    )
-
-        assert result is not None
-        assert result["success"] is True
-        assert result["storage_type"] == "server"
-        mock_queue_next.assert_called_once_with(
-            redis_client=mock_redis,
-            current_stage="object_storage",
-            workflow_name="wf",
-            execution_id="exec_1",
-            output_path_str=str(output_dir),
-        )
-
-    def test_process_object_storage_upload_output_path_missing_returns_failure(self):
-        """When object storage enabled but output path does not exist, returns fail_workflow dict."""
-        with patch("earth2studio.serve.server.cpu_worker.config") as mock_config:
-            with patch(
-                "earth2studio.serve.server.cpu_worker.fail_workflow"
-            ) as mock_fail:
-                mock_config.object_storage.enabled = True
-                mock_config.object_storage.bucket = "my-bucket"
-                mock_fail.return_value = {
-                    "success": False,
-                    "error": "Output path does not exist",
-                }
-
-                result = process_object_storage_upload(
-                    workflow_name="wf",
-                    execution_id="exec_1",
-                    output_path_str="/nonexistent/path",
-                )
-
-        assert result is not None and result.get("success") is False
-        mock_fail.assert_called_once()
-        assert "does not exist" in mock_fail.call_args[0][2]
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])
diff --git a/test/serve/server/test_object_storage.py b/test/serve/server/test_object_storage.py
deleted file mode 100644
index 907441cc2..000000000
--- a/test/serve/server/test_object_storage.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import base64
-import sys
-import tempfile
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from earth2studio.serve.server.object_storage import (
-    MSCObjectStorage,
-    ObjectStorage,
-    ObjectStorageError,
-    UploadResult,
-)
-
-
-class TestUploadResult:
-    """Tests for UploadResult dataclass."""
-
-    def test_repr_success(self):
-        """__repr__ shows SUCCESS when success is True."""
-        r = UploadResult(
-            success=True,
-            files_uploaded=10,
-            total_bytes=1000,
-            destination="s3://bucket/prefix",
-            errors=[],
-        )
-        assert "SUCCESS" in repr(r)
-        assert "files=10" in repr(r)
-        assert "bytes=1000" in repr(r)
-
-    def test_repr_failed(self):
-        """__repr__ shows FAILED when success is False."""
-        r = UploadResult(
-            success=False,
-            files_uploaded=0,
-            total_bytes=0,
-            destination="s3://bucket/prefix",
-            errors=["error1"],
-        )
-        assert "FAILED" in repr(r)
-
-
-class TestObjectStorageError:
-    """Tests for ObjectStorageError."""
-
-    def test_is_exception(self):
-        """ObjectStorageError is a subclass of Exception."""
-        assert issubclass(ObjectStorageError, Exception)
-
-    def test_raise_and_catch(self):
-        """Can raise and catch ObjectStorageError with message."""
-        with pytest.raises(ObjectStorageError, match="test message"):
-            raise ObjectStorageError("test message")
-
-
-class TestObjectStorage:
-    """Tests for ObjectStorage abstract base class."""
-
-    def test_cannot_instantiate_directly(self):
-        """ObjectStorage cannot be instantiated (abstract)."""
-        with pytest.raises(TypeError):
-            ObjectStorage()
-
-
-class TestMSCObjectStorage:
-    """Tests for MSCObjectStorage (with mocked multistorageclient)."""
-
-    @pytest.fixture
-    def mock_msc(self):
-        """Mock multistorageclient module so MSCObjectStorage can be instantiated."""
-        mock_module = MagicMock()
-        mock_module.StorageClientConfig.from_dict.side_effect = [
-            MagicMock(),
-            MagicMock(),
-        ]
-        mock_module.StorageClient.return_value = MagicMock()
-        with patch.dict(sys.modules, {"multistorageclient": mock_module}):
-            yield mock_module
-
-    def test_init_requires_multistorageclient(self):
-        """MSCObjectStorage raises ImportError if multistorageclient is not available."""
-        import builtins
-
-        real_import = builtins.__import__
-
-        def fail_msc(name, *args, **kwargs):
-            if name == "multistorageclient":
-                raise ImportError("No module named 'multistorageclient'")
-            return real_import(name, *args, **kwargs)
-
-        with patch.object(builtins, "__import__", side_effect=fail_msc):
-            with pytest.raises(ImportError, match="multi-storage-client"):
-                MSCObjectStorage(bucket="my-bucket")
-
-    def test_upload_directory_raises_when_dir_not_found(self, mock_msc):
-        """upload_directory raises FileNotFoundError when path does not exist."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-
-        with pytest.raises(FileNotFoundError, match="not found"):
-            storage.upload_directory(
-                local_directory="/nonexistent/path",
-                remote_prefix="prefix",
-            )
-
-    def test_upload_directory_raises_when_path_not_directory(self, mock_msc):
-        """upload_directory raises ValueError when path is not a directory."""
-        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
-            file_path = f.name
-        try:
-            storage = MSCObjectStorage(bucket="b", region="us-east-1")
-
-            with pytest.raises(ValueError, match="not a directory"):
-                storage.upload_directory(
-                    local_directory=file_path,
-                    remote_prefix="prefix",
-                )
-        finally:
-            Path(file_path).unlink(missing_ok=True)
-
-    def test_upload_directory_success_returns_upload_result(self, mock_msc):
-        """upload_directory returns UploadResult with success when sync_from succeeds."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            (Path(tmpdir) / "f1.txt").write_text("hello")
-            storage = MSCObjectStorage(bucket="b", region="us-east-1")
-            storage._s3_client.sync_from.return_value = None
-
-            result = storage.upload_directory(
-                local_directory=tmpdir,
-                remote_prefix="prefix",
-            )
-
-            assert isinstance(result, UploadResult)
-            assert result.success is True
-            assert result.files_uploaded == 1
-            assert result.total_bytes == 5
-            assert "s3://b/prefix" in result.destination
-            assert result.errors == []
-
-    def test_upload_directory_failure_appends_errors(self, mock_msc):
-        """upload_directory returns success=False and appends error when sync_from raises."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            (Path(tmpdir) / "f1.txt").write_text("x")
-            storage = MSCObjectStorage(bucket="b", region="us-east-1")
-            storage._s3_client.sync_from.side_effect = Exception("sync failed")
-
-            result = storage.upload_directory(
-                local_directory=tmpdir,
-                remote_prefix="prefix",
-            )
-
-            assert result.success is False
-            assert result.files_uploaded == 0
-            assert len(result.errors) == 1
-            assert "sync failed" in result.errors[0]
-
-    def test_upload_file_returns_false_when_file_not_found(self, mock_msc):
-        """upload_file returns False when local file does not exist."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-
-        assert (
-            storage.upload_file(
-                local_file="/nonexistent/file.txt",
-                remote_key="key.txt",
-            )
-            is False
-        )
-
-    def test_upload_file_returns_true_on_success(self, mock_msc):
-        """upload_file returns True when upload succeeds."""
-        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
-            f.write(b"data")
-            path = f.name
-        try:
-            storage = MSCObjectStorage(bucket="b", region="us-east-1")
-
-            result = storage.upload_file(
-                local_file=path,
-                remote_key="key.txt",
-            )
-            assert result is True
-            storage._s3_client.upload_file.assert_called_once()
-        finally:
-            Path(path).unlink(missing_ok=True)
-
-    def test_upload_file_returns_false_on_exception(self, mock_msc):
-        """upload_file returns False when upload_file raises."""
-        with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f:
-            path = f.name
-        try:
-            storage = MSCObjectStorage(bucket="b", region="us-east-1")
-            storage._s3_client.upload_file.side_effect = Exception("upload failed")
-
-            result = storage.upload_file(
-                local_file=path,
-                remote_key="key.txt",
-            )
-            assert result is False
-        finally:
-            Path(path).unlink(missing_ok=True)
-
-    def test_file_exists_returns_true_when_info_succeeds(self, mock_msc):
-        """file_exists returns True when info() does not raise."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-        storage._s3_client.info.return_value = None
-
-        assert storage.file_exists("my/key") is True
-
-    def test_file_exists_returns_false_when_file_not_found(self, mock_msc):
-        """file_exists returns False when info() raises FileNotFoundError."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-        storage._s3_client.info.side_effect = FileNotFoundError()
-
-        assert storage.file_exists("my/key") is False
-
-    def test_delete_file_returns_true_on_success(self, mock_msc):
-        """delete_file returns True when delete succeeds."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-
-        assert storage.delete_file("my/key") is True
-        storage._s3_client.delete.assert_called_once()
-
-    def test_delete_file_returns_false_when_file_not_found(self, mock_msc):
-        """delete_file returns False when delete raises FileNotFoundError."""
-        storage = MSCObjectStorage(bucket="b", region="us-east-1")
-        storage._s3_client.delete.side_effect = FileNotFoundError()
-
-        assert storage.delete_file("my/key") is False
-
-    def test_url_safe_b64(self, mock_msc):
-        """_url_safe_b64 produces URL-safe base64 (no +, /, =)."""
-        _ = MSCObjectStorage(
-            bucket="b", region="us-east-1"
-        )  # need instance so mock_msc context is active
-        raw = b"hello"
-        result = MSCObjectStorage._url_safe_b64(raw)
-        # Standard b64 could have +, /, =
-        assert "+" not in result
-        assert "/" not in result
-        assert "=" not in result
-        # Decode and check round-trip (with URL-safe mapping)
-        decoded = base64.urlsafe_b64decode(
-            result.replace("-", "+").replace("_", "=").replace("~", "/")
-        )
-        assert decoded == raw
-
-    def test_generate_signed_url_raises_when_cloudfront_not_configured(self, mock_msc):
-        """generate_signed_url raises ObjectStorageError when CloudFront not configured."""
-        storage = MSCObjectStorage(
-            bucket="b",
-            region="us-east-1",
-            cloudfront_domain=None,
-            cloudfront_key_pair_id=None,
-            cloudfront_private_key=None,
-        )
-
-        with pytest.raises(ObjectStorageError, match="CloudFront configuration"):
-            storage.generate_signed_url("key.txt")
-
-    def test_generate_signed_url_returns_url_when_configured(self, mock_msc):
-        """generate_signed_url returns URL string when CloudFront is configured."""
-        storage = MSCObjectStorage(
-            bucket="b",
-            region="us-east-1",
-            cloudfront_domain="d123.cloudfront.net",
-            cloudfront_key_pair_id="KP123",
-            cloudfront_private_key="-----BEGIN RSA PRIVATE KEY-----\nMIIEow==\n-----END RSA PRIVATE KEY-----",
-        )
-        storage._rsa_signer = MagicMock(return_value=b"signature")
-
-        url = storage.generate_signed_url("path/to/file", expires_in=3600)
-
-        assert isinstance(url, str)
-        assert "d123.cloudfront.net" in url
-        assert "Policy=" in url
-        assert "Signature=" in url
-        assert "Key-Pair-Id=KP123" in url
diff --git a/test/serve/server/test_server_main.py b/test/serve/server/test_server_main.py
deleted file mode 100644
index 6c9894ff0..000000000
--- a/test/serve/server/test_server_main.py
+++ /dev/null
@@ -1,2132 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-from datetime import datetime, timezone
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from fastapi.testclient import TestClient
-from pydantic import Field
-
-# Set API environment variable before importing main (DANGER!!! REMOVE THIS)
-os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
-
-# Patch FastAPI route creation to handle union return types
-# This fixes the issue where FastAPI can't handle dict[str, Any] | StreamingResponse
-# ruff: noqa: E402
-import fastapi  # type: ignore[import-untyped]
-import fastapi.routing  # type: ignore[import-untyped]
-from fastapi.exceptions import FastAPIError  # type: ignore[import-untyped]
-
-_original_route_init = fastapi.routing.APIRoute.__init__
-
-
-def _patched_route_init(self, *args, **kwargs):
-    """Patched APIRoute.__init__ that handles union return types with StreamingResponse"""
-    import inspect
-
-    from starlette.responses import StreamingResponse
-
-    # Check if the endpoint has a union return type with StreamingResponse
-    endpoint = kwargs.get("endpoint") or (args[1] if len(args) > 1 else None)
-    if endpoint:
-        sig = inspect.signature(endpoint)
-        return_annotation = sig.return_annotation
-        if (
-            hasattr(return_annotation, "__args__")
-            and StreamingResponse in return_annotation.__args__
-        ):
-            # Set response_model=None to skip response model generation
-            kwargs["response_model"] = None
-
-    try:
-        return _original_route_init(self, *args, **kwargs)
-    except FastAPIError as e:
-        if "Invalid args for response field" in str(e):
-            # Retry with response_model=None
-            kwargs["response_model"] = None
-            return _original_route_init(self, *args, **kwargs)
-        raise
-
-
-# Apply the patch before any imports of earth2studio.serve.server.main
-fastapi.routing.APIRoute.__init__ = _patched_route_init
-
-
-class TestWorkflowSchemaEndpoint:
-    """Tests for GET /v1/workflows/{workflow_name}/schema endpoint"""
-
-    @pytest.fixture
-    def mock_workflow_class(self):
-        """Create a mock workflow class with Parameters"""
-        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
-
-        class TestWorkflowParameters(WorkflowParameters):
-            """Test parameters for unit testing"""
-
-            forecast_times: list[str] = Field(
-                default=["2024-01-01T00:00:00"],
-                description="List of forecast initialization times",
-            )
-            nsteps: int = Field(
-                default=10,
-                ge=1,
-                le=100,
-                description="Number of forecast steps",
-            )
-            model_type: str = Field(
-                default="fcn",
-                description="Model type to use",
-            )
-
-        class TestWorkflow(Workflow):
-            name = "test_workflow"
-            description = "Test workflow for unit testing"
-            Parameters = TestWorkflowParameters
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestWorkflowParameters
-            ) -> TestWorkflowParameters:
-                return TestWorkflowParameters.validate(parameters)
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        return TestWorkflow
-
-    @pytest.fixture
-    def client(self, mock_workflow_class):
-        """Create test client with mocked dependencies"""
-        # Mock Redis and other dependencies at their source locations
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue"),
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            # Setup mock async Redis
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-
-            # Setup mock sync Redis
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import workflow_registry
-
-            # Register the test workflow
-            workflow_registry._workflows["test_workflow"] = mock_workflow_class
-
-            with TestClient(app, raise_server_exceptions=False) as client:
-                yield client
-
-            # Cleanup
-            if "test_workflow" in workflow_registry._workflows:
-                del workflow_registry._workflows["test_workflow"]
-
-    def test_schema_endpoint_returns_valid_json_schema(
-        self, client, mock_workflow_class
-    ):
-        """Test that schema endpoint returns valid JSON Schema"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        # Verify it's a valid JSON Schema structure
-        assert "type" in schema
-        assert schema["type"] == "object"
-        assert "properties" in schema
-        assert "title" in schema
-
-    def test_schema_endpoint_includes_all_parameters(self, client, mock_workflow_class):
-        """Test that schema includes all defined parameters"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check all parameters are present
-        assert "forecast_times" in properties
-        assert "nsteps" in properties
-        assert "model_type" in properties
-
-    def test_schema_endpoint_includes_field_metadata(self, client, mock_workflow_class):
-        """Test that schema includes field descriptions and constraints"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check forecast_times has correct type
-        assert properties["forecast_times"]["type"] == "array"
-        assert "description" in properties["forecast_times"]
-
-        # Check nsteps has constraints
-        nsteps = properties["nsteps"]
-        assert nsteps["type"] == "integer"
-        assert "description" in nsteps
-        # Pydantic uses "minimum" and "maximum" for ge/le constraints
-        assert nsteps.get("minimum") == 1 or nsteps.get("exclusiveMinimum") == 0
-        assert nsteps.get("maximum") == 100 or nsteps.get("exclusiveMaximum") == 101
-
-    def test_schema_endpoint_includes_defaults(self, client, mock_workflow_class):
-        """Test that schema includes default values"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        properties = schema["properties"]
-
-        # Check defaults are included
-        assert properties["forecast_times"]["default"] == ["2024-01-01T00:00:00"]
-        assert properties["nsteps"]["default"] == 10
-        assert properties["model_type"]["default"] == "fcn"
-
-    def test_schema_endpoint_workflow_not_found(self, client):
-        """Test that 404 is returned for non-existent workflow"""
-        response = client.get("/v1/workflows/nonexistent_workflow/schema")
-
-        assert response.status_code == 404
-        error = response.json()
-        assert "not found" in error["detail"].lower()
-
-    def test_schema_endpoint_openapi_compatibility(self, client, mock_workflow_class):
-        """Test that returned schema is OpenAPI 3.1 compatible (JSON Schema draft 2020-12)"""
-        response = client.get("/v1/workflows/test_workflow/schema")
-
-        assert response.status_code == 200
-        schema = response.json()
-
-        # OpenAPI 3.1 / JSON Schema required fields
-        assert "type" in schema
-        assert "properties" in schema
-
-        # Schema should be directly usable (no wrapper objects)
-        assert "workflow_name" not in schema
-        assert "parameters_schema" not in schema
-
-
-class TestWorkflowParameterValidation:
-    """Test workflow parameter validation at submission time"""
-
-    @pytest.fixture
-    def client(self):
-        """Create test client"""
-        from typing import Any, Literal
-        from unittest.mock import MagicMock, patch
-
-        from pydantic import Field
-
-        from earth2studio.serve.server.main import app
-        from earth2studio.serve.server.workflow import (
-            Workflow,
-            WorkflowParameters,
-            workflow_registry,
-        )
-
-        # Create test workflow parameter classes with Literal validation
-        class TestDeterministicParams(WorkflowParameters):
-            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
-            nsteps: int = Field(default=6, ge=1, le=100)
-            model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
-            data_source: Literal["gfs"] = Field(default="gfs")
-            output_format: Literal["zarr"] = Field(default="zarr")
-            plot_variable: Literal["t2m", "msl", "u10m", "v10m", "tcwv", "z500"] = (
-                Field(default="t2m")
-            )
-
-        class TestDiagnosticParams(WorkflowParameters):
-            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
-            nsteps: int = Field(default=6, ge=1, le=100)
-            prognostic_model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
-            diagnostic_model_type: Literal["precipitation_afno"] = Field(
-                default="precipitation_afno"
-            )
-            data_source: Literal["gfs"] = Field(default="gfs")
-            plot_variable: Literal[
-                "tp", "t2m", "msl", "u10m", "v10m", "tcwv", "z500"
-            ] = Field(default="tp")
-
-        # Create mock workflow classes
-        class TestDeterministicWorkflow(Workflow):
-            name = "deterministic_workflow"
-            description = "Test deterministic workflow"
-            Parameters = TestDeterministicParams
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestDeterministicParams
-            ) -> TestDeterministicParams:
-                try:
-                    return TestDeterministicParams.validate(parameters)
-                except Exception as e:
-                    raise ValueError(f"Invalid parameters: {e}") from e
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        class TestDiagnosticWorkflow(Workflow):
-            name = "diagnostic_workflow"
-            description = "Test diagnostic workflow"
-            Parameters = TestDiagnosticParams
-
-            @classmethod
-            def validate_parameters(
-                cls, parameters: dict[str, Any] | TestDiagnosticParams
-            ) -> TestDiagnosticParams:
-                try:
-                    return TestDiagnosticParams.validate(parameters)
-                except Exception as e:
-                    raise ValueError(f"Invalid parameters: {e}") from e
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        # Register test workflows directly in the registry
-        workflow_registry._workflows["deterministic_workflow"] = (
-            TestDeterministicWorkflow
-        )
-        workflow_registry._workflows["diagnostic_workflow"] = TestDiagnosticWorkflow
-
-        # Create mocks
-        mock_redis = MagicMock()
-        mock_queue = MagicMock()
-        mock_queue.__len__ = MagicMock(return_value=0)
-        mock_queue.max_size = 100
-
-        with patch("earth2studio.serve.server.main.redis_sync_client", mock_redis):
-            with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-                test_client = TestClient(app)
-                yield test_client, mock_queue, mock_redis
-
-    def test_valid_deterministic_workflow_parameters(self, client):
-        """Test that valid parameters for deterministic workflow are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "output_format": "zarr",
-                    "plot_variable": "t2m",
-                }
-            },
-        )
-
-        assert response.status_code == 200
-        data = response.json()
-        assert data["status"] == "queued"
-
-    def test_invalid_model_type_rejected(self, client):
-        """Test that invalid model_type is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "invalid_model",  # Invalid value
-                    "data_source": "gfs",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "model_type" in data["detail"]
-
-    def test_invalid_data_source_rejected(self, client):
-        """Test that invalid data_source is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "invalid_source",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "data_source" in data["detail"]
-
-    def test_invalid_output_format_rejected(self, client):
-        """Test that invalid output_format is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "output_format": "invalid_format",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "output_format" in data["detail"]
-
-    def test_invalid_plot_variable_rejected(self, client):
-        """Test that invalid plot_variable is rejected at submission"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                    "data_source": "gfs",
-                    "plot_variable": "invalid_variable",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "plot_variable" in data["detail"]
-
-    def test_invalid_nsteps_range_rejected(self, client):
-        """Test that nsteps outside valid range is rejected"""
-        test_client, _, _ = client
-
-        # Test nsteps too low
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 0,  # Below minimum of 1
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-        # Test nsteps too high
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 101,  # Above maximum of 100
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-    def test_diagnostic_workflow_invalid_model_types_rejected(self, client):
-        """Test that invalid model types for diagnostic workflow are rejected"""
-        test_client, _, _ = client
-
-        # Test invalid prognostic model
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "invalid_prognostic",  # Invalid
-                    "diagnostic_model_type": "precipitation_afno",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "prognostic_model_type" in data["detail"]
-
-        # Test invalid diagnostic model
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "fcn",
-                    "diagnostic_model_type": "invalid_diagnostic",  # Invalid
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "diagnostic_model_type" in data["detail"]
-
-    def test_valid_diagnostic_workflow_parameters(self, client):
-        """Test that valid parameters for diagnostic workflow are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        response = test_client.post(
-            "/v1/infer/diagnostic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 10,
-                    "prognostic_model_type": "fcn",
-                    "diagnostic_model_type": "precipitation_afno",
-                    "data_source": "gfs",
-                    "plot_variable": "tp",
-                }
-            },
-        )
-
-        assert response.status_code == 200
-        data = response.json()
-        assert data["status"] == "queued"
-
-    def test_multiple_invalid_parameters_rejected(self, client):
-        """Test that multiple invalid parameters are caught"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": ["2024-01-01T00:00:00"],
-                    "nsteps": 101,  # Invalid: too high
-                    "model_type": "invalid_model",  # Invalid value
-                    "data_source": "invalid_source",  # Invalid value
-                    "output_format": "invalid_format",  # Invalid value
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-
-    def test_valid_forecast_times_iso8601_accepted(self, client):
-        """Test that valid ISO 8601 forecast_times are accepted"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_job"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        # Test various valid ISO 8601 formats
-        valid_times = [
-            ["2024-01-01T00:00:00"],
-            ["2024-01-01T00:00:00Z"],
-            ["2024-01-01T00:00:00+00:00"],
-            ["2024-01-01T00:00:00.123456"],
-            ["2024-01-01 00:00:00"],  # Space separator also accepted
-            ["2024-01-01T00:00:00", "2024-01-02T00:00:00", "2024-01-03T00:00:00"],
-        ]
-
-        for times in valid_times:
-            response = test_client.post(
-                "/v1/infer/deterministic_workflow",
-                json={
-                    "parameters": {
-                        "forecast_times": times,
-                        "nsteps": 10,
-                        "model_type": "fcn",
-                    }
-                },
-            )
-
-            assert response.status_code == 200, f"Failed for times: {times}"
-            data = response.json()
-            assert data["status"] == "queued"
-
-    def test_invalid_forecast_times_rejected(self, client):
-        """Test that invalid forecast_times formats are rejected"""
-        test_client, _, _ = client
-
-        # Test various invalid formats
-        invalid_times = [
-            (["2024-01-01"], "date without time"),
-            (["01/01/2024"], "wrong date format"),
-            (["not-a-date"], "completely invalid"),
-            (["2024-13-01T00:00:00"], "invalid month"),
-            (["2024-01-32T00:00:00"], "invalid day"),
-            (["2024-01-01T25:00:00"], "invalid hour"),
-        ]
-
-        for times, description in invalid_times:
-            response = test_client.post(
-                "/v1/infer/deterministic_workflow",
-                json={
-                    "parameters": {
-                        "forecast_times": times,
-                        "nsteps": 10,
-                        "model_type": "fcn",
-                    }
-                },
-            )
-
-            assert (
-                response.status_code == 422
-            ), f"Expected 422 for {description}, got {response.status_code}"
-            data = response.json()
-            assert (
-                "Invalid parameters" in data["detail"]
-            ), f"Expected validation error for {description}"
-            assert (
-                "forecast_times" in data["detail"]
-            ), f"Expected forecast_times in error for {description}"
-
-    def test_forecast_times_mixed_valid_invalid_rejected(self, client):
-        """Test that forecast_times with one invalid value fails"""
-        test_client, _, _ = client
-
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": [
-                        "2024-01-01T00:00:00",  # Valid
-                        "not-a-date",  # Invalid
-                        "2024-01-03T00:00:00",  # Valid
-                    ],
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "forecast_times" in data["detail"]
-
-    def test_forecast_times_wrong_type_rejected(self, client):
-        """Test that forecast_times with non-string values are rejected"""
-        test_client, _, _ = client
-
-        # Test with integer
-        response = test_client.post(
-            "/v1/infer/deterministic_workflow",
-            json={
-                "parameters": {
-                    "forecast_times": [12345],  # Not a string
-                    "nsteps": 10,
-                    "model_type": "fcn",
-                }
-            },
-        )
-
-        assert response.status_code == 422
-        data = response.json()
-        assert "Invalid parameters" in data["detail"]
-        assert "forecast_times" in data["detail"]
-
-
-class TestWorkflowExecutionRequest:
-    """Tests for WorkflowExecutionRequest validation"""
-
-    def test_valid_request_with_parameters(self):
-        """Test that valid request with parameters field is accepted"""
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Valid request
-        request = WorkflowExecutionRequest(
-            parameters={"field1": "value1", "field2": 123}
-        )
-        assert request.parameters == {"field1": "value1", "field2": 123}
-
-    def test_valid_request_with_empty_parameters(self):
-        """Test that request with empty parameters dict is accepted"""
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Empty parameters is valid
-        request = WorkflowExecutionRequest(parameters={})
-        assert request.parameters == {}
-
-    def test_valid_request_without_parameters_uses_default(self):
-        """Test that request without parameters field uses default empty dict"""
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # No parameters provided - should use default
-        request = WorkflowExecutionRequest()
-        assert request.parameters == {}
-
-    def test_invalid_request_with_extra_fields(self):
-        """Test that request with unknown fields is rejected"""
-        from pydantic import ValidationError
-
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Request with unknown field should be rejected
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"valid": "param"}, unknown_field="should_fail"
-            )
-
-    def test_invalid_request_with_multiple_extra_fields(self):
-        """Test that request with multiple unknown fields is rejected"""
-        from pydantic import ValidationError
-
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Multiple unknown fields
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"valid": "param"},
-                extra1="bad",
-                extra2="also_bad",
-                extra3="still_bad",
-            )
-
-    def test_invalid_request_flat_structure(self):
-        """Test that flat request structure (without parameters wrapper) is rejected"""
-        from pydantic import ValidationError
-
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Flat structure - fields that should be inside 'parameters'
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                forecast_times=["2024-01-01"],
-                nsteps=6,
-                model_type="fcn",
-            )
-
-    def test_invalid_request_mixed_structure(self):
-        """Test that mixed structure (parameters + extra fields) is rejected"""
-        from pydantic import ValidationError
-
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Mixed: valid parameters field + invalid extra fields
-        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
-            WorkflowExecutionRequest(
-                parameters={"forecast_times": ["2024-01-01"], "nsteps": 6},
-                workflow_type="deterministic",  # This should not be at this level
-                device="auto",  # This should not be at this level
-            )
-
-    def test_parameters_field_type_validation(self):
-        """Test that parameters field must be a dict"""
-        from pydantic import ValidationError
-
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # parameters must be a dict, not a string
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters="not_a_dict")
-
-        # parameters must be a dict, not a list
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters=["not", "a", "dict"])
-
-        # parameters must be a dict, not None
-        with pytest.raises(ValidationError):
-            WorkflowExecutionRequest(parameters=None)
-
-    def test_valid_request_serialization(self):
-        """Test that valid request can be serialized to dict"""
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        request = WorkflowExecutionRequest(
-            parameters={"forecast_times": ["2024-01-01"], "nsteps": 10}
-        )
-
-        # Serialize to dict
-        data = request.model_dump()
-        assert isinstance(data, dict)
-        assert data == {"parameters": {"forecast_times": ["2024-01-01"], "nsteps": 10}}
-
-    def test_valid_request_with_nested_parameters(self):
-        """Test that request with nested parameter structures is accepted"""
-        from earth2studio.serve.server.main import WorkflowExecutionRequest
-
-        # Complex nested parameters
-        request = WorkflowExecutionRequest(
-            parameters={
-                "forecast_times": ["2024-01-01", "2024-01-02"],
-                "nsteps": 10,
-                "config": {
-                    "model": {"type": "fcn", "version": "1.0"},
-                    "data": {"source": "gfs", "cache": True},
-                },
-                "options": ["opt1", "opt2", "opt3"],
-            }
-        )
-
-        assert request.parameters["nsteps"] == 10
-        assert request.parameters["config"]["model"]["type"] == "fcn"
-        assert len(request.parameters["options"]) == 3
-
-
-class TestQueuePosition:
-    """Tests for queue position functionality"""
-
-    @pytest.fixture
-    def mock_queue(self):
-        """Create a mock RQ queue with job IDs"""
-        from unittest.mock import MagicMock
-
-        queue = MagicMock()
-        queue.job_ids = [
-            "workflow1_exec_001",
-            "workflow2_exec_002",
-            "workflow1_exec_003",
-            "workflow3_exec_004",
-        ]
-        return queue
-
-    def test_get_queue_position_first_in_queue(self, mock_queue):
-        """Test getting position for first job in queue"""
-        from unittest.mock import patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow1_exec_001")
-            assert position == 0  # 0-indexed: first job is at position 0
-
-    def test_get_queue_position_middle_of_queue(self, mock_queue):
-        """Test getting position for job in middle of queue"""
-        from unittest.mock import patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow1_exec_003")
-            assert position == 2  # 0-indexed: third job is at position 2
-
-    def test_get_queue_position_last_in_queue(self, mock_queue):
-        """Test getting position for last job in queue"""
-        from unittest.mock import patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            position = get_queue_position("workflow3_exec_004")
-            assert position == 3  # 0-indexed: fourth job is at position 3
-
-    def test_get_queue_position_not_in_queue(self, mock_queue):
-        """Test getting position for job not in queue returns None"""
-        from unittest.mock import patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            position = get_queue_position("nonexistent_job")
-            assert position is None
-
-    def test_get_queue_position_no_queue(self):
-        """Test getting position when queue is not initialized returns None"""
-        from unittest.mock import patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        with patch("earth2studio.serve.server.main.inference_queue", None):
-            position = get_queue_position("any_job")
-            assert position is None
-
-    def test_get_queue_position_empty_queue(self):
-        """Test getting position when queue is empty"""
-        from unittest.mock import MagicMock, patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        empty_queue = MagicMock()
-        empty_queue.job_ids = []
-
-        with patch("earth2studio.serve.server.main.inference_queue", empty_queue):
-            position = get_queue_position("any_job")
-            assert position is None
-
-    def test_get_queue_position_exception_handling(self):
-        """Test that exceptions in get_queue_position are handled gracefully"""
-        from unittest.mock import MagicMock, PropertyMock, patch
-
-        from earth2studio.serve.server.main import get_queue_position
-
-        queue_with_error = MagicMock()
-        type(queue_with_error).job_ids = PropertyMock(
-            side_effect=Exception("Queue connection error")
-        )
-
-        with patch("earth2studio.serve.server.main.inference_queue", queue_with_error):
-            position = get_queue_position("any_job")
-            assert position is None
-
-
-class TestWorkflowExecutionWithQueuePosition:
-    """Tests for workflow execution endpoint with queue position"""
-
-    @pytest.fixture
-    def mock_workflow_class(self):
-        """Create a mock workflow class"""
-        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
-
-        class TestWorkflowParameters(WorkflowParameters):
-            """Test parameters"""
-
-            test_param: str = "default"
-
-        class TestWorkflow(Workflow):
-            name = "test_workflow"
-            description = "Test workflow"
-            Parameters = TestWorkflowParameters
-
-            @classmethod
-            def validate_parameters(cls, parameters):
-                return TestWorkflowParameters.validate(parameters)
-
-            def run(self, parameters, execution_id):
-                return {"status": "success"}
-
-        return TestWorkflow
-
-    @pytest.fixture
-    def client(self, mock_workflow_class):
-        """Create test client with mocked dependencies"""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            # Setup mock async Redis
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-
-            # Setup mock sync Redis
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_instance.setex = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            # Setup mock queue
-            mock_queue = MagicMock()
-            mock_queue.__len__ = MagicMock(return_value=0)
-            mock_queue.job_ids = []
-            mock_queue_class.return_value = mock_queue
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import workflow_registry
-
-            # Register the test workflow
-            workflow_registry._workflows["test_workflow"] = mock_workflow_class
-
-            with TestClient(app, raise_server_exceptions=False) as client:
-                yield client, mock_queue, mock_sync_instance
-
-            # Cleanup
-            if "test_workflow" in workflow_registry._workflows:
-                del workflow_registry._workflows["test_workflow"]
-
-    def test_execute_workflow_includes_queue_position(self, client):
-        """Test that POST /v1/infer/{workflow_name} includes queue position"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_123"
-
-        # Before enqueue: queue size is 0
-        mock_queue.__len__ = MagicMock(return_value=0)
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Redis llen returns 0 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=0)
-
-        # Patch the inference_queue at module level
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is included in response
-        assert "position" in data
-        assert data["position"] == 0  # Redis llen returns 0 (first job, position 0)
-        assert data["status"] == "queued"
-
-    def test_execute_workflow_position_with_existing_jobs(self, client):
-        """Test that position is correct when there are existing jobs in queue"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_456"
-
-        # Before enqueue: queue has 2 existing jobs
-        mock_queue.__len__ = MagicMock(return_value=2)
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Redis llen returns 2 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=2)
-
-        # Patch the inference_queue at module level
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is correct
-        assert "position" in data
-        assert data["position"] == 2  # Redis llen returns 2 (third job, position 2)
-        assert data["status"] == "queued"
-
-    def test_execute_workflow_position_with_five_existing_jobs(self, client):
-        """Test that position is correct with multiple existing jobs in queue"""
-        from unittest.mock import patch
-
-        test_client, mock_queue, mock_redis = client
-
-        # Setup mock job
-        mock_job = MagicMock()
-        mock_job.id = "test_workflow_exec_789"
-        mock_queue.enqueue = MagicMock(return_value=mock_job)
-
-        # Before enqueue: queue has 5 existing jobs
-        mock_queue.__len__ = MagicMock(return_value=5)
-
-        # Redis llen returns 5 (the queue size before the job is fully committed)
-        mock_redis.llen = MagicMock(return_value=5)
-
-        # Patch the inference_queue at module level
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = test_client.post(
-                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
-            )
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is correct
-        assert "position" in data
-        assert data["position"] == 5  # Redis llen returns 5 (sixth job, position 5)
-        assert data["status"] == "queued"
-
-    def test_get_workflow_status_includes_position_when_queued(self, client):
-        """Test that GET /v1/infer/{workflow_name}/{execution_id}/status includes position when status is queued"""
-        test_client, mock_queue, mock_redis = client
-
-        # Setup queue with multiple jobs
-        execution_id = "exec_456"
-        job_id = f"test_workflow_{execution_id}"
-
-        # Set up mock queue job_ids to include our job at position 1
-        mock_queue.job_ids = ["other_job_1", job_id, "other_job_2"]
-
-        # Mock Redis response for queued execution
-        import json
-        from datetime import datetime, timezone
-        from unittest.mock import patch
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "queued",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        # Patch the inference_queue for get_queue_position to use
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Check that position is included and correct
-        assert "position" in data
-        assert data["position"] == 1  # Second in queue (0-indexed, so position 1)
-        assert data["status"] == "queued"
-
-    def test_get_workflow_status_queued_but_not_in_queue(self, client):
-        """Test race condition: status is QUEUED but job not in queue (worker picked it up)"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_race"
-
-        # Set up mock queue with empty job_ids (job already picked up by worker)
-        mock_queue.job_ids = []
-
-        # Mock Redis response with QUEUED status (worker hasn't updated it yet)
-        import json
-        from datetime import datetime, timezone
-        from unittest.mock import patch
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "queued",  # Still queued in Redis
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        # Patch the inference_queue for get_queue_position to use
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Should be treated as RUNNING since job is not in queue
-        assert data["status"] == "running"
-        # Position should be None for running status
-        if "position" in data:
-            assert data["position"] is None
-
-    def test_get_workflow_status_no_position_when_running(self, client):
-        """Test that position is None when workflow is running (not queued)"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_789"
-
-        # Mock Redis response for running execution
-        import json
-        from datetime import datetime, timezone
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "running",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Position should be None or not included for non-queued status
-        # (The model allows None, so it could be None or absent)
-        if "position" in data:
-            assert data["position"] is None
-        assert data["status"] == "running"
-
-    def test_get_workflow_status_no_position_when_completed(self, client):
-        """Test that position is None when workflow is completed"""
-        test_client, mock_queue, mock_redis = client
-
-        execution_id = "exec_999"
-
-        # Mock Redis response for completed execution
-        import json
-        from datetime import datetime, timezone
-
-        execution_data = {
-            "workflow_name": "test_workflow",
-            "execution_id": execution_id,
-            "status": "completed",
-            "start_time": datetime.now(timezone.utc).isoformat(),
-            "end_time": datetime.now(timezone.utc).isoformat(),
-            "metadata": {},
-        }
-        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
-
-        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
-
-        assert response.status_code == 200
-        data = response.json()
-
-        # Position should be None for completed workflows
-        if "position" in data:
-            assert data["position"] is None
-        assert data["status"] == "completed"
-
-
-class TestHealthAndProbes:
-    """Tests for /health, /readiness, /liveness, and /metrics endpoints."""
-
-    @pytest.fixture
-    def client_with_mocks(self):
-        """Client with mocked Redis and RQ for probe endpoints."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue"),
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_redis.return_value = mock_async_instance
-
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            from earth2studio.serve.server.main import app
-
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c
-
-    def test_liveness_returns_alive(self, client_with_mocks):
-        """GET /liveness returns status alive."""
-        response = client_with_mocks.get("/liveness")
-        assert response.status_code == 200
-        assert response.json() == {"status": "alive"}
-
-    def test_health_healthy_when_script_returns_zero(self, client_with_mocks):
-        """GET /health and /readiness return healthy when status script exits 0."""
-        with patch(
-            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
-        ) as mock_exec:
-            mock_proc = MagicMock()
-            mock_proc.returncode = 0
-            mock_proc.communicate = AsyncMock(return_value=(b"", b""))
-            mock_exec.return_value = mock_proc
-
-            for path in ("/health", "/readiness"):
-                response = client_with_mocks.get(path)
-                assert response.status_code == 200
-                data = response.json()
-                assert data["status"] == "healthy"
-                assert "timestamp" in data
-
-    def test_health_unhealthy_when_script_returns_nonzero(self, client_with_mocks):
-        """GET /health returns 503 when status script exits non-zero."""
-        with patch(
-            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
-        ) as mock_exec:
-            mock_proc = MagicMock()
-            mock_proc.returncode = 1
-            mock_proc.communicate = AsyncMock(return_value=(b"", b"stderr"))
-            mock_exec.return_value = mock_proc
-
-            response = client_with_mocks.get("/health")
-            assert response.status_code == 503
-            data = response.json()
-            assert data["detail"]["status"] == "unhealthy"
-
-    def test_metrics_returns_prometheus_format(self, client_with_mocks):
-        """GET /metrics returns Prometheus text format."""
-        with patch("earth2studio.serve.server.main.generate_latest") as mock_gen:
-            mock_gen.return_value = (
-                b"# HELP mock_metric Mock\n# TYPE mock_metric gauge\nmock_metric 1.0\n"
-            )
-
-            response = client_with_mocks.get("/metrics")
-            assert response.status_code == 200
-            assert "mock_metric" in response.text
-            assert "text/plain" in response.headers.get("content-type", "")
-
-
-class TestListWorkflows:
-    """Tests for GET /v1/workflows and /v1/infer/workflows."""
-
-    @pytest.fixture
-    def client_with_workflows(self):
-        """Client with a registered workflow for list_workflows."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue"),
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_redis.return_value = mock_async_instance
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import workflow_registry
-
-            class FakeWorkflow:
-                name = "fake_wf"
-                description = "Fake workflow for list test"
-
-            workflow_registry._workflows["fake_wf"] = FakeWorkflow
-
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c
-
-            if "fake_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["fake_wf"]
-
-    def test_list_workflows_returns_registry(self, client_with_workflows):
-        """GET /v1/workflows returns workflows dict."""
-        response = client_with_workflows.get("/v1/workflows")
-        assert response.status_code == 200
-        data = response.json()
-        assert "workflows" in data
-        assert "fake_wf" in data["workflows"]
-        assert data["workflows"]["fake_wf"] == "Fake workflow for list test"
-
-    def test_list_workflows_infer_path(self, client_with_workflows):
-        """GET /v1/infer/workflows returns same structure."""
-        response = client_with_workflows.get("/v1/infer/workflows")
-        assert response.status_code == 200
-        data = response.json()
-        assert "workflows" in data
-
-
-class TestAdmissionControl:
-    """Tests for check_admission_control (queue full, Redis None)."""
-
-    @pytest.fixture
-    def client_for_admission(self):
-        """Client with mocked queue and redis for admission tests."""
-        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
-
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_redis.return_value = mock_async_instance
-
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.llen = MagicMock(return_value=0)
-            mock_sync_redis.return_value = mock_sync_instance
-
-            mock_queue = MagicMock()
-            mock_queue.enqueue = MagicMock(return_value=MagicMock(id="job_1"))
-            mock_queue_class.return_value = mock_queue
-
-            class AdmitParams(WorkflowParameters):
-                x: int = Field(default=1, ge=0)
-
-            class AdmitWorkflow(Workflow):
-                name = "admit_wf"
-                description = "For admission test"
-                Parameters = AdmitParams
-
-                @classmethod
-                def validate_parameters(
-                    cls, parameters: dict[str, Any] | AdmitParams
-                ) -> AdmitParams:
-                    return AdmitParams.validate(parameters)
-
-                def run(self, parameters, execution_id):
-                    return {"status": "ok"}
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import workflow_registry
-
-            workflow_registry._workflows["admit_wf"] = AdmitWorkflow
-
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c, mock_sync_instance, mock_queue
-
-            if "admit_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["admit_wf"]
-
-    def test_admission_control_redis_none_returns_503(self, client_for_admission):
-        """When redis_sync_client is None, execute returns 503."""
-        client, mock_sync_redis, mock_queue = client_for_admission
-        with patch("earth2studio.serve.server.main.redis_sync_client", None):
-            response = client.post(
-                "/v1/infer/admit_wf",
-                json={"parameters": {}},
-            )
-        assert response.status_code == 503
-        assert "Redis" in response.json().get("detail", "")
-
-    def test_admission_control_queue_full_returns_429(self, client_for_admission):
-        """When queue is full, execute returns 429."""
-        client, mock_sync_redis, mock_queue = client_for_admission
-        mock_sync_redis.llen.return_value = 100
-        with patch("earth2studio.serve.server.main.config") as mock_config:
-            mock_config.queue.max_size = 10
-            mock_config.queue.name = "inference"
-            mock_config.queue.result_zip_queue_name = "result_zip"
-            mock_config.queue.object_storage_queue_name = "object_storage"
-            mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
-            response = client.post(
-                "/v1/infer/admit_wf",
-                json={"parameters": {}},
-            )
-        assert response.status_code == 429
-        assert "full" in response.json().get("detail", "").lower()
-
-    def test_admission_control_llen_raises_returns_500(self, client_for_admission):
-        """When redis_sync_client.llen raises, execute returns 500."""
-        client, mock_sync_redis, mock_queue = client_for_admission
-        mock_sync_redis.llen.side_effect = RuntimeError("Redis connection lost")
-        response = client.post(
-            "/v1/infer/admit_wf",
-            json={"parameters": {}},
-        )
-        assert response.status_code == 500
-        assert "queue" in response.json().get("detail", "").lower()
-
-
-class TestExecuteWorkflowBranches:
-    """Tests for execute_workflow branches (404, 400, 503, 500)."""
-
-    @pytest.fixture
-    def client_exec(self):
-        """Client with one workflow for execute tests."""
-        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
-
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_instance.setex = MagicMock()
-            mock_sync_instance.llen = MagicMock(return_value=0)
-            mock_sync_redis.return_value = mock_sync_instance
-
-            mock_queue = MagicMock()
-            mock_job = MagicMock()
-            mock_job.id = "exec_wf_exec_123"
-            mock_queue.enqueue = MagicMock(return_value=mock_job)
-            mock_queue_class.return_value = mock_queue
-
-            class ExecParams(WorkflowParameters):
-                x: int = Field(default=1, ge=0)
-
-            class ExecWorkflow(Workflow):
-                name = "exec_wf"
-                description = "For execute tests"
-                Parameters = ExecParams
-
-                @classmethod
-                def validate_parameters(
-                    cls, parameters: dict[str, Any] | ExecParams
-                ) -> ExecParams:
-                    return ExecParams.validate(parameters)
-
-                def run(self, parameters, execution_id):
-                    return {"status": "ok"}
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import workflow_registry
-
-            workflow_registry._workflows["exec_wf"] = ExecWorkflow
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c, mock_sync_instance, mock_queue
-            if "exec_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["exec_wf"]
-
-    def test_execute_workflow_not_found_404(self, client_exec):
-        """POST /v1/infer/{workflow_name} with unknown workflow returns 404."""
-        client, _, _ = client_exec
-        response = client.post(
-            "/v1/infer/nonexistent_workflow",
-            json={"parameters": {}},
-        )
-        assert response.status_code == 404
-        assert "not found" in response.json().get("detail", "").lower()
-
-    def test_execute_workflow_validate_parameters_unexpected_error_400(
-        self, client_exec
-    ):
-        """When validate_parameters raises non-ValueError, returns 400."""
-        client, _, _ = client_exec
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
-            mock_wf = MagicMock()
-            mock_wf.validate_parameters.side_effect = AttributeError("no model_dump")
-            mock_registry.get_workflow_class.return_value = mock_wf
-            response = client.post(
-                "/v1/infer/exec_wf",
-                json={"parameters": {"x": 1}},
-            )
-        assert response.status_code == 400
-        assert "validating" in response.json().get("detail", "").lower()
-
-    def test_execute_workflow_inference_queue_none_503(self, client_exec):
-        """When inference_queue is None, returns 503."""
-        client, mock_redis, mock_queue = client_exec
-        with patch("earth2studio.serve.server.main.inference_queue", None):
-            response = client.post(
-                "/v1/infer/exec_wf",
-                json={"parameters": {}},
-            )
-        assert response.status_code == 503
-        assert "queue" in response.json().get("detail", "").lower()
-
-    def test_execute_workflow_llen_raises_after_enqueue_500(self, client_exec):
-        """When llen raises after enqueue (queue position lookup), returns 500."""
-        client, mock_redis, mock_queue = client_exec
-        # First 4 calls: admission (4 queues); 5th: position lookup. Make 5th raise.
-        mock_redis.llen.side_effect = [0, 0, 0, 0, RuntimeError("redis error")]
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = client.post(
-                "/v1/infer/exec_wf",
-                json={"parameters": {}},
-            )
-        assert response.status_code == 500
-        assert "queue position" in response.json().get(
-            "detail", ""
-        ).lower() or "Internal" in response.json().get("detail", "")
-
-    def test_execute_workflow_enqueue_failure_500(self, client_exec):
-        """When enqueue raises, returns 500."""
-        client, mock_redis, mock_queue = client_exec
-        mock_queue.enqueue.side_effect = RuntimeError("RQ enqueue failed")
-        with patch("earth2studio.serve.server.main.inference_queue", mock_queue):
-            response = client.post(
-                "/v1/infer/exec_wf",
-                json={"parameters": {}},
-            )
-        assert response.status_code == 500
-        assert "enqueue" in response.json().get("detail", "").lower()
-
-
-class TestHealthMetricsSchemaExceptions:
-    """Tests for health, metrics, and schema exception paths."""
-
-    @pytest.fixture
-    def client_probes(self):
-        """Client for probe endpoints."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue"),
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_redis.return_value = mock_async_instance
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-            from earth2studio.serve.server.main import app
-
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c
-
-    def test_health_check_subprocess_exception_500(self, client_probes):
-        """When create_subprocess_exec raises, health returns 500."""
-        with patch(
-            "earth2studio.serve.server.main.asyncio.create_subprocess_exec"
-        ) as mock_exec:
-            mock_exec.side_effect = FileNotFoundError("status.sh not found")
-            response = client_probes.get("/health")
-        assert response.status_code == 500
-        assert "Health check failed" in response.json().get("detail", "")
-
-    def test_metrics_generate_latest_raises_500(self, client_probes):
-        """When generate_latest raises, metrics returns 500."""
-        with patch("earth2studio.serve.server.main.generate_latest") as mock_gen:
-            mock_gen.side_effect = RuntimeError("metrics error")
-            response = client_probes.get("/metrics")
-        assert response.status_code == 500
-        detail = response.json().get("detail", {})
-        if isinstance(detail, dict):
-            assert "metrics" in str(detail).lower()
-        else:
-            assert "metrics" in detail.lower()
-
-    def test_schema_generation_exception_500(self, client_probes):
-        """When model_json_schema raises, schema endpoint returns 500."""
-        from earth2studio.serve.server.workflow import Workflow, WorkflowParameters
-
-        class BadSchemaParams(WorkflowParameters):
-            x: int = Field(default=1)
-
-        class BadSchemaWorkflow(Workflow):
-            name = "bad_schema_wf"
-            description = "Bad schema"
-            Parameters = BadSchemaParams
-
-            @classmethod
-            def validate_parameters(cls, parameters):
-                return BadSchemaParams.validate(parameters)
-
-            def run(self, parameters, execution_id):
-                return {"status": "ok"}
-
-        from earth2studio.serve.server.workflow import workflow_registry
-
-        workflow_registry._workflows["bad_schema_wf"] = BadSchemaWorkflow
-        try:
-            with patch.object(
-                BadSchemaParams,
-                "model_json_schema",
-                side_effect=RuntimeError("schema error"),
-            ):
-                response = client_probes.get("/v1/workflows/bad_schema_wf/schema")
-            assert response.status_code == 500
-            detail = response.json().get("detail", {})
-            assert "schema" in str(detail).lower()
-        finally:
-            if "bad_schema_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["bad_schema_wf"]
-
-
-class TestGetWorkflowStatusBranches:
-    """Tests for get_workflow_status branches (404 ValueError, 500 Exception)."""
-
-    @pytest.fixture
-    def client_status(self):
-        """Client with workflow for status tests."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-            mock_queue_class.return_value = MagicMock()
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import (
-                Workflow,
-                WorkflowParameters,
-                workflow_registry,
-            )
-
-            class StatusParams(WorkflowParameters):
-                x: int = Field(default=1)
-
-            class StatusWorkflow(Workflow):
-                name = "status_wf"
-                description = "Status test"
-                Parameters = StatusParams
-
-                @classmethod
-                def validate_parameters(cls, parameters):
-                    return StatusParams.validate(parameters)
-
-                def run(self, parameters, execution_id):
-                    return {"status": "ok"}
-
-            workflow_registry._workflows["status_wf"] = StatusWorkflow
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c, mock_sync_instance
-            if "status_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["status_wf"]
-
-    def test_get_workflow_status_value_error_404(self, client_status):
-        """When _get_execution_data raises ValueError, returns 404."""
-        client, mock_redis = client_status
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(
-                side_effect=ValueError("Execution not found")
-            )
-            mock_registry.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/status_wf/exec_123/status")
-        assert response.status_code == 404
-
-    def test_get_workflow_status_exception_500(self, client_status):
-        """When _get_execution_data raises Exception, returns 500."""
-        client, mock_redis = client_status
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_registry:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(
-                side_effect=RuntimeError("Redis error")
-            )
-            mock_registry.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/status_wf/exec_123/status")
-        assert response.status_code == 500
-        assert "status" in response.json().get("detail", "").lower()
-
-
-class TestGetWorkflowResultsBranches:
-    """Tests for get_workflow_results (EXPIRED, 202, FAILED, CANCELLED, 404, 200, 500)."""
-
-    @pytest.fixture
-    def client_results(self, tmp_path):
-        """Client and tmp dir for results tests."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-            mock_queue_class.return_value = MagicMock()
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import (
-                Workflow,
-                WorkflowParameters,
-                WorkflowStatus,
-                workflow_registry,
-            )
-
-            class ResultsParams(WorkflowParameters):
-                x: int = Field(default=1)
-
-            class ResultsWorkflow(Workflow):
-                name = "results_wf"
-                description = "Results test"
-                Parameters = ResultsParams
-
-                @classmethod
-                def validate_parameters(cls, parameters):
-                    return ResultsParams.validate(parameters)
-
-                def run(self, parameters, execution_id):
-                    return {"status": "ok"}
-
-            workflow_registry._workflows["results_wf"] = ResultsWorkflow
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c, mock_sync_instance, tmp_path, WorkflowStatus
-            if "results_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["results_wf"]
-
-    def _make_execution_data(self, status, execution_id="exec_1"):
-        from earth2studio.serve.server.workflow import WorkflowResult
-
-        return WorkflowResult(
-            workflow_name="results_wf",
-            execution_id=execution_id,
-            status=status,
-            start_time=datetime.now(timezone.utc).isoformat(),
-            end_time=(
-                datetime.now(timezone.utc).isoformat()
-                if status in ("completed", "failed", "expired", "cancelled")
-                else None
-            ),
-            error_message="failed" if status == "failed" else None,
-        )
-
-    def test_get_workflow_results_expired_400(self, client_results):
-        """When status is EXPIRED, returns 400."""
-        client, mock_redis, _, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.EXPIRED)
-        mock_redis.get.return_value = json.dumps(exec_data.model_dump()).encode()
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 400
-        assert "expired" in response.json().get("detail", {}).get("error", "").lower()
-
-    def test_get_workflow_results_still_running_202(self, client_results):
-        """When status is RUNNING, returns 202."""
-        client, mock_redis, _, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.RUNNING)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 202
-
-    def test_get_workflow_results_failed_404(self, client_results):
-        """When status is FAILED, returns 404."""
-        client, mock_redis, _, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.FAILED)
-        exec_data.error_message = "Something failed"
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 404
-        assert "failed" in response.json().get("detail", {}).get("error", "").lower()
-
-    def test_get_workflow_results_cancelled_404(self, client_results):
-        """When status is CANCELLED, returns 404."""
-        client, mock_redis, _, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.CANCELLED)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 404
-        assert "cancelled" in response.json().get("detail", {}).get("error", "").lower()
-
-    def test_get_workflow_results_metadata_not_found_404(self, client_results):
-        """When status is COMPLETED but metadata file missing, returns 404."""
-        client, mock_redis, tmp_path, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 404
-        assert "Metadata" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_results_success_200(self, client_results):
-        """When status is COMPLETED and metadata exists, returns 200 with JSON."""
-        client, mock_redis, tmp_path, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
-        metadata_path = tmp_path / "metadata_results_wf:exec_1.json"
-        metadata_path.write_text(
-            json.dumps({"request_id": "results_wf:exec_1", "status": "completed"})
-        )
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 200
-        assert response.json().get("request_id") == "results_wf:exec_1"
-
-    def test_get_workflow_results_value_error_400(self, client_results):
-        """When _get_execution_data raises ValueError, returns 400."""
-        client, _, _, _ = client_results
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(
-                side_effect=ValueError("Invalid key")
-            )
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 400
-
-    def test_get_workflow_results_exception_500(self, client_results):
-        """When reading metadata raises, returns 500."""
-        client, mock_redis, tmp_path, WorkflowStatus = client_results
-        exec_data = self._make_execution_data(WorkflowStatus.COMPLETED)
-        metadata_path = tmp_path / "metadata_results_wf:exec_1.json"
-        metadata_path.write_text("not valid json {{")
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/results_wf/exec_1/results")
-        assert response.status_code == 500
-
-
-class TestGetWorkflowResultFileBranches:
-    """Tests for get_workflow_result_file (404, 503, 403, 400, 200)."""
-
-    @pytest.fixture
-    def client_file(self, tmp_path):
-        """Client and tmp dir for result file tests."""
-        with (
-            patch("redis.asyncio.Redis") as mock_async_redis,
-            patch("redis.Redis") as mock_sync_redis,
-            patch("rq.Queue") as mock_queue_class,
-            patch("earth2studio.serve.server.workflow.register_all_workflows"),
-        ):
-            mock_async_instance = MagicMock()
-            mock_async_instance.ping = AsyncMock(return_value=True)
-            mock_async_instance.close = AsyncMock()
-            mock_async_instance.get = AsyncMock(return_value=None)
-            mock_async_redis.return_value = mock_async_instance
-            mock_sync_instance = MagicMock()
-            mock_sync_instance.ping = MagicMock(return_value=True)
-            mock_sync_instance.close = MagicMock()
-            mock_sync_redis.return_value = mock_sync_instance
-            mock_queue_class.return_value = MagicMock()
-
-            from earth2studio.serve.server.main import app
-            from earth2studio.serve.server.workflow import (
-                Workflow,
-                WorkflowParameters,
-                WorkflowStatus,
-                workflow_registry,
-            )
-
-            class FileParams(WorkflowParameters):
-                x: int = Field(default=1)
-
-            class FileWorkflow(Workflow):
-                name = "file_wf"
-                description = "File test"
-                Parameters = FileParams
-
-                @classmethod
-                def validate_parameters(cls, parameters):
-                    return FileParams.validate(parameters)
-
-                def run(self, parameters, execution_id):
-                    return {"status": "ok"}
-
-            workflow_registry._workflows["file_wf"] = FileWorkflow
-            with TestClient(app, raise_server_exceptions=False) as c:
-                yield c, mock_async_instance, mock_sync_instance, tmp_path, WorkflowStatus
-            if "file_wf" in workflow_registry._workflows:
-                del workflow_registry._workflows["file_wf"]
-
-    def _exec_data(self, status=None):
-        from earth2studio.serve.server.workflow import (
-            WorkflowResult,
-            WorkflowStatus,
-        )
-
-        if status is None:
-            status = WorkflowStatus.COMPLETED
-        return WorkflowResult(
-            workflow_name="file_wf",
-            execution_id="exec_1",
-            status=status,
-            start_time=datetime.now(timezone.utc).isoformat(),
-            end_time=datetime.now(timezone.utc).isoformat(),
-        )
-
-    def test_get_workflow_result_file_workflow_not_found_404(self, client_file):
-        """When workflow not in registry, returns 404."""
-        client, *_ = client_file
-        response = client.get("/v1/infer/nonexistent_wf/exec_1/results/out.nc")
-        assert response.status_code == 404
-
-    def test_get_workflow_result_file_status_not_completed_404(self, client_file):
-        """When status is not COMPLETED, returns 404."""
-        client, mock_async, mock_sync, _, WorkflowStatus = client_file
-        exec_data = self._exec_data(WorkflowStatus.RUNNING)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            mock_wf = MagicMock()
-            mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-            mock_reg.get_workflow_class.return_value = mock_wf
-            response = client.get("/v1/infer/file_wf/exec_1/results/out.nc")
-        assert response.status_code == 404
-        assert (
-            "not available"
-            in response.json().get("detail", {}).get("error", "").lower()
-        )
-
-    def test_get_workflow_result_file_redis_none_503(self, client_file):
-        """When redis_client (async) is None for file request, returns 503."""
-        client, mock_async, mock_sync, _, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", None):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/file_wf/exec_1/results/out.nc")
-        assert response.status_code == 503
-
-    def test_get_workflow_result_file_zip_not_in_redis_404(self, client_file):
-        """When filepath equals request_id but zip not in Redis, returns 404."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        mock_async.get = AsyncMock(return_value=None)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                with patch("earth2studio.serve.server.main.RESULTS_ZIP_DIR", tmp_path):
-                    mock_wf = MagicMock()
-                    mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                    mock_reg.get_workflow_class.return_value = mock_wf
-                    response = client.get(
-                        "/v1/infer/file_wf/exec_1/results/file_wf:exec_1"
-                    )
-        assert response.status_code == 404
-        assert "Zip" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_result_file_output_dir_not_found_404(self, client_file):
-        """When output_path_key not in Redis, returns 404."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        mock_async.get = AsyncMock(return_value=None)
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/file_wf/exec_1/results/somefile.txt")
-        assert response.status_code == 404
-        assert "Output directory" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_result_file_path_traversal_403(self, client_file):
-        """When filepath escapes output dir, returns 403 (or 404 if path invalid)."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        output_dir = tmp_path / "output"
-        output_dir.mkdir()
-        # Create a file inside output_dir; request it via path that escapes (..)
-        inner = output_dir / "inner.txt"
-        inner.write_text("data")
-        mock_async.get = AsyncMock(return_value=str(output_dir))
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                # Path ../output/inner.txt from output_dir resolves to output_dir/inner.txt
-                # Use path that clearly escapes: .. with resolved path outside
-                response = client.get(
-                    "/v1/infer/file_wf/exec_1/results/..%2F..%2F..%2Fetc%2Fpasswd"
-                )
-        # Path traversal must not succeed: either 403 (access denied) or 404
-        assert response.status_code in (403, 404)
-        if response.status_code == 403:
-            assert "Access denied" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_result_file_not_found_404(self, client_file):
-        """When file does not exist in output dir, returns 404."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        output_dir = tmp_path / "output"
-        output_dir.mkdir()
-        mock_async.get = AsyncMock(return_value=str(output_dir))
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get(
-                    "/v1/infer/file_wf/exec_1/results/nonexistent.txt"
-                )
-        assert response.status_code == 404
-        assert "File not found" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_result_file_not_a_file_400(self, client_file):
-        """When path is a directory, returns 400."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        output_dir = tmp_path / "output"
-        output_dir.mkdir()
-        subdir = output_dir / "subdir"
-        subdir.mkdir()
-        mock_async.get = AsyncMock(return_value=str(output_dir))
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/file_wf/exec_1/results/subdir")
-        assert response.status_code == 400
-        assert "Not a file" in response.json().get("detail", {}).get("error", "")
-
-    def test_get_workflow_result_file_stream_file_200(self, client_file):
-        """When file exists, returns 200 with streamed content."""
-        client, mock_async, mock_sync, tmp_path, WorkflowStatus = client_file
-        exec_data = self._exec_data()
-        output_dir = tmp_path / "output"
-        output_dir.mkdir()
-        out_file = output_dir / "data.txt"
-        out_file.write_text("hello world")
-        mock_async.get = AsyncMock(return_value=str(output_dir))
-        with patch("earth2studio.serve.server.main.workflow_registry") as mock_reg:
-            with patch("earth2studio.serve.server.main.redis_client", mock_async):
-                mock_wf = MagicMock()
-                mock_wf._get_execution_data = MagicMock(return_value=exec_data)
-                mock_reg.get_workflow_class.return_value = mock_wf
-                response = client.get("/v1/infer/file_wf/exec_1/results/data.txt")
-        assert response.status_code == 200
-        assert response.text == "hello world"
-
-
-class TestMainEntrypoint:
-    """Test main module entrypoint (covers line 1044)."""
-
-    def test_main_entrypoint_calls_uvicorn(self):
-        """When run as __main__, uvicorn.run is called."""
-        import runpy
-
-        import earth2studio.serve.server.main as main_module
-
-        with patch("uvicorn.run") as mock_run:
-            runpy.run_path(main_module.__file__, run_name="__main__")
-            mock_run.assert_called_once()
-            call = mock_run.call_args
-            # runpy re-executes the module so app is a new instance; check type and args
-            assert call[0][0].__class__.__name__ == "FastAPI"
-            assert call[1]["host"] == main_module.config.server.host
-            assert call[1]["port"] == main_module.config.server.port
diff --git a/test/serve/server/test_utils.py b/test/serve/server/test_utils.py
deleted file mode 100644
index 3e3b663ff..000000000
--- a/test/serve/server/test_utils.py
+++ /dev/null
@@ -1,266 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from unittest.mock import MagicMock, patch
-
-from earth2studio.serve.server.utils import (
-    get_inference_request_metadata_key,
-    get_inference_request_output_path_key,
-    get_inference_request_zip_key,
-    get_results_zip_dir_key,
-    get_signed_url_key,
-    queue_next_stage,
-)
-
-
-class TestRedisKeyFunctions:
-    """Tests for Redis key helper functions."""
-
-    def test_get_inference_request_zip_key(self):
-        """get_inference_request_zip_key returns expected key format."""
-        assert get_inference_request_zip_key("req_1") == (
-            "inference_request_zips:req_1:zip_file"
-        )
-
-    def test_get_inference_request_output_path_key(self):
-        """get_inference_request_output_path_key returns expected key format."""
-        assert get_inference_request_output_path_key("req_1") == (
-            "inference_request:req_1:output_path"
-        )
-
-    def test_get_inference_request_metadata_key(self):
-        """get_inference_request_metadata_key returns expected key format."""
-        assert get_inference_request_metadata_key("req_1") == (
-            "inference_request:req_1:pending_metadata"
-        )
-
-    def test_get_results_zip_dir_key(self):
-        """get_results_zip_dir_key returns expected key format."""
-        assert get_results_zip_dir_key("req_1") == (
-            "inference_request:req_1:results_zip_dir"
-        )
-
-    def test_get_signed_url_key(self):
-        """get_signed_url_key returns expected key format."""
-        assert get_signed_url_key("req_1") == ("inference_request:req_1:signed_url")
-
-
-class TestQueueNextStage:
-    """Tests for queue_next_stage."""
-
-    def test_unknown_stage_returns_none(self):
-        """Unknown current_stage logs error and returns None."""
-        mock_redis = MagicMock()
-        result = queue_next_stage(
-            redis_client=mock_redis,
-            current_stage="unknown_stage",  # type: ignore[arg-type]
-            workflow_name="wf",
-            execution_id="exec_1",
-            output_path_str="/path",
-        )
-        assert result is None
-
-    def test_inference_stage_queues_result_zip(self):
-        """current_stage=inference enqueues process_result_zip to result_zip queue."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.paths.result_zip_enabled = True
-        mock_config.object_storage.enabled = False
-        mock_config.queue.result_zip_queue_name = "result_zip"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_job = MagicMock()
-        mock_job.id = "job_123"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.return_value = mock_job
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            result = queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="inference",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-                results_zip_dir_str="/zip",
-            )
-
-        assert result == "job_123"
-        mock_queue.enqueue.assert_called_once()
-        call_kw = mock_queue.enqueue.call_args
-        assert (
-            call_kw[0][0] == "earth2studio.serve.server.cpu_worker.process_result_zip"
-        )
-        assert call_kw[0][1:5] == ("wf", "exec_1", "/out", "/zip")
-        assert call_kw[0][5] is True  # create_zip
-
-    def test_inference_stage_create_zip_false_when_object_storage_enabled(self):
-        """When object_storage enabled, create_zip is False for inference stage."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.paths.result_zip_enabled = True
-        mock_config.object_storage.enabled = True
-        mock_config.queue.result_zip_queue_name = "result_zip"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_job = MagicMock()
-        mock_job.id = "job_123"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.return_value = mock_job
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="inference",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-                results_zip_dir_str="/zip",
-            )
-
-        assert mock_queue.enqueue.call_args[0][5] is False  # create_zip
-
-    def test_result_zip_stage_object_storage_enabled_queues_object_storage(self):
-        """current_stage=result_zip with object_storage enabled enqueues process_object_storage_upload."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.object_storage.enabled = True
-        mock_config.queue.object_storage_queue_name = "object_storage"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_job = MagicMock()
-        mock_job.id = "job_456"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.return_value = mock_job
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            result = queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="result_zip",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-            )
-
-        assert result == "job_456"
-        mock_queue.enqueue.assert_called_once()
-        assert "process_object_storage_upload" in mock_queue.enqueue.call_args[0][0]
-        assert mock_queue.enqueue.call_args[0][1:4] == ("wf", "exec_1", "/out")
-
-    def test_result_zip_stage_object_storage_disabled_queues_finalize_metadata(self):
-        """current_stage=result_zip with object_storage disabled enqueues process_finalize_metadata."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.object_storage.enabled = False
-        mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_job = MagicMock()
-        mock_job.id = "job_789"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.return_value = mock_job
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            result = queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="result_zip",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-            )
-
-        assert result == "job_789"
-        mock_queue.enqueue.assert_called_once()
-        assert "process_finalize_metadata" in mock_queue.enqueue.call_args[0][0]
-        assert mock_queue.enqueue.call_args[0][1:3] == ("wf", "exec_1")
-
-    def test_object_storage_stage_queues_finalize_metadata(self):
-        """current_stage=object_storage enqueues process_finalize_metadata."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.queue.finalize_metadata_queue_name = "finalize_metadata"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_job = MagicMock()
-        mock_job.id = "job_final"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.return_value = mock_job
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            result = queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="object_storage",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-            )
-
-        assert result == "job_final"
-        mock_queue.enqueue.assert_called_once()
-        assert "process_finalize_metadata" in mock_queue.enqueue.call_args[0][0]
-
-    def test_enqueue_exception_returns_none(self):
-        """When Queue.enqueue raises, queue_next_stage returns None."""
-        mock_redis = MagicMock()
-        mock_config = MagicMock()
-        mock_config.paths.result_zip_enabled = False
-        mock_config.object_storage.enabled = False
-        mock_config.queue.result_zip_queue_name = "result_zip"
-        mock_config.queue.default_timeout = "1h"
-        mock_config.queue.job_timeout = "2h"
-        mock_queue = MagicMock()
-        mock_queue.enqueue.side_effect = Exception("RQ error")
-
-        with (
-            patch(
-                "earth2studio.serve.server.utils.get_config", return_value=mock_config
-            ),
-            patch("earth2studio.serve.server.utils.Queue", return_value=mock_queue),
-        ):
-            result = queue_next_stage(
-                redis_client=mock_redis,
-                current_stage="inference",
-                workflow_name="wf",
-                execution_id="exec_1",
-                output_path_str="/out",
-                results_zip_dir_str="/zip",
-            )
-
-        assert result is None
diff --git a/test/serve/server/test_worker.py b/test/serve/server/test_worker.py
deleted file mode 100644
index f8b4e02b8..000000000
--- a/test/serve/server/test_worker.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
-# SPDX-FileCopyrightText: All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import tempfile
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from earth2studio.serve.server.worker import get_output_path, run_custom_workflow
-
-
-class TestGetOutputPath:
-    """Tests for get_output_path."""
-
-    def test_returns_path_with_default_backend_when_io_config_none(self):
-        """When io_config is None, backend_type defaults to zarr."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            default_dir = Path(tmpdir)
-            with (
-                patch(
-                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
-                    default_dir,
-                ),
-            ):
-                out = get_output_path(
-                    io_config=None,
-                    timestamp="2024-01-01T12:00:00Z",
-                    workflow_type="my_workflow",
-                    request_id="req_123",
-                )
-            assert out.suffix == ".zarr"
-            assert out.name == "forecast.zarr"
-            assert "my_workflow" in str(out)
-            assert "req_123" in str(out)
-            assert out.parent.exists()
-
-    def test_uses_backend_type_from_io_config(self):
-        """When io_config has backend_type, output file uses that extension."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            default_dir = Path(tmpdir)
-            with (
-                patch(
-                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
-                    default_dir,
-                ),
-            ):
-                out = get_output_path(
-                    io_config={"backend_type": "netcdf4"},
-                    timestamp="2024-01-01T00:00:00",
-                    workflow_type="wf",
-                    request_id="r1",
-                )
-            assert out.suffix == ".netcdf4" or "netcdf4" in out.name
-            assert out.name == "forecast.netcdf4"
-
-    def test_normalizes_timestamp_in_path(self):
-        """Timestamp is normalized (colons, Z, + removed) in directory name."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            default_dir = Path(tmpdir)
-            with (
-                patch(
-                    "earth2studio.serve.server.worker.DEFAULT_OUTPUT_DIR",
-                    default_dir,
-                ),
-            ):
-                out = get_output_path(
-                    io_config=None,
-                    timestamp="2024-01-01T12:00:00+00:00",
-                    workflow_type="w",
-                    request_id="r",
-                )
-            # Directory should not contain : or + or Z
-            dir_name = out.parent.name
-            assert ":" not in dir_name
-            assert "Z" not in dir_name
-            assert "+" not in dir_name
-
-
-class TestRunCustomWorkflow:
-    """Tests for run_custom_workflow."""
-
-    def test_raises_value_error_when_workflow_not_in_registry(self):
-        """Raises ValueError when workflow name is not registered."""
-        with patch(
-            "earth2studio.serve.server.worker.workflow_registry"
-        ) as mock_registry:
-            mock_registry.get_workflow_class.return_value = None
-
-            with pytest.raises(ValueError, match="not found in registry"):
-                run_custom_workflow(
-                    workflow_name="nonexistent",
-                    execution_id="exec_1",
-                    parameters={},
-                )
-
-    def test_raises_value_error_when_workflow_cannot_be_instantiated(self):
-        """Raises ValueError when registry.get returns None (instantiation failed)."""
-        with patch(
-            "earth2studio.serve.server.worker.workflow_registry"
-        ) as mock_registry:
-            mock_registry.get_workflow_class.return_value = MagicMock()
-            mock_registry.get.return_value = None
-
-            with pytest.raises(ValueError, match="could not be instantiated"):
-                run_custom_workflow(
-                    workflow_name="my_workflow",
-                    execution_id="exec_1",
-                    parameters={},
-                )
-
-    def test_success_returns_result_and_queues_next_stage(self):
-        """On success, returns workflow result and queue_next_stage is called."""
-        mock_workflow_class = MagicMock()
-        mock_workflow = MagicMock()
-        mock_workflow.run.return_value = {"result": "ok"}
-        with tempfile.TemporaryDirectory() as tmpdir:
-            safe_out = Path(tmpdir) / "out"
-            safe_results_zip = Path(tmpdir) / "results_zip"
-            mock_workflow.get_output_path.return_value = safe_out
-
-            with (
-                patch(
-                    "earth2studio.serve.server.worker.workflow_registry"
-                ) as mock_registry,
-                patch(
-                    "earth2studio.serve.server.worker.redis_client",
-                    MagicMock(),
-                ),
-                patch(
-                    "earth2studio.serve.server.worker.queue_next_stage",
-                    return_value="job_123",
-                ) as mock_queue,
-                patch(
-                    "earth2studio.serve.server.worker.RESULTS_ZIP_DIR",
-                    safe_results_zip,
-                ),
-            ):
-                mock_registry.get_workflow_class.return_value = mock_workflow_class
-                mock_registry.get.return_value = mock_workflow
-
-                result = run_custom_workflow(
-                    workflow_name="my_workflow",
-                    execution_id="exec_1",
-                    parameters={"key": "value"},
-                )
-
-            assert result == {"result": "ok"}
-            mock_workflow.run.assert_called_once_with({"key": "value"}, "exec_1")
-            mock_queue.assert_called_once()
-            call_kw = mock_queue.call_args[1]
-            assert call_kw["workflow_name"] == "my_workflow"
-            assert call_kw["execution_id"] == "exec_1"
-            assert call_kw["current_stage"] == "inference"
-
-    def test_raises_runtime_error_when_queue_next_stage_fails(self):
-        """When queue_next_stage returns None, raises RuntimeError and updates status."""
-        mock_workflow_class = MagicMock()
-        mock_workflow = MagicMock()
-        mock_workflow.run.return_value = {}
-        with tempfile.TemporaryDirectory() as tmpdir:
-            safe_out = Path(tmpdir) / "out"
-            safe_results_zip = Path(tmpdir) / "results_zip"
-            mock_workflow.get_output_path.return_value = safe_out
-
-            with (
-                patch(
-                    "earth2studio.serve.server.worker.workflow_registry"
-                ) as mock_registry,
-                patch(
-                    "earth2studio.serve.server.worker.redis_client",
-                    MagicMock(),
-                ),
-                patch(
-                    "earth2studio.serve.server.worker.queue_next_stage",
-                    return_value=None,
-                ),
-                patch(
-                    "earth2studio.serve.server.worker.RESULTS_ZIP_DIR",
-                    safe_results_zip,
-                ),
-            ):
-                mock_registry.get_workflow_class.return_value = mock_workflow_class
-                mock_registry.get.return_value = mock_workflow
-
-                with pytest.raises(RuntimeError, match="Failed to queue next pipeline"):
-                    run_custom_workflow(
-                        workflow_name="my_workflow",
-                        execution_id="exec_1",
-                        parameters={},
-                    )
-
-        # Status should have been updated to FAILED
-        mock_workflow_class._update_execution_data.assert_called()
-        calls = mock_workflow_class._update_execution_data.call_args_list
-        # Last call before raise is the one with status FAILED
-        fail_calls = [c for c in calls if c[0][3].get("status") == "failed"]
-        assert len(fail_calls) >= 1
-
-    def test_on_run_exception_updates_status_and_reraises(self):
-        """When workflow.run raises, status is updated to FAILED and exception is re-raised."""
-        mock_workflow_class = MagicMock()
-        mock_workflow = MagicMock()
-        mock_workflow.run.side_effect = ValueError("run failed")
-
-        with (
-            patch(
-                "earth2studio.serve.server.worker.workflow_registry"
-            ) as mock_registry,
-            patch(
-                "earth2studio.serve.server.worker.redis_client",
-                MagicMock(),
-            ),
-        ):
-            mock_registry.get_workflow_class.return_value = mock_workflow_class
-            mock_registry.get.return_value = mock_workflow
-
-            with pytest.raises(ValueError, match="run failed"):
-                run_custom_workflow(
-                    workflow_name="my_workflow",
-                    execution_id="exec_1",
-                    parameters={},
-                )
-
-        mock_workflow_class._update_execution_data.assert_called()
-        calls = mock_workflow_class._update_execution_data.call_args_list
-        fail_updates = [c for c in calls if c[0][3].get("status") == "failed"]
-        assert len(fail_updates) >= 1
diff --git a/test/serve/test_cleanup_daemon.py b/test/serve/test_cleanup_daemon.py
new file mode 100644
index 000000000..e25cc9944
--- /dev/null
+++ b/test/serve/test_cleanup_daemon.py
@@ -0,0 +1,502 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Unit tests for the cleanup daemon.
+
+This module tests the cleanup daemon functionality with mocked Redis and filesystem operations.
+"""
+
+import json
+from datetime import datetime, timedelta, timezone
+from unittest.mock import MagicMock, patch
+
+import pytest
+from api_server.cleanup_daemon import (
+    _delete_result_files,
+    _process_expired_key,
+    cleanup_expired_results,
+)
+from api_server.config import get_config
+from api_server.workflow import WorkflowStatus
+
+
+class TestDeleteResultFiles:
+    """Test suite for _delete_result_files function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    def test_delete_workflow_files_with_workflow_name(self, mock_redis):
+        """Test deletion of workflow execution files including workflow-specific directories and metadata"""
+        # Setup
+        workflow_name = "custom_workflow"
+        execution_id = "exec_456"
+        combined_id = f"{workflow_name}:{execution_id}"
+        zip_filename = f"workflow_{combined_id}.zip"
+
+        mock_redis.get.return_value = zip_filename
+
+        # Mock zip file
+        mock_zip_file = MagicMock()
+        mock_zip_file.exists.return_value = True
+        mock_zip_file.unlink = MagicMock()
+
+        # Mock metadata file for workflow
+        mock_metadata_file = MagicMock()
+        mock_metadata_file.is_file.return_value = True
+        mock_metadata_file.is_dir.return_value = False
+        mock_metadata_file.name = f"metadata_{combined_id}.json"
+        mock_metadata_file.unlink = MagicMock()
+
+        # Mock workflow directory
+        mock_workflow_dir = MagicMock()
+        mock_workflow_dir.exists.return_value = True
+
+        mock_workflow_subdir = MagicMock()
+        mock_workflow_subdir.is_dir.return_value = True
+        mock_workflow_subdir.is_file.return_value = False
+        mock_workflow_subdir.name = f"output_{execution_id}"
+
+        mock_workflow_dir.iterdir.return_value = [mock_workflow_subdir]
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: mock_workflow_dir
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.__truediv__ = lambda self, other: mock_zip_file
+        mock_zip_dir.iterdir.return_value = [mock_metadata_file]
+
+        with (
+            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
+            patch(
+                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                mock_output_dir,
+            ),
+            patch("api_server.cleanup_daemon.shutil.rmtree") as mock_rmtree,
+        ):
+            # Execute
+            _delete_result_files(
+                mock_redis, combined_id, execution_id, workflow_name=workflow_name
+            )
+
+            # Verify zip file deleted
+            mock_zip_file.unlink.assert_called_once()
+
+            # Verify metadata file deleted
+            mock_metadata_file.unlink.assert_called_once()
+
+            # Verify workflow-specific directory deleted
+            mock_rmtree.assert_called_once_with(mock_workflow_subdir)
+
+    def test_delete_files_no_zip_in_redis(self, mock_redis):
+        """Test deletion when no zip file is found in Redis"""
+        # Setup
+        result_id = "result_789"
+        mock_redis.get.return_value = None
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        with (
+            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
+            patch(
+                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                mock_output_dir,
+            ),
+        ):
+            # Execute - should not raise exception
+            _delete_result_files(mock_redis, result_id, result_id)
+
+            # Verify Redis was queried
+            mock_redis.get.assert_called_once()
+
+
+class TestProcessExpiredKey:
+    """Test suite for _process_expired_key function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    @pytest.fixture
+    def mock_delete_func(self):
+        """Create a mock delete function"""
+        return MagicMock()
+
+    def test_process_expired_key_success(self, mock_redis, mock_delete_func):
+        """Test successful processing of an expired key"""
+        # Setup
+        key = "workflow_execution:wf1:exec_123"
+        current_time = datetime.now(timezone.utc)
+        old_time = current_time - timedelta(hours=25)  # 25 hours ago
+        results_ttl_hours = 24
+
+        request_data = {
+            "status": WorkflowStatus.COMPLETED,
+            "end_time": old_time.isoformat(),
+            "execution_id": "exec_123",
+        }
+
+        mock_redis.get.return_value = json.dumps(request_data)
+        config = get_config()
+
+        # Execute
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key=key,
+            current_time=current_time,
+            results_ttl_hours=results_ttl_hours,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        # Verify
+        assert result is True
+        mock_delete_func.assert_called_once_with(mock_redis, key)
+        mock_redis.setex.assert_called_once()
+        args = mock_redis.setex.call_args[0]
+        assert args[0] == key
+        assert args[1] == config.redis.retention_ttl
+        updated_data = json.loads(args[2])
+        assert updated_data["status"] == WorkflowStatus.EXPIRED
+
+    def test_process_expired_key_not_expired(self, mock_redis, mock_delete_func):
+        """Test processing of a key that is not yet expired"""
+        # Setup
+        key = "workflow_execution:wf1:exec_456"
+        current_time = datetime.now(timezone.utc)
+        recent_time = current_time - timedelta(hours=12)  # 12 hours ago
+        results_ttl_hours = 24
+
+        request_data = {
+            "status": WorkflowStatus.COMPLETED,
+            "end_time": recent_time.isoformat(),
+            "execution_id": "exec_456",
+        }
+
+        mock_redis.get.return_value = json.dumps(request_data)
+
+        # Execute
+        result = _process_expired_key(
+            redis_client=mock_redis,
+            key=key,
+            current_time=current_time,
+            results_ttl_hours=results_ttl_hours,
+            expected_status=WorkflowStatus.COMPLETED,
+            get_end_time_field="end_time",
+            delete_files_func=mock_delete_func,
+            log_prefix="workflow",
+        )
+
+        # Verify - should not clean up
+        assert result is False
+        mock_delete_func.assert_not_called()
+        mock_redis.setex.assert_not_called()
+
+
+class TestCleanupExpiredResults:
+    """Test suite for cleanup_expired_results function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Create a mock Redis client"""
+        return MagicMock()
+
+    @pytest.fixture
+    def mock_config(self):
+        """Mock the config"""
+        with patch("api_server.cleanup_daemon.config") as mock_cfg:
+            mock_cfg.server.results_ttl_hours = 24
+            mock_cfg.redis.retention_ttl = 604800
+            yield mock_cfg
+
+    def test_cleanup_expired_results_workflow_only(self, mock_redis, mock_config):
+        """Test cleanup with workflow executions only"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+        recent_time = (current_time - timedelta(hours=12)).isoformat()
+
+        # Mock Redis keys - only workflow keys
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf1:exec_old",
+            "workflow_execution:wf2:exec_recent",
+        ]
+
+        # Mock Redis get responses
+        def get_side_effect(key):
+            if key == "workflow_execution:wf1:exec_old":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+                )
+            elif key == "workflow_execution:wf2:exec_recent":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+            exists=lambda: False
+        )
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        with (
+            patch("api_server.cleanup_daemon._delete_result_files"),
+            patch(
+                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                mock_output_dir,
+            ),
+            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
+        ):
+            # Execute
+            cleanup_expired_results(mock_redis)
+
+            # Verify - should process 1 workflow (exec_old), skip exec_recent
+            assert mock_redis.setex.call_count == 1
+
+            # Verify the expired key was updated
+            setex_calls = mock_redis.setex.call_args_list
+            expired_keys = [call[0][0] for call in setex_calls]
+            assert "workflow_execution:wf1:exec_old" in expired_keys
+
+    def test_cleanup_expired_results_no_keys(self, mock_redis, mock_config):
+        """Test cleanup when there are no keys in Redis"""
+        # Setup
+        mock_redis.keys.return_value = []
+
+        # Execute
+        cleanup_expired_results(mock_redis)
+
+        # Verify - no cleanup operations
+        mock_redis.setex.assert_not_called()
+
+    def test_cleanup_expired_results_all_recent(self, mock_redis, mock_config):
+        """Test cleanup when all workflow keys are recent (not expired)"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        recent_time = (current_time - timedelta(hours=5)).isoformat()
+
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf1:exec_1",
+            "workflow_execution:wf2:exec_2",
+        ]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": recent_time}
+        )
+
+        # Execute
+        cleanup_expired_results(mock_redis)
+
+        # Verify - no cleanup operations (not expired)
+        mock_redis.setex.assert_not_called()
+
+    def test_cleanup_expired_results_redis_get_failure(self, mock_redis, mock_config):
+        """Test cleanup handles Redis get failures gracefully"""
+        # Setup
+        mock_redis.keys.return_value = [
+            "workflow_execution:wf_fail:exec_1",
+            "workflow_execution:wf_ok:exec_2",
+        ]
+
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        # First key causes exception, second succeeds
+        def get_side_effect(key):
+            if key == "workflow_execution:wf_fail:exec_1":
+                raise Exception("Redis connection error")
+            elif key == "workflow_execution:wf_ok:exec_2":
+                return json.dumps(
+                    {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+                )
+            return None
+
+        mock_redis.get.side_effect = get_side_effect
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+            exists=lambda: False
+        )
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        with (
+            patch("api_server.cleanup_daemon._delete_result_files"),
+            patch(
+                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                mock_output_dir,
+            ),
+            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
+        ):
+            # Execute - should not raise exception
+            cleanup_expired_results(mock_redis)
+
+            # Verify - exec_2 should still be processed despite exec_1 error
+            assert mock_redis.setex.call_count == 1
+            assert mock_redis.setex.call_args[0][0] == "workflow_execution:wf_ok:exec_2"
+
+    def test_cleanup_expired_results_delete_files_failure(
+        self, mock_redis, mock_config
+    ):
+        """Test cleanup handles file deletion failures gracefully and does not mark as expired"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        mock_redis.keys.return_value = ["workflow_execution:wf_fail:exec_fail"]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+        )
+
+        with patch("api_server.cleanup_daemon._delete_result_files") as mock_delete:
+            mock_delete.side_effect = Exception("File deletion error")
+
+            # Execute - should handle exception and continue
+            cleanup_expired_results(mock_redis)
+
+            # Verify delete was attempted
+            mock_delete.assert_called_once()
+
+            # Status should NOT be updated if file deletion fails
+            # (we want to retry cleanup on next run)
+            assert mock_redis.setex.call_count == 0
+
+    def test_cleanup_expired_results_workflow_execution(self, mock_redis, mock_config):
+        """Test cleanup of workflow execution keys"""
+        # Setup
+        current_time = datetime.now(timezone.utc)
+        old_time = (current_time - timedelta(hours=30)).isoformat()
+
+        mock_redis.keys.return_value = ["workflow_execution:my_workflow:exec_123"]
+
+        mock_redis.get.return_value = json.dumps(
+            {"status": WorkflowStatus.COMPLETED, "end_time": old_time}
+        )
+
+        mock_output_dir = MagicMock()
+        mock_output_dir.iterdir.return_value = []
+        mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+            exists=lambda: False
+        )
+
+        mock_zip_dir = MagicMock()
+        mock_zip_dir.iterdir.return_value = []
+
+        with (
+            patch("api_server.cleanup_daemon._delete_result_files") as mock_delete,
+            patch(
+                "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                mock_output_dir,
+            ),
+            patch("api_server.cleanup_daemon.RESULTS_ZIP_DIR", mock_zip_dir),
+        ):
+            # Execute
+            cleanup_expired_results(mock_redis)
+
+            # Verify workflow was cleaned up
+            assert mock_redis.setex.call_count == 1
+            assert (
+                mock_redis.setex.call_args[0][0]
+                == "workflow_execution:my_workflow:exec_123"
+            )
+
+            # Verify delete was called with correct parameters
+            mock_delete.assert_called_once()
+            call_args = mock_delete.call_args
+            assert call_args[1]["result_id"] == "my_workflow:exec_123"
+            assert call_args[1]["search_id"] == "exec_123"
+            assert call_args[1]["workflow_name"] == "my_workflow"
+
+    def test_cleanup_expired_results_custom_ttl_config(self, mock_redis):
+        """Test cleanup with custom TTL configuration"""
+        # Setup custom config
+        with patch("api_server.cleanup_daemon.config") as mock_cfg:
+            mock_cfg.server.results_ttl_hours = 48  # Custom 48 hour TTL
+            mock_cfg.redis.retention_ttl = 604800
+
+            current_time = datetime.now(timezone.utc)
+            # 30 hours ago - should NOT be expired with 48 hour TTL
+            time_30h_ago = (current_time - timedelta(hours=30)).isoformat()
+            # 50 hours ago - should be expired with 48 hour TTL
+            time_50h_ago = (current_time - timedelta(hours=50)).isoformat()
+
+            mock_redis.keys.return_value = [
+                "workflow_execution:wf_30h:exec_30h",
+                "workflow_execution:wf_50h:exec_50h",
+            ]
+
+            def get_side_effect(key):
+                if key == "workflow_execution:wf_30h:exec_30h":
+                    return json.dumps(
+                        {"status": WorkflowStatus.COMPLETED, "end_time": time_30h_ago}
+                    )
+                elif key == "workflow_execution:wf_50h:exec_50h":
+                    return json.dumps(
+                        {"status": WorkflowStatus.COMPLETED, "end_time": time_50h_ago}
+                    )
+                return None
+
+            mock_redis.get.side_effect = get_side_effect
+
+            mock_output_dir = MagicMock()
+            mock_output_dir.iterdir.return_value = []
+            mock_output_dir.__truediv__ = lambda self, other: MagicMock(
+                exists=lambda: False
+            )
+
+            mock_zip_dir = MagicMock()
+            mock_zip_dir.iterdir.return_value = []
+
+            with (
+                patch("api_server.cleanup_daemon._delete_result_files"),
+                patch(
+                    "api_server.cleanup_daemon.DEFAULT_OUTPUT_DIR",
+                    mock_output_dir,
+                ),
+                patch(
+                    "api_server.cleanup_daemon.RESULTS_ZIP_DIR",
+                    mock_zip_dir,
+                ),
+            ):
+                # Execute
+                cleanup_expired_results(mock_redis)
+
+                # Verify - only exec_50h should be cleaned up
+                assert mock_redis.setex.call_count == 1
+                assert (
+                    mock_redis.setex.call_args[0][0]
+                    == "workflow_execution:wf_50h:exec_50h"
+                )
diff --git a/test/serve/server/test_config.py b/test/serve/test_config.py
similarity index 96%
rename from test/serve/server/test_config.py
rename to test/serve/test_config.py
index 854b1c248..9dd7e40a5 100644
--- a/test/serve/server/test_config.py
+++ b/test/serve/test_config.py
@@ -14,6 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Unit tests for api_server.config module.
+
+Tests configuration management, environment variable overrides, and singleton behavior.
+"""
+
 import logging
 import os
 import sys
@@ -42,8 +48,8 @@
 mock_omegaconf.OmegaConf.to_container = MagicMock(return_value={})
 
 # Import config after mocking dependencies (noqa: E402 - import after mocks is intentional)
-# Note: serve/server path is added by conftest.py
-from earth2studio.serve.server.config import (  # noqa: E402
+# Note: service/inferenceserver path is added by conftest.py
+from api_server.config import (  # noqa: E402
     AppConfig,
     ConfigManager,
     CORSConfig,
@@ -177,7 +183,7 @@ def test_reset_config_creates_new_instance(self) -> None:
         # Reset clears the global
         reset_config()
         # Manually clear the instance to verify reset worked
-        from earth2studio.serve.server.config import ConfigManager
+        from api_server.config import ConfigManager
 
         old_instance = ConfigManager._instance
         ConfigManager._instance = None
@@ -189,9 +195,9 @@ def test_reset_config_creates_new_instance(self) -> None:
 class TestConfigManagerInitialization:
     """Test ConfigManager initialization and fallback behavior"""
 
-    @patch("earth2studio.serve.server.config.initialize_config_dir")
-    @patch("earth2studio.serve.server.config.compose")
-    @patch("earth2studio.serve.server.config.GlobalHydra")
+    @patch("api_server.config.initialize_config_dir")
+    @patch("api_server.config.compose")
+    @patch("api_server.config.GlobalHydra")
     def test_initialize_config_success(
         self,
         mock_global_hydra: MagicMock,
@@ -207,7 +213,7 @@ def test_initialize_config_success(
         mock_compose.side_effect = [mock_cfg, mock_workflow_cfg]
 
         # Mock OmegaConf.to_container
-        with patch("earth2studio.serve.server.config.OmegaConf") as mock_omega:
+        with patch("api_server.config.OmegaConf") as mock_omega:
             mock_omega.to_container.side_effect = [
                 {"redis": {"host": "test_host"}},
                 {"workflow1": {}},
@@ -219,8 +225,8 @@ def test_initialize_config_success(
             assert config is not None
             assert isinstance(config, AppConfig)
 
-    @patch("earth2studio.serve.server.config.initialize_config_dir")
-    @patch("earth2studio.serve.server.config.compose")
+    @patch("api_server.config.initialize_config_dir")
+    @patch("api_server.config.compose")
     def test_initialize_config_fallback_to_defaults(
         self, mock_compose: MagicMock, mock_init: MagicMock
     ) -> None:
@@ -515,8 +521,8 @@ def test_setup_logging_adds_execution_id_filter(self) -> None:
 class TestGetWorkflowConfig:
     """Test get_workflow_config function"""
 
-    @patch("earth2studio.serve.server.config.initialize_config_dir")
-    @patch("earth2studio.serve.server.config.compose")
+    @patch("api_server.config.initialize_config_dir")
+    @patch("api_server.config.compose")
     def test_get_workflow_config_returns_dict(
         self, mock_compose: MagicMock, mock_init: MagicMock
     ) -> None:
@@ -526,7 +532,7 @@ def test_get_workflow_config_returns_dict(
         mock_workflow_cfg = MagicMock()
         mock_compose.side_effect = [MagicMock(), mock_workflow_cfg]
 
-        with patch("earth2studio.serve.server.config.OmegaConf") as mock_omega:
+        with patch("api_server.config.OmegaConf") as mock_omega:
             mock_omega.to_container.side_effect = [
                 {},  # config
                 {"test_workflow": {"param": "value"}},  # workflow_config
diff --git a/test/serve/test_cpu_worker.py b/test/serve/test_cpu_worker.py
new file mode 100644
index 000000000..f29836e26
--- /dev/null
+++ b/test/serve/test_cpu_worker.py
@@ -0,0 +1,743 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Unit tests for the CPU worker module.
+
+This module tests CPU-intensive functions that can be offloaded from the main worker process.
+"""
+
+import json
+import sys
+import tempfile
+import zipfile
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Literal
+from unittest.mock import Mock
+
+import pytest
+import redis
+
+
+# Create mock config classes before importing cpu_worker
+@dataclass
+class MockRedisConfig:
+    """Mock Redis configuration"""
+
+    host: str = "localhost"
+    port: int = 6379
+    db: int = 0
+    password: str | None = None
+    socket_connect_timeout: int = 5
+    socket_timeout: int = 5
+    decode_responses: bool = True
+    retention_ttl: int = 604800  # Redis key retention TTL in seconds (7 days)
+
+
+@dataclass
+class MockQueueConfig:
+    """Mock queue configuration"""
+
+    name: str = "inference"
+    result_zip_queue_name: str = "result_zip"
+    object_storage_queue_name: str = "object_storage"
+    finalize_metadata_queue_name: str = "finalize_metadata"
+    max_size: int = 10
+    default_timeout: str = "1h"
+    job_timeout: str = "2h"
+
+
+# Secure temp dir for mock path defaults (S108)
+_secure_test_dir = tempfile.mkdtemp(prefix="e2s_testing_")
+
+
+@dataclass
+class MockPathsConfig:
+    """Mock paths configuration"""
+
+    default_output_dir: str = field(default_factory=lambda: _secure_test_dir)
+    results_zip_dir: str = field(default_factory=lambda: _secure_test_dir)
+    output_format: Literal["zarr", "netcdf4"] = "zarr"
+    result_zip_enabled: bool = False
+
+
+@dataclass
+class MockLoggingConfig:
+    """Mock logging configuration"""
+
+    level: str = "INFO"
+    format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+
+
+@dataclass
+class MockServerConfig:
+    """Mock server configuration"""
+
+    host: str = "0.0.0.0"  # noqa: S104
+    port: int = 8000
+    workers: int = 1
+    title: str = "REST API"
+    description: str = "REST API for running workflows"
+    version: str = "1.0.0"
+    docs_url: str = "/docs"
+    redoc_url: str = "/redoc"
+
+
+@dataclass
+class MockCORSConfig:
+    """Mock CORS configuration"""
+
+    allow_origins: list = field(default_factory=lambda: ["*"])
+    allow_credentials: bool = True
+    allow_methods: list = field(default_factory=lambda: ["*"])
+    allow_headers: list = field(default_factory=lambda: ["*"])
+
+
+@dataclass
+class MockObjectStorageConfig:
+    """Mock object storage configuration"""
+
+    enabled: bool = False
+    bucket: str | None = None
+    region: str = "us-east-1"
+    prefix: str = "outputs"
+    access_key_id: str | None = None
+    secret_access_key: str | None = None
+    session_token: str | None = None
+    cloudfront_domain: str | None = None
+    cloudfront_key_pair_id: str | None = None
+    cloudfront_private_key_path: str | None = None
+    signed_url_expires_in: int = 3600
+
+
+@dataclass
+class MockAppConfig:
+    """Mock root configuration"""
+
+    redis: MockRedisConfig = field(default_factory=MockRedisConfig)
+    queue: MockQueueConfig = field(default_factory=MockQueueConfig)
+    paths: MockPathsConfig = field(default_factory=MockPathsConfig)
+    logging: MockLoggingConfig = field(default_factory=MockLoggingConfig)
+    server: MockServerConfig = field(default_factory=MockServerConfig)
+    cors: MockCORSConfig = field(default_factory=MockCORSConfig)
+    object_storage: MockObjectStorageConfig = field(
+        default_factory=MockObjectStorageConfig
+    )
+
+
+# Create a mock config module
+mock_config_module = Mock()
+mock_config_module.AppConfig = MockAppConfig
+mock_config_module.get_config = Mock(return_value=MockAppConfig())
+mock_config_module.get_config_manager = Mock()
+
+# Inject the mock before importing cpu_worker
+sys.modules["api_server.config"] = mock_config_module
+
+# Now import the module under test
+from api_server.cpu_worker import create_results_zip  # noqa: E402
+
+
+class TestCreateResultsZip:
+    """Test suite for create_results_zip function"""
+
+    @pytest.fixture
+    def mock_redis(self):
+        """Mock Redis client"""
+        return Mock(spec=redis.Redis)
+
+    @pytest.fixture
+    def inference_request(self):
+        """Sample inference request data"""
+        return {
+            "id": "test_req_123",
+            "type": "deterministic",
+            "status": "completed",
+            "completion_time": "2024-01-01T12:00:00Z",
+            "execution_time_seconds": 45.5,
+            "created_at": "2024-01-01T11:00:00Z",
+            "peak_memory_usage": "2.5GB",
+            "device": "cuda:0",
+            "request": {
+                "workflow_type": "deterministic",
+                "time": "2024-01-01T00:00:00Z",
+                "nsteps": 10,
+                "prognostic": {"model_type": "fcn"},
+                "data": {"source_type": "gfs"},
+                "io": {"backend_type": "zarr"},
+            },
+        }
+
+    def test_create_results_zip_with_single_file(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip with a single output file"""
+        # Create a temporary output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data content")
+        file_size = output_file.stat().st_size
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_123",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip file was created - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_123"
+
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify Redis was updated (4 times: metadata, results_zip_dir, zip, output_path)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+
+        # First call: metadata (stored in Redis for finalize worker)
+        assert calls[0][0][0] == "inference_request:test_req_123:pending_metadata"
+
+        # Second call: results_zip_dir path
+        assert calls[1][0][0] == "inference_request:test_req_123:results_zip_dir"
+        assert calls[1][0][2] == str(results_zip_dir)
+
+        # Third call: zip filename
+        assert calls[2][0][0] == "inference_request_zips:test_req_123:zip_file"
+        assert calls[2][0][2] == zip_filename
+
+        # Fourth call: output path
+        assert calls[3][0][0] == "inference_request:test_req_123:output_path"
+        assert calls[3][0][1] == 86400  # 24 hours
+        assert calls[3][0][2] == str(output_file)
+
+        # Verify zip contents - metadata.json is NOT in the zip
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert "metadata.json" not in file_list  # metadata is now separate
+            assert "output.nc" in file_list
+
+        # Verify metadata is stored in Redis (not as file - file is created by finalize worker)
+        # Get the metadata from the first setex call
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify metadata structure
+        assert metadata["request_id"] == "test_req_123"
+        assert metadata["status"] == "completed"
+        assert metadata["workflow_type"] == "deterministic"
+        assert metadata["execution_time_seconds"] == 45.5
+        assert metadata["device"] == "cuda:0"
+        assert "zip_created_at" in metadata
+
+        # Verify parameters field
+        assert "parameters" in metadata
+        assert metadata["parameters"] == inference_request["request"]
+        assert metadata["parameters"]["workflow_type"] == "deterministic"
+        assert metadata["parameters"]["nsteps"] == 10
+
+        # Verify output_files manifest includes both the file AND the zip
+        assert "output_files" in metadata
+        assert isinstance(metadata["output_files"], list)
+        assert len(metadata["output_files"]) == 2  # output.nc + zip file entry
+
+        # Convert to dict for easier checking
+        files_dict = {f["path"]: f["size"] for f in metadata["output_files"]}
+
+        # Check the output file entry
+        assert "output.nc" in files_dict
+        assert files_dict["output.nc"] == file_size
+
+        # Check the zip file entry (path = request_id)
+        assert "test_req_123" in files_dict
+        assert files_dict["test_req_123"] == zip_path.stat().st_size
+
+    def test_create_results_zip_with_directory(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip with a directory containing multiple files"""
+        # Create a temporary output directory with files
+        output_dir = tmp_path / "output_data"
+        output_dir.mkdir()
+
+        file1 = output_dir / "data1.nc"
+        file1.write_text("data 1 content")
+        file1_size = file1.stat().st_size
+
+        file2 = output_dir / "data2.nc"
+        file2.write_text("data 2 content with more text")
+        file2_size = file2.stat().st_size
+
+        # Create subdirectory
+        subdir = output_dir / "plots"
+        subdir.mkdir()
+        file3 = subdir / "plot.png"
+        file3.write_bytes(b"fake png data")
+        file3_size = file3.stat().st_size
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_456",
+            output_path=output_dir,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip file was created - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_456"
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify zip contents - metadata.json is NOT in the zip
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert "metadata.json" not in file_list
+
+            # Verify files exist in zip
+            assert "output_data/data1.nc" in file_list
+            assert "output_data/data2.nc" in file_list
+            assert "output_data/plots/plot.png" in file_list
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify output_files manifest (3 files + 1 zip entry)
+        assert "output_files" in metadata
+        output_files = metadata["output_files"]
+        assert len(output_files) == 4  # 3 files + zip entry
+
+        # Convert to dict for easier checking
+        files_dict = {f["path"]: f["size"] for f in output_files}
+
+        # Verify all files are in manifest with correct sizes
+        assert "output_data/data1.nc" in files_dict
+        assert files_dict["output_data/data1.nc"] == file1_size
+
+        assert "output_data/data2.nc" in files_dict
+        assert files_dict["output_data/data2.nc"] == file2_size
+
+        assert "output_data/plots/plot.png" in files_dict
+        assert files_dict["output_data/plots/plot.png"] == file3_size
+
+        # Verify zip file entry (path = request_id)
+        assert "test_req_456" in files_dict
+        assert files_dict["test_req_456"] == zip_path.stat().st_size
+
+    def test_create_results_zip_with_nonexistent_output(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test creating zip when output path doesn't exist"""
+        # Use nonexistent path
+        output_path = tmp_path / "nonexistent"
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_789",
+            output_path=output_path,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should still create zip - filename is now just request_id
+        assert zip_filename is not None
+        assert zip_filename == "test_req_789"
+        zip_path = results_zip_dir / zip_filename
+        assert zip_path.exists()
+
+        # Verify zip contents - should be empty (no output files)
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            file_list = zf.namelist()
+            assert len(file_list) == 0  # No files in zip
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # output_files should only contain the zip entry
+        assert "output_files" in metadata
+        assert len(metadata["output_files"]) == 1  # Only zip entry
+        assert metadata["output_files"][0]["path"] == "test_req_789"
+
+    def test_create_results_zip_parameters_validation(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that parameters are correctly stored in metadata"""
+        # Create a temporary output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Modify inference request to have specific parameters
+        inference_request["request"] = {
+            "workflow_type": "ensemble",
+            "time": "2024-01-15T00:00:00Z",
+            "nsteps": 20,
+            "nensemble": 10,
+            "prognostic": {"model_type": "graphcast"},
+            "data": {"source_type": "era5"},
+            "io": {"backend_type": "netcdf"},
+            "perturbation": {"method": "spherical_gaussian"},
+        }
+
+        # Call create_results_zip
+        result = create_results_zip(
+            request_id="test_req_params",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+        assert result is not None
+
+        # Verify metadata is stored in Redis (not as file)
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Verify parameters field exists and matches
+        assert "parameters" in metadata
+        params = metadata["parameters"]
+
+        assert params["workflow_type"] == "ensemble"
+        assert params["time"] == "2024-01-15T00:00:00Z"
+        assert params["nsteps"] == 20
+        assert params["nensemble"] == 10
+        assert params["prognostic"]["model_type"] == "graphcast"
+        assert params["data"]["source_type"] == "era5"
+        assert params["io"]["backend_type"] == "netcdf"
+        assert params["perturbation"]["method"] == "spherical_gaussian"
+
+    def test_create_results_zip_file_manifest_structure(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that file manifest has correct structure"""
+        # Create output directory with multiple files
+        output_dir = tmp_path / "outputs"
+        output_dir.mkdir()
+
+        for i in range(5):
+            file = output_dir / f"file_{i}.dat"
+            file.write_text(f"content {i}" * 100)
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_manifest",
+            output_path=output_dir,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify metadata is stored in Redis
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        manifest = metadata["output_files"]
+
+        # Verify manifest structure (5 files + 1 zip entry)
+        assert isinstance(manifest, list)
+        assert len(manifest) == 6  # 5 files + zip entry
+
+        # Find the zip entry (path = request_id)
+        zip_entry = None
+        file_entries = []
+        for entry in manifest:
+            if entry["path"] == "test_manifest":
+                zip_entry = entry
+            else:
+                file_entries.append(entry)
+
+        assert zip_entry is not None
+        assert len(file_entries) == 5
+
+        # Verify file entries structure
+        zip_path = results_zip_dir / zip_filename
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            for entry in file_entries:
+                assert isinstance(entry, dict)
+                assert "path" in entry
+                assert "size" in entry
+                assert isinstance(entry["path"], str)
+                assert isinstance(entry["size"], int)
+                assert entry["size"] > 0
+
+                # Verify path starts with output directory name
+                assert entry["path"].startswith("outputs/")
+
+                # Verify size matches actual file in zip
+                actual_size = zf.getinfo(entry["path"]).file_size
+                assert entry["size"] == actual_size
+
+    def test_create_results_zip_missing_request_parameters(self, mock_redis, tmp_path):
+        """Test handling when request doesn't have parameters field"""
+        # Create inference request without 'request' field
+        inference_request = {
+            "id": "test_req_no_params",
+            "type": "deterministic",
+            "status": "completed",
+        }
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_req_no_params",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Verify zip was created
+        assert zip_filename is not None
+
+        # Verify metadata stored in Redis - parameters should be None
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Parameters should be None or not present
+        assert metadata.get("parameters") is None
+
+    def test_create_results_zip_redis_failure(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test handling when Redis operation fails"""
+        # Make Redis setex raise an exception
+        mock_redis.setex.side_effect = Exception("Redis connection failed")
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip - should handle exception gracefully
+        zip_filename = create_results_zip(
+            request_id="test_redis_fail",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should return None since storing in Redis failed
+        assert zip_filename is None
+
+    def test_create_results_zip_exception_handling(self, mock_redis, tmp_path):
+        """Test general exception handling"""
+        # Use invalid inference request that will cause issues
+        invalid_request = None
+
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip with invalid data
+        zip_filename = create_results_zip(
+            request_id="test_exception",
+            output_path=output_file,
+            inference_request=invalid_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        # Should return None on exception
+        assert zip_filename is None
+
+    def test_create_results_zip_metadata_completeness(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that all expected metadata fields are present"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_metadata",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        # Verify metadata stored in Redis
+        assert mock_redis.setex.call_count == 4
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Check all expected fields
+        required_fields = [
+            "request_id",
+            "status",
+            "completion_time",
+            "execution_time_seconds",
+            "workflow_type",
+            "created_at",
+            "peak_memory_usage",
+            "device",
+            "zip_created_at",
+            "parameters",
+            "output_files",
+        ]
+
+        for f in required_fields:
+            assert f in metadata, f"Missing required field: {field}"
+
+        # Verify zip_created_at is a valid timestamp
+        zip_created_at = metadata["zip_created_at"]
+        assert zip_created_at.endswith("Z")
+        # Should be parseable as ISO format
+        datetime.fromisoformat(zip_created_at.replace("Z", "+00:00"))
+
+    def test_create_results_zip_manifest_includes_zip_entry(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that the manifest includes an entry for the zip file itself"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data content")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        request_id = "test_zip_entry"
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id=request_id,
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        zip_path = results_zip_dir / zip_filename
+        zip_size = zip_path.stat().st_size
+
+        # Verify metadata stored in Redis
+        calls = mock_redis.setex.call_args_list
+        metadata_json = calls[0][0][2]
+        metadata = json.loads(metadata_json)
+
+        # Find the zip entry in manifest
+        zip_entry = None
+        for entry in metadata["output_files"]:
+            if entry["path"] == request_id:
+                zip_entry = entry
+                break
+
+        # Verify zip entry exists and has correct size
+        assert zip_entry is not None, "Zip file entry not found in manifest"
+        assert zip_entry["path"] == request_id
+        assert zip_entry["size"] == zip_size
+
+    def test_create_results_zip_output_path_stored_in_redis(
+        self, mock_redis, inference_request, tmp_path
+    ):
+        """Test that output path is stored in Redis"""
+        # Create output file
+        output_file = tmp_path / "output.nc"
+        output_file.write_text("test data")
+
+        # Create results directory
+        results_zip_dir = tmp_path / "results"
+        results_zip_dir.mkdir()
+
+        # Call create_results_zip
+        zip_filename = create_results_zip(
+            request_id="test_output_path",
+            output_path=output_file,
+            inference_request=inference_request,
+            results_zip_dir=results_zip_dir,
+            redis_client=mock_redis,
+        )
+
+        assert zip_filename is not None
+
+        # Verify Redis setex was called 4 times (metadata, results_zip_dir, zip, output_path)
+        assert mock_redis.setex.call_count == 4
+
+        # Check the output path call (4th call)
+        calls = mock_redis.setex.call_args_list
+        output_path_call = calls[3]
+
+        assert (
+            output_path_call[0][0] == "inference_request:test_output_path:output_path"
+        )
+        assert output_path_call[0][1] == 86400  # 24 hours
+        assert output_path_call[0][2] == str(output_file)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/test/serve/test_main.py b/test/serve/test_main.py
new file mode 100644
index 000000000..964fc9922
--- /dev/null
+++ b/test/serve/test_main.py
@@ -0,0 +1,1213 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Unit tests for api_server.main module.
+
+Tests the FastAPI endpoints including the workflow schema endpoint.
+"""
+
+import os
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+from pydantic import Field
+
+# Set API environment variable before importing main
+os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
+
+# Patch FastAPI route creation to handle union return types
+# This fixes the issue where FastAPI can't handle dict[str, Any] | StreamingResponse
+# ruff: noqa: E402
+import fastapi  # type: ignore[import-untyped]
+import fastapi.routing  # type: ignore[import-untyped]
+from fastapi.exceptions import FastAPIError  # type: ignore[import-untyped]
+
+_original_route_init = fastapi.routing.APIRoute.__init__
+
+
+def _patched_route_init(self, *args, **kwargs):
+    """Patched APIRoute.__init__ that handles union return types with StreamingResponse"""
+    import inspect
+
+    from starlette.responses import StreamingResponse
+
+    # Check if the endpoint has a union return type with StreamingResponse
+    endpoint = kwargs.get("endpoint") or (args[1] if len(args) > 1 else None)
+    if endpoint:
+        sig = inspect.signature(endpoint)
+        return_annotation = sig.return_annotation
+        if (
+            hasattr(return_annotation, "__args__")
+            and StreamingResponse in return_annotation.__args__
+        ):
+            # Set response_model=None to skip response model generation
+            kwargs["response_model"] = None
+
+    try:
+        return _original_route_init(self, *args, **kwargs)
+    except FastAPIError as e:
+        if "Invalid args for response field" in str(e):
+            # Retry with response_model=None
+            kwargs["response_model"] = None
+            return _original_route_init(self, *args, **kwargs)
+        raise
+
+
+# Apply the patch before any imports of api_server.main
+fastapi.routing.APIRoute.__init__ = _patched_route_init
+
+
+class TestWorkflowSchemaEndpoint:
+    """Tests for GET /v1/workflows/{workflow_name}/schema endpoint"""
+
+    @pytest.fixture
+    def mock_workflow_class(self):
+        """Create a mock workflow class with Parameters"""
+        from api_server.workflow import Workflow, WorkflowParameters
+
+        class TestWorkflowParameters(WorkflowParameters):
+            """Test parameters for unit testing"""
+
+            forecast_times: list[str] = Field(
+                default=["2024-01-01T00:00:00"],
+                description="List of forecast initialization times",
+            )
+            nsteps: int = Field(
+                default=10,
+                ge=1,
+                le=100,
+                description="Number of forecast steps",
+            )
+            model_type: str = Field(
+                default="fcn",
+                description="Model type to use",
+            )
+
+        class TestWorkflow(Workflow):
+            name = "test_workflow"
+            description = "Test workflow for unit testing"
+            Parameters = TestWorkflowParameters
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestWorkflowParameters
+            ) -> TestWorkflowParameters:
+                return TestWorkflowParameters.validate(parameters)
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        return TestWorkflow
+
+    @pytest.fixture
+    def client(self, mock_workflow_class):
+        """Create test client with mocked dependencies"""
+        # Mock Redis and other dependencies at their source locations
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue"),
+            patch("api_server.workflow.register_all_workflows"),
+        ):
+            # Setup mock async Redis
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+
+            # Setup mock sync Redis
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            from api_server.main import app
+            from api_server.workflow import workflow_registry
+
+            # Register the test workflow
+            workflow_registry._workflows["test_workflow"] = mock_workflow_class
+
+            with TestClient(app, raise_server_exceptions=False) as client:
+                yield client
+
+            # Cleanup
+            if "test_workflow" in workflow_registry._workflows:
+                del workflow_registry._workflows["test_workflow"]
+
+    def test_schema_endpoint_returns_valid_json_schema(
+        self, client, mock_workflow_class
+    ):
+        """Test that schema endpoint returns valid JSON Schema"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        # Verify it's a valid JSON Schema structure
+        assert "type" in schema
+        assert schema["type"] == "object"
+        assert "properties" in schema
+        assert "title" in schema
+
+    def test_schema_endpoint_includes_all_parameters(self, client, mock_workflow_class):
+        """Test that schema includes all defined parameters"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check all parameters are present
+        assert "forecast_times" in properties
+        assert "nsteps" in properties
+        assert "model_type" in properties
+
+    def test_schema_endpoint_includes_field_metadata(self, client, mock_workflow_class):
+        """Test that schema includes field descriptions and constraints"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check forecast_times has correct type
+        assert properties["forecast_times"]["type"] == "array"
+        assert "description" in properties["forecast_times"]
+
+        # Check nsteps has constraints
+        nsteps = properties["nsteps"]
+        assert nsteps["type"] == "integer"
+        assert "description" in nsteps
+        # Pydantic uses "minimum" and "maximum" for ge/le constraints
+        assert nsteps.get("minimum") == 1 or nsteps.get("exclusiveMinimum") == 0
+        assert nsteps.get("maximum") == 100 or nsteps.get("exclusiveMaximum") == 101
+
+    def test_schema_endpoint_includes_defaults(self, client, mock_workflow_class):
+        """Test that schema includes default values"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        properties = schema["properties"]
+
+        # Check defaults are included
+        assert properties["forecast_times"]["default"] == ["2024-01-01T00:00:00"]
+        assert properties["nsteps"]["default"] == 10
+        assert properties["model_type"]["default"] == "fcn"
+
+    def test_schema_endpoint_workflow_not_found(self, client):
+        """Test that 404 is returned for non-existent workflow"""
+        response = client.get("/v1/workflows/nonexistent_workflow/schema")
+
+        assert response.status_code == 404
+        error = response.json()
+        assert "not found" in error["detail"].lower()
+
+    def test_schema_endpoint_openapi_compatibility(self, client, mock_workflow_class):
+        """Test that returned schema is OpenAPI 3.1 compatible (JSON Schema draft 2020-12)"""
+        response = client.get("/v1/workflows/test_workflow/schema")
+
+        assert response.status_code == 200
+        schema = response.json()
+
+        # OpenAPI 3.1 / JSON Schema required fields
+        assert "type" in schema
+        assert "properties" in schema
+
+        # Schema should be directly usable (no wrapper objects)
+        assert "workflow_name" not in schema
+        assert "parameters_schema" not in schema
+
+
+class TestWorkflowParameterValidation:
+    """Test workflow parameter validation at submission time"""
+
+    @pytest.fixture
+    def client(self):
+        """Create test client"""
+        from typing import Any, Literal
+        from unittest.mock import MagicMock, patch
+
+        from api_server.main import app
+        from api_server.workflow import (
+            Workflow,
+            WorkflowParameters,
+            workflow_registry,
+        )
+        from pydantic import Field
+
+        # Create test workflow parameter classes with Literal validation
+        class TestDeterministicParams(WorkflowParameters):
+            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
+            nsteps: int = Field(default=6, ge=1, le=100)
+            model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
+            data_source: Literal["gfs"] = Field(default="gfs")
+            output_format: Literal["zarr"] = Field(default="zarr")
+            plot_variable: Literal["t2m", "msl", "u10m", "v10m", "tcwv", "z500"] = (
+                Field(default="t2m")
+            )
+
+        class TestDiagnosticParams(WorkflowParameters):
+            forecast_times: list[str] = Field(default=["2024-01-01T00:00:00"])
+            nsteps: int = Field(default=6, ge=1, le=100)
+            prognostic_model_type: Literal["dlwp", "fcn"] = Field(default="fcn")
+            diagnostic_model_type: Literal["precipitation_afno"] = Field(
+                default="precipitation_afno"
+            )
+            data_source: Literal["gfs"] = Field(default="gfs")
+            plot_variable: Literal[
+                "tp", "t2m", "msl", "u10m", "v10m", "tcwv", "z500"
+            ] = Field(default="tp")
+
+        # Create mock workflow classes
+        class TestDeterministicWorkflow(Workflow):
+            name = "deterministic_workflow"
+            description = "Test deterministic workflow"
+            Parameters = TestDeterministicParams
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestDeterministicParams
+            ) -> TestDeterministicParams:
+                try:
+                    return TestDeterministicParams.validate(parameters)
+                except Exception as e:
+                    raise ValueError(f"Invalid parameters: {e}") from e
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        class TestDiagnosticWorkflow(Workflow):
+            name = "diagnostic_workflow"
+            description = "Test diagnostic workflow"
+            Parameters = TestDiagnosticParams
+
+            @classmethod
+            def validate_parameters(
+                cls, parameters: dict[str, Any] | TestDiagnosticParams
+            ) -> TestDiagnosticParams:
+                try:
+                    return TestDiagnosticParams.validate(parameters)
+                except Exception as e:
+                    raise ValueError(f"Invalid parameters: {e}") from e
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        # Register test workflows directly in the registry
+        workflow_registry._workflows["deterministic_workflow"] = (
+            TestDeterministicWorkflow
+        )
+        workflow_registry._workflows["diagnostic_workflow"] = TestDiagnosticWorkflow
+
+        # Create mocks
+        mock_redis = MagicMock()
+        mock_queue = MagicMock()
+        mock_queue.__len__ = MagicMock(return_value=0)
+        mock_queue.max_size = 100
+
+        with patch("api_server.main.redis_sync_client", mock_redis):
+            with patch("api_server.main.inference_queue", mock_queue):
+                test_client = TestClient(app)
+                yield test_client, mock_queue, mock_redis
+
+    def test_valid_deterministic_workflow_parameters(self, client):
+        """Test that valid parameters for deterministic workflow are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "output_format": "zarr",
+                    "plot_variable": "t2m",
+                }
+            },
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "queued"
+
+    def test_invalid_model_type_rejected(self, client):
+        """Test that invalid model_type is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "invalid_model",  # Invalid value
+                    "data_source": "gfs",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "model_type" in data["detail"]
+
+    def test_invalid_data_source_rejected(self, client):
+        """Test that invalid data_source is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "invalid_source",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "data_source" in data["detail"]
+
+    def test_invalid_output_format_rejected(self, client):
+        """Test that invalid output_format is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "output_format": "invalid_format",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "output_format" in data["detail"]
+
+    def test_invalid_plot_variable_rejected(self, client):
+        """Test that invalid plot_variable is rejected at submission"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                    "data_source": "gfs",
+                    "plot_variable": "invalid_variable",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "plot_variable" in data["detail"]
+
+    def test_invalid_nsteps_range_rejected(self, client):
+        """Test that nsteps outside valid range is rejected"""
+        test_client, _, _ = client
+
+        # Test nsteps too low
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 0,  # Below minimum of 1
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+        # Test nsteps too high
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 101,  # Above maximum of 100
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+    def test_diagnostic_workflow_invalid_model_types_rejected(self, client):
+        """Test that invalid model types for diagnostic workflow are rejected"""
+        test_client, _, _ = client
+
+        # Test invalid prognostic model
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "invalid_prognostic",  # Invalid
+                    "diagnostic_model_type": "precipitation_afno",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "prognostic_model_type" in data["detail"]
+
+        # Test invalid diagnostic model
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "fcn",
+                    "diagnostic_model_type": "invalid_diagnostic",  # Invalid
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "diagnostic_model_type" in data["detail"]
+
+    def test_valid_diagnostic_workflow_parameters(self, client):
+        """Test that valid parameters for diagnostic workflow are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        response = test_client.post(
+            "/v1/infer/diagnostic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 10,
+                    "prognostic_model_type": "fcn",
+                    "diagnostic_model_type": "precipitation_afno",
+                    "data_source": "gfs",
+                    "plot_variable": "tp",
+                }
+            },
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "queued"
+
+    def test_multiple_invalid_parameters_rejected(self, client):
+        """Test that multiple invalid parameters are caught"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": ["2024-01-01T00:00:00"],
+                    "nsteps": 101,  # Invalid: too high
+                    "model_type": "invalid_model",  # Invalid value
+                    "data_source": "invalid_source",  # Invalid value
+                    "output_format": "invalid_format",  # Invalid value
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+
+    def test_valid_forecast_times_iso8601_accepted(self, client):
+        """Test that valid ISO 8601 forecast_times are accepted"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_job"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        # Test various valid ISO 8601 formats
+        valid_times = [
+            ["2024-01-01T00:00:00"],
+            ["2024-01-01T00:00:00Z"],
+            ["2024-01-01T00:00:00+00:00"],
+            ["2024-01-01T00:00:00.123456"],
+            ["2024-01-01 00:00:00"],  # Space separator also accepted
+            ["2024-01-01T00:00:00", "2024-01-02T00:00:00", "2024-01-03T00:00:00"],
+        ]
+
+        for times in valid_times:
+            response = test_client.post(
+                "/v1/infer/deterministic_workflow",
+                json={
+                    "parameters": {
+                        "forecast_times": times,
+                        "nsteps": 10,
+                        "model_type": "fcn",
+                    }
+                },
+            )
+
+            assert response.status_code == 200, f"Failed for times: {times}"
+            data = response.json()
+            assert data["status"] == "queued"
+
+    def test_invalid_forecast_times_rejected(self, client):
+        """Test that invalid forecast_times formats are rejected"""
+        test_client, _, _ = client
+
+        # Test various invalid formats
+        invalid_times = [
+            (["2024-01-01"], "date without time"),
+            (["01/01/2024"], "wrong date format"),
+            (["not-a-date"], "completely invalid"),
+            (["2024-13-01T00:00:00"], "invalid month"),
+            (["2024-01-32T00:00:00"], "invalid day"),
+            (["2024-01-01T25:00:00"], "invalid hour"),
+        ]
+
+        for times, description in invalid_times:
+            response = test_client.post(
+                "/v1/infer/deterministic_workflow",
+                json={
+                    "parameters": {
+                        "forecast_times": times,
+                        "nsteps": 10,
+                        "model_type": "fcn",
+                    }
+                },
+            )
+
+            assert (
+                response.status_code == 422
+            ), f"Expected 422 for {description}, got {response.status_code}"
+            data = response.json()
+            assert (
+                "Invalid parameters" in data["detail"]
+            ), f"Expected validation error for {description}"
+            assert (
+                "forecast_times" in data["detail"]
+            ), f"Expected forecast_times in error for {description}"
+
+    def test_forecast_times_mixed_valid_invalid_rejected(self, client):
+        """Test that forecast_times with one invalid value fails"""
+        test_client, _, _ = client
+
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": [
+                        "2024-01-01T00:00:00",  # Valid
+                        "not-a-date",  # Invalid
+                        "2024-01-03T00:00:00",  # Valid
+                    ],
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "forecast_times" in data["detail"]
+
+    def test_forecast_times_wrong_type_rejected(self, client):
+        """Test that forecast_times with non-string values are rejected"""
+        test_client, _, _ = client
+
+        # Test with integer
+        response = test_client.post(
+            "/v1/infer/deterministic_workflow",
+            json={
+                "parameters": {
+                    "forecast_times": [12345],  # Not a string
+                    "nsteps": 10,
+                    "model_type": "fcn",
+                }
+            },
+        )
+
+        assert response.status_code == 422
+        data = response.json()
+        assert "Invalid parameters" in data["detail"]
+        assert "forecast_times" in data["detail"]
+
+
+class TestWorkflowExecutionRequest:
+    """Tests for WorkflowExecutionRequest validation"""
+
+    def test_valid_request_with_parameters(self):
+        """Test that valid request with parameters field is accepted"""
+        from api_server.main import WorkflowExecutionRequest
+
+        # Valid request
+        request = WorkflowExecutionRequest(
+            parameters={"field1": "value1", "field2": 123}
+        )
+        assert request.parameters == {"field1": "value1", "field2": 123}
+
+    def test_valid_request_with_empty_parameters(self):
+        """Test that request with empty parameters dict is accepted"""
+        from api_server.main import WorkflowExecutionRequest
+
+        # Empty parameters is valid
+        request = WorkflowExecutionRequest(parameters={})
+        assert request.parameters == {}
+
+    def test_valid_request_without_parameters_uses_default(self):
+        """Test that request without parameters field uses default empty dict"""
+        from api_server.main import WorkflowExecutionRequest
+
+        # No parameters provided - should use default
+        request = WorkflowExecutionRequest()
+        assert request.parameters == {}
+
+    def test_invalid_request_with_extra_fields(self):
+        """Test that request with unknown fields is rejected"""
+        from api_server.main import WorkflowExecutionRequest
+        from pydantic import ValidationError
+
+        # Request with unknown field should be rejected
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"valid": "param"}, unknown_field="should_fail"
+            )
+
+    def test_invalid_request_with_multiple_extra_fields(self):
+        """Test that request with multiple unknown fields is rejected"""
+        from api_server.main import WorkflowExecutionRequest
+        from pydantic import ValidationError
+
+        # Multiple unknown fields
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"valid": "param"},
+                extra1="bad",
+                extra2="also_bad",
+                extra3="still_bad",
+            )
+
+    def test_invalid_request_flat_structure(self):
+        """Test that flat request structure (without parameters wrapper) is rejected"""
+        from api_server.main import WorkflowExecutionRequest
+        from pydantic import ValidationError
+
+        # Flat structure - fields that should be inside 'parameters'
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                forecast_times=["2024-01-01"],
+                nsteps=6,
+                model_type="fcn",
+            )
+
+    def test_invalid_request_mixed_structure(self):
+        """Test that mixed structure (parameters + extra fields) is rejected"""
+        from api_server.main import WorkflowExecutionRequest
+        from pydantic import ValidationError
+
+        # Mixed: valid parameters field + invalid extra fields
+        with pytest.raises(ValidationError, match="Extra inputs are not permitted"):
+            WorkflowExecutionRequest(
+                parameters={"forecast_times": ["2024-01-01"], "nsteps": 6},
+                workflow_type="deterministic",  # This should not be at this level
+                device="auto",  # This should not be at this level
+            )
+
+    def test_parameters_field_type_validation(self):
+        """Test that parameters field must be a dict"""
+        from api_server.main import WorkflowExecutionRequest
+        from pydantic import ValidationError
+
+        # parameters must be a dict, not a string
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters="not_a_dict")
+
+        # parameters must be a dict, not a list
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters=["not", "a", "dict"])
+
+        # parameters must be a dict, not None
+        with pytest.raises(ValidationError):
+            WorkflowExecutionRequest(parameters=None)
+
+    def test_valid_request_serialization(self):
+        """Test that valid request can be serialized to dict"""
+        from api_server.main import WorkflowExecutionRequest
+
+        request = WorkflowExecutionRequest(
+            parameters={"forecast_times": ["2024-01-01"], "nsteps": 10}
+        )
+
+        # Serialize to dict
+        data = request.model_dump()
+        assert isinstance(data, dict)
+        assert data == {"parameters": {"forecast_times": ["2024-01-01"], "nsteps": 10}}
+
+    def test_valid_request_with_nested_parameters(self):
+        """Test that request with nested parameter structures is accepted"""
+        from api_server.main import WorkflowExecutionRequest
+
+        # Complex nested parameters
+        request = WorkflowExecutionRequest(
+            parameters={
+                "forecast_times": ["2024-01-01", "2024-01-02"],
+                "nsteps": 10,
+                "config": {
+                    "model": {"type": "fcn", "version": "1.0"},
+                    "data": {"source": "gfs", "cache": True},
+                },
+                "options": ["opt1", "opt2", "opt3"],
+            }
+        )
+
+        assert request.parameters["nsteps"] == 10
+        assert request.parameters["config"]["model"]["type"] == "fcn"
+        assert len(request.parameters["options"]) == 3
+
+
+class TestQueuePosition:
+    """Tests for queue position functionality"""
+
+    @pytest.fixture
+    def mock_queue(self):
+        """Create a mock RQ queue with job IDs"""
+        from unittest.mock import MagicMock
+
+        queue = MagicMock()
+        queue.job_ids = [
+            "workflow1_exec_001",
+            "workflow2_exec_002",
+            "workflow1_exec_003",
+            "workflow3_exec_004",
+        ]
+        return queue
+
+    def test_get_queue_position_first_in_queue(self, mock_queue):
+        """Test getting position for first job in queue"""
+        from unittest.mock import patch
+
+        from api_server.main import get_queue_position
+
+        with patch("api_server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow1_exec_001")
+            assert position == 0  # 0-indexed: first job is at position 0
+
+    def test_get_queue_position_middle_of_queue(self, mock_queue):
+        """Test getting position for job in middle of queue"""
+        from unittest.mock import patch
+
+        from api_server.main import get_queue_position
+
+        with patch("api_server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow1_exec_003")
+            assert position == 2  # 0-indexed: third job is at position 2
+
+    def test_get_queue_position_last_in_queue(self, mock_queue):
+        """Test getting position for last job in queue"""
+        from unittest.mock import patch
+
+        from api_server.main import get_queue_position
+
+        with patch("api_server.main.inference_queue", mock_queue):
+            position = get_queue_position("workflow3_exec_004")
+            assert position == 3  # 0-indexed: fourth job is at position 3
+
+    def test_get_queue_position_not_in_queue(self, mock_queue):
+        """Test getting position for job not in queue returns None"""
+        from unittest.mock import patch
+
+        from api_server.main import get_queue_position
+
+        with patch("api_server.main.inference_queue", mock_queue):
+            position = get_queue_position("nonexistent_job")
+            assert position is None
+
+    def test_get_queue_position_no_queue(self):
+        """Test getting position when queue is not initialized returns None"""
+        from unittest.mock import patch
+
+        from api_server.main import get_queue_position
+
+        with patch("api_server.main.inference_queue", None):
+            position = get_queue_position("any_job")
+            assert position is None
+
+    def test_get_queue_position_empty_queue(self):
+        """Test getting position when queue is empty"""
+        from unittest.mock import MagicMock, patch
+
+        from api_server.main import get_queue_position
+
+        empty_queue = MagicMock()
+        empty_queue.job_ids = []
+
+        with patch("api_server.main.inference_queue", empty_queue):
+            position = get_queue_position("any_job")
+            assert position is None
+
+    def test_get_queue_position_exception_handling(self):
+        """Test that exceptions in get_queue_position are handled gracefully"""
+        from unittest.mock import MagicMock, PropertyMock, patch
+
+        from api_server.main import get_queue_position
+
+        queue_with_error = MagicMock()
+        type(queue_with_error).job_ids = PropertyMock(
+            side_effect=Exception("Queue connection error")
+        )
+
+        with patch("api_server.main.inference_queue", queue_with_error):
+            position = get_queue_position("any_job")
+            assert position is None
+
+
+class TestWorkflowExecutionWithQueuePosition:
+    """Tests for workflow execution endpoint with queue position"""
+
+    @pytest.fixture
+    def mock_workflow_class(self):
+        """Create a mock workflow class"""
+        from api_server.workflow import Workflow, WorkflowParameters
+
+        class TestWorkflowParameters(WorkflowParameters):
+            """Test parameters"""
+
+            test_param: str = "default"
+
+        class TestWorkflow(Workflow):
+            name = "test_workflow"
+            description = "Test workflow"
+            Parameters = TestWorkflowParameters
+
+            @classmethod
+            def validate_parameters(cls, parameters):
+                return TestWorkflowParameters.validate(parameters)
+
+            def run(self, parameters, execution_id):
+                return {"status": "success"}
+
+        return TestWorkflow
+
+    @pytest.fixture
+    def client(self, mock_workflow_class):
+        """Create test client with mocked dependencies"""
+        with (
+            patch("redis.asyncio.Redis") as mock_async_redis,
+            patch("redis.Redis") as mock_sync_redis,
+            patch("rq.Queue") as mock_queue_class,
+            patch("api_server.workflow.register_all_workflows"),
+        ):
+            # Setup mock async Redis
+            mock_async_instance = MagicMock()
+            mock_async_instance.ping = AsyncMock(return_value=True)
+            mock_async_instance.close = AsyncMock()
+            mock_async_instance.get = AsyncMock(return_value=None)
+            mock_async_redis.return_value = mock_async_instance
+
+            # Setup mock sync Redis
+            mock_sync_instance = MagicMock()
+            mock_sync_instance.ping = MagicMock(return_value=True)
+            mock_sync_instance.close = MagicMock()
+            mock_sync_instance.setex = MagicMock()
+            mock_sync_redis.return_value = mock_sync_instance
+
+            # Setup mock queue
+            mock_queue = MagicMock()
+            mock_queue.__len__ = MagicMock(return_value=0)
+            mock_queue.job_ids = []
+            mock_queue_class.return_value = mock_queue
+
+            from api_server.main import app
+            from api_server.workflow import workflow_registry
+
+            # Register the test workflow
+            workflow_registry._workflows["test_workflow"] = mock_workflow_class
+
+            with TestClient(app, raise_server_exceptions=False) as client:
+                yield client, mock_queue, mock_sync_instance
+
+            # Cleanup
+            if "test_workflow" in workflow_registry._workflows:
+                del workflow_registry._workflows["test_workflow"]
+
+    def test_execute_workflow_includes_queue_position(self, client):
+        """Test that POST /v1/infer/{workflow_name} includes queue position"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_123"
+
+        # Before enqueue: queue size is 0
+        mock_queue.__len__ = MagicMock(return_value=0)
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Redis llen returns 0 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=0)
+
+        # Patch the inference_queue at module level
+        with patch("api_server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is included in response
+        assert "position" in data
+        assert data["position"] == 0  # Redis llen returns 0 (first job, position 0)
+        assert data["status"] == "queued"
+
+    def test_execute_workflow_position_with_existing_jobs(self, client):
+        """Test that position is correct when there are existing jobs in queue"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_456"
+
+        # Before enqueue: queue has 2 existing jobs
+        mock_queue.__len__ = MagicMock(return_value=2)
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Redis llen returns 2 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=2)
+
+        # Patch the inference_queue at module level
+        with patch("api_server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is correct
+        assert "position" in data
+        assert data["position"] == 2  # Redis llen returns 2 (third job, position 2)
+        assert data["status"] == "queued"
+
+    def test_execute_workflow_position_with_five_existing_jobs(self, client):
+        """Test that position is correct with multiple existing jobs in queue"""
+        from unittest.mock import patch
+
+        test_client, mock_queue, mock_redis = client
+
+        # Setup mock job
+        mock_job = MagicMock()
+        mock_job.id = "test_workflow_exec_789"
+        mock_queue.enqueue = MagicMock(return_value=mock_job)
+
+        # Before enqueue: queue has 5 existing jobs
+        mock_queue.__len__ = MagicMock(return_value=5)
+
+        # Redis llen returns 5 (the queue size before the job is fully committed)
+        mock_redis.llen = MagicMock(return_value=5)
+
+        # Patch the inference_queue at module level
+        with patch("api_server.main.inference_queue", mock_queue):
+            response = test_client.post(
+                "/v1/infer/test_workflow", json={"parameters": {"test_param": "value"}}
+            )
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is correct
+        assert "position" in data
+        assert data["position"] == 5  # Redis llen returns 5 (sixth job, position 5)
+        assert data["status"] == "queued"
+
+    def test_get_workflow_status_includes_position_when_queued(self, client):
+        """Test that GET /v1/infer/{workflow_name}/{execution_id}/status includes position when status is queued"""
+        test_client, mock_queue, mock_redis = client
+
+        # Setup queue with multiple jobs
+        execution_id = "exec_456"
+        job_id = f"test_workflow_{execution_id}"
+
+        # Set up mock queue job_ids to include our job at position 1
+        mock_queue.job_ids = ["other_job_1", job_id, "other_job_2"]
+
+        # Mock Redis response for queued execution
+        import json
+        from datetime import datetime, timezone
+        from unittest.mock import patch
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "queued",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        # Patch the inference_queue for get_queue_position to use
+        with patch("api_server.main.inference_queue", mock_queue):
+            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Check that position is included and correct
+        assert "position" in data
+        assert data["position"] == 1  # Second in queue (0-indexed, so position 1)
+        assert data["status"] == "queued"
+
+    def test_get_workflow_status_queued_but_not_in_queue(self, client):
+        """Test race condition: status is QUEUED but job not in queue (worker picked it up)"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_race"
+
+        # Set up mock queue with empty job_ids (job already picked up by worker)
+        mock_queue.job_ids = []
+
+        # Mock Redis response with QUEUED status (worker hasn't updated it yet)
+        import json
+        from datetime import datetime, timezone
+        from unittest.mock import patch
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "queued",  # Still queued in Redis
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        # Patch the inference_queue for get_queue_position to use
+        with patch("api_server.main.inference_queue", mock_queue):
+            response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Should be treated as RUNNING since job is not in queue
+        assert data["status"] == "running"
+        # Position should be None for running status
+        if "position" in data:
+            assert data["position"] is None
+
+    def test_get_workflow_status_no_position_when_running(self, client):
+        """Test that position is None when workflow is running (not queued)"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_789"
+
+        # Mock Redis response for running execution
+        import json
+        from datetime import datetime, timezone
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "running",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Position should be None or not included for non-queued status
+        # (The model allows None, so it could be None or absent)
+        if "position" in data:
+            assert data["position"] is None
+        assert data["status"] == "running"
+
+    def test_get_workflow_status_no_position_when_completed(self, client):
+        """Test that position is None when workflow is completed"""
+        test_client, mock_queue, mock_redis = client
+
+        execution_id = "exec_999"
+
+        # Mock Redis response for completed execution
+        import json
+        from datetime import datetime, timezone
+
+        execution_data = {
+            "workflow_name": "test_workflow",
+            "execution_id": execution_id,
+            "status": "completed",
+            "start_time": datetime.now(timezone.utc).isoformat(),
+            "end_time": datetime.now(timezone.utc).isoformat(),
+            "metadata": {},
+        }
+        mock_redis.get = MagicMock(return_value=json.dumps(execution_data).encode())
+
+        response = test_client.get(f"/v1/infer/test_workflow/{execution_id}/status")
+
+        assert response.status_code == 200
+        data = response.json()
+
+        # Position should be None for completed workflows
+        if "position" in data:
+            assert data["position"] is None
+        assert data["status"] == "completed"
diff --git a/test/serve/server/test_workflow.py b/test/serve/test_workflow.py
similarity index 97%
rename from test/serve/server/test_workflow.py
rename to test/serve/test_workflow.py
index 4fb7e4259..36fc7af4f 100644
--- a/test/serve/server/test_workflow.py
+++ b/test/serve/test_workflow.py
@@ -24,10 +24,8 @@
 
 import pytest
 import redis  # type: ignore[import-untyped]
-from pydantic import Field, ValidationError  # type: ignore[import-untyped]
-
-from earth2studio.serve.server.config import get_config  # type: ignore[import-untyped]
-from earth2studio.serve.server.workflow import (  # type: ignore[import-untyped]
+from api_server.config import get_config  # type: ignore[import-untyped]
+from api_server.workflow import (  # type: ignore[import-untyped]
     Workflow,
     WorkflowParameters,
     WorkflowProgress,
@@ -38,25 +36,10 @@
     register_all_workflows,
     workflow_registry,
 )
+from pydantic import Field, ValidationError  # type: ignore[import-untyped]
 
-
-@pytest.fixture(scope="module", autouse=True)
-def api_active_env():  # type: ignore[no-untyped-def]
-    """
-    Set EARTH2STUDIO_API_ACTIVE=1 for tests in this module only; restore afterward.
-
-    Ensures workflow registration is enabled during these tests without
-    affecting other test modules.
-    """
-    old = os.environ.pop("EARTH2STUDIO_API_ACTIVE", None)
-    os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
-    try:
-        yield
-    finally:
-        if old is not None:
-            os.environ["EARTH2STUDIO_API_ACTIVE"] = old
-        else:
-            os.environ.pop("EARTH2STUDIO_API_ACTIVE", None)
+# imitate API server environment
+os.environ["EARTH2STUDIO_API_ACTIVE"] = "1"
 
 
 def get_test_config(base_path: str | None = None):  # type: ignore[no-untyped-def]
@@ -78,13 +61,11 @@ def setup_test_paths():  # type: ignore[no-untyped-def]
         test_config = get_test_config(tmpdir)
         with (
             patch(
-                "earth2studio.serve.server.workflow.get_config",
+                "api_server.config.get_config",
                 return_value=test_config,
             ),
-            patch(
-                "earth2studio.serve.server.config.get_config", return_value=test_config
-            ),
-            patch("earth2studio.serve.server.workflow.config", test_config),
+            patch("api_server.config.get_config", return_value=test_config),
+            patch("api_server.workflow.workflow.config", test_config),
         ):
             yield test_config
 
@@ -1649,7 +1630,7 @@ def test_discover_and_register_from_directories_with_python_file(self):
             workflow_file = Path(tmpdir) / "test_workflow_module.py"
             workflow_file.write_text(
                 """
-from earth2studio.serve.server.workflow import Workflow, WorkflowParameters, workflow_registry
+from api_server.workflow import Workflow, WorkflowParameters, workflow_registry
 
 class SimpleWorkflow(Workflow):
     # No __init__ needed - name and description set by registry
@@ -1671,7 +1652,7 @@ def run(self, parameters, execution_id):
             successful, failed = self.registry.discover_and_register_from_directories(
                 [tmpdir], include_builtin=False
             )
-            from earth2studio.serve.server.workflow import workflow_registry
+            from api_server.workflow import workflow_registry
 
             assert successful == 1
             assert failed == 0
@@ -1705,7 +1686,7 @@ def test_discover_and_register_filters_init_files(self):
             assert successful == 0
             assert failed == 0
 
-    @patch("earth2studio.serve.server.workflow.Path")
+    @patch("api_server.workflow.workflow.Path")
     def test_discover_and_register_with_builtin(self, mock_path):
         """Test discovering workflows with built-in workflows"""
         # Mock the builtin workflows directory to not exist
@@ -1728,7 +1709,7 @@ def test_auto_register_workflows(self):
 
         with (
             patch(
-                "earth2studio.serve.server.workflow.parse_workflow_directories_from_env",
+                "api_server.workflow.workflow.parse_workflow_directories_from_env",
                 return_value=[],
             ),
             patch.object(
@@ -1748,7 +1729,7 @@ def test_auto_register_workflows_with_error(self):
         mock_redis = Mock(spec=redis.Redis)
 
         with patch(
-            "earth2studio.serve.server.workflow.parse_workflow_directories_from_env",
+            "api_server.workflow.workflow.parse_workflow_directories_from_env",
             side_effect=Exception("Test error"),
         ):
             with pytest.raises(Exception, match="Test error"):
diff --git a/test/utils/test_coords.py b/test/utils/test_coords.py
index 8a526303c..4d8134225 100644
--- a/test/utils/test_coords.py
+++ b/test/utils/test_coords.py
@@ -26,12 +26,7 @@
     handshake_dim,
     handshake_size,
 )
-from earth2studio.utils.coords import (
-    cat_coords,
-    map_coords,
-    split_coords,
-    tile_coords,
-)
+from earth2studio.utils.coords import map_coords, split_coords
 
 
 @pytest.mark.parametrize(
@@ -305,26 +300,15 @@ def test_convert_multidim_to_singledim_error():
 
 def test_handshake_coords():
     """Test handshake_coords function"""
-    coords1 = OrderedDict(
-        [
-            ("lat", np.array([1, 2, 3])),
-            ("lon", np.array([4, 5, 6])),
-        ]
-    )
-    coords2 = OrderedDict(
-        [
-            ("lat", np.array([1, 2, 3])),
-            ("lon", np.array([4, 5, 6])),
-        ]
-    )
+    coords1 = OrderedDict([("lat", np.array([1, 2, 3]))])
+    coords2 = OrderedDict([("lat", np.array([1, 2, 3]))])
 
     # Should pass
     handshake_coords(coords1, coords2, "lat")
-    handshake_coords(coords1, coords2, ["lat", "lon"])
 
     # Test missing dimension in input coords
     with pytest.raises(KeyError, match="not found in input coordinates"):
-        handshake_coords(coords1, coords2, "variable")
+        handshake_coords(coords1, coords2, "lon")
 
     # Test missing dimension in target coords
     coords2 = OrderedDict([("lon", np.array([1, 2, 3]))])
@@ -436,425 +420,3 @@ def test_convert_multidim_to_singledim_additional():
         ValueError, match="Assumed that if an n-dimensional coordinate exists"
     ):
         convert_multidim_to_singledim(coords)
-
-
-def test_tile_coords():
-    """Test tiling function for expanding dimensions"""
-
-    x = torch.randn(2, 721, 1440)
-    coords = OrderedDict(
-        {
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    target_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "lead_time": np.array([0, 1, 2, 3, 4]),
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 4, 5, 2, 721, 1440)
-    assert "variable" in result_coords
-    assert "time" in result_coords
-
-
-def test_tile_coords_ignores_common_dims():
-    x = torch.randn(2, 721, 1440)
-    coords = OrderedDict(
-        {
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    # target_coords has "variable", "lat", "lon" which should be ignored
-    # Only "ensemble", "time", "lead_time" should be used for tiling
-    target_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "lead_time": np.array([0, 1, 2, 3, 4]),
-            "variable": np.array(["different", "values"]),  # Should be ignored
-            "lat": np.linspace(0, 90, 100),  # Should be ignored
-            "lon": np.linspace(0, 180, 200),  # Should be ignored
-        }
-    )
-
-    result, result_coords = tile_coords(x, coords, target_coords)
-    # Should tile to (3, 4, 5, 2, 721, 1440) - leading dims from target_coords (not in coords)
-    assert result.shape == (3, 4, 5, 2, 721, 1440)
-    # Should use coords values, not target_coords values for common dims
-    assert np.array_equal(result_coords["variable"], coords["variable"])
-    assert np.array_equal(result_coords["lat"], coords["lat"])
-    assert np.array_equal(result_coords["lon"], coords["lon"])
-
-
-def test_tile_coords_edge_cases():
-    # Test: no leading dimensions (all target_coords dims exist in coords)
-    x = torch.randn(2, 3, 4)
-    coords = OrderedDict(
-        {
-            "a": np.array([0, 1]),
-            "b": np.array([0, 1, 2]),
-            "c": np.array([0, 1, 2, 3]),
-        }
-    )
-    target_coords = OrderedDict(
-        {
-            "a": np.array([0, 1]),
-            "b": np.array([0, 1, 2]),
-            "c": np.array([0, 1, 2, 3]),
-        }
-    )
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == x.shape
-    assert result_coords == coords
-
-    # Test: common dimensions in target_coords are ignored (uses coords values)
-    x = torch.randn(2, 721, 1440)
-    coords = OrderedDict(
-        {
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    target_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "variable": np.array(["different"]),  # Should be ignored
-            "lat": np.linspace(0, 90, 100),  # Should be ignored
-            "lon": np.linspace(0, 180, 200),  # Should be ignored
-        }
-    )
-    # Only "ensemble" and "time" from target_coords should be used for tiling
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 4, 2, 721, 1440)
-    assert np.array_equal(result_coords["variable"], coords["variable"])
-    assert np.array_equal(result_coords["lat"], coords["lat"])
-    assert np.array_equal(result_coords["lon"], coords["lon"])
-
-    # Test: single dimension x
-    x = torch.randn(721)
-    coords = OrderedDict({"lat": np.linspace(90, -90, 721)})
-    target_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "lat": np.linspace(0, 90, 100),  # Should be ignored
-        }
-    )
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 4, 721)
-    assert "lat" in result_coords
-    assert np.array_equal(result_coords["lat"], coords["lat"])
-
-    # Test: empty tensors (0-sized dimensions)
-    x = torch.randn(0, 721, 1440)
-    coords = OrderedDict(
-        {
-            "variable": np.array([]),  # Empty array
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    target_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "variable": np.array([1, 2, 3]),  # Should be ignored
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 4, 0, 721, 1440)
-    assert len(result_coords["variable"]) == 0
-
-    # Test: mismatched coordinate values (common dims use coords values)
-    x = torch.randn(2, 10)
-    coords = OrderedDict(
-        {
-            "a": np.array([0, 1]),
-            "b": np.arange(10),
-        }
-    )
-    target_coords = OrderedDict(
-        {
-            "c": np.array([0, 1, 2]),
-            "a": np.array([10, 20]),  # Should be ignored
-            "b": np.arange(10, 20),  # Should be ignored
-        }
-    )
-    result, result_coords = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 2, 10)
-    assert np.array_equal(result_coords["a"], coords["a"])
-    assert np.array_equal(result_coords["b"], coords["b"])
-
-
-def test_tile_coords_validation():
-    """Test that tile_coords validates new dims lead common dims"""
-    x = torch.randn(2, 10)
-    coords = OrderedDict(
-        {
-            "a": np.array([0, 1]),
-            "b": np.arange(10),
-        }
-    )
-
-    # Valid: new dims before common dims
-    target_coords = OrderedDict(
-        {
-            "c": np.array([0, 1, 2]),
-            "a": np.array([10, 20]),
-            "b": np.arange(10, 20),
-        }
-    )
-    result, _ = tile_coords(x, coords, target_coords)
-    assert result.shape == (3, 2, 10)
-
-    # Invalid: new dim after common dim
-    target_coords_bad = OrderedDict(
-        {
-            "a": np.array([10, 20]),
-            "c": np.array([0, 1, 2]),  # New dim after common dim
-            "b": np.arange(10, 20),
-        }
-    )
-    with pytest.raises(ValueError):
-        tile_coords(x, coords, target_coords_bad)
-
-
-def test_cat_coords():
-    xx = torch.randn(1, 2, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["u10m", "v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(1, 1, 721, 1440)
-    coy = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="variable")
-
-    assert result.shape == (1, 3, 721, 1440)
-    assert len(result_coords["variable"]) == 3
-    assert np.array_equal(result_coords["variable"], ["u10m", "v10m", "msl"])
-
-
-def test_cat_coords_different_dims():
-    """Test concatenation along different dimensions"""
-    # Test concatenation along "time" dimension
-    xx = torch.randn(2, 3, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0, 1]),
-            "variable": np.array(["u10m", "v10m", "msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(1, 3, 721, 1440)
-    coy = OrderedDict(
-        {
-            "time": np.array([2]),
-            "variable": np.array(["u10m", "v10m", "msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="time")
-    assert result.shape == (3, 3, 721, 1440)
-    assert len(result_coords["time"]) == 3
-    assert np.array_equal(result_coords["time"], [0, 1, 2])
-
-    # Test concatenation along "batch" dimension
-    xx = torch.randn(2, 3, 721, 1440)
-    cox = OrderedDict(
-        {
-            "batch": np.array([0, 1]),
-            "variable": np.array(["u10m", "v10m", "msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(1, 3, 721, 1440)
-    coy = OrderedDict(
-        {
-            "batch": np.array([2]),
-            "variable": np.array(["u10m", "v10m", "msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = cat_coords((xx, yy), (cox, coy), dim="batch")
-    assert result.shape == (3, 3, 721, 1440)
-    assert len(result_coords["batch"]) == 3
-    assert np.array_equal(result_coords["batch"], [0, 1, 2])
-
-
-def test_cat_coords_multiple():
-    xx = torch.randn(1, 1, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["u10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(1, 1, 721, 1440)
-    coy = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    zz = torch.randn(1, 1, 721, 1440)
-    coz = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = cat_coords((xx, yy, zz), (cox, coy, coz), dim="variable")
-    assert result.shape == (1, 3, 721, 1440)
-    assert len(result_coords["variable"]) == 3
-    assert np.array_equal(result_coords["variable"], ["u10m", "v10m", "msl"])
-
-
-def test_cat_coords_single():
-    """Test concatenation with a single tensor (should return as-is)"""
-    xx = torch.randn(1, 2, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["u10m", "v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = cat_coords((xx,), (cox,), dim="variable")
-    assert torch.equal(result, xx)  # Should return the same tensor
-    for dim in cox.keys():
-        assert np.all(result_coords[dim] == cox[dim])
-
-
-def test_cat_coords_errors():
-    """Test error cases for cat_coords"""
-    # Test length mismatch
-    xx = torch.randn(1, 2, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["u10m", "v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    yy = torch.randn(1, 1, 721, 1440)
-    coy = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    with pytest.raises(ValueError):
-        cat_coords((xx, yy), (cox,), dim="variable")
-
-    # Test empty input
-    with pytest.raises(ValueError):
-        cat_coords((), (), dim="variable")
-
-    xx = torch.randn(1, 2, 721, 1440)
-    cox = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["u10m", "v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(1, 1, 721, 1440)
-    coy = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    # Test missing dimension in first coords (handshake_dim on cox)
-    with pytest.raises(KeyError):
-        cat_coords((xx, yy), (cox, coy), dim="nonexistent")
-
-    # Test cox has extra dim (key-equality check catches mismatched dimension sets)
-    cox_with_extra = cox.copy()
-    cox_with_extra["extra_dim"] = np.array([0])
-    xx_extra = torch.randn(1, 2, 1, 721, 1440)
-    with pytest.raises(KeyError):
-        cat_coords((xx_extra, yy), (cox_with_extra, coy), dim="extra_dim")
-
-    # Test mismatched non-cat coords (handshake_coords catches shape mismatch)
-    yy_bad = torch.randn(1, 1, 361, 1440)
-    coy_bad = OrderedDict(
-        {
-            "time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 361),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    with pytest.raises(ValueError):
-        cat_coords((xx, yy_bad), (cox, coy_bad), dim="variable")
-
-    # Test mismatched dimension names
-    yy_bad = torch.randn(1, 1, 361, 1440)
-    coy_bad = OrderedDict(
-        {
-            "lead_time": np.array([0]),
-            "variable": np.array(["msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-    with pytest.raises(ValueError):
-        cat_coords((xx, yy_bad), (cox, coy_bad), dim="variable")
diff --git a/tox-full.ini b/tox-full.ini
index 58b3a0c4f..a2c79803d 100644
--- a/tox-full.ini
+++ b/tox-full.ini
@@ -47,8 +47,6 @@ package = editable
 basepython = python3.12
 passenv =
     EARTH2STUDIO_CACHE
-    EARTH2STUDIO_DATA_CACHE
-    EARTH2STUDIO_MODEL_CACHE
     EARTH2STUDIO_PACKAGE_TIMEOUT
     TESTMON_DATAFILE
     UV_CACHE_DIR
diff --git a/tox.ini b/tox.ini
index 98e5ef839..f3ae05d6b 100644
--- a/tox.ini
+++ b/tox.ini
@@ -9,8 +9,6 @@ envlist =
     test-stats
     test-px-models
     test-dx-models
-    test-da-models
-    test-px-models-ace2
 
 [testenv]
 # Base configuration for all environments
@@ -20,8 +18,6 @@ package = editable
 basepython = python3.12
 passenv =
     EARTH2STUDIO_CACHE
-    EARTH2STUDIO_DATA_CACHE
-    EARTH2STUDIO_MODEL_CACHE
     EARTH2STUDIO_PACKAGE_TIMEOUT
     TESTMON_DATAFILE
     UV_CACHE_DIR
@@ -56,7 +52,11 @@ description = Run tests for the serve
 extras = serve
 commands =
     pytest {posargs:-s --cov --cov-append --slow --testmon} \
-        test/serve
+        test/serve/test_config.py \
+        test/serve/test_cleanup_daemon.py \
+        test/serve/test_cpu_worker.py \
+        test/serve/test_main.py \
+        test/serve/test_workflow.py
 
 [testenv:test-data]
 runner = uv-venv-lock-runner
@@ -100,21 +100,6 @@ commands =
     pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
         test/models/dx
 
-[testenv:test-da-models]
-runner = uv-venv-lock-runner
-description = Run tests for da models using all env
-extras = all
-commands =
-    pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
-        test/models/da
-
 # =====================
 # Conflict environments
 # =====================
-[testenv:test-px-models-ace2]
-runner = uv-venv-lock-runner
-description = Run tests for px models with conflicts
-extras = ace2
-commands =
-    pytest {posargs:-s --cov --cov-append --slow --package --testmon} \
-        test/models/px/test_ace2.py
diff --git a/uv.lock b/uv.lock
index 2c59325f9..e83b710f9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
@@ -27,27 +27,14 @@ resolution-markers = [
     "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
     "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'linux'",
 ]
-conflicts = [[
-    { package = "earth2studio", extra = "ace2" },
-    { package = "earth2studio", extra = "atlas" },
-], [
-    { package = "earth2studio", extra = "ace2" },
-    { package = "earth2studio", extra = "fcn3" },
-], [
-    { package = "earth2studio", extra = "ace2" },
-    { package = "earth2studio", extra = "perturbation" },
-], [
-    { package = "earth2studio", extra = "ace2" },
-    { package = "earth2studio", extra = "sfno" },
-]]
 
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+    { name = "nvidia-cuda-nvcc-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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+    { name = "cuda-python", version = "12.9.5", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux'" },
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 [[package]]
 name = "earth2grid"
-version = "2025.11.1+torch29"
+version = "2025.11.1+torch210"
 source = { git = "https://github.com/NVlabs/earth2grid.git?rev=11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3#11dcf1b0787a7eb6a8497a3a5a5e1fdcc31232d3" }
 dependencies = [
     { name = "einops" },
@@ -1610,6 +1706,7 @@ dependencies = [
 [package.optional-dependencies]
 ace2 = [
     { name = "fme" },
+    { name = "h5py" },
     { name = "pandas" },
     { name = "scipy" },
 ]
@@ -1627,29 +1724,29 @@ aifsens = [
 ]
 all = [
     { name = "anemoi-models" },
-    { name = "cbottle", marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "cbottle", marker = "python_full_version >= '3.12'" },
     { name = "cdsapi" },
     { name = "cfgrib" },
     { name = "cucim-cu12" },
-    { name = "cudf-cu12" },
     { name = "cupy-cuda12x" },
     { name = "dm-haiku" },
     { name = "dm-tree" },
     { name = "earth2grid" },
     { name = "earthkit-regrid" },
     { name = "eccodes" },
-    { name = "eccodeslib" },
     { name = "ecmwf-opendata" },
     { name = "einops" },
     { name = "flash-attn" },
     { name = "flax" },
+    { name = "fme" },
     { name = "globus-sdk" },
     { name = "graphcast" },
+    { name = "h5py" },
     { name = "httpx" },
     { name = "hydra-core" },
     { name = "importlib-metadata" },
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+    { name = "intake-esgf", version = "2025.10.22", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "intake-esgf", version = "2026.1.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "jax", extra = ["cuda12"] },
     { name = "makani" },
     { name = "microsoft-aurora" },
@@ -1667,35 +1764,37 @@ all = [
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     { name = "pyproj" },
     { name = "pystac-client" },
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+    { name = "pytz" },
+    { name = "rasterio", version = "1.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "rasterio", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "rioxarray", version = "0.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "rioxarray", version = "0.20.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "ruamel-yaml" },
     { name = "scikit-image" },
     { name = "scipy" },
     { name = "timm" },
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+    { name = "torch-harmonics" },
 ]
 atlas = [
     { name = "einops" },
     { name = "natten" },
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
     { name = "timm" },
-    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
+    { name = "torch-harmonics" },
 ]
 aurora = [
     { name = "microsoft-aurora" },
 ]
 cbottle = [
-    { name = "cbottle", marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "cbottle", marker = "python_full_version >= '3.12'" },
     { name = "earth2grid" },
 ]
 corrdiff = [
     { name = "einops" },
     { name = "nvidia-physicsnemo" },
     { name = "nvtx" },
+    { name = "pytz" },
     { name = "scipy" },
 ]
 cyclone = [
@@ -1707,35 +1806,24 @@ da-interp = [
     { name = "cudf-cu12" },
     { name = "cupy-cuda12x" },
 ]
-da-stormcast = [
-    { name = "cudf-cu12" },
-    { name = "cupy-cuda12x" },
-    { name = "einops" },
-    { name = "nvidia-physicsnemo" },
-    { name = "nvtx" },
-    { name = "omegaconf" },
-    { name = "pyproj" },
-    { name = "scipy" },
-]
 data = [
     { name = "cdsapi" },
     { name = "cfgrib" },
     { name = "eccodes" },
-    { name = "eccodeslib" },
     { name = "ecmwf-opendata" },
     { name = "globus-sdk" },
     { name = "httpx" },
-    { name = "intake-esgf", version = "2025.10.22", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
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+    { name = "intake-esgf", version = "2025.10.22", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "intake-esgf", version = "2026.1.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "multi-storage-client", extra = ["boto3", "fsspec", "google-cloud-storage"] },
     { name = "pandas" },
     { name = "planetary-computer" },
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+    { name = "rasterio", version = "1.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "rasterio", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "rioxarray", version = "0.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "rioxarray", version = "0.20.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "scipy" },
 ]
 dlesym = [
@@ -1743,20 +1831,24 @@ dlesym = [
     { name = "hydra-core" },
     { name = "nvidia-physicsnemo" },
     { name = "omegaconf" },
+    { name = "pytz" },
 ]
 dlwp = [
     { name = "importlib-metadata" },
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 fcn = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 fcn3 = [
     { name = "makani" },
     { name = "moviepy" },
+    { name = "pytz" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
-    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
+    { name = "torch-harmonics" },
 ]
 fengwu = [
     { name = "onnx" },
@@ -1777,6 +1869,7 @@ graphcast = [
 ]
 interp-modafno = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 pangu = [
     { name = "onnx" },
@@ -1785,29 +1878,32 @@ pangu = [
 ]
 perturbation = [
     { name = "scipy" },
-    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
+    { name = "torch-harmonics" },
 ]
 precip-afno = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 precip-afno-v2 = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 serve = [
     { name = "aiofiles" },
+    { name = "azure-identity" },
+    { name = "azure-storage-blob" },
+    { name = "cryptography" },
     { name = "fastapi" },
     { name = "hiredis" },
     { name = "httpx" },
     { name = "hydra-core" },
+    { name = "multi-storage-client" },
     { name = "prometheus-client" },
     { name = "pydantic" },
     { name = "python-dotenv" },
     { name = "python-multipart" },
     { name = "redis" },
-    { name = "requests" },
     { name = "rq" },
-    { name = "types-redis" },
-    { name = "urllib3" },
     { name = "uvicorn", extra = ["standard"] },
 ]
 sfno = [
@@ -1816,13 +1912,15 @@ sfno = [
     { name = "pynvml" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
-    { name = "torch-harmonics", version = "0.8.1", source = { git = "https://github.com/NVIDIA/torch-harmonics.git?rev=a632ca748a12bd9f74dbc1e00653317810991f74#a632ca748a12bd9f74dbc1e00653317810991f74" } },
+    { name = "torch-harmonics" },
 ]
 solarradiation-afno = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 statistics = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 stormcast = [
     { name = "einops" },
@@ -1830,6 +1928,7 @@ stormcast = [
     { name = "nvtx" },
     { name = "omegaconf" },
     { name = "pyproj" },
+    { name = "pytz" },
     { name = "scipy" },
 ]
 stormscope = [
@@ -1837,6 +1936,7 @@ stormscope = [
     { name = "natten" },
     { name = "nvidia-physicsnemo" },
     { name = "nvtx" },
+    { name = "pytz" },
     { name = "scipy" },
 ]
 utils = [
@@ -1845,6 +1945,7 @@ utils = [
 ]
 windgust-afno = [
     { name = "nvidia-physicsnemo" },
+    { name = "pytz" },
 ]
 
 [package.dev-dependencies]
@@ -1870,7 +1971,6 @@ dev = [
     { name = "ruff" },
     { name = "tox" },
     { name = "tox-uv" },
-    { name = "types-redis" },
     { name = "types-requests" },
 ]
 docs = [
@@ -1878,12 +1978,12 @@ docs = [
     { name = "jupytext" },
     { name = "matplotlib" },
     { name = "myst-parser" },
-    { name = "nvidia-sphinx-theme" },
+    { name = "pydata-sphinx-theme" },
     { name = "scipy" },
-    { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
-    { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
-    { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
-    { name = "sphinx-autodoc-typehints", version = "3.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-atlas') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-fcn3') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-perturbation') or (extra == 'extra-12-earth2studio-ace2' and extra == 'extra-12-earth2studio-sfno')" },
+    { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "sphinx-autodoc-typehints", version = "3.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "sphinx-design" },
     { name = "sphinx-favicon" },
     { name = "sphinx-gallery" },
@@ -1896,22 +1996,22 @@ requires-dist = [
     { name = "anemoi-models", marker = "extra == 'aifs'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'aifsens'", specifier = "==0.5.1" },
     { name = "anemoi-models", marker = "extra == 'all'", specifier = "==0.5.1" },
-    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'all'", git = "https://github.com/NVlabs/cBottle.git?rev=8b8b358466e6b2f50d1779009790002ceb596e72" },
-    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'cbottle'", git = "https://github.com/NVlabs/cBottle.git?rev=8b8b358466e6b2f50d1779009790002ceb596e72" },
+    { name = "azure-identity", marker = "extra == 'serve'", specifier = ">=1.15.0" },
+    { name = "azure-storage-blob", marker = "extra == 'serve'", specifier = ">=12.19.0" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'all'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
+    { name = "cbottle", marker = "python_full_version >= '3.12' and extra == 'cbottle'", git = "https://github.com/NVlabs/cBottle.git?rev=1d4435abe730d6b966e983633b466b345d60ac78" },
     { name = "cdsapi", marker = "extra == 'all'", specifier = ">=0.7.5" },
     { name = "cdsapi", marker = "extra == 'data'", specifier = ">=0.7.5" },
     { name = "cfgrib", marker = "extra == 'all'", git = "https://github.com/NickGeneva/cfgrib.git?rev=6a9af3173d7050b5fa5d221755f97e6ecb8c3433" },
     { name = "cfgrib", marker = "extra == 'data'", git = "https://github.com/NickGeneva/cfgrib.git?rev=6a9af3173d7050b5fa5d221755f97e6ecb8c3433" },
     { name = "cftime" },
+    { name = "cryptography", marker = "extra == 'serve'", specifier = ">=41.0.0" },
     { name = "cucim-cu12", marker = "extra == 'all'", specifier = ">=25.4.0" },
     { name = "cucim-cu12", marker = "extra == 'cyclone'", specifier = ">=25.4.0" },
-    { name = "cudf-cu12", marker = "extra == 'all'", specifier = "==26.2.*" },
     { name = "cudf-cu12", marker = "extra == 'da-interp'", specifier = "==26.2.*" },
-    { name = "cudf-cu12", marker = "extra == 'da-stormcast'", specifier = "==26.2.*" },
     { name = "cupy-cuda12x", marker = "extra == 'all'", specifier = "<14.0.0" },
     { name = "cupy-cuda12x", marker = "extra == 'cyclone'", specifier = "<14.0.0" },
     { name = "cupy-cuda12x", marker = "extra == 'da-interp'", specifier = "<14.0.0" },
-    { name = "cupy-cuda12x", marker = "extra == 'da-stormcast'", specifier = "<14.0.0" },
     { name = "dm-haiku", marker = "extra == 'all'", specifier = ">=0.0.14" },
     { name = "dm-haiku", marker = "extra == 'graphcast'", specifier = ">=0.0.14" },
     { name = "dm-tree", marker = "extra == 'all'", specifier = ">=0.1.9" },
@@ -1924,10 +2024,8 @@ requires-dist = [
     { name = "earthkit-regrid", marker = "extra == 'aifs'", specifier = "==0.4.0" },
     { name = "earthkit-regrid", marker = "extra == 'aifsens'", specifier = "==0.4.0" },
     { name = "earthkit-regrid", marker = "extra == 'all'", specifier = "==0.4.0" },
-    { name = "eccodes", marker = "extra == 'all'", specifier = ">=2.39.2" },
-    { name = "eccodes", marker = "extra == 'data'", specifier = ">=2.39.2" },
-    { name = "eccodeslib", marker = "extra == 'all'", specifier = ">=2.39.2" },
-    { name = "eccodeslib", marker = "extra == 'data'", specifier = ">=2.39.2" },
+    { name = "eccodes", marker = "extra == 'all'", specifier = ">=2.38.0" },
+    { name = "eccodes", marker = "extra == 'data'", specifier = ">=2.38.0" },
     { name = "ecmwf-opendata", marker = "extra == 'aifs'", specifier = ">=0.3.3" },
     { name = "ecmwf-opendata", marker = "extra == 'aifsens'", specifier = ">=0.3.3" },
     { name = "ecmwf-opendata", marker = "extra == 'all'", specifier = ">=0.3.3" },
@@ -1936,7 +2034,6 @@ requires-dist = [
     { name = "einops", marker = "extra == 'all'", specifier = ">=0.8.1" },
     { name = "einops", marker = "extra == 'atlas'" },
     { name = "einops", marker = "extra == 'corrdiff'", specifier = ">=0.8.1" },
-    { name = "einops", marker = "extra == 'da-stormcast'", specifier = ">=0.8.1" },
     { name = "einops", marker = "extra == 'stormcast'", specifier = ">=0.8.1" },
     { name = "fastapi", marker = "extra == 'serve'", specifier = ">=0.104.0" },
     { name = "flash-attn", marker = "extra == 'aifs'" },
@@ -1945,6 +2042,7 @@ requires-dist = [
     { name = "flax", marker = "extra == 'all'", specifier = ">=0.10.6" },
     { name = "flax", marker = "extra == 'graphcast'", specifier = ">=0.10.6" },
     { name = "fme", marker = "extra == 'ace2'", git = "https://github.com/ai2cm/ace.git?rev=e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" },
+    { name = "fme", marker = "extra == 'all'", git = "https://github.com/ai2cm/ace.git?rev=e211cad3e1a5cff0fa84e8d2f0ee67042eff3d4d" },
     { name = "fsspec", specifier = ">=2024.2.0" },
     { name = "gcsfs" },
     { name = "globus-sdk", marker = "extra == 'all'", specifier = "<4.0.0" },
@@ -1953,6 +2051,8 @@ requires-dist = [
     { name = "graphcast", marker = "extra == 'graphcast'", git = "https://github.com/google-deepmind/graphcast.git?rev=7077d40a36db6541e3ed72ccaed1c0d202fa6014" },
     { name = "h5netcdf", specifier = ">=1.0.0" },
     { name = "h5py", specifier = ">=3.2.0" },
+    { name = "h5py", marker = "extra == 'ace2'", specifier = "<=3.14.0" },
+    { name = "h5py", marker = "extra == 'all'", specifier = "<=3.14.0" },
     { name = "hiredis", marker = "extra == 'serve'", specifier = ">=2.0.0" },
     { name = "httpx", marker = "extra == 'all'", specifier = "==0.28.1" },
     { name = "httpx", marker = "extra == 'data'", specifier = "==0.28.1" },
@@ -1968,14 +2068,15 @@ requires-dist = [
     { name = "jax", extras = ["cuda12"], marker = "extra == 'all'", specifier = ">=0.4.26" },
     { name = "jax", extras = ["cuda12"], marker = "extra == 'graphcast'", specifier = ">=0.4.26" },
     { name = "loguru" },
-    { name = "makani", marker = "extra == 'all'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
-    { name = "makani", marker = "extra == 'fcn3'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
-    { name = "makani", marker = "extra == 'sfno'", git = "https://github.com/NVIDIA/makani.git?rev=b38fcb2799d7dbc146fa60459f3f9823394a8bf1" },
+    { name = "makani", marker = "extra == 'all'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
+    { name = "makani", marker = "extra == 'fcn3'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
+    { name = "makani", marker = "extra == 'sfno'", git = "https://github.com/NVIDIA/makani.git?rev=fd1a62b9f27aebbc793f0cbafd838a3811de43fd" },
     { name = "microsoft-aurora", marker = "extra == 'all'", specifier = ">=1.6.0" },
     { name = "microsoft-aurora", marker = "extra == 'aurora'", specifier = ">=1.6.0" },
     { name = "moviepy", marker = "extra == 'all'" },
     { name = "moviepy", marker = "extra == 'fcn3'" },
     { name = "moviepy", marker = "extra == 'sfno'" },
+    { name = "multi-storage-client", marker = "extra == 'serve'", specifier = ">=0.44.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'all'", specifier = ">=0.33.0" },
     { name = "multi-storage-client", extras = ["boto3", "fsspec", "google-cloud-storage"], marker = "extra == 'data'", specifier = ">=0.33.0" },
     { name = "natten", marker = "extra == 'all'" },
@@ -1983,30 +2084,28 @@ requires-dist = [
     { name = "natten", marker = "extra == 'stormscope'" },
     { name = "nest-asyncio" },
     { name = "netcdf4", specifier = ">=1.6.4,<1.7.3" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.3.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'atlas'", specifier = ">=1.3.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'corrdiff'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'da-stormcast'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'dlesym'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'dlwp'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'fcn'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'interp-modafno'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno-v2'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'solarradiation-afno'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'statistics'", specifier = ">=1.0.1" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'stormcast'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'stormscope'", specifier = ">=2.0" },
-    { name = "nvidia-physicsnemo", marker = "extra == 'windgust-afno'", specifier = ">=1.0.1" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = "==1.3.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.2.0,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'all'", specifier = ">=1.3.0,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'atlas'", specifier = ">=1.3.0,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'corrdiff'", specifier = ">=1.2.0,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'dlesym'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'dlwp'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'fcn'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'interp-modafno'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'precip-afno-v2'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'solarradiation-afno'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'statistics'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'stormcast'", specifier = ">=1.0.1,<2.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'stormscope'", specifier = "==1.3.0" },
+    { name = "nvidia-physicsnemo", marker = "extra == 'windgust-afno'", specifier = ">=1.0.1,<2.0" },
     { name = "nvtx", marker = "extra == 'all'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'corrdiff'", specifier = ">=0.2.11" },
-    { name = "nvtx", marker = "extra == 'da-stormcast'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'stormcast'", specifier = ">=0.2.11" },
     { name = "nvtx", marker = "extra == 'stormscope'", specifier = ">=0.2.11" },
     { name = "omegaconf", marker = "extra == 'all'", specifier = ">=2.3.0" },
-    { name = "omegaconf", marker = "extra == 'da-stormcast'", specifier = ">=2.3.0" },
     { name = "omegaconf", marker = "extra == 'dlesym'", specifier = ">=2.3.0" },
     { name = "omegaconf", marker = "extra == 'stormcast'", specifier = ">=2.3.0" },
     { name = "onnx", marker = "extra == 'all'" },
@@ -2024,6 +2123,7 @@ requires-dist = [
     { name = "pandas", specifier = "<3.0" },
     { name = "pandas", marker = "extra == 'ace2'", specifier = ">=2.3.2" },
     { name = "pandas", marker = "extra == 'all'", specifier = "<3.0" },
+    { name = "pandas", marker = "extra == 'all'", specifier = ">=2.3.2" },
     { name = "pandas", marker = "extra == 'data'", specifier = "<3.0" },
     { name = "planetary-computer", marker = "extra == 'all'", specifier = ">=0.5.0" },
     { name = "planetary-computer", marker = "extra == 'data'", specifier = ">=0.5.0" },
@@ -2034,7 +2134,6 @@ requires-dist = [
     { name = "pynvml", marker = "extra == 'all'", specifier = ">=12.0.0" },
     { name = "pynvml", marker = "extra == 'sfno'", specifier = ">=12.0.0" },
     { name = "pyproj", marker = "extra == 'all'", specifier = ">=3.7.1" },
-    { name = "pyproj", marker = "extra == 'da-stormcast'", specifier = ">=3.7.1" },
     { name = "pyproj", marker = "extra == 'data'", specifier = ">=3.7.1" },
     { name = "pyproj", marker = "extra == 'stormcast'", specifier = ">=3.7.1" },
     { name = "pystac-client", marker = "extra == 'all'", specifier = ">=0.9.0" },
@@ -2042,10 +2141,24 @@ requires-dist = [
     { name = "python-dotenv" },
     { name = "python-dotenv", marker = "extra == 'serve'", specifier = ">=1.0.0" },
     { name = "python-multipart", marker = "extra == 'serve'", specifier = ">=0.0.6" },
+    { name = "pytz", marker = "extra == 'all'" },
+    { name = "pytz", marker = "extra == 'atlas'" },
+    { name = "pytz", marker = "extra == 'corrdiff'" },
+    { name = "pytz", marker = "extra == 'dlesym'" },
+    { name = "pytz", marker = "extra == 'dlwp'" },
+    { name = "pytz", marker = "extra == 'fcn'" },
+    { name = "pytz", marker = "extra == 'fcn3'" },
+    { name = "pytz", marker = "extra == 'interp-modafno'" },
+    { name = "pytz", marker = "extra == 'precip-afno'" },
+    { name = "pytz", marker = "extra == 'precip-afno-v2'" },
+    { name = "pytz", marker = "extra == 'solarradiation-afno'" },
+    { name = "pytz", marker = "extra == 'statistics'" },
+    { name = "pytz", marker = "extra == 'stormcast'" },
+    { name = "pytz", marker = "extra == 'stormscope'" },
+    { name = "pytz", marker = "extra == 'windgust-afno'" },
     { name = "rasterio", marker = "extra == 'all'", specifier = ">=1.3.9" },
     { name = "rasterio", marker = "extra == 'data'", specifier = ">=1.3.9" },
     { name = "redis", marker = "extra == 'serve'", specifier = ">=5.0.0" },
-    { name = "requests", marker = "extra == 'serve'", specifier = ">=2.25.0" },
     { name = "rich" },
     { name = "rioxarray", marker = "extra == 'all'", specifier = ">=0.15.5" },
     { name = "rioxarray", marker = "extra == 'data'", specifier = ">=0.15.5" },
@@ -2061,7 +2174,6 @@ requires-dist = [
     { name = "scipy", marker = "extra == 'all'", specifier = ">=1.10.0" },
     { name = "scipy", marker = "extra == 'all'", specifier = ">=1.15.2" },
     { name = "scipy", marker = "extra == 'corrdiff'", specifier = ">=1.15.2" },
-    { name = "scipy", marker = "extra == 'da-stormcast'", specifier = ">=1.15.2" },
     { name = "scipy", marker = "extra == 'data'", specifier = ">=1.15.2" },
     { name = "scipy", marker = "extra == 'fcn3'", specifier = ">=1.10.0" },
     { name = "scipy", marker = "extra == 'perturbation'", specifier = ">=1.15.2" },
@@ -2075,18 +2187,16 @@ requires-dist = [
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+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvcc-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
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