From d3a22452b09bf68c2a708603cbc3bc5474f7b800 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 1 Dec 2025 07:52:27 -0800
Subject: [PATCH 01/69] resolved conflict

---
 earth2studio/models/px/fcn3.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 653d9ba1e..12768a863 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -173,6 +173,7 @@ def __init__(
         super().__init__()
         self.model = core_model
         self.variables = variables
+        self.seed = seed
         if "2d" in self.variables:
             self.variables[self.variables == "2d"] = "d2m"
 

From 0189d524fbf589098762d381ba8e83086863fc71 Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 01:08:08 -0700
Subject: [PATCH 02/69] update changelog

---
 CHANGELOG.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index adefc312b..d7b1671f2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,6 +14,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added general PlanetaryComputerData source for pulling Planetary Computer STAC assets
 - Added ECMWF AIFSENS ensemble prognostic model wrapper
 - Added MRMS data source
+- Added CMIP6 data source
+- Added CBottle Tropical Cyclone guidance diagnostic
+- Added CBottle Video prognostic model
+- Exposed backend arguments of netcdf/zarr to datasource_to_file signature
+- Added vertical wind speed support in GFS
+- Added ModelOutputDatasetSource to use written model output to start a new model run
+- Added FCN3 noise handling routines.
 
 ### Changed
 

From f11b18b136ed141eed4646ee1f4b5b634b1b767f Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 11:40:11 -0700
Subject: [PATCH 03/69] move seed initialization and fix dxwrapper tests

---
 earth2studio/models/px/fcn3.py   | 1 -
 test/models/px/test_dxwrapper.py | 4 +++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 12768a863..653d9ba1e 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -173,7 +173,6 @@ def __init__(
         super().__init__()
         self.model = core_model
         self.variables = variables
-        self.seed = seed
         if "2d" in self.variables:
             self.variables[self.variables == "2d"] = "d2m"
 
diff --git a/test/models/px/test_dxwrapper.py b/test/models/px/test_dxwrapper.py
index 9d44eba28..cedb7d054 100644
--- a/test/models/px/test_dxwrapper.py
+++ b/test/models/px/test_dxwrapper.py
@@ -283,7 +283,9 @@ def test_dxwrapper_iter(device, times, number_of_samples):
         ([np.datetime64("2025-08-21T00:00:00")], 1),
     ],
 )
-def test_dxwrapper_run(device, times, number_of_samples):
+@pytest.mark.parametrize("keep_px_output", [False, True])
+def test_fcn3_tc_tracker(device, times, keep_px_output):
+    # Spoof models
 
     model = PhooCorrDiff()
     in_center = torch.zeros(12, 1, 1)

From d063760b37242e4515b48302c047778186075271 Mon Sep 17 00:00:00 2001
From: Mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 15 Sep 2025 06:13:38 -0700
Subject: [PATCH 04/69] tempest extremes diagnostic model

---
 earth2studio/models/dx/__init__.py         |   4 +
 earth2studio/models/dx/tempest_extremes.py | 935 +++++++++++++++++++++
 2 files changed, 939 insertions(+)
 create mode 100644 earth2studio/models/dx/tempest_extremes.py

diff --git a/earth2studio/models/dx/__init__.py b/earth2studio/models/dx/__init__.py
index 448e1d3df..7625f367c 100644
--- a/earth2studio/models/dx/__init__.py
+++ b/earth2studio/models/dx/__init__.py
@@ -38,6 +38,10 @@
     TCTrackerVitart,
     TCTrackerWuDuan,
 )
+from earth2studio.models.dx.tempest_extremes import (
+    TempestExtremes,
+    AsyncTempestExtremes,
+)
 from earth2studio.models.dx.wind_gust import WindgustAFNO  # noqa
 
 __all__ = [
diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
new file mode 100644
index 000000000..9eee25035
--- /dev/null
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -0,0 +1,935 @@
+import os
+import shutil
+import time
+import copy
+import numpy as np
+import atexit
+import threading
+import torch
+from collections import OrderedDict
+from subprocess import run
+from concurrent.futures import ThreadPoolExecutor, Future
+from typing import Optional, Tuple, Dict, List, Any, Union
+
+from physicsnemo.distributed import DistributedManager
+from earth2studio.io import KVBackend
+from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
+
+
+def tile_xx_to_yy(xx: torch.Tensor,
+                  xx_coords: CoordSystem,
+                  yy: torch.Tensor,
+                  yy_coords: CoordSystem) -> Tuple[torch.Tensor, CoordSystem]:
+    """Tile tensor xx to match the leading dimensions of tensor yy.
+
+    Parameters
+    ----------
+    xx : torch.Tensor
+        Source tensor to be tiled
+    xx_coords : CoordSystem
+        Coordinate system for xx tensor
+    yy : torch.Tensor
+        Target tensor whose shape determines tiling
+    yy_coords : CoordSystem
+        Coordinate system for yy tensor
+
+    Returns
+    -------
+    Tuple[torch.Tensor, CoordSystem]
+        Tuple containing the tiled tensor and updated coordinate system
+
+    Raises
+    ------
+    ValueError
+        If xx has more dimensions than yy
+    """
+    n_lead = len(yy.shape) - len(xx.shape)
+
+    if n_lead < 0:
+        raise ValueError("xx must have fewer dimensions than yy.")
+
+    out_shape = yy.shape[:n_lead] + tuple([-1 for _ in range(len(xx.shape))])
+    out_coords = copy.deepcopy(yy_coords)
+    for key, val in xx_coords.items():
+        out_coords[key] = val
+
+    return xx.expand(out_shape), out_coords
+
+
+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
+
+
+class TempestExtremes:
+    """TempestExtremes cyclone tracking diagnostic class.
+
+    This class provides functionality for detecting and tracking tropical cyclones
+    using the TempestExtremes (TE) software package directly on model outputs.
+    To be compatible with TE, model outputs are buffered as a netcdf file with the
+    coordinates (time, lat, lon). Optionally, this netcdf file can be stored in RAM to
+    avoid writing large amount of data to disk.
+    Since TE natively only provides a command-line interface, a subporcess is spawned
+    calling TE. To provide users with as much flexibility as possible, the command to
+    execude TE has to be specified by the user and passed to the class during
+    initialisation. Any arguments regarding input and output files will be ignored
+    and automatically populated with the files holding the model output.
+    Additionally, this apporach allows the user to point the class to TE executibles
+    at any location of the system.
+    TE is purely CPU-based, but for most appliations still faster than producing the
+    atmospheric data on the GPU. AsyncTempestExtremes provides an implementation in
+    which a CPU thread applies TE to the gerneated data, while the GPU continues
+    producing the next prediction.
+
+    The result will be a TE-genereated track file, stored in a user-defined location
+
+    NOTE: currently works on batch size 1 only
+
+    Tip: To iterate on the TE command without having to always run a model, set
+    keep_raw_data to True and work with TE directly on the netcdf file that the
+    class generates.
+
+    Parameters
+    ----------
+    detect_cmd : str
+        TempestExtremes DetectNodes command with arguments
+        Note that --in_data_list and --out_file_list will be ignored if provdied
+    stitch_cmd : str
+        TempestExtremes StitchNodes command with arguments
+        Note that --in and --out will be ignored if provdied
+    input_vars :
+        List of variables which are required for the tracking algorithm
+    n_steps :
+        Number of time steps
+    time_step :
+        Time step interval
+    lats :
+        Latitude coordinates
+    lons :
+        Longitude coordinates
+    static_vars : torch.Tensor, optional
+        A tensor holding static data like orography, by default None
+    static_coords : dict, optional
+        Coordinates of the static data, by default None
+    store_dir : str, optional
+        Path for storing output files, by default None.
+        if use_ram==False, intermediate files will also be written to that path
+    keep_raw_data : bool, optional
+        Whether to keep raw data files, by default False
+    print_te_output : bool, optional
+        Whether to print TempestExtremes command line output, by default False
+    use_ram : bool, optional
+        Whether to use RAM (/dev/shm) for temporary storage, by default True
+    **kwargs
+        Additional keyword arguments for robustness
+    """
+
+    def __init__(self,
+                 detect_cmd: str,
+                 stitch_cmd: str,
+                 input_vars: List[str],
+                 n_steps: int,
+                 time_step: np.ndarray | List[np.timedelta64] | np.timedelta64,
+                 lats: np.ndarray | torch.Tensor,
+                 lons: np.ndarray | torch.Tensor,
+                 static_vars: Optional[torch.Tensor] = None,
+                 static_coords: Optional[Dict] = None,
+                 store_dir: Optional[str] = None,
+                 keep_raw_data: bool = False,
+                 print_te_output: bool = False,
+                 use_ram: bool = True,
+                 **kwargs): # leave kwars to make call robust to switching between this and async version
+
+        self.rank = DistributedManager().rank
+        self.capture_output = not print_te_output
+        self.keep_raw = keep_raw_data
+        self.static_vars = static_vars
+        self.static_coords = static_coords
+        self.time_step = time_step
+        self.input_vars = input_vars
+        self.n_steps = n_steps
+        self.lats = lats
+        self.lons = lons
+
+        if isinstance(self.time_step, np.ndarray) or isinstance(self.time_step, list):
+            assert(len(self.time_step)==1)
+            self.time_step = self.time_step[0]
+        if (static_vars==None) != (static_coords==None):
+            raise ValueError("provide both values and coords for static fields or don't." +
+                             " currently, only one of them is provided")
+
+        self.format_tempestextremes_commands(detect_cmd, stitch_cmd)
+
+        self.initialise_store_coords()
+
+        self.initialise_te_kvstore()
+
+        self.setup_output_directories(use_ram, store_dir)
+
+        return
+
+
+    def format_tempestextremes_commands(self, detect_cmd: str, stitch_cmd: str) -> None:
+        """Format TempestExtremes commands by removing input/output file arguments.
+        These arguments are populated dynamically by the method.
+
+        Parameters
+        ----------
+        detect_cmd : str
+            TempestExtremes detect command
+        stitch_cmd : str
+            TempestExtremes stitch command
+        """
+        self.detect_cmd = detect_cmd.split(" ")
+        self.stitch_cmd = stitch_cmd.split(" ")
+
+        self.detect_cmd = self.remove_arguments(self.detect_cmd, ['--in_data_list', '--out_file_list'])
+        self.stitch_cmd = self.remove_arguments(self.stitch_cmd, ['--in', '--out'])
+
+        return
+
+
+    @staticmethod
+    def remove_arguments(cmd: List[str], args: List[str]) -> List[str]:
+        """Remove specified arguments from command list.
+
+        Parameters
+        ----------
+        cmd : List[str]
+            Command as list of strings
+        args : List[str]
+            Arguments to remove
+
+        Returns
+        -------
+        List[str]
+            Command with specified arguments removed
+        """
+        for arg in args:
+            if arg in cmd:
+                remove = [cmd.index(arg)]
+                if len(cmd) > remove[0]+1:
+                    if not cmd[remove[0]+1].startswith('--'):
+                        remove.append(remove[0]+1)
+                cmd = [_arg for ii, _arg in enumerate(cmd) if ii not in remove]
+
+        return cmd
+
+
+    def setup_output_directories(self,
+                                 use_ram: bool,
+                                 store_dir: Optional[str]) -> None:
+        """Set up output directories for final data storage and for storing
+        auxiliary files.
+
+        Parameters
+        ----------
+        use_ram : bool
+            Whether to use RAM for temporary storage
+        store_dir : Optional[str]
+            Base storage directory path
+        """
+        self.store_dir = os.path.abspath(os.path.join(store_dir, 'cyclone_tracks_te'))
+
+        if self.rank==0:
+            os.makedirs(self.store_dir, exist_ok=True) # output for track data
+            if self.keep_raw:
+                os.makedirs(os.path.join(self.store_dir, 'raw_data'), exist_ok=True) # output for raw data
+
+        if use_ram:
+            self.ram_dir = os.path.join('/dev/shm', 'cyclone_tracking')
+            if DistributedManager().local_rank==0:
+                os.makedirs(self.ram_dir, exist_ok=True) # RAM location for processing fields
+        else:
+            self.ram_dir = os.path.join(self.store_dir, 'raw_data')
+            if self.rank==0:
+                os.makedirs(self.ram_dir, exist_ok=True) # disk location for processing fields
+
+        return
+
+
+    def initialise_store_coords(self) -> None:
+        """Initialize coordinate system for data storage.
+
+        Creates the internal coordinate system for storing to the internal
+        KV io backend, with optionally adding static variables at each time step.
+        """
+        # extract time step in case it's passed as list or array
+        _input_vars = self.input_vars
+        if self.static_coords:
+            _input_vars = np.array(list(self.input_vars) + list(self.static_coords['variable']))
+
+        self._store_coords = OrderedDict(
+            {
+                'lead_time': np.asarray(
+                                    [self.time_step * i for i in range(self.n_steps + 1)]
+                                ).flatten(),
+                'variable': np.array(_input_vars),
+                'lat': np.array(self.lats),
+                'lon': np.array(self.lons),
+            }
+        )
+
+        return
+
+
+    @property
+    def input_coords(self) -> OrderedDict:
+        """Returns the coords of the data to be passed to record_state.
+
+        Returns
+        -------
+        OrderedDict
+            Coordinate system for input data
+        """
+        return OrderedDict(
+                            {
+                                'time': np.empty(0),
+                                'lead_time': np.asarray(
+                                                    [self.time_step * i for i in range(self.n_steps + 1)]
+                                                ).flatten(),
+                                'variable': np.array(self.input_vars),
+                                'lat': np.array(self.lats),
+                                'lon': np.array(self.lons),
+                            }
+                          )
+
+
+    def initialise_te_kvstore(self) -> None:
+        """Initialize the KV store that stores model output in TE compatible format.
+        """
+        # create store
+        self.store = KVBackend()
+
+        # add arrays to the store
+        oco = copy.deepcopy(self._store_coords)
+        out_vars = oco.pop("variable")
+
+        oco["time"] = np.array([None])
+        oco["ensemble"] = np.array([None])
+        oco.move_to_end("time", last=False)
+        oco.move_to_end("ensemble", last=False)
+
+        self.store.add_array(coords=oco, array_name=out_vars)
+
+        return
+
+
+    def dump_raw_data(self) -> str:
+        """Dump raw data from store to NetCDF file.
+
+        Morphs the store to TempestExtremes-compatible [time, lat, lon] structure
+        and writes to a (optianlly in-RAM) NetCDF file, which can be ingested by TE.
+
+        Returns
+        -------
+        str
+            Path to the created NetCDF file
+        """
+        ic = self.store.coords['time'][0]
+        mem = self.store.coords.pop('ensemble')[0]
+
+        # morph store to tempes extremes-compatible [time, lat, lon] structure
+        lead_times = self.store.coords.pop('lead_time')
+        self.store.coords['time'] = [ic + lt for lt in lead_times]
+
+        for var in self.store.root.keys():
+            self.store.root[var] = self.store.root[var].squeeze((0,1))
+            self.store.dims[var]=['time', 'lat', 'lon']
+
+        # write file to ram
+        raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mems{mem:04d}.nc"#.replace(':', '.')
+        raw_dat = os.path.join(self.ram_dir, raw_dat)
+        kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
+
+        _store = self.store.to_xarray()
+        _store.to_netcdf(**kw_args)
+
+        # new clean store
+        self.initialise_te_kvstore()
+
+        return raw_dat
+
+
+    def setup_files(self) -> Tuple[str, str, str, str]:
+        """Set up input and output files for TE processing.
+
+        Creates necessary file lists and paths for TE detect and stitch operations.
+
+        Returns
+        -------
+        Tuple[str, str, str, str]
+            Tuple containing (input_file_list, output_file_list, node_file, track_file)
+        """
+        # write store to in-memory file
+        raw_dat = self.dump_raw_data()
+
+        # in_files to file
+        ins = os.path.join(self.ram_dir, f'input_files_{self.rank:04d}.txt')
+        with open(ins, 'w') as fl:
+            fl.write(raw_dat + '\n')
+
+        outs = os.path.join(self.ram_dir, f'output_files_{self.rank:04d}.txt')
+        base_name = os.path.basename(raw_dat)
+        base_name = base_name.rsplit('.',1)[0]
+        node_file = os.path.join(self.ram_dir, base_name + '_tc_centres.txt')
+        with open(outs, 'w') as fl:
+            fl.write(node_file + '\n')
+
+        track_file = os.path.join(self.store_dir, 'tracks_' + base_name + '.csv')
+
+        return ins, outs, node_file, track_file
+
+
+    def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
+        """Record state data to the internal store.
+
+        Updates store with current time and ensemble information,
+        concatenates static data if available, and writes to store.
+
+        Parameters
+        ----------
+        xx : torch.Tensor
+            Input tensor data
+        coords : CoordSystem
+            Coordinate system for the input data
+        """
+        # update store mem and time
+        for dim in ['time', 'ensemble']:
+            if self.store.coords[dim] != coords[dim]:
+                self.store.coords[dim] = coords[dim]
+
+        # concatenate static data
+        if self.static_coords:
+            self.static_vars = self.static_vars.to(xx.device)
+            if len(xx.shape) > len(self.static_vars.shape):
+                _static_vars, _static_coords = tile_xx_to_yy(self.static_vars, self.static_coords, xx, coords)
+            else:
+                _static_vars, _static_coords = self.static_vars, self.static_coords
+            xx, coords = cat_coords(xx, coords, _static_vars, _static_coords)
+
+        # select output data and write to store
+        xx_sub, coords_sub = map_coords(xx, coords, self._store_coords)
+        self.store.write(*split_coords(xx_sub, coords_sub, dim="variable"))
+
+        return
+
+
+    def track_cyclones(self) -> None:
+        """Execute cyclone tracking using TempestExtremes.
+
+        Runs the complete tracking pipeline including node detection
+        and stitching operations.
+
+        Raises
+        ------
+        ChildProcessError
+            If TempestExtremes detect or stitch operations fail
+        """
+        then = time.time()
+
+        # set up TE helper files
+        ins, outs, node_file, self.track_file = self.setup_files()
+
+        # detect nodes
+        out = run(self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
+            capture_output=self.capture_output)
+
+        # unfortunately, TE does not fail with proper returncode
+        if 'EXCEPTION' in out.stdout.decode('utf-8'):
+            print(out.stdout.decode('utf-8'))
+            raise ChildProcessError('\nERROR: detect nodes failed, see output above from TempestExtremes for details.')
+
+        # stitch nodes
+        out = run(self.stitch_cmd + ['--in', node_file, '--out', self.track_file],
+            capture_output=self.capture_output)
+
+        # unfortunately, TE does not fail with proper returncode
+        if 'EXCEPTION' in out.stdout.decode('utf-8'):
+            print(out.stdout.decode('utf-8'))
+            raise ChildProcessError('\nERROR: stitch nodes failed, see output above from TempestExtremes for details.')
+
+        # remove helper files
+        self.tidy_up(ins, outs)
+
+        print(f'took {(time.time() - then):.1f}s to track cyclones')
+
+        return
+
+
+    def __call__(self) -> None:
+        """Make the class callable to execute cyclone tracking.
+
+        Returns
+        -------
+        None
+            Calls track_cyclones method
+        """
+        return self.track_cyclones()
+
+
+    def tidy_up(self, ins: str, outs: str) -> None:
+        """Clean up temporary files after processing.
+
+        Handles raw data files based on keep_raw_data setting and
+        removes TempestExtremes helper files.
+
+        Parameters
+        ----------
+        ins : str
+            Path to input file list
+        outs : str
+            Path to output file list
+        """
+
+        with open(ins, 'r') as fl:
+            raw_dat = fl.read().strip()
+
+        if self.keep_raw: # move field data to disk
+            out_path = os.path.join(self.store_dir, 'raw_data')
+            raw_dest = os.path.join(out_path, os.path.basename(raw_dat))
+            if raw_dat != raw_dest: # nothing to do if use_ram=False
+                if os.path.exists(raw_dest):
+                    os.remove(raw_dest)
+                shutil.move(raw_dat, out_path)
+        else:             # delete field data
+            os.remove(raw_dat)
+
+        # delete TE helper files
+        files = [ins, outs]
+        with open(outs, 'r') as fl:
+            for line in fl:
+                files.append(line.strip())
+
+        for file in files:
+            os.remove(file)
+
+        return
+
+
+# Global thread pool for background TempestExtremes processing
+_tempest_executor = None
+_tempest_background_tasks = []
+_tempest_executor_lock = threading.Lock()
+
+
+def get_tempest_executor() -> ThreadPoolExecutor:
+    """Get or create the thread pool executor for TempestExtremes processing."""
+    global _tempest_executor
+    with _tempest_executor_lock:
+        if _tempest_executor is None:
+            # Reduced worker count for long-running tasks to avoid resource exhaustion
+            max_workers = min(4, os.cpu_count() or 1)
+            _tempest_executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="tempest_extremes")
+            # Ensure cleanup on exit
+            atexit.register(cleanup_tempest_executor)
+    return _tempest_executor
+
+
+def cleanup_tempest_executor(timeout_per_task: int = 360) -> None:
+    """Clean up the thread pool executor.
+
+    Parameters
+    ----------
+    timeout_per_task : int, optional
+        Timeout in seconds for each task, by default 360
+
+    Raises
+    ------
+    ChildProcessError
+        If one or more background TempestExtremes tasks failed
+    """
+    """Clean up the thread pool executor."""
+    global _tempest_executor, _tempest_background_tasks
+    with _tempest_executor_lock:
+        if _tempest_executor is not None:
+            # Wait for all background tasks to complete
+            child_process_errors = []
+            for i, future in enumerate(_tempest_background_tasks):
+                try:
+                    future.result(timeout=timeout_per_task)  # Configurable timeout per task
+                except ChildProcessError as e:
+                    print(f"Background TempestExtremes task {i+1} failed with ChildProcessError: {e}")
+                    child_process_errors.append(e)
+                except Exception as e:
+                    print(f"Background TempestExtremes task {i+1} failed: {e}")
+            _tempest_executor.shutdown(wait=True)
+            _tempest_executor = None
+            _tempest_background_tasks.clear()
+
+            # Re-raise ChildProcessError if any occurred
+            if child_process_errors:
+                raise ChildProcessError(f"One or more background TempestExtremes tasks failed: {child_process_errors}")
+
+
+class AsyncTempestExtremes(TempestExtremes):
+    """Asynchronous version of TempestExtremes that runs cyclone tracking in background threads.
+
+    This class extends TempestExtremes to provide asynchronous cyclone tracking capabilities
+    using background thread pools. It allows for non-blocking cyclone tracking operations
+    while maintaining data integrity through synchronization mechanisms.
+
+    Follows the parallelization paradigm from async_tracking_utils.py.
+
+    Parameters
+    ----------
+    *args
+        Positional arguments passed to parent TempestExtremes class
+    **kwargs
+        Keyword arguments passed to parent TempestExtremes class.
+        Special parameters:
+        - timeout : int, optional
+            Timeout in seconds for operations, by default 60
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        # Extract timeout parameter with default value
+        self.timeout = kwargs.pop('timeout', 60)
+
+        # Initialize the parent class
+        super().__init__(*args, **kwargs)
+
+        # Track background tasks for this instance
+        self._instance_tasks = []
+        self._instance_lock = threading.Lock()
+        self._dump_in_progress = threading.Event()
+        self._dump_in_progress.set()  # Initially no dump in progress
+        self._has_failed = False  # Track if any previous task failed
+
+
+    def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
+        """Record state but ensure dump_raw_data has finished successfully before proceeding.
+
+        This method ensures data integrity by waiting for any ongoing dump operations
+        to complete before recording new state data.
+
+        Parameters
+        ----------
+        xx : torch.Tensor
+            Input tensor data
+        coords : CoordSystem
+            Coordinate system for the input data
+
+        Raises
+        ------
+        ChildProcessError
+            If any previous cyclone tracking task failed
+        """
+        # Check if any previous task failed and raise error immediately
+        self._check_for_failures()
+
+        # Wait for any ongoing dump_raw_data to complete
+        self._dump_in_progress.wait()
+
+        # Call parent's record_state method
+        super().record_state(xx, coords)
+
+
+    def dump_raw_data(self) -> str:
+        """Dump raw data with synchronization to prevent concurrent access.
+
+        Uses threading events to ensure only one dump operation occurs at a time
+        to maintain data integrity.
+
+        Returns
+        -------
+        str
+            Path to the created NetCDF file
+        """
+        # Signal that dump is in progress
+        self._dump_in_progress.clear()
+
+        try:
+            # Call parent's dump_raw_data method
+            result = super().dump_raw_data()
+            return result
+        finally:
+            # Signal that dump is complete
+            self._dump_in_progress.set()
+
+
+    def _check_for_failures(self) -> None:
+        """Check if any previous tasks failed with ChildProcessError and raise immediately.
+
+        This method checks the status of all background tasks and raises an exception
+        if any have failed, ensuring early failure detection.
+
+        Raises
+        ------
+        ChildProcessError
+            If any previous cyclone tracking task failed
+        Exception
+            If any task failed with a different exception type
+        """
+        if self._has_failed:
+            raise ChildProcessError("Previous cyclone tracking task failed - stopping execution")
+
+        # Check completed tasks for failures
+        with self._instance_lock:
+            for future in self._instance_tasks:
+                if future.done():
+                    try:
+                        future.result()  # This will raise if the task failed
+                    except ChildProcessError as e:
+                        self._has_failed = True
+                        raise e  # Re-raise immediately
+                    except Exception as e:
+                        self._has_failed = True
+                        raise e  # Re-raise other exceptions too
+
+
+    def track_cyclones_async(self) -> Future:
+        """Submit cyclone tracking to background thread pool.
+
+        This method submits cyclone tracking operations to a background thread pool
+        for asynchronous execution, allowing the main thread to continue processing.
+
+        Returns
+        -------
+        Future
+            A Future object that can be used to check status or get results
+
+        Raises
+        ------
+        ChildProcessError
+            If any previous cyclone tracking task failed
+        """
+        # Check if any previous task failed and raise error immediately
+        self._check_for_failures()
+
+        global _tempest_background_tasks
+
+        # Clean up completed tasks to avoid memory buildup
+        with _tempest_executor_lock:
+            _tempest_background_tasks = [f for f in _tempest_background_tasks if not f.done()]
+
+        with self._instance_lock:
+            self._instance_tasks = [f for f in self._instance_tasks if not f.done()]
+
+        # Check if we have too many running tasks
+        with _tempest_executor_lock:
+            running_tasks = len(_tempest_background_tasks)
+            if running_tasks >= 3:  # Conservative limit for long-running tasks
+                print(f"Warning: {running_tasks} TempestExtremes tasks already running. Consider waiting for some to complete.")
+
+        # Submit the task
+        executor = get_tempest_executor()
+        future = executor.submit(self.track_cyclones)
+
+        # Add to both global and instance task lists
+        with _tempest_executor_lock:
+            _tempest_background_tasks.append(future)
+
+        with self._instance_lock:
+            self._instance_tasks.append(future)
+
+        return future
+
+
+    def get_task_status(self) -> Dict[str, int]:
+        """Get status of background tasks for this instance.
+
+        Returns
+        -------
+        Dict[str, int]
+            Dictionary containing task status counts with keys:
+            - 'running': Number of currently running tasks
+            - 'pending': Number of pending tasks
+            - 'completed': Number of completed tasks
+            - 'failed': Number of failed tasks
+            - 'total': Total number of tasks
+        """
+        with self._instance_lock:
+            running = sum(1 for f in self._instance_tasks if f.running())
+            pending = sum(1 for f in self._instance_tasks if not f.done() and not f.running())
+            completed = sum(1 for f in self._instance_tasks if f.done())
+            failed = sum(1 for f in self._instance_tasks if f.done() and f.exception() is not None)
+
+            return {
+                "running": running,
+                "pending": pending,
+                "completed": completed,
+                "failed": failed,
+                "total": len(self._instance_tasks)
+            }
+
+
+    def get_task_results(self, raise_on_error: bool = True) -> Union[Dict[str, Any], Tuple[List, List]]:
+        """Get results from all completed tasks.
+
+        Parameters
+        ----------
+        raise_on_error : bool, optional
+            If True, raises ChildProcessError if any task failed with that error.
+            If False, returns results and exceptions separately, by default True
+
+        Returns
+        -------
+        Union[Dict[str, Any], Tuple[List, List]]
+            If raise_on_error=True: dict with successful results
+            If raise_on_error=False: tuple of (results, exceptions)
+
+        Raises
+        ------
+        ChildProcessError
+            If raise_on_error=True and any task failed with ChildProcessError
+        Exception
+            If raise_on_error=True and any task failed with other exceptions
+        """
+        with self._instance_lock:
+            tasks_to_check = list(self._instance_tasks)
+
+        results = []
+        exceptions = []
+
+        for i, future in enumerate(tasks_to_check):
+            if future.done():
+                try:
+                    result = future.result()
+                    results.append(result)
+                except ChildProcessError as e:
+                    if raise_on_error:
+                        raise  # Re-raise ChildProcessError immediately
+                    exceptions.append(('ChildProcessError', e))
+                except Exception as e:
+                    if raise_on_error:
+                        raise  # Re-raise any other exceptions
+                    exceptions.append(('Exception', e))
+
+        if raise_on_error:
+            return {"results": results, "task_count": len(tasks_to_check)}
+        else:
+            return results, exceptions
+
+
+    def wait_for_completion(self, timeout_per_task: int = 360) -> None:
+        """Wait for all background tasks associated with this instance to complete.
+
+        This method blocks until all background tasks have finished execution.
+
+        Parameters
+        ----------
+        timeout_per_task : int, optional
+            Timeout in seconds for each task, by default 360
+
+        Raises
+        ------
+        ChildProcessError
+            If any background task failed with ChildProcessError
+        Exception
+            If any background task failed with other exceptions
+        """
+        with self._instance_lock:
+            tasks_to_wait = list(self._instance_tasks)
+
+        if tasks_to_wait:
+            print(f"Waiting for {len(tasks_to_wait)} TempestExtremes tasks to complete...")
+
+            for i, future in enumerate(tasks_to_wait):
+                try:
+                    print(f"Waiting for task {i+1}/{len(tasks_to_wait)} to complete...")
+                    future.result(timeout=timeout_per_task)
+                except ChildProcessError as e:
+                    print(f"Task {i+1} failed with ChildProcessError: {e}")
+                    raise  # Re-raise ChildProcessError to propagate it
+                except Exception as e:
+                    print(f"Task {i+1} failed: {e}")
+                    raise  # Re-raise any other exceptions as well
+        else:
+            print("No background tasks to wait for.")
+
+
+    def __del__(self) -> None:
+        """Destructor that ensures all processes have finished before the class gets destroyed.
+
+        This method performs cleanup operations when the object is being destroyed,
+        ensuring all background tasks complete and resources are properly released.
+        """
+        try:
+            # Wait for any ongoing dump to complete
+            if hasattr(self, '_dump_in_progress'):
+                self._dump_in_progress.wait(timeout=60)  # Max 1 minute wait
+
+            # Wait for all instance tasks to complete
+            if hasattr(self, '_instance_tasks') and hasattr(self, '_instance_lock'):
+                with self._instance_lock:
+                    tasks_to_wait = list(self._instance_tasks)
+
+                if tasks_to_wait:
+                    print(f"AsyncTempestExtremes is waiting for {len(tasks_to_wait)} tasks to complete...")
+
+                    for future in tasks_to_wait:
+                        try:
+                            future.result(timeout=30)  # Shorter timeout in destructor
+                        except ChildProcessError as e:
+                            print(f"Task failed during cleanup with ChildProcessError: {e}")
+                            # Note: In destructor, we log but don't re-raise to avoid issues
+                        except Exception as e:
+                            print(f"Task failed during cleanup: {e}")
+        except Exception as e:
+            print(f"Error in AsyncTempestExtremes destructor: {e}")
+
+
+    def __call__(self) -> Future:
+        """Override call method to use async version by default.
+
+        This method makes the class callable and uses the asynchronous
+        tracking method by default.
+
+        Returns
+        -------
+        Future
+            A Future object for the submitted cyclone tracking task
+
+        Raises
+        ------
+        ChildProcessError
+            If any previous cyclone tracking task failed
+        """
+        # Check if any previous task failed and raise error immediately
+        self._check_for_failures()
+        return self.track_cyclones_async()

From a4d25440dd743b73ac4bf04c038ce589ee4c52db Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 25 Sep 2025 03:31:57 -0700
Subject: [PATCH 05/69] error message

---
 earth2studio/models/dx/tempest_extremes.py | 61 ++++++++++++++++------
 1 file changed, 44 insertions(+), 17 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 9eee25035..a92e8762b 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -6,6 +6,7 @@
 import atexit
 import threading
 import torch
+
 from collections import OrderedDict
 from subprocess import run
 from concurrent.futures import ThreadPoolExecutor, Future
@@ -15,6 +16,11 @@
 from earth2studio.io import KVBackend
 from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
 
+from earth2studio.utils.imports import (
+    OptionalDependencyFailure,
+    check_optional_dependencies,
+)
+
 
 def tile_xx_to_yy(xx: torch.Tensor,
                   xx_coords: CoordSystem,
@@ -185,7 +191,7 @@ def __init__(self,
                  **kwargs): # leave kwars to make call robust to switching between this and async version
 
         self.rank = DistributedManager().rank
-        self.capture_output = not print_te_output
+        self.print_te_output = print_te_output
         self.keep_raw = keep_raw_data
         self.static_vars = static_vars
         self.static_coords = static_coords
@@ -204,6 +210,8 @@ def __init__(self,
 
         self.format_tempestextremes_commands(detect_cmd, stitch_cmd)
 
+        self.check_tempest_extremes_availability()
+
         self.initialise_store_coords()
 
         self.initialise_te_kvstore()
@@ -233,6 +241,18 @@ def format_tempestextremes_commands(self, detect_cmd: str, stitch_cmd: str) -> N
         return
 
 
+    def check_tempest_extremes_availability(self):
+        for cmd in (self.detect_cmd[0], self.stitch_cmd[0]):
+            try:
+                run(cmd, capture_output=True)
+            except:
+                # raise ChildProcessError(f'{cmd} not working')
+                print('what?!')
+                OptionalDependencyFailure("fcn3")
+
+        return
+
+
     @staticmethod
     def remove_arguments(cmd: List[str], args: List[str]) -> List[str]:
         """Remove specified arguments from command list.
@@ -459,6 +479,24 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         return
 
 
+    @staticmethod
+    def run_te(command, print_output):
+        # detect nodes
+        out = run(command, capture_output=True)
+
+        # Print output to terminal if requested
+        if print_output:
+            print(out.stdout.decode('utf-8'))
+
+        # unfortunately, TE does not fail with proper returncode
+        if 'EXCEPTION' in out.stdout.decode('utf-8'):
+            print(out.stdout.decode('utf-8'))
+            raise ChildProcessError(f'\nERROR: {command[0]} failed, see output above from TempestExtremes for details.')
+
+        return
+
+
+    @check_optional_dependencies
     def track_cyclones(self) -> None:
         """Execute cyclone tracking using TempestExtremes.
 
@@ -476,22 +514,12 @@ def track_cyclones(self) -> None:
         ins, outs, node_file, self.track_file = self.setup_files()
 
         # detect nodes
-        out = run(self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
-            capture_output=self.capture_output)
-
-        # unfortunately, TE does not fail with proper returncode
-        if 'EXCEPTION' in out.stdout.decode('utf-8'):
-            print(out.stdout.decode('utf-8'))
-            raise ChildProcessError('\nERROR: detect nodes failed, see output above from TempestExtremes for details.')
+        self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
+                    print_output=self.print_te_output)
 
-        # stitch nodes
-        out = run(self.stitch_cmd + ['--in', node_file, '--out', self.track_file],
-            capture_output=self.capture_output)
-
-        # unfortunately, TE does not fail with proper returncode
-        if 'EXCEPTION' in out.stdout.decode('utf-8'):
-            print(out.stdout.decode('utf-8'))
-            raise ChildProcessError('\nERROR: stitch nodes failed, see output above from TempestExtremes for details.')
+        # stitch them together
+        self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', self.track_file],
+                    print_output=self.print_te_output)
 
         # remove helper files
         self.tidy_up(ins, outs)
@@ -721,7 +749,6 @@ def _check_for_failures(self) -> None:
                         self._has_failed = True
                         raise e  # Re-raise other exceptions too
 
-
     def track_cyclones_async(self) -> Future:
         """Submit cyclone tracking to background thread pool.
 

From c1cdca02d44a3fb123b988bdf56c09b7ad42015c Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 25 Sep 2025 05:55:46 -0700
Subject: [PATCH 06/69] testing if TE is available and works

---
 earth2studio/models/dx/tempest_extremes.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index a92e8762b..c08500402 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -16,10 +16,7 @@
 from earth2studio.io import KVBackend
 from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
 
-from earth2studio.utils.imports import (
-    OptionalDependencyFailure,
-    check_optional_dependencies,
-)
+from earth2studio.utils.imports import OptionalDependencyFailure, check_optional_dependencies
 
 
 def tile_xx_to_yy(xx: torch.Tensor,
@@ -246,9 +243,8 @@ def check_tempest_extremes_availability(self):
             try:
                 run(cmd, capture_output=True)
             except:
-                # raise ChildProcessError(f'{cmd} not working')
-                print('what?!')
-                OptionalDependencyFailure("fcn3")
+                # TODO discuss implementation of below before merging
+                OptionalDependencyFailure("tempest_extremes")
 
         return
 
@@ -312,6 +308,7 @@ def setup_output_directories(self,
         return
 
 
+    @check_optional_dependencies()
     def initialise_store_coords(self) -> None:
         """Initialize coordinate system for data storage.
 
@@ -496,7 +493,7 @@ def run_te(command, print_output):
         return
 
 
-    @check_optional_dependencies
+    @check_optional_dependencies()
     def track_cyclones(self) -> None:
         """Execute cyclone tracking using TempestExtremes.
 

From 016f16b4b10abed0406d2b94a14845f21803cd7f Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 25 Sep 2025 06:59:51 -0700
Subject: [PATCH 07/69] started working on support for batch sizes >1,
 currently works for bs 1

---
 earth2studio/models/dx/tempest_extremes.py | 67 +++++++++++-----------
 1 file changed, 34 insertions(+), 33 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index c08500402..1741b9cc3 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -399,7 +399,7 @@ def dump_raw_data(self) -> str:
             self.store.dims[var]=['time', 'lat', 'lon']
 
         # write file to ram
-        raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mems{mem:04d}.nc"#.replace(':', '.')
+        raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc"
         raw_dat = os.path.join(self.ram_dir, raw_dat)
         kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
 
@@ -439,7 +439,7 @@ def setup_files(self) -> Tuple[str, str, str, str]:
 
         track_file = os.path.join(self.store_dir, 'tracks_' + base_name + '.csv')
 
-        return ins, outs, node_file, track_file
+        return [ins], [outs], [node_file], [track_file]
 
 
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
@@ -508,18 +508,19 @@ def track_cyclones(self) -> None:
         then = time.time()
 
         # set up TE helper files
-        ins, outs, node_file, self.track_file = self.setup_files()
+        insies, outsies, node_files, self.track_files = self.setup_files()
 
-        # detect nodes
-        self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
-                    print_output=self.print_te_output)
+        for ins, outs, node_file, track_file in zip(insies, outsies, node_files, self.track_files):
+            # detect nodes
+            self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
+                        print_output=self.print_te_output)
 
-        # stitch them together
-        self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', self.track_file],
-                    print_output=self.print_te_output)
+            # stitch them together
+            self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', track_file],
+                        print_output=self.print_te_output)
 
         # remove helper files
-        self.tidy_up(ins, outs)
+        self.tidy_up(insies, outsies)
 
         print(f'took {(time.time() - then):.1f}s to track cyclones')
 
@@ -537,7 +538,7 @@ def __call__(self) -> None:
         return self.track_cyclones()
 
 
-    def tidy_up(self, ins: str, outs: str) -> None:
+    def tidy_up(self, insies: str, outsies: str) -> None:
         """Clean up temporary files after processing.
 
         Handles raw data files based on keep_raw_data setting and
@@ -550,28 +551,28 @@ def tidy_up(self, ins: str, outs: str) -> None:
         outs : str
             Path to output file list
         """
-
-        with open(ins, 'r') as fl:
-            raw_dat = fl.read().strip()
-
-        if self.keep_raw: # move field data to disk
-            out_path = os.path.join(self.store_dir, 'raw_data')
-            raw_dest = os.path.join(out_path, os.path.basename(raw_dat))
-            if raw_dat != raw_dest: # nothing to do if use_ram=False
-                if os.path.exists(raw_dest):
-                    os.remove(raw_dest)
-                shutil.move(raw_dat, out_path)
-        else:             # delete field data
-            os.remove(raw_dat)
-
-        # delete TE helper files
-        files = [ins, outs]
-        with open(outs, 'r') as fl:
-            for line in fl:
-                files.append(line.strip())
-
-        for file in files:
-            os.remove(file)
+        for ins, outs in zip(insies, outsies):
+            with open(ins, 'r') as fl:
+                raw_dat = fl.read().strip()
+
+            if self.keep_raw: # move field data to disk
+                out_path = os.path.join(self.store_dir, 'raw_data')
+                raw_dest = os.path.join(out_path, os.path.basename(raw_dat))
+                if raw_dat != raw_dest: # nothing to do if use_ram=False
+                    if os.path.exists(raw_dest):
+                        os.remove(raw_dest)
+                    shutil.move(raw_dat, out_path)
+            else:             # delete field data
+                os.remove(raw_dat)
+
+            # delete TE helper files
+            files = [ins, outs]
+            with open(outs, 'r') as fl:
+                for line in fl:
+                    files.append(line.strip())
+
+            for file in files:
+                os.remove(file)
 
         return
 

From 68e33b555d24ade54c4f0ca3bf09c5da31d23d4e Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 25 Sep 2025 08:27:05 -0700
Subject: [PATCH 08/69] halfway to larger batch support

---
 earth2studio/models/dx/tempest_extremes.py | 62 +++++++++++++---------
 1 file changed, 36 insertions(+), 26 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 1741b9cc3..fc92c39c9 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -388,28 +388,31 @@ def dump_raw_data(self) -> str:
             Path to the created NetCDF file
         """
         ic = self.store.coords['time'][0]
-        mem = self.store.coords.pop('ensemble')[0]
+        mems = self.store.coords['ensemble']
 
         # morph store to tempes extremes-compatible [time, lat, lon] structure
         lead_times = self.store.coords.pop('lead_time')
         self.store.coords['time'] = [ic + lt for lt in lead_times]
 
         for var in self.store.root.keys():
-            self.store.root[var] = self.store.root[var].squeeze((0,1))
-            self.store.dims[var]=['time', 'lat', 'lon']
+            self.store.root[var] = self.store.root[var].squeeze((1))
+            self.store.dims[var]=['ensemble', 'time', 'lat', 'lon']
 
         # write file to ram
-        raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc"
-        raw_dat = os.path.join(self.ram_dir, raw_dat)
-        kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
+        raw_files = []
+        for mem in mems:
+            raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc"
+            raw_dat = os.path.join(self.ram_dir, raw_dat)
+            raw_files.append(raw_dat)
+            kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
 
-        _store = self.store.to_xarray()
-        _store.to_netcdf(**kw_args)
+            _store = self.store.to_xarray()
+            _store.sel(ensemble=mem).drop_vars('ensemble').to_netcdf(**kw_args)
 
         # new clean store
         self.initialise_te_kvstore()
 
-        return raw_dat
+        return raw_files, mems
 
 
     def setup_files(self) -> Tuple[str, str, str, str]:
@@ -423,23 +426,30 @@ def setup_files(self) -> Tuple[str, str, str, str]:
             Tuple containing (input_file_list, output_file_list, node_file, track_file)
         """
         # write store to in-memory file
-        raw_dat = self.dump_raw_data()
-
-        # in_files to file
-        ins = os.path.join(self.ram_dir, f'input_files_{self.rank:04d}.txt')
-        with open(ins, 'w') as fl:
-            fl.write(raw_dat + '\n')
-
-        outs = os.path.join(self.ram_dir, f'output_files_{self.rank:04d}.txt')
-        base_name = os.path.basename(raw_dat)
-        base_name = base_name.rsplit('.',1)[0]
-        node_file = os.path.join(self.ram_dir, base_name + '_tc_centres.txt')
-        with open(outs, 'w') as fl:
-            fl.write(node_file + '\n')
-
-        track_file = os.path.join(self.store_dir, 'tracks_' + base_name + '.csv')
-
-        return [ins], [outs], [node_file], [track_file]
+        raw_files, mems = self.dump_raw_data()
+
+        ins, outs, node_files, track_files = [], [], [], []
+        for mem, raw_dat in zip(mems, raw_files):
+            # in_files to file
+            _ins = os.path.join(self.ram_dir, f'input_files_{self.rank:04d}_mem_{mem:05d}.txt')
+            with open(_ins, 'w') as fl:
+                fl.write(raw_dat + '\n')
+
+            _outs = os.path.join(self.ram_dir, f'output_files_{self.rank:04d}_mem_{mem:05d}.txt')
+            base_name = os.path.basename(raw_dat)
+            base_name = base_name.rsplit('.',1)[0]
+            node_file = os.path.join(self.ram_dir, base_name + f'_mem_{mem:05d}_tc_centres.txt')
+            with open(_outs, 'w') as fl:
+                fl.write(node_file + '\n')
+
+            track_file = os.path.join(self.store_dir, 'tracks_' + base_name + '.csv')
+
+            ins.append(_ins)
+            outs.append(_outs)
+            node_files.append(node_file)
+            track_files.append(track_file)
+
+        return ins, outs, node_files, track_files
 
 
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:

From 7bd60e1418289699d7847db40535c4a01bfd531d Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 26 Sep 2025 03:51:51 -0700
Subject: [PATCH 09/69] enabling TE for batch sizes of >1. async version seems
 to work as well but needs further testing with batching.

---
 earth2studio/models/dx/tempest_extremes.py | 24 ++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index fc92c39c9..6b7b371b1 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -130,7 +130,9 @@ class TempestExtremes:
 
     The result will be a TE-genereated track file, stored in a user-defined location
 
-    NOTE: currently works on batch size 1 only
+    NOTE:
+        - currently works on batch size 1 only (currently worked on)
+        - length of time direction in input coords must be 1 (add test)
 
     Tip: To iterate on the TE command without having to always run a model, set
     keep_raw_data to True and work with TE directly on the netcdf file that the
@@ -175,6 +177,7 @@ def __init__(self,
                  detect_cmd: str,
                  stitch_cmd: str,
                  input_vars: List[str],
+                 batch_size: int,
                  n_steps: int,
                  time_step: np.ndarray | List[np.timedelta64] | np.timedelta64,
                  lats: np.ndarray | torch.Tensor,
@@ -194,6 +197,7 @@ def __init__(self,
         self.static_coords = static_coords
         self.time_step = time_step
         self.input_vars = input_vars
+        self.batch_size = batch_size
         self.n_steps = n_steps
         self.lats = lats
         self.lons = lons
@@ -367,7 +371,7 @@ def initialise_te_kvstore(self) -> None:
         out_vars = oco.pop("variable")
 
         oco["time"] = np.array([None])
-        oco["ensemble"] = np.array([None])
+        oco["ensemble"] = np.array([None]*self.batch_size)
         oco.move_to_end("time", last=False)
         oco.move_to_end("ensemble", last=False)
 
@@ -387,6 +391,9 @@ def dump_raw_data(self) -> str:
         str
             Path to the created NetCDF file
         """
+        if len(self.store.coords['time']) > 1:
+            raise ValueError('Currently, TempestExtremes interface only works for single IC per evaluation cycle.')
+
         ic = self.store.coords['time'][0]
         mems = self.store.coords['ensemble']
 
@@ -398,16 +405,21 @@ def dump_raw_data(self) -> str:
             self.store.root[var] = self.store.root[var].squeeze((1))
             self.store.dims[var]=['ensemble', 'time', 'lat', 'lon']
 
-        # write file to ram
+        # for each member, write te compatible nc file
         raw_files = []
+
+        # in case last batch has less ensemble members than batch size, expand coords of store so to_xarray() does not fail
+        if len(mems) < self.batch_size:
+            self.store.coords['ensemble'] = list(self.store.coords['ensemble']) + [None]*(self.batch_size - len(mems))
+
         for mem in mems:
             raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc"
             raw_dat = os.path.join(self.ram_dir, raw_dat)
             raw_files.append(raw_dat)
             kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
 
-            _store = self.store.to_xarray()
-            _store.sel(ensemble=mem).drop_vars('ensemble').to_netcdf(**kw_args)
+            _store = self.store.to_xarray().sel(ensemble=mem).drop_vars('ensemble')
+            _store.to_netcdf(**kw_args)
 
         # new clean store
         self.initialise_te_kvstore()
@@ -467,7 +479,7 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         """
         # update store mem and time
         for dim in ['time', 'ensemble']:
-            if self.store.coords[dim] != coords[dim]:
+            if not (self.store.coords[dim] == coords[dim]).all():
                 self.store.coords[dim] = coords[dim]
 
         # concatenate static data

From 3b0c00e3f8dec4007e64f0f97ca93a7b0e90b30f Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 29 Sep 2025 06:15:51 -0700
Subject: [PATCH 10/69] option to pass file names to TE connector

---
 earth2studio/models/dx/tempest_extremes.py | 28 +++++++++++++---------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 6b7b371b1..b77e572bc 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -427,7 +427,7 @@ def dump_raw_data(self) -> str:
         return raw_files, mems
 
 
-    def setup_files(self) -> Tuple[str, str, str, str]:
+    def setup_files(self, out_file_names=None) -> Tuple[str, str, str, str]:
         """Set up input and output files for TE processing.
 
         Creates necessary file lists and paths for TE detect and stitch operations.
@@ -440,8 +440,13 @@ def setup_files(self) -> Tuple[str, str, str, str]:
         # write store to in-memory file
         raw_files, mems = self.dump_raw_data()
 
+        # make sure if file names are passed that there's one per member
+        if out_file_names:
+            if len(out_file_names) != len(mems):
+                raise ValueError(f'{len(out_file_names)} passed for {len(mems)} ensemble members')
+
         ins, outs, node_files, track_files = [], [], [], []
-        for mem, raw_dat in zip(mems, raw_files):
+        for ii, (mem, raw_dat) in enumerate(zip(mems, raw_files)):
             # in_files to file
             _ins = os.path.join(self.ram_dir, f'input_files_{self.rank:04d}_mem_{mem:05d}.txt')
             with open(_ins, 'w') as fl:
@@ -454,7 +459,8 @@ def setup_files(self) -> Tuple[str, str, str, str]:
             with open(_outs, 'w') as fl:
                 fl.write(node_file + '\n')
 
-            track_file = os.path.join(self.store_dir, 'tracks_' + base_name + '.csv')
+            track_file = out_file_names[ii] if out_file_names else 'tracks_' + base_name + '.csv'
+            track_file = os.path.join(self.store_dir, track_file)
 
             ins.append(_ins)
             outs.append(_outs)
@@ -516,7 +522,7 @@ def run_te(command, print_output):
 
 
     @check_optional_dependencies()
-    def track_cyclones(self) -> None:
+    def track_cyclones(self, out_file_names=None) -> None:
         """Execute cyclone tracking using TempestExtremes.
 
         Runs the complete tracking pipeline including node detection
@@ -530,7 +536,7 @@ def track_cyclones(self) -> None:
         then = time.time()
 
         # set up TE helper files
-        insies, outsies, node_files, self.track_files = self.setup_files()
+        insies, outsies, node_files, self.track_files = self.setup_files(out_file_names)
 
         for ins, outs, node_file, track_file in zip(insies, outsies, node_files, self.track_files):
             # detect nodes
@@ -549,7 +555,7 @@ def track_cyclones(self) -> None:
         return
 
 
-    def __call__(self) -> None:
+    def __call__(self, out_file_names=None) -> None:
         """Make the class callable to execute cyclone tracking.
 
         Returns
@@ -557,7 +563,7 @@ def __call__(self) -> None:
         None
             Calls track_cyclones method
         """
-        return self.track_cyclones()
+        return self.track_cyclones(out_file_names)
 
 
     def tidy_up(self, insies: str, outsies: str) -> None:
@@ -769,7 +775,7 @@ def _check_for_failures(self) -> None:
                         self._has_failed = True
                         raise e  # Re-raise other exceptions too
 
-    def track_cyclones_async(self) -> Future:
+    def track_cyclones_async(self, out_file_names: str=None) -> Future:
         """Submit cyclone tracking to background thread pool.
 
         This method submits cyclone tracking operations to a background thread pool
@@ -805,7 +811,7 @@ def track_cyclones_async(self) -> Future:
 
         # Submit the task
         executor = get_tempest_executor()
-        future = executor.submit(self.track_cyclones)
+        future = executor.submit(self.track_cyclones, out_file_names)
 
         # Add to both global and instance task lists
         with _tempest_executor_lock:
@@ -961,7 +967,7 @@ def __del__(self) -> None:
             print(f"Error in AsyncTempestExtremes destructor: {e}")
 
 
-    def __call__(self) -> Future:
+    def __call__(self, out_file_names=None) -> Future:
         """Override call method to use async version by default.
 
         This method makes the class callable and uses the asynchronous
@@ -979,4 +985,4 @@ def __call__(self) -> Future:
         """
         # Check if any previous task failed and raise error immediately
         self._check_for_failures()
-        return self.track_cyclones_async()
+        return self.track_cyclones_async(out_file_names)

From 1e9bbe8c82b3b91813dc188bc996a012ac699ede Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 29 Sep 2025 06:56:26 -0700
Subject: [PATCH 11/69] array equal test

---
 earth2studio/models/dx/tempest_extremes.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index b77e572bc..21ed9d34a 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -485,7 +485,7 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         """
         # update store mem and time
         for dim in ['time', 'ensemble']:
-            if not (self.store.coords[dim] == coords[dim]).all():
+            if not np.array_equal(self.store.coords[dim], coords[dim]):
                 self.store.coords[dim] = coords[dim]
 
         # concatenate static data
@@ -775,6 +775,7 @@ def _check_for_failures(self) -> None:
                         self._has_failed = True
                         raise e  # Re-raise other exceptions too
 
+
     def track_cyclones_async(self, out_file_names: str=None) -> Future:
         """Submit cyclone tracking to background thread pool.
 

From d6be6dd5abbe7701db9767c23fe4af3df4c32d84 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 7 Oct 2025 02:09:49 -0700
Subject: [PATCH 12/69] first stable try

---
 earth2studio/models/dx/tempest_extremes.py | 157 +++++++++++++++++++--
 1 file changed, 146 insertions(+), 11 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 21ed9d34a..75a1e0dfe 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -693,6 +693,7 @@ def __init__(self, *args, **kwargs) -> None:
         self._dump_in_progress = threading.Event()
         self._dump_in_progress.set()  # Initially no dump in progress
         self._has_failed = False  # Track if any previous task failed
+        self._cleanup_done = False  # Track if cleanup has been performed
 
 
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
@@ -937,16 +938,126 @@ def wait_for_completion(self, timeout_per_task: int = 360) -> None:
             print("No background tasks to wait for.")
 
 
-    def __del__(self) -> None:
-        """Destructor that ensures all processes have finished before the class gets destroyed.
+    def _process_single_member(self, ins: str, outs: str, node_file: str, track_file: str) -> None:
+        """Process a single ensemble member (detect + stitch).
 
-        This method performs cleanup operations when the object is being destroyed,
-        ensuring all background tasks complete and resources are properly released.
+        This method is designed to run in a separate thread for parallel processing
+        of ensemble members.
+
+        Parameters
+        ----------
+        ins : str
+            Path to input file list for this member
+        outs : str
+            Path to output file list for this member
+        node_file : str
+            Path to node file for this member
+        track_file : str
+            Path to track file for this member
+
+        Raises
+        ------
+        ChildProcessError
+            If TempestExtremes detect or stitch operations fail
         """
+        then = time.time()
+
+        # detect nodes
+        self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
+                    print_output=self.print_te_output)
+
+        # stitch them together
+        self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', track_file],
+                    print_output=self.print_te_output)
+
+        if self.print_te_output:
+            print(f'took {(time.time() - then):.1f}s to track cyclones for one member')
+
+
+    def track_cyclones(self, out_file_names=None) -> None:
+        """Execute cyclone tracking with parallel processing per ensemble member.
+
+        This override of the parent method dumps data synchronously (within this thread),
+        then spawns separate threads for each ensemble member's processing. This allows
+        multiple ensemble members to be processed in parallel while avoiding race
+        conditions during data dumping.
+
+        Uses a dedicated local thread pool for member processing to avoid conflicts
+        with the global executor during shutdown.
+
+        Parameters
+        ----------
+        out_file_names : list of str, optional
+            Custom output file names for each ensemble member
+
+        Raises
+        ------
+        ChildProcessError
+            If TempestExtremes detect or stitch operations fail for any member
+        """
+        then = time.time()
+
+        # Dump data and setup files (synchronous within this thread to avoid race conditions)
+        insies, outsies, node_files, self.track_files = self.setup_files(out_file_names)
+
+        # Create a dedicated local thread pool for member processing
+        # This avoids conflicts with the global executor during shutdown
+        n_members = len(insies)
+        max_workers = min(n_members, os.cpu_count() or 1)
+
+        with ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="te_members") as executor:
+            # Submit one task per member to process in parallel
+            member_futures = []
+
+            for ins, outs, node_file, track_file in zip(insies, outsies, node_files, self.track_files):
+                future = executor.submit(self._process_single_member, ins, outs, node_file, track_file)
+                member_futures.append(future)
+
+            # Wait for all members to complete
+            exceptions = []
+            for i, future in enumerate(member_futures):
+                try:
+                    future.result()  # This will raise if the task failed
+                except Exception as e:
+                    print(f"Member {i+1} processing failed: {e}")
+                    exceptions.append((i, e))
+
+        # Raise if any failed
+        if exceptions:
+            error_msg = f"Processing failed for {len(exceptions)} member(s): {exceptions}"
+            raise ChildProcessError(error_msg)
+
+        # Clean up after all are done
+        self.tidy_up(insies, outsies)
+
+        print(f'took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members')
+
+
+    def cleanup(self, timeout_per_task: int = 360) -> None:
+        """Explicitly clean up and wait for all background tasks to complete.
+
+        This method should be called before the object is destroyed or the program exits
+        to ensure all cyclone tracking tasks complete successfully.
+
+        Parameters
+        ----------
+        timeout_per_task : int, optional
+            Timeout in seconds for each task, by default 360
+
+        Raises
+        ------
+        ChildProcessError
+            If any background task failed
+        Exception
+            If any task failed with other exceptions
+        """
+        if self._cleanup_done:
+            return
+
         try:
             # Wait for any ongoing dump to complete
             if hasattr(self, '_dump_in_progress'):
-                self._dump_in_progress.wait(timeout=60)  # Max 1 minute wait
+                self._dump_in_progress.wait(timeout=60)
 
             # Wait for all instance tasks to complete
             if hasattr(self, '_instance_tasks') and hasattr(self, '_instance_lock'):
@@ -954,18 +1065,42 @@ def __del__(self) -> None:
                     tasks_to_wait = list(self._instance_tasks)
 
                 if tasks_to_wait:
-                    print(f"AsyncTempestExtremes is waiting for {len(tasks_to_wait)} tasks to complete...")
+                    print(f"AsyncTempestExtremes: waiting for {len(tasks_to_wait)} background tasks to complete...")
 
-                    for future in tasks_to_wait:
+                    for i, future in enumerate(tasks_to_wait):
                         try:
-                            future.result(timeout=30)  # Shorter timeout in destructor
+                            print(f"  Waiting for task {i+1}/{len(tasks_to_wait)}...")
+                            future.result(timeout=timeout_per_task)
+                            print(f"  Task {i+1}/{len(tasks_to_wait)} completed successfully")
                         except ChildProcessError as e:
-                            print(f"Task failed during cleanup with ChildProcessError: {e}")
-                            # Note: In destructor, we log but don't re-raise to avoid issues
+                            print(f"  Task {i+1}/{len(tasks_to_wait)} failed with ChildProcessError: {e}")
+                            raise  # Re-raise to propagate the error
                         except Exception as e:
-                            print(f"Task failed during cleanup: {e}")
+                            print(f"  Task {i+1}/{len(tasks_to_wait)} failed: {e}")
+                            raise  # Re-raise to propagate the error
+
+                    print(f"All {len(tasks_to_wait)} background tasks completed successfully")
+
+            self._cleanup_done = True
+
+        except Exception as e:
+            self._cleanup_done = True  # Mark as done even on failure to avoid retry
+            raise
+
+
+    def __del__(self) -> None:
+        """Destructor that ensures all processes have finished before the class gets destroyed.
+
+        This method performs cleanup operations when the object is being destroyed,
+        ensuring all background tasks complete and resources are properly released.
+        """
+        try:
+            if not self._cleanup_done:
+                print("AsyncTempestExtremes: Destructor called - cleaning up background tasks...")
+                self.cleanup(timeout_per_task=30)  # Shorter timeout in destructor
         except Exception as e:
             print(f"Error in AsyncTempestExtremes destructor: {e}")
+            # Note: In destructor, we log but don't re-raise to avoid issues during interpreter shutdown
 
 
     def __call__(self, out_file_names=None) -> Future:

From 1e9b2750934ce92afbafefe930192764e5b91e7b Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 7 Oct 2025 02:23:12 -0700
Subject: [PATCH 13/69] support for per-member parallel execution and lets user
 controll max num workers

---
 earth2studio/models/dx/tempest_extremes.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 75a1e0dfe..6b05f9244 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -678,11 +678,16 @@ class AsyncTempestExtremes(TempestExtremes):
         Special parameters:
         - timeout : int, optional
             Timeout in seconds for operations, by default 60
+        - max_workers : int, optional
+            Maximum number of worker threads for parallel ensemble member processing.
+            If None (default), uses min(n_members, cpu_count). If provided, caps
+            the number of workers to this value, by default None
     """
 
     def __init__(self, *args, **kwargs) -> None:
-        # Extract timeout parameter with default value
+        # Extract async-specific parameters with default values
         self.timeout = kwargs.pop('timeout', 60)
+        self.max_workers = kwargs.pop('max_workers', None)
 
         # Initialize the parent class
         super().__init__(*args, **kwargs)
@@ -1004,6 +1009,8 @@ def track_cyclones(self, out_file_names=None) -> None:
         # This avoids conflicts with the global executor during shutdown
         n_members = len(insies)
         max_workers = min(n_members, os.cpu_count() or 1)
+        if self.max_workers is not None:
+            max_workers = min(self.max_workers, max_workers)
 
         with ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="te_members") as executor:
             # Submit one task per member to process in parallel

From b5f5c18afcd7aeee136374996310d7ea4808b482 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 7 Oct 2025 05:18:55 -0700
Subject: [PATCH 14/69] precommit hooks

---
 earth2studio/models/dx/tempest_extremes.py | 496 ++++++++++++---------
 1 file changed, 293 insertions(+), 203 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 6b05f9244..5eee793e3 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -1,28 +1,29 @@
+import atexit
+import copy
 import os
 import shutil
-import time
-import copy
-import numpy as np
-import atexit
 import threading
-import torch
-
+import time
 from collections import OrderedDict
+from concurrent.futures import Future, ThreadPoolExecutor
 from subprocess import run
-from concurrent.futures import ThreadPoolExecutor, Future
-from typing import Optional, Tuple, Dict, List, Any, Union
+from typing import Any
 
+import numpy as np
+import torch
 from physicsnemo.distributed import DistributedManager
+
 from earth2studio.io import KVBackend
 from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
-
-from earth2studio.utils.imports import OptionalDependencyFailure, check_optional_dependencies
+from earth2studio.utils.imports import (
+    OptionalDependencyFailure,
+    check_optional_dependencies,
+)
 
 
-def tile_xx_to_yy(xx: torch.Tensor,
-                  xx_coords: CoordSystem,
-                  yy: torch.Tensor,
-                  yy_coords: CoordSystem) -> Tuple[torch.Tensor, CoordSystem]:
+def tile_xx_to_yy(
+    xx: torch.Tensor, xx_coords: CoordSystem, yy: torch.Tensor, yy_coords: CoordSystem
+) -> tuple[torch.Tensor, CoordSystem]:
     """Tile tensor xx to match the leading dimensions of tensor yy.
 
     Parameters
@@ -173,22 +174,24 @@ class generates.
         Additional keyword arguments for robustness
     """
 
-    def __init__(self,
-                 detect_cmd: str,
-                 stitch_cmd: str,
-                 input_vars: List[str],
-                 batch_size: int,
-                 n_steps: int,
-                 time_step: np.ndarray | List[np.timedelta64] | np.timedelta64,
-                 lats: np.ndarray | torch.Tensor,
-                 lons: np.ndarray | torch.Tensor,
-                 static_vars: Optional[torch.Tensor] = None,
-                 static_coords: Optional[Dict] = None,
-                 store_dir: Optional[str] = None,
-                 keep_raw_data: bool = False,
-                 print_te_output: bool = False,
-                 use_ram: bool = True,
-                 **kwargs): # leave kwars to make call robust to switching between this and async version
+    def __init__(
+        self,
+        detect_cmd: str,
+        stitch_cmd: str,
+        input_vars: list[str],
+        batch_size: int,
+        n_steps: int,
+        time_step: np.ndarray | list[np.timedelta64] | np.timedelta64,
+        lats: np.ndarray | torch.Tensor,
+        lons: np.ndarray | torch.Tensor,
+        store_dir: str,
+        static_vars: torch.Tensor | None = None,
+        static_coords: dict | None = None,
+        keep_raw_data: bool = False,
+        print_te_output: bool = False,
+        use_ram: bool = True,
+        **kwargs: Any,
+    ):  # leave kwars to make call robust to switching between this and async version
 
         self.rank = DistributedManager().rank
         self.print_te_output = print_te_output
@@ -203,11 +206,16 @@ def __init__(self,
         self.lons = lons
 
         if isinstance(self.time_step, np.ndarray) or isinstance(self.time_step, list):
-            assert(len(self.time_step)==1)
+            if not len(self.time_step) == 1:
+                raise ValueError(
+                    "TempestExtremes connector currently only accepts multiple ensemble members from a single ICs."
+                )
             self.time_step = self.time_step[0]
-        if (static_vars==None) != (static_coords==None):
-            raise ValueError("provide both values and coords for static fields or don't." +
-                             " currently, only one of them is provided")
+        if (static_vars is None) != (static_coords is None):
+            raise ValueError(
+                "provide both values and coords for static fields or don't."
+                + " currently, only one of them is provided"
+            )
 
         self.format_tempestextremes_commands(detect_cmd, stitch_cmd)
 
@@ -221,7 +229,6 @@ def __init__(self,
 
         return
 
-
     def format_tempestextremes_commands(self, detect_cmd: str, stitch_cmd: str) -> None:
         """Format TempestExtremes commands by removing input/output file arguments.
         These arguments are populated dynamically by the method.
@@ -236,25 +243,35 @@ def format_tempestextremes_commands(self, detect_cmd: str, stitch_cmd: str) -> N
         self.detect_cmd = detect_cmd.split(" ")
         self.stitch_cmd = stitch_cmd.split(" ")
 
-        self.detect_cmd = self.remove_arguments(self.detect_cmd, ['--in_data_list', '--out_file_list'])
-        self.stitch_cmd = self.remove_arguments(self.stitch_cmd, ['--in', '--out'])
+        self.detect_cmd = self.remove_arguments(
+            self.detect_cmd, ["--in_data_list", "--out_file_list"]
+        )
+        self.stitch_cmd = self.remove_arguments(self.stitch_cmd, ["--in", "--out"])
 
         return
 
+    def check_tempest_extremes_availability(self) -> None:
+        """Check if TempestExtremes executables are available on the system.
+
+        Verifies that both detect and stitch commands can be executed by attempting
+        to run them. If either command fails, raises an OptionalDependencyFailure.
 
-    def check_tempest_extremes_availability(self):
+        Raises
+        ------
+        OptionalDependencyFailure
+            If TempestExtremes executables are not available or cannot be executed
+        """
         for cmd in (self.detect_cmd[0], self.stitch_cmd[0]):
             try:
-                run(cmd, capture_output=True)
-            except:
+                run(cmd, capture_output=True)  # noqa: S603
+            except Exception as _:
                 # TODO discuss implementation of below before merging
                 OptionalDependencyFailure("tempest_extremes")
 
         return
 
-
     @staticmethod
-    def remove_arguments(cmd: List[str], args: List[str]) -> List[str]:
+    def remove_arguments(cmd: list[str], args: list[str]) -> list[str]:
         """Remove specified arguments from command list.
 
         Parameters
@@ -272,17 +289,14 @@ def remove_arguments(cmd: List[str], args: List[str]) -> List[str]:
         for arg in args:
             if arg in cmd:
                 remove = [cmd.index(arg)]
-                if len(cmd) > remove[0]+1:
-                    if not cmd[remove[0]+1].startswith('--'):
-                        remove.append(remove[0]+1)
+                if len(cmd) > remove[0] + 1:
+                    if not cmd[remove[0] + 1].startswith("--"):
+                        remove.append(remove[0] + 1)
                 cmd = [_arg for ii, _arg in enumerate(cmd) if ii not in remove]
 
         return cmd
 
-
-    def setup_output_directories(self,
-                                 use_ram: bool,
-                                 store_dir: Optional[str]) -> None:
+    def setup_output_directories(self, use_ram: bool, store_dir: str) -> None:
         """Set up output directories for final data storage and for storing
         auxiliary files.
 
@@ -293,25 +307,30 @@ def setup_output_directories(self,
         store_dir : Optional[str]
             Base storage directory path
         """
-        self.store_dir = os.path.abspath(os.path.join(store_dir, 'cyclone_tracks_te'))
+        self.store_dir = os.path.abspath(os.path.join(store_dir, "cyclone_tracks_te"))
 
-        if self.rank==0:
-            os.makedirs(self.store_dir, exist_ok=True) # output for track data
+        if self.rank == 0:
+            os.makedirs(self.store_dir, exist_ok=True)  # output for track data
             if self.keep_raw:
-                os.makedirs(os.path.join(self.store_dir, 'raw_data'), exist_ok=True) # output for raw data
+                os.makedirs(
+                    os.path.join(self.store_dir, "raw_data"), exist_ok=True
+                )  # output for raw data
 
         if use_ram:
-            self.ram_dir = os.path.join('/dev/shm', 'cyclone_tracking')
-            if DistributedManager().local_rank==0:
-                os.makedirs(self.ram_dir, exist_ok=True) # RAM location for processing fields
+            self.ram_dir = os.path.join("/dev/shm", "cyclone_tracking")  # noqa: S108
+            if DistributedManager().local_rank == 0:
+                os.makedirs(
+                    self.ram_dir, exist_ok=True
+                )  # RAM location for processing fields
         else:
-            self.ram_dir = os.path.join(self.store_dir, 'raw_data')
-            if self.rank==0:
-                os.makedirs(self.ram_dir, exist_ok=True) # disk location for processing fields
+            self.ram_dir = os.path.join(self.store_dir, "raw_data")
+            if self.rank == 0:
+                os.makedirs(
+                    self.ram_dir, exist_ok=True
+                )  # disk location for processing fields
 
         return
 
-
     @check_optional_dependencies()
     def initialise_store_coords(self) -> None:
         """Initialize coordinate system for data storage.
@@ -322,22 +341,23 @@ def initialise_store_coords(self) -> None:
         # extract time step in case it's passed as list or array
         _input_vars = self.input_vars
         if self.static_coords:
-            _input_vars = np.array(list(self.input_vars) + list(self.static_coords['variable']))
+            _input_vars = np.array(
+                list(self.input_vars) + list(self.static_coords["variable"])
+            )
 
         self._store_coords = OrderedDict(
             {
-                'lead_time': np.asarray(
-                                    [self.time_step * i for i in range(self.n_steps + 1)]
-                                ).flatten(),
-                'variable': np.array(_input_vars),
-                'lat': np.array(self.lats),
-                'lon': np.array(self.lons),
+                "lead_time": np.asarray(
+                    [self.time_step * i for i in range(self.n_steps + 1)]
+                ).flatten(),
+                "variable": np.array(_input_vars),
+                "lat": np.array(self.lats),
+                "lon": np.array(self.lons),
             }
         )
 
         return
 
-
     @property
     def input_coords(self) -> OrderedDict:
         """Returns the coords of the data to be passed to record_state.
@@ -348,21 +368,19 @@ def input_coords(self) -> OrderedDict:
             Coordinate system for input data
         """
         return OrderedDict(
-                            {
-                                'time': np.empty(0),
-                                'lead_time': np.asarray(
-                                                    [self.time_step * i for i in range(self.n_steps + 1)]
-                                                ).flatten(),
-                                'variable': np.array(self.input_vars),
-                                'lat': np.array(self.lats),
-                                'lon': np.array(self.lons),
-                            }
-                          )
-
+            {
+                "time": np.empty(0),
+                "lead_time": np.asarray(
+                    [self.time_step * i for i in range(self.n_steps + 1)]
+                ).flatten(),
+                "variable": np.array(self.input_vars),
+                "lat": np.array(self.lats),
+                "lon": np.array(self.lons),
+            }
+        )
 
     def initialise_te_kvstore(self) -> None:
-        """Initialize the KV store that stores model output in TE compatible format.
-        """
+        """Initialize the KV store that stores model output in TE compatible format."""
         # create store
         self.store = KVBackend()
 
@@ -371,7 +389,7 @@ def initialise_te_kvstore(self) -> None:
         out_vars = oco.pop("variable")
 
         oco["time"] = np.array([None])
-        oco["ensemble"] = np.array([None]*self.batch_size)
+        oco["ensemble"] = np.array([None] * self.batch_size)
         oco.move_to_end("time", last=False)
         oco.move_to_end("ensemble", last=False)
 
@@ -379,8 +397,7 @@ def initialise_te_kvstore(self) -> None:
 
         return
 
-
-    def dump_raw_data(self) -> str:
+    def dump_raw_data(self) -> tuple[list[str], np.ndarray]:
         """Dump raw data from store to NetCDF file.
 
         Morphs the store to TempestExtremes-compatible [time, lat, lon] structure
@@ -391,26 +408,30 @@ def dump_raw_data(self) -> str:
         str
             Path to the created NetCDF file
         """
-        if len(self.store.coords['time']) > 1:
-            raise ValueError('Currently, TempestExtremes interface only works for single IC per evaluation cycle.')
+        if len(self.store.coords["time"]) > 1:
+            raise ValueError(
+                "Currently, TempestExtremes interface only works for single IC per evaluation cycle."
+            )
 
-        ic = self.store.coords['time'][0]
-        mems = self.store.coords['ensemble']
+        ic = self.store.coords["time"][0]
+        mems = self.store.coords["ensemble"]
 
         # morph store to tempes extremes-compatible [time, lat, lon] structure
-        lead_times = self.store.coords.pop('lead_time')
-        self.store.coords['time'] = [ic + lt for lt in lead_times]
+        lead_times = self.store.coords.pop("lead_time")
+        self.store.coords["time"] = [ic + lt for lt in lead_times]
 
         for var in self.store.root.keys():
-            self.store.root[var] = self.store.root[var].squeeze((1))
-            self.store.dims[var]=['ensemble', 'time', 'lat', 'lon']
+            self.store.root[var] = self.store.root[var].squeeze(1)
+            self.store.dims[var] = ["ensemble", "time", "lat", "lon"]
 
         # for each member, write te compatible nc file
         raw_files = []
 
         # in case last batch has less ensemble members than batch size, expand coords of store so to_xarray() does not fail
         if len(mems) < self.batch_size:
-            self.store.coords['ensemble'] = list(self.store.coords['ensemble']) + [None]*(self.batch_size - len(mems))
+            self.store.coords["ensemble"] = list(self.store.coords["ensemble"]) + [
+                None
+            ] * (self.batch_size - len(mems))
 
         for mem in mems:
             raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc"
@@ -418,7 +439,7 @@ def dump_raw_data(self) -> str:
             raw_files.append(raw_dat)
             kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"}
 
-            _store = self.store.to_xarray().sel(ensemble=mem).drop_vars('ensemble')
+            _store = self.store.to_xarray().sel(ensemble=mem).drop_vars("ensemble")
             _store.to_netcdf(**kw_args)
 
         # new clean store
@@ -426,40 +447,51 @@ def dump_raw_data(self) -> str:
 
         return raw_files, mems
 
-
-    def setup_files(self, out_file_names=None) -> Tuple[str, str, str, str]:
+    def setup_files(
+        self, out_file_names: list[str] | None = None
+    ) -> tuple[list[str], list[str], list[str], list[str]]:
         """Set up input and output files for TE processing.
 
         Creates necessary file lists and paths for TE detect and stitch operations.
 
         Returns
         -------
-        Tuple[str, str, str, str]
+        tuple[list[str], list[str], list[str], list[str]]
             Tuple containing (input_file_list, output_file_list, node_file, track_file)
         """
         # write store to in-memory file
         raw_files, mems = self.dump_raw_data()
 
         # make sure if file names are passed that there's one per member
-        if out_file_names:
+        if out_file_names is not None:
             if len(out_file_names) != len(mems):
-                raise ValueError(f'{len(out_file_names)} passed for {len(mems)} ensemble members')
+                raise ValueError(
+                    f"{len(out_file_names)} passed for {len(mems)} ensemble members"
+                )
 
         ins, outs, node_files, track_files = [], [], [], []
         for ii, (mem, raw_dat) in enumerate(zip(mems, raw_files)):
             # in_files to file
-            _ins = os.path.join(self.ram_dir, f'input_files_{self.rank:04d}_mem_{mem:05d}.txt')
-            with open(_ins, 'w') as fl:
-                fl.write(raw_dat + '\n')
-
-            _outs = os.path.join(self.ram_dir, f'output_files_{self.rank:04d}_mem_{mem:05d}.txt')
+            _ins = os.path.join(
+                self.ram_dir, f"input_files_{self.rank:04d}_mem_{mem:05d}.txt"
+            )
+            with open(_ins, "w") as fl:
+                fl.write(raw_dat + "\n")
+
+            _outs = os.path.join(
+                self.ram_dir, f"output_files_{self.rank:04d}_mem_{mem:05d}.txt"
+            )
             base_name = os.path.basename(raw_dat)
-            base_name = base_name.rsplit('.',1)[0]
-            node_file = os.path.join(self.ram_dir, base_name + f'_mem_{mem:05d}_tc_centres.txt')
-            with open(_outs, 'w') as fl:
-                fl.write(node_file + '\n')
-
-            track_file = out_file_names[ii] if out_file_names else 'tracks_' + base_name + '.csv'
+            base_name = base_name.rsplit(".", 1)[0]
+            node_file = os.path.join(
+                self.ram_dir, base_name + f"_mem_{mem:05d}_tc_centres.txt"
+            )
+            with open(_outs, "w") as fl:
+                fl.write(node_file + "\n")
+
+            track_file = (
+                out_file_names[ii] if out_file_names else "tracks_" + base_name + ".csv"
+            )
             track_file = os.path.join(self.store_dir, track_file)
 
             ins.append(_ins)
@@ -469,7 +501,6 @@ def setup_files(self, out_file_names=None) -> Tuple[str, str, str, str]:
 
         return ins, outs, node_files, track_files
 
-
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         """Record state data to the internal store.
 
@@ -484,15 +515,17 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
             Coordinate system for the input data
         """
         # update store mem and time
-        for dim in ['time', 'ensemble']:
+        for dim in ["time", "ensemble"]:
             if not np.array_equal(self.store.coords[dim], coords[dim]):
                 self.store.coords[dim] = coords[dim]
 
         # concatenate static data
-        if self.static_coords:
+        if self.static_vars is not None:
             self.static_vars = self.static_vars.to(xx.device)
             if len(xx.shape) > len(self.static_vars.shape):
-                _static_vars, _static_coords = tile_xx_to_yy(self.static_vars, self.static_coords, xx, coords)
+                _static_vars, _static_coords = tile_xx_to_yy(
+                    self.static_vars, self.static_coords, xx, coords
+                )
             else:
                 _static_vars, _static_coords = self.static_vars, self.static_coords
             xx, coords = cat_coords(xx, coords, _static_vars, _static_coords)
@@ -503,26 +536,40 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
 
         return
 
-
     @staticmethod
-    def run_te(command, print_output):
+    def run_te(command: list[str], print_output: bool) -> None:
+        """Run TempestExtremes command and handle output.
+
+        Parameters
+        ----------
+        command : list[str]
+            Command to execute as list of strings
+        print_output : bool
+            Whether to print command output to terminal
+
+        Raises
+        ------
+        ChildProcessError
+            If TempestExtremes command fails (detected by "EXCEPTION" in output)
+        """
         # detect nodes
-        out = run(command, capture_output=True)
+        out = run(command, capture_output=True)  # noqa: S603
 
         # Print output to terminal if requested
         if print_output:
-            print(out.stdout.decode('utf-8'))
+            print(out.stdout.decode("utf-8"))
 
         # unfortunately, TE does not fail with proper returncode
-        if 'EXCEPTION' in out.stdout.decode('utf-8'):
-            print(out.stdout.decode('utf-8'))
-            raise ChildProcessError(f'\nERROR: {command[0]} failed, see output above from TempestExtremes for details.')
+        if "EXCEPTION" in out.stdout.decode("utf-8"):
+            print(out.stdout.decode("utf-8"))
+            raise ChildProcessError(
+                f"\nERROR: {command[0]} failed, see output above from TempestExtremes for details."
+            )
 
         return
 
-
     @check_optional_dependencies()
-    def track_cyclones(self, out_file_names=None) -> None:
+    def track_cyclones(self, out_file_names: list[str] | None = None) -> None:
         """Execute cyclone tracking using TempestExtremes.
 
         Runs the complete tracking pipeline including node detection
@@ -538,24 +585,30 @@ def track_cyclones(self, out_file_names=None) -> None:
         # set up TE helper files
         insies, outsies, node_files, self.track_files = self.setup_files(out_file_names)
 
-        for ins, outs, node_file, track_file in zip(insies, outsies, node_files, self.track_files):
+        for ins, outs, node_file, track_file in zip(
+            insies, outsies, node_files, self.track_files
+        ):
             # detect nodes
-            self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
-                        print_output=self.print_te_output)
+            self.run_te(
+                command=self.detect_cmd
+                + ["--in_data_list", ins, "--out_file_list", outs],
+                print_output=self.print_te_output,
+            )
 
             # stitch them together
-            self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', track_file],
-                        print_output=self.print_te_output)
+            self.run_te(
+                command=self.stitch_cmd + ["--in", node_file, "--out", track_file],
+                print_output=self.print_te_output,
+            )
 
         # remove helper files
         self.tidy_up(insies, outsies)
 
-        print(f'took {(time.time() - then):.1f}s to track cyclones')
+        print(f"took {(time.time() - then):.1f}s to track cyclones")
 
         return
 
-
-    def __call__(self, out_file_names=None) -> None:
+    def __call__(self, out_file_names: list[str] | None = None) -> None:
         """Make the class callable to execute cyclone tracking.
 
         Returns
@@ -565,8 +618,7 @@ def __call__(self, out_file_names=None) -> None:
         """
         return self.track_cyclones(out_file_names)
 
-
-    def tidy_up(self, insies: str, outsies: str) -> None:
+    def tidy_up(self, insies: list[str], outsies: list[str]) -> None:
         """Clean up temporary files after processing.
 
         Handles raw data files based on keep_raw_data setting and
@@ -580,24 +632,24 @@ def tidy_up(self, insies: str, outsies: str) -> None:
             Path to output file list
         """
         for ins, outs in zip(insies, outsies):
-            with open(ins, 'r') as fl:
+            with open(ins) as fl:
                 raw_dat = fl.read().strip()
 
-            if self.keep_raw: # move field data to disk
-                out_path = os.path.join(self.store_dir, 'raw_data')
+            if self.keep_raw:  # move field data to disk
+                out_path = os.path.join(self.store_dir, "raw_data")
                 raw_dest = os.path.join(out_path, os.path.basename(raw_dat))
-                if raw_dat != raw_dest: # nothing to do if use_ram=False
+                if raw_dat != raw_dest:  # nothing to do if use_ram=False
                     if os.path.exists(raw_dest):
                         os.remove(raw_dest)
                     shutil.move(raw_dat, out_path)
-            else:             # delete field data
+            else:  # delete field data
                 os.remove(raw_dat)
 
             # delete TE helper files
             files = [ins, outs]
-            with open(outs, 'r') as fl:
-                for line in fl:
-                    files.append(line.strip())
+            with open(outs) as fl:
+                _files = [line.strip() for line in fl]
+            files.extend(_files)
 
             for file in files:
                 os.remove(file)
@@ -606,9 +658,9 @@ def tidy_up(self, insies: str, outsies: str) -> None:
 
 
 # Global thread pool for background TempestExtremes processing
-_tempest_executor = None
-_tempest_background_tasks = []
-_tempest_executor_lock = threading.Lock()
+_tempest_executor: ThreadPoolExecutor | None = None
+_tempest_background_tasks: list[Future] = []
+_tempest_executor_lock: threading.Lock = threading.Lock()
 
 
 def get_tempest_executor() -> ThreadPoolExecutor:
@@ -618,7 +670,9 @@ def get_tempest_executor() -> ThreadPoolExecutor:
         if _tempest_executor is None:
             # Reduced worker count for long-running tasks to avoid resource exhaustion
             max_workers = min(4, os.cpu_count() or 1)
-            _tempest_executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="tempest_extremes")
+            _tempest_executor = ThreadPoolExecutor(
+                max_workers=max_workers, thread_name_prefix="tempest_extremes"
+            )
             # Ensure cleanup on exit
             atexit.register(cleanup_tempest_executor)
     return _tempest_executor
@@ -645,9 +699,13 @@ def cleanup_tempest_executor(timeout_per_task: int = 360) -> None:
             child_process_errors = []
             for i, future in enumerate(_tempest_background_tasks):
                 try:
-                    future.result(timeout=timeout_per_task)  # Configurable timeout per task
+                    future.result(
+                        timeout=timeout_per_task
+                    )  # Configurable timeout per task
                 except ChildProcessError as e:
-                    print(f"Background TempestExtremes task {i+1} failed with ChildProcessError: {e}")
+                    print(
+                        f"Background TempestExtremes task {i+1} failed with ChildProcessError: {e}"
+                    )
                     child_process_errors.append(e)
                 except Exception as e:
                     print(f"Background TempestExtremes task {i+1} failed: {e}")
@@ -657,7 +715,9 @@ def cleanup_tempest_executor(timeout_per_task: int = 360) -> None:
 
             # Re-raise ChildProcessError if any occurred
             if child_process_errors:
-                raise ChildProcessError(f"One or more background TempestExtremes tasks failed: {child_process_errors}")
+                raise ChildProcessError(
+                    f"One or more background TempestExtremes tasks failed: {child_process_errors}"
+                )
 
 
 class AsyncTempestExtremes(TempestExtremes):
@@ -684,23 +744,22 @@ class AsyncTempestExtremes(TempestExtremes):
             the number of workers to this value, by default None
     """
 
-    def __init__(self, *args, **kwargs) -> None:
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
         # Extract async-specific parameters with default values
-        self.timeout = kwargs.pop('timeout', 60)
-        self.max_workers = kwargs.pop('max_workers', None)
+        self.timeout = kwargs.pop("timeout", 60)
+        self.max_workers = kwargs.pop("max_workers", None)
 
         # Initialize the parent class
         super().__init__(*args, **kwargs)
 
         # Track background tasks for this instance
-        self._instance_tasks = []
+        self._instance_tasks: list[Future] = []
         self._instance_lock = threading.Lock()
         self._dump_in_progress = threading.Event()
         self._dump_in_progress.set()  # Initially no dump in progress
         self._has_failed = False  # Track if any previous task failed
         self._cleanup_done = False  # Track if cleanup has been performed
 
-
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         """Record state but ensure dump_raw_data has finished successfully before proceeding.
 
@@ -728,8 +787,7 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         # Call parent's record_state method
         super().record_state(xx, coords)
 
-
-    def dump_raw_data(self) -> str:
+    def dump_raw_data(self) -> tuple[list[str], np.ndarray]:
         """Dump raw data with synchronization to prevent concurrent access.
 
         Uses threading events to ensure only one dump operation occurs at a time
@@ -737,8 +795,8 @@ def dump_raw_data(self) -> str:
 
         Returns
         -------
-        str
-            Path to the created NetCDF file
+        tuple[list[str], np.ndarray]
+            Tuple containing list of raw file paths and array of member indices
         """
         # Signal that dump is in progress
         self._dump_in_progress.clear()
@@ -751,7 +809,6 @@ def dump_raw_data(self) -> str:
             # Signal that dump is complete
             self._dump_in_progress.set()
 
-
     def _check_for_failures(self) -> None:
         """Check if any previous tasks failed with ChildProcessError and raise immediately.
 
@@ -766,7 +823,9 @@ def _check_for_failures(self) -> None:
             If any task failed with a different exception type
         """
         if self._has_failed:
-            raise ChildProcessError("Previous cyclone tracking task failed - stopping execution")
+            raise ChildProcessError(
+                "Previous cyclone tracking task failed - stopping execution"
+            )
 
         # Check completed tasks for failures
         with self._instance_lock:
@@ -781,8 +840,7 @@ def _check_for_failures(self) -> None:
                         self._has_failed = True
                         raise e  # Re-raise other exceptions too
 
-
-    def track_cyclones_async(self, out_file_names: str=None) -> Future:
+    def track_cyclones_async(self, out_file_names: list[str] | None = None) -> Future:
         """Submit cyclone tracking to background thread pool.
 
         This method submits cyclone tracking operations to a background thread pool
@@ -805,7 +863,9 @@ def track_cyclones_async(self, out_file_names: str=None) -> Future:
 
         # Clean up completed tasks to avoid memory buildup
         with _tempest_executor_lock:
-            _tempest_background_tasks = [f for f in _tempest_background_tasks if not f.done()]
+            _tempest_background_tasks = [
+                f for f in _tempest_background_tasks if not f.done()
+            ]
 
         with self._instance_lock:
             self._instance_tasks = [f for f in self._instance_tasks if not f.done()]
@@ -814,7 +874,9 @@ def track_cyclones_async(self, out_file_names: str=None) -> Future:
         with _tempest_executor_lock:
             running_tasks = len(_tempest_background_tasks)
             if running_tasks >= 3:  # Conservative limit for long-running tasks
-                print(f"Warning: {running_tasks} TempestExtremes tasks already running. Consider waiting for some to complete.")
+                print(
+                    f"Warning: {running_tasks} TempestExtremes tasks already running. Consider waiting for some to complete."
+                )
 
         # Submit the task
         executor = get_tempest_executor()
@@ -829,8 +891,7 @@ def track_cyclones_async(self, out_file_names: str=None) -> Future:
 
         return future
 
-
-    def get_task_status(self) -> Dict[str, int]:
+    def get_task_status(self) -> dict[str, int]:
         """Get status of background tasks for this instance.
 
         Returns
@@ -845,20 +906,27 @@ def get_task_status(self) -> Dict[str, int]:
         """
         with self._instance_lock:
             running = sum(1 for f in self._instance_tasks if f.running())
-            pending = sum(1 for f in self._instance_tasks if not f.done() and not f.running())
+            pending = sum(
+                1 for f in self._instance_tasks if not f.done() and not f.running()
+            )
             completed = sum(1 for f in self._instance_tasks if f.done())
-            failed = sum(1 for f in self._instance_tasks if f.done() and f.exception() is not None)
+            failed = sum(
+                1
+                for f in self._instance_tasks
+                if f.done() and f.exception() is not None
+            )
 
             return {
                 "running": running,
                 "pending": pending,
                 "completed": completed,
                 "failed": failed,
-                "total": len(self._instance_tasks)
+                "total": len(self._instance_tasks),
             }
 
-
-    def get_task_results(self, raise_on_error: bool = True) -> Union[Dict[str, Any], Tuple[List, List]]:
+    def get_task_results(
+        self, raise_on_error: bool = True
+    ) -> dict[str, Any] | tuple[list[Any], list[tuple[str, Exception]]]:
         """Get results from all completed tasks.
 
         Parameters
@@ -869,7 +937,7 @@ def get_task_results(self, raise_on_error: bool = True) -> Union[Dict[str, Any],
 
         Returns
         -------
-        Union[Dict[str, Any], Tuple[List, List]]
+        Union[Dict[str, Any], tuple[list[Any], list[tuple[str, Exception]]]]
             If raise_on_error=True: dict with successful results
             If raise_on_error=False: tuple of (results, exceptions)
 
@@ -884,7 +952,7 @@ def get_task_results(self, raise_on_error: bool = True) -> Union[Dict[str, Any],
             tasks_to_check = list(self._instance_tasks)
 
         results = []
-        exceptions = []
+        exceptions: list[tuple[str, Exception]] = []
 
         for i, future in enumerate(tasks_to_check):
             if future.done():
@@ -894,18 +962,17 @@ def get_task_results(self, raise_on_error: bool = True) -> Union[Dict[str, Any],
                 except ChildProcessError as e:
                     if raise_on_error:
                         raise  # Re-raise ChildProcessError immediately
-                    exceptions.append(('ChildProcessError', e))
+                    exceptions.append(("ChildProcessError", e))
                 except Exception as e:
                     if raise_on_error:
                         raise  # Re-raise any other exceptions
-                    exceptions.append(('Exception', e))
+                    exceptions.append(("Exception", e))
 
         if raise_on_error:
             return {"results": results, "task_count": len(tasks_to_check)}
         else:
             return results, exceptions
 
-
     def wait_for_completion(self, timeout_per_task: int = 360) -> None:
         """Wait for all background tasks associated with this instance to complete.
 
@@ -927,7 +994,9 @@ def wait_for_completion(self, timeout_per_task: int = 360) -> None:
             tasks_to_wait = list(self._instance_tasks)
 
         if tasks_to_wait:
-            print(f"Waiting for {len(tasks_to_wait)} TempestExtremes tasks to complete...")
+            print(
+                f"Waiting for {len(tasks_to_wait)} TempestExtremes tasks to complete..."
+            )
 
             for i, future in enumerate(tasks_to_wait):
                 try:
@@ -942,8 +1011,9 @@ def wait_for_completion(self, timeout_per_task: int = 360) -> None:
         else:
             print("No background tasks to wait for.")
 
-
-    def _process_single_member(self, ins: str, outs: str, node_file: str, track_file: str) -> None:
+    def _process_single_member(
+        self, ins: str, outs: str, node_file: str, track_file: str
+    ) -> None:
         """Process a single ensemble member (detect + stitch).
 
         This method is designed to run in a separate thread for parallel processing
@@ -968,18 +1038,21 @@ def _process_single_member(self, ins: str, outs: str, node_file: str, track_file
         then = time.time()
 
         # detect nodes
-        self.run_te(command=self.detect_cmd + ['--in_data_list', ins, '--out_file_list', outs],
-                    print_output=self.print_te_output)
+        self.run_te(
+            command=self.detect_cmd + ["--in_data_list", ins, "--out_file_list", outs],
+            print_output=self.print_te_output,
+        )
 
         # stitch them together
-        self.run_te(command=self.stitch_cmd + ['--in', node_file, '--out', track_file],
-                    print_output=self.print_te_output)
+        self.run_te(
+            command=self.stitch_cmd + ["--in", node_file, "--out", track_file],
+            print_output=self.print_te_output,
+        )
 
         if self.print_te_output:
-            print(f'took {(time.time() - then):.1f}s to track cyclones for one member')
-
+            print(f"took {(time.time() - then):.1f}s to track cyclones for one member")
 
-    def track_cyclones(self, out_file_names=None) -> None:
+    def track_cyclones(self, out_file_names: list[str] | None = None) -> None:
         """Execute cyclone tracking with parallel processing per ensemble member.
 
         This override of the parent method dumps data synchronously (within this thread),
@@ -1012,12 +1085,18 @@ def track_cyclones(self, out_file_names=None) -> None:
         if self.max_workers is not None:
             max_workers = min(self.max_workers, max_workers)
 
-        with ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="te_members") as executor:
+        with ThreadPoolExecutor(
+            max_workers=max_workers, thread_name_prefix="te_members"
+        ) as executor:
             # Submit one task per member to process in parallel
             member_futures = []
 
-            for ins, outs, node_file, track_file in zip(insies, outsies, node_files, self.track_files):
-                future = executor.submit(self._process_single_member, ins, outs, node_file, track_file)
+            for ins, outs, node_file, track_file in zip(
+                insies, outsies, node_files, self.track_files
+            ):
+                future = executor.submit(
+                    self._process_single_member, ins, outs, node_file, track_file
+                )
                 member_futures.append(future)
 
             # Wait for all members to complete
@@ -1031,14 +1110,17 @@ def track_cyclones(self, out_file_names=None) -> None:
 
         # Raise if any failed
         if exceptions:
-            error_msg = f"Processing failed for {len(exceptions)} member(s): {exceptions}"
+            error_msg = (
+                f"Processing failed for {len(exceptions)} member(s): {exceptions}"
+            )
             raise ChildProcessError(error_msg)
 
         # Clean up after all are done
         self.tidy_up(insies, outsies)
 
-        print(f'took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members')
-
+        print(
+            f"took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members"
+        )
 
     def cleanup(self, timeout_per_task: int = 360) -> None:
         """Explicitly clean up and wait for all background tasks to complete.
@@ -1063,38 +1145,45 @@ def cleanup(self, timeout_per_task: int = 360) -> None:
 
         try:
             # Wait for any ongoing dump to complete
-            if hasattr(self, '_dump_in_progress'):
+            if hasattr(self, "_dump_in_progress"):
                 self._dump_in_progress.wait(timeout=60)
 
             # Wait for all instance tasks to complete
-            if hasattr(self, '_instance_tasks') and hasattr(self, '_instance_lock'):
+            if hasattr(self, "_instance_tasks") and hasattr(self, "_instance_lock"):
                 with self._instance_lock:
                     tasks_to_wait = list(self._instance_tasks)
 
                 if tasks_to_wait:
-                    print(f"AsyncTempestExtremes: waiting for {len(tasks_to_wait)} background tasks to complete...")
+                    print(
+                        f"AsyncTempestExtremes: waiting for {len(tasks_to_wait)} background tasks to complete..."
+                    )
 
                     for i, future in enumerate(tasks_to_wait):
                         try:
                             print(f"  Waiting for task {i+1}/{len(tasks_to_wait)}...")
                             future.result(timeout=timeout_per_task)
-                            print(f"  Task {i+1}/{len(tasks_to_wait)} completed successfully")
+                            print(
+                                f"  Task {i+1}/{len(tasks_to_wait)} completed successfully"
+                            )
                         except ChildProcessError as e:
-                            print(f"  Task {i+1}/{len(tasks_to_wait)} failed with ChildProcessError: {e}")
+                            print(
+                                f"  Task {i+1}/{len(tasks_to_wait)} failed with ChildProcessError: {e}"
+                            )
                             raise  # Re-raise to propagate the error
                         except Exception as e:
                             print(f"  Task {i+1}/{len(tasks_to_wait)} failed: {e}")
                             raise  # Re-raise to propagate the error
 
-                    print(f"All {len(tasks_to_wait)} background tasks completed successfully")
+                    print(
+                        f"All {len(tasks_to_wait)} background tasks completed successfully"
+                    )
 
             self._cleanup_done = True
 
-        except Exception as e:
+        except Exception as _:
             self._cleanup_done = True  # Mark as done even on failure to avoid retry
             raise
 
-
     def __del__(self) -> None:
         """Destructor that ensures all processes have finished before the class gets destroyed.
 
@@ -1103,14 +1192,15 @@ def __del__(self) -> None:
         """
         try:
             if not self._cleanup_done:
-                print("AsyncTempestExtremes: Destructor called - cleaning up background tasks...")
+                print(
+                    "AsyncTempestExtremes: Destructor called - cleaning up background tasks..."
+                )
                 self.cleanup(timeout_per_task=30)  # Shorter timeout in destructor
         except Exception as e:
             print(f"Error in AsyncTempestExtremes destructor: {e}")
             # Note: In destructor, we log but don't re-raise to avoid issues during interpreter shutdown
 
-
-    def __call__(self, out_file_names=None) -> Future:
+    def __call__(self, out_file_names: list[str] | None = None) -> Future[None]:  # type: ignore[override]
         """Override call method to use async version by default.
 
         This method makes the class callable and uses the asynchronous
@@ -1118,7 +1208,7 @@ def __call__(self, out_file_names=None) -> Future:
 
         Returns
         -------
-        Future
+        Future[None]
             A Future object for the submitted cyclone tracking task
 
         Raises

From af8bc71a88c22ba4a0bd68fd936788ffe5ab860f Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 7 Oct 2025 06:00:14 -0700
Subject: [PATCH 15/69] vibe-coded some tests, need to be hand-tested and
 selected

---
 test/models/dx/test_tempest_extremes.py | 719 ++++++++++++++++++++++++
 1 file changed, 719 insertions(+)
 create mode 100644 test/models/dx/test_tempest_extremes.py

diff --git a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
new file mode 100644
index 000000000..8ebe55949
--- /dev/null
+++ b/test/models/dx/test_tempest_extremes.py
@@ -0,0 +1,719 @@
+# test_tempest_extremes.py
+
+import os
+import tempfile
+from unittest.mock import Mock, patch, MagicMock
+import numpy as np
+import pytest
+import torch
+from collections import OrderedDict
+
+from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
+from earth2studio.io import KVBackend
+from physicsnemo.distributed import DistributedManager
+
+
+@pytest.fixture(scope="session", autouse=True)
+def initialize_distributed_manager():
+    """Initialize DistributedManager once for all tests."""
+    # Initialize if not already initialized
+    if not DistributedManager.is_initialized():
+        DistributedManager.initialize()
+    yield
+    # Note: We don't cleanup torch.distributed as it can't be reinitialized in the same process
+
+
+# ============================================================================
+# 1. INITIALIZATION AND SETUP TESTS
+# ============================================================================
+
+def test_tempest_extremes_initialization():
+    """Test basic initialization with required parameters"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes --verbosity 0",
+            stitch_cmd="StitchNodes --verbosity 0",
+            input_vars=["u10m", "v10m", "msl"],
+            batch_size=2,
+            n_steps=10,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        assert te.batch_size == 2
+        assert te.n_steps == 10
+        assert len(te.input_vars) == 3
+        assert os.path.exists(te.store_dir)
+
+
+def test_initialization_with_static_vars():
+    """Test initialization with static variables"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        static_vars = torch.randn(2, 721, 1440)
+        static_coords = OrderedDict({
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        })
+
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m", "v10m", "msl"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            static_vars=static_vars,
+            static_coords=static_coords,
+            use_ram=False,
+        )
+
+        assert te.static_vars is not None
+        assert te.static_coords is not None
+
+
+def test_initialization_missing_static_coords_raises_error():
+    """Test that providing static_vars without static_coords raises error"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with pytest.raises(ValueError, match="provide both values and coords"):
+            TempestExtremes(
+                detect_cmd="DetectNodes",
+                stitch_cmd="StitchNodes",
+                input_vars=["u10m"],
+                batch_size=1,
+                n_steps=5,
+                time_step=np.timedelta64(6, 'h'),
+                lats=np.linspace(90, -90, 721),
+                lons=np.linspace(0, 360, 1440),
+                store_dir=tmpdir,
+                static_vars=torch.randn(1, 721, 1440),
+                static_coords=None,
+                use_ram=False,
+            )
+
+
+# ============================================================================
+# 2. COMMAND FORMATTING TESTS
+# ============================================================================
+
+def test_remove_arguments():
+    """Test that specified arguments are correctly removed from commands"""
+    cmd = ["DetectNodes", "--in_data_list", "input.txt", "--verbosity", "0"]
+    result = TempestExtremes.remove_arguments(cmd, ["--in_data_list"])
+
+    assert "--in_data_list" not in result
+    assert "input.txt" not in result
+    assert "--verbosity" in result
+    assert "0" in result
+
+
+def test_format_tempestextremes_commands():
+    """Test command formatting removes input/output arguments"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        detect_cmd = "DetectNodes --in_data_list old.txt --verbosity 0"
+        stitch_cmd = "StitchNodes --in old.txt --out old_tracks.txt"
+
+        te = TempestExtremes(
+            detect_cmd=detect_cmd,
+            stitch_cmd=stitch_cmd,
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        assert "--in_data_list" not in te.detect_cmd
+        assert "--out_file_list" not in te.detect_cmd
+        assert "--in" not in te.stitch_cmd
+        assert "--out" not in te.stitch_cmd
+        assert "--verbosity" in te.detect_cmd
+
+
+# ============================================================================
+# 3. COORDINATE SYSTEM TESTS
+# ============================================================================
+
+def test_input_coords():
+    """Test that input_coords returns correct coordinate system"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        input_vars = ["u10m", "v10m", "msl"]
+        lats = np.linspace(90, -90, 721)
+        lons = np.linspace(0, 360, 1440)
+
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=input_vars,
+            batch_size=1,
+            n_steps=10,
+            time_step=np.timedelta64(6, 'h'),
+            lats=lats,
+            lons=lons,
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        coords = te.input_coords
+
+        assert "time" in coords
+        assert "lead_time" in coords
+        assert "variable" in coords
+        assert "lat" in coords
+        assert "lon" in coords
+        assert len(coords["lead_time"]) == 11  # n_steps + 1
+        assert np.array_equal(coords["variable"], input_vars)
+
+
+def test_store_coords_with_static_vars():
+    """Test that store coords include static variables"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        static_vars = torch.randn(1, 721, 1440)
+        static_coords = OrderedDict({
+            "variable": np.array(["z"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        })
+
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m", "v10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            static_vars=static_vars,
+            static_coords=static_coords,
+            use_ram=False,
+        )
+
+        # Check that static vars are in store coords
+        assert "z" in te._store_coords["variable"]
+
+
+# ============================================================================
+# 4. DATA RECORDING TESTS
+# ============================================================================
+
+def test_record_state_basic():
+    """Test that record_state correctly stores data"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m", "v10m", "msl"],
+            batch_size=1,
+            n_steps=2,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        # Create test data
+        time = np.datetime64('2024-01-01T00:00:00')
+        lead_time = np.timedelta64(0, 'h')
+        x = torch.randn(1, 1, 1, 3, 721, 1440)
+        coords = OrderedDict({
+            "ensemble": np.array([0]),
+            "time": np.array([time]),
+            "lead_time": np.array([lead_time]),
+            "variable": np.array(["u10m", "v10m", "msl"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        })
+
+        # Record state
+        te.record_state(x, coords)
+
+        # Check that data was written to store
+        assert len(te.store.coords["time"]) == 1
+        assert len(te.store.coords["ensemble"]) == 1
+
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_record_state_with_static_vars(mock_check):
+    """Test record_state correctly concatenates static variables"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        static_vars = torch.ones(2, 721, 1440) * 0.5
+        static_coords = OrderedDict({
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        })
+
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m", "v10m"],
+            batch_size=1,
+            n_steps=2,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            static_vars=static_vars,
+            static_coords=static_coords,
+            use_ram=False,
+        )
+
+        # Create test data (without static vars)
+        time = np.datetime64('2024-01-01T00:00:00')
+        x = torch.randn(1, 1, 1, 2, 721, 1440)
+        coords = OrderedDict({
+            "ensemble": np.array([0]),
+            "time": np.array([time]),
+            "lead_time": np.array([np.timedelta64(0, 'h')]),
+            "variable": np.array(["u10m", "v10m"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        })
+
+        te.record_state(x, coords)
+
+        # Verify static vars were concatenated
+        assert "z" in te._store_coords["variable"]
+        assert "lsm" in te._store_coords["variable"]
+
+
+# ============================================================================
+# 5. DATA DUMPING TESTS
+# ============================================================================
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_dump_raw_data_creates_netcdf(mock_check):
+    """Test that dump_raw_data creates NetCDF files with correct structure"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m", "v10m", "msl"],
+            batch_size=2,
+            n_steps=3,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        # Record some data
+        time = np.datetime64('2024-01-01T00:00:00')
+        for lt in range(4):  # n_steps + 1
+            x = torch.randn(2, 1, 1, 3, 721, 1440)
+            coords = OrderedDict({
+                "ensemble": np.array([0, 1]),
+                "time": np.array([time]),
+                "lead_time": np.array([np.timedelta64(lt * 6, 'h')]),
+                "variable": np.array(["u10m", "v10m", "msl"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            })
+            te.record_state(x, coords)
+
+        # Dump data
+        raw_files, mems = te.dump_raw_data()
+
+        # Check that files were created
+        assert len(raw_files) == 2  # batch_size
+        assert len(mems) == 2
+        for f in raw_files:
+            assert os.path.exists(f)
+            assert f.endswith('.nc')
+
+
+# ============================================================================
+# 6. FILE SETUP TESTS
+# ============================================================================
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data')
+def test_setup_files(mock_dump, mock_check):
+    """Test that setup_files creates correct file structure"""
+    mock_check.return_value = None
+    mock_dump.return_value = (
+        ["/tmp/data_mem_0000.nc", "/tmp/data_mem_0001.nc"],
+        np.array([0, 1])
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=2,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        ins, outs, node_files, track_files = te.setup_files()
+
+        # Check that file lists were created
+        assert len(ins) == 2
+        assert len(outs) == 2
+        assert len(node_files) == 2
+        assert len(track_files) == 2
+
+        # Check that input/output lists exist and contain expected content
+        for i, in_file in enumerate(ins):
+            assert os.path.exists(in_file)
+            with open(in_file, 'r') as f:
+                content = f.read()
+                assert "mem_000" in content
+
+
+def test_setup_files_with_custom_names():
+    """Test setup_files with custom output file names"""
+    # Similar structure to above, but with out_file_names parameter
+    pass
+
+
+# ============================================================================
+# 7. ASYNC FUNCTIONALITY TESTS
+# ============================================================================
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_async_initialization(mock_check):
+    """Test AsyncTempestExtremes initialization"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ate = AsyncTempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            timeout=120,
+            max_workers=2,
+            use_ram=False,
+        )
+
+        assert ate.timeout == 120
+        assert ate.max_workers == 2
+        assert hasattr(ate, '_instance_tasks')
+        assert hasattr(ate, '_dump_in_progress')
+
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_async_task_status(mock_check):
+    """Test getting task status from AsyncTempestExtremes"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ate = AsyncTempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        status = ate.get_task_status()
+
+        assert "running" in status
+        assert "pending" in status
+        assert "completed" in status
+        assert "failed" in status
+        assert "total" in status
+        assert status["total"] == 0  # No tasks submitted yet
+
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_async_record_state_waits_for_dump(mock_check):
+    """Test that async record_state waits for ongoing dumps"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ate = AsyncTempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        # Simulate dump in progress
+        ate._dump_in_progress.clear()
+
+        # Start recording in a thread (would block)
+        import threading
+        record_started = threading.Event()
+        record_completed = threading.Event()
+
+        def record():
+            record_started.set()
+            time = np.datetime64('2024-01-01T00:00:00')
+            x = torch.randn(1, 1, 1, 1, 721, 1440)
+            coords = OrderedDict({
+                "ensemble": np.array([0]),
+                "time": np.array([time]),
+                "lead_time": np.array([np.timedelta64(0, 'h')]),
+                "variable": np.array(["u10m"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            })
+            ate.record_state(x, coords)
+            record_completed.set()
+
+        thread = threading.Thread(target=record)
+        thread.start()
+
+        # Wait a bit and verify record hasn't completed
+        record_started.wait(timeout=1)
+        import time
+        time.sleep(0.1)
+        assert not record_completed.is_set()
+
+        # Signal dump complete
+        ate._dump_in_progress.set()
+
+        # Now record should complete
+        thread.join(timeout=2)
+        assert record_completed.is_set()
+
+
+# ============================================================================
+# 8. UTILITY FUNCTION TESTS
+# ============================================================================
+
+def test_tile_xx_to_yy():
+    """Test tiling function for expanding dimensions"""
+    from earth2studio.models.dx.tempest_extremes import tile_xx_to_yy
+    from collections import OrderedDict
+
+    xx = torch.randn(2, 721, 1440)
+    xx_coords = OrderedDict({
+        "variable": np.array(["z", "lsm"]),
+        "lat": np.linspace(90, -90, 721),
+        "lon": np.linspace(0, 360, 1440),
+    })
+
+    yy = torch.randn(3, 4, 5, 721, 1440)
+    yy_coords = OrderedDict({
+        "ensemble": np.array([0, 1, 2]),
+        "time": np.array([1, 2, 3, 4]),
+        "lead_time": np.array([0, 1, 2, 3, 4]),
+        "lat": np.linspace(90, -90, 721),
+        "lon": np.linspace(0, 360, 1440),
+    })
+
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+
+    # Result should have yy's leading dims + all of xx's dims
+    # yy.shape = (3, 4, 5, 721, 1440), xx.shape = (2, 721, 1440)
+    # n_lead = 5 - 3 = 2, so we prepend yy's first 2 dims to xx
+    # Result shape should be (3, 4, 2, 721, 1440)
+    assert result.shape == (3, 4, 2, 721, 1440)
+    assert "variable" in result_coords
+    assert "time" in result_coords
+
+
+def test_cat_coords():
+    """Test coordinate concatenation"""
+    from earth2studio.models.dx.tempest_extremes import cat_coords
+    from collections import OrderedDict
+
+    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, cox, yy, 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"])
+
+
+# ============================================================================
+# 9. ERROR HANDLING TESTS
+# ============================================================================
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data')
+def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check):
+    """Test that providing wrong number of output file names raises error"""
+    mock_check.return_value = None
+    mock_dump.return_value = (["/tmp/data1.nc", "/tmp/data2.nc"], np.array([0, 1]))
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=2,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        with pytest.raises(ValueError, match="passed for.*ensemble members"):
+            te.setup_files(out_file_names=["only_one_file.csv"])
+
+
+def test_multiple_ics_raises_error():
+    """Test that multiple initial conditions raise an error"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with pytest.raises(ValueError, match="only accepts multiple ensemble members"):
+            TempestExtremes(
+                detect_cmd="DetectNodes",
+                stitch_cmd="StitchNodes",
+                input_vars=["u10m"],
+                batch_size=1,
+                n_steps=5,
+                time_step=[np.timedelta64(6, 'h'), np.timedelta64(12, 'h')],
+                lats=np.linspace(90, -90, 721),
+                lons=np.linspace(0, 360, 1440),
+                store_dir=tmpdir,
+                use_ram=False,
+            )
+
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_async_check_for_failures_raises(mock_check):
+    """Test that async tracker raises error if previous task failed"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ate = AsyncTempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        # Manually set failure flag
+        ate._has_failed = True
+
+        # Should raise on next operation
+        with pytest.raises(ChildProcessError, match="Previous cyclone tracking"):
+            ate._check_for_failures()
+
+
+# ============================================================================
+# 10. CLEANUP TESTS
+# ============================================================================
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch('earth2studio.models.dx.tempest_extremes.run')
+def test_tidy_up_keeps_raw_data(mock_run, mock_check):
+    """Test that tidy_up keeps raw data when keep_raw=True"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        te = TempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            keep_raw_data=True,
+            use_ram=False,
+        )
+
+        # Create dummy files
+        raw_file = os.path.join(tmpdir, "raw_data", "test_data.nc")
+        os.makedirs(os.path.dirname(raw_file), exist_ok=True)
+        with open(raw_file, 'w') as f:
+            f.write("dummy")
+
+        in_list = os.path.join(tmpdir, "raw_data", "input_list.txt")
+        with open(in_list, 'w') as f:
+            f.write(raw_file)
+
+        out_list = os.path.join(tmpdir, "raw_data", "output_list.txt")
+        node_file = os.path.join(tmpdir, "raw_data", "nodes.txt")
+        with open(out_list, 'w') as f:
+            f.write(node_file)
+        with open(node_file, 'w') as f:
+            f.write("dummy")
+
+        # Run cleanup
+        te.tidy_up([in_list], [out_list])
+
+        # Raw data should still exist (moved to raw_data subdir)
+        assert os.path.exists(os.path.join(te.store_dir, "raw_data"))
+
+
+@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+def test_async_cleanup(mock_check):
+    """Test that async cleanup waits for all tasks"""
+    mock_check.return_value = None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ate = AsyncTempestExtremes(
+            detect_cmd="DetectNodes",
+            stitch_cmd="StitchNodes",
+            input_vars=["u10m"],
+            batch_size=1,
+            n_steps=5,
+            time_step=np.timedelta64(6, 'h'),
+            lats=np.linspace(90, -90, 721),
+            lons=np.linspace(0, 360, 1440),
+            store_dir=tmpdir,
+            use_ram=False,
+        )
+
+        # Cleanup should not raise when no tasks exist
+        ate.cleanup()
+        assert ate._cleanup_done
\ No newline at end of file

From a9fd2bcb8482a8f54a8578dbc932b745ad8bab94 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 7 Oct 2025 06:00:28 -0700
Subject: [PATCH 16/69] vibe-coded some tests, need to be hand-tested and
 selected

---
 test/models/dx/test_tempest_extremes.py | 303 ++++++++++++++----------
 1 file changed, 180 insertions(+), 123 deletions(-)

diff --git a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
index 8ebe55949..6b0b31198 100644
--- a/test/models/dx/test_tempest_extremes.py
+++ b/test/models/dx/test_tempest_extremes.py
@@ -2,15 +2,16 @@
 
 import os
 import tempfile
-from unittest.mock import Mock, patch, MagicMock
+from collections import OrderedDict
+from unittest.mock import MagicMock, Mock, patch
+
 import numpy as np
 import pytest
 import torch
-from collections import OrderedDict
+from physicsnemo.distributed import DistributedManager
 
-from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
 from earth2studio.io import KVBackend
-from physicsnemo.distributed import DistributedManager
+from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -27,6 +28,7 @@ def initialize_distributed_manager():
 # 1. INITIALIZATION AND SETUP TESTS
 # ============================================================================
 
+
 def test_tempest_extremes_initialization():
     """Test basic initialization with required parameters"""
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -36,7 +38,7 @@ def test_tempest_extremes_initialization():
             input_vars=["u10m", "v10m", "msl"],
             batch_size=2,
             n_steps=10,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -53,11 +55,13 @@ def test_initialization_with_static_vars():
     """Test initialization with static variables"""
     with tempfile.TemporaryDirectory() as tmpdir:
         static_vars = torch.randn(2, 721, 1440)
-        static_coords = OrderedDict({
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        })
+        static_coords = OrderedDict(
+            {
+                "variable": np.array(["z", "lsm"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            }
+        )
 
         te = TempestExtremes(
             detect_cmd="DetectNodes",
@@ -65,7 +69,7 @@ def test_initialization_with_static_vars():
             input_vars=["u10m", "v10m", "msl"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -88,7 +92,7 @@ def test_initialization_missing_static_coords_raises_error():
                 input_vars=["u10m"],
                 batch_size=1,
                 n_steps=5,
-                time_step=np.timedelta64(6, 'h'),
+                time_step=np.timedelta64(6, "h"),
                 lats=np.linspace(90, -90, 721),
                 lons=np.linspace(0, 360, 1440),
                 store_dir=tmpdir,
@@ -102,6 +106,7 @@ def test_initialization_missing_static_coords_raises_error():
 # 2. COMMAND FORMATTING TESTS
 # ============================================================================
 
+
 def test_remove_arguments():
     """Test that specified arguments are correctly removed from commands"""
     cmd = ["DetectNodes", "--in_data_list", "input.txt", "--verbosity", "0"]
@@ -125,7 +130,7 @@ def test_format_tempestextremes_commands():
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -143,6 +148,7 @@ def test_format_tempestextremes_commands():
 # 3. COORDINATE SYSTEM TESTS
 # ============================================================================
 
+
 def test_input_coords():
     """Test that input_coords returns correct coordinate system"""
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -156,7 +162,7 @@ def test_input_coords():
             input_vars=input_vars,
             batch_size=1,
             n_steps=10,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=lats,
             lons=lons,
             store_dir=tmpdir,
@@ -178,11 +184,13 @@ def test_store_coords_with_static_vars():
     """Test that store coords include static variables"""
     with tempfile.TemporaryDirectory() as tmpdir:
         static_vars = torch.randn(1, 721, 1440)
-        static_coords = OrderedDict({
-            "variable": np.array(["z"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        })
+        static_coords = OrderedDict(
+            {
+                "variable": np.array(["z"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            }
+        )
 
         te = TempestExtremes(
             detect_cmd="DetectNodes",
@@ -190,7 +198,7 @@ def test_store_coords_with_static_vars():
             input_vars=["u10m", "v10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -207,6 +215,7 @@ def test_store_coords_with_static_vars():
 # 4. DATA RECORDING TESTS
 # ============================================================================
 
+
 def test_record_state_basic():
     """Test that record_state correctly stores data"""
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -216,7 +225,7 @@ def test_record_state_basic():
             input_vars=["u10m", "v10m", "msl"],
             batch_size=1,
             n_steps=2,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -224,17 +233,19 @@ def test_record_state_basic():
         )
 
         # Create test data
-        time = np.datetime64('2024-01-01T00:00:00')
-        lead_time = np.timedelta64(0, 'h')
+        time = np.datetime64("2024-01-01T00:00:00")
+        lead_time = np.timedelta64(0, "h")
         x = torch.randn(1, 1, 1, 3, 721, 1440)
-        coords = OrderedDict({
-            "ensemble": np.array([0]),
-            "time": np.array([time]),
-            "lead_time": np.array([lead_time]),
-            "variable": np.array(["u10m", "v10m", "msl"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        })
+        coords = OrderedDict(
+            {
+                "ensemble": np.array([0]),
+                "time": np.array([time]),
+                "lead_time": np.array([lead_time]),
+                "variable": np.array(["u10m", "v10m", "msl"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            }
+        )
 
         # Record state
         te.record_state(x, coords)
@@ -244,18 +255,22 @@ def test_record_state_basic():
         assert len(te.store.coords["ensemble"]) == 1
 
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_record_state_with_static_vars(mock_check):
     """Test record_state correctly concatenates static variables"""
     mock_check.return_value = None
 
     with tempfile.TemporaryDirectory() as tmpdir:
         static_vars = torch.ones(2, 721, 1440) * 0.5
-        static_coords = OrderedDict({
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        })
+        static_coords = OrderedDict(
+            {
+                "variable": np.array(["z", "lsm"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            }
+        )
 
         te = TempestExtremes(
             detect_cmd="DetectNodes",
@@ -263,7 +278,7 @@ def test_record_state_with_static_vars(mock_check):
             input_vars=["u10m", "v10m"],
             batch_size=1,
             n_steps=2,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -273,16 +288,18 @@ def test_record_state_with_static_vars(mock_check):
         )
 
         # Create test data (without static vars)
-        time = np.datetime64('2024-01-01T00:00:00')
+        time = np.datetime64("2024-01-01T00:00:00")
         x = torch.randn(1, 1, 1, 2, 721, 1440)
-        coords = OrderedDict({
-            "ensemble": np.array([0]),
-            "time": np.array([time]),
-            "lead_time": np.array([np.timedelta64(0, 'h')]),
-            "variable": np.array(["u10m", "v10m"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        })
+        coords = OrderedDict(
+            {
+                "ensemble": np.array([0]),
+                "time": np.array([time]),
+                "lead_time": np.array([np.timedelta64(0, "h")]),
+                "variable": np.array(["u10m", "v10m"]),
+                "lat": np.linspace(90, -90, 721),
+                "lon": np.linspace(0, 360, 1440),
+            }
+        )
 
         te.record_state(x, coords)
 
@@ -295,7 +312,10 @@ def test_record_state_with_static_vars(mock_check):
 # 5. DATA DUMPING TESTS
 # ============================================================================
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_dump_raw_data_creates_netcdf(mock_check):
     """Test that dump_raw_data creates NetCDF files with correct structure"""
     mock_check.return_value = None
@@ -307,7 +327,7 @@ def test_dump_raw_data_creates_netcdf(mock_check):
             input_vars=["u10m", "v10m", "msl"],
             batch_size=2,
             n_steps=3,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -315,17 +335,19 @@ def test_dump_raw_data_creates_netcdf(mock_check):
         )
 
         # Record some data
-        time = np.datetime64('2024-01-01T00:00:00')
+        time = np.datetime64("2024-01-01T00:00:00")
         for lt in range(4):  # n_steps + 1
             x = torch.randn(2, 1, 1, 3, 721, 1440)
-            coords = OrderedDict({
-                "ensemble": np.array([0, 1]),
-                "time": np.array([time]),
-                "lead_time": np.array([np.timedelta64(lt * 6, 'h')]),
-                "variable": np.array(["u10m", "v10m", "msl"]),
-                "lat": np.linspace(90, -90, 721),
-                "lon": np.linspace(0, 360, 1440),
-            })
+            coords = OrderedDict(
+                {
+                    "ensemble": np.array([0, 1]),
+                    "time": np.array([time]),
+                    "lead_time": np.array([np.timedelta64(lt * 6, "h")]),
+                    "variable": np.array(["u10m", "v10m", "msl"]),
+                    "lat": np.linspace(90, -90, 721),
+                    "lon": np.linspace(0, 360, 1440),
+                }
+            )
             te.record_state(x, coords)
 
         # Dump data
@@ -336,21 +358,24 @@ def test_dump_raw_data_creates_netcdf(mock_check):
         assert len(mems) == 2
         for f in raw_files:
             assert os.path.exists(f)
-            assert f.endswith('.nc')
+            assert f.endswith(".nc")
 
 
 # ============================================================================
 # 6. FILE SETUP TESTS
 # ============================================================================
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data')
+
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
+@patch("earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data")
 def test_setup_files(mock_dump, mock_check):
     """Test that setup_files creates correct file structure"""
     mock_check.return_value = None
     mock_dump.return_value = (
         ["/tmp/data_mem_0000.nc", "/tmp/data_mem_0001.nc"],
-        np.array([0, 1])
+        np.array([0, 1]),
     )
 
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -360,7 +385,7 @@ def test_setup_files(mock_dump, mock_check):
             input_vars=["u10m"],
             batch_size=2,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -378,7 +403,7 @@ def test_setup_files(mock_dump, mock_check):
         # Check that input/output lists exist and contain expected content
         for i, in_file in enumerate(ins):
             assert os.path.exists(in_file)
-            with open(in_file, 'r') as f:
+            with open(in_file) as f:
                 content = f.read()
                 assert "mem_000" in content
 
@@ -393,7 +418,10 @@ def test_setup_files_with_custom_names():
 # 7. ASYNC FUNCTIONALITY TESTS
 # ============================================================================
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_async_initialization(mock_check):
     """Test AsyncTempestExtremes initialization"""
     mock_check.return_value = None
@@ -405,7 +433,7 @@ def test_async_initialization(mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -416,11 +444,13 @@ def test_async_initialization(mock_check):
 
         assert ate.timeout == 120
         assert ate.max_workers == 2
-        assert hasattr(ate, '_instance_tasks')
-        assert hasattr(ate, '_dump_in_progress')
+        assert hasattr(ate, "_instance_tasks")
+        assert hasattr(ate, "_dump_in_progress")
 
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_async_task_status(mock_check):
     """Test getting task status from AsyncTempestExtremes"""
     mock_check.return_value = None
@@ -432,7 +462,7 @@ def test_async_task_status(mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -449,7 +479,9 @@ def test_async_task_status(mock_check):
         assert status["total"] == 0  # No tasks submitted yet
 
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_async_record_state_waits_for_dump(mock_check):
     """Test that async record_state waits for ongoing dumps"""
     mock_check.return_value = None
@@ -461,7 +493,7 @@ def test_async_record_state_waits_for_dump(mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -473,21 +505,24 @@ def test_async_record_state_waits_for_dump(mock_check):
 
         # Start recording in a thread (would block)
         import threading
+
         record_started = threading.Event()
         record_completed = threading.Event()
 
         def record():
             record_started.set()
-            time = np.datetime64('2024-01-01T00:00:00')
+            time = np.datetime64("2024-01-01T00:00:00")
             x = torch.randn(1, 1, 1, 1, 721, 1440)
-            coords = OrderedDict({
-                "ensemble": np.array([0]),
-                "time": np.array([time]),
-                "lead_time": np.array([np.timedelta64(0, 'h')]),
-                "variable": np.array(["u10m"]),
-                "lat": np.linspace(90, -90, 721),
-                "lon": np.linspace(0, 360, 1440),
-            })
+            coords = OrderedDict(
+                {
+                    "ensemble": np.array([0]),
+                    "time": np.array([time]),
+                    "lead_time": np.array([np.timedelta64(0, "h")]),
+                    "variable": np.array(["u10m"]),
+                    "lat": np.linspace(90, -90, 721),
+                    "lon": np.linspace(0, 360, 1440),
+                }
+            )
             ate.record_state(x, coords)
             record_completed.set()
 
@@ -497,6 +532,7 @@ def record():
         # Wait a bit and verify record hasn't completed
         record_started.wait(timeout=1)
         import time
+
         time.sleep(0.1)
         assert not record_completed.is_set()
 
@@ -512,26 +548,32 @@ def record():
 # 8. UTILITY FUNCTION TESTS
 # ============================================================================
 
+
 def test_tile_xx_to_yy():
     """Test tiling function for expanding dimensions"""
-    from earth2studio.models.dx.tempest_extremes import tile_xx_to_yy
     from collections import OrderedDict
 
+    from earth2studio.models.dx.tempest_extremes import tile_xx_to_yy
+
     xx = torch.randn(2, 721, 1440)
-    xx_coords = OrderedDict({
-        "variable": np.array(["z", "lsm"]),
-        "lat": np.linspace(90, -90, 721),
-        "lon": np.linspace(0, 360, 1440),
-    })
+    xx_coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
 
     yy = torch.randn(3, 4, 5, 721, 1440)
-    yy_coords = OrderedDict({
-        "ensemble": np.array([0, 1, 2]),
-        "time": np.array([1, 2, 3, 4]),
-        "lead_time": np.array([0, 1, 2, 3, 4]),
-        "lat": np.linspace(90, -90, 721),
-        "lon": np.linspace(0, 360, 1440),
-    })
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lead_time": np.array([0, 1, 2, 3, 4]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
 
     result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
 
@@ -546,24 +588,29 @@ def test_tile_xx_to_yy():
 
 def test_cat_coords():
     """Test coordinate concatenation"""
-    from earth2studio.models.dx.tempest_extremes import cat_coords
     from collections import OrderedDict
 
+    from earth2studio.models.dx.tempest_extremes import 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),
-    })
+    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),
-    })
+    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, cox, yy, coy, dim="variable")
 
@@ -576,8 +623,11 @@ def test_cat_coords():
 # 9. ERROR HANDLING TESTS
 # ============================================================================
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data')
+
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
+@patch("earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data")
 def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check):
     """Test that providing wrong number of output file names raises error"""
     mock_check.return_value = None
@@ -590,7 +640,7 @@ def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check):
             input_vars=["u10m"],
             batch_size=2,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -611,7 +661,7 @@ def test_multiple_ics_raises_error():
                 input_vars=["u10m"],
                 batch_size=1,
                 n_steps=5,
-                time_step=[np.timedelta64(6, 'h'), np.timedelta64(12, 'h')],
+                time_step=[np.timedelta64(6, "h"), np.timedelta64(12, "h")],
                 lats=np.linspace(90, -90, 721),
                 lons=np.linspace(0, 360, 1440),
                 store_dir=tmpdir,
@@ -619,7 +669,9 @@ def test_multiple_ics_raises_error():
             )
 
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_async_check_for_failures_raises(mock_check):
     """Test that async tracker raises error if previous task failed"""
     mock_check.return_value = None
@@ -631,7 +683,7 @@ def test_async_check_for_failures_raises(mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -650,8 +702,11 @@ def test_async_check_for_failures_raises(mock_check):
 # 10. CLEANUP TESTS
 # ============================================================================
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
-@patch('earth2studio.models.dx.tempest_extremes.run')
+
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
+@patch("earth2studio.models.dx.tempest_extremes.run")
 def test_tidy_up_keeps_raw_data(mock_run, mock_check):
     """Test that tidy_up keeps raw data when keep_raw=True"""
     mock_check.return_value = None
@@ -663,7 +718,7 @@ def test_tidy_up_keeps_raw_data(mock_run, mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -674,18 +729,18 @@ def test_tidy_up_keeps_raw_data(mock_run, mock_check):
         # Create dummy files
         raw_file = os.path.join(tmpdir, "raw_data", "test_data.nc")
         os.makedirs(os.path.dirname(raw_file), exist_ok=True)
-        with open(raw_file, 'w') as f:
+        with open(raw_file, "w") as f:
             f.write("dummy")
 
         in_list = os.path.join(tmpdir, "raw_data", "input_list.txt")
-        with open(in_list, 'w') as f:
+        with open(in_list, "w") as f:
             f.write(raw_file)
 
         out_list = os.path.join(tmpdir, "raw_data", "output_list.txt")
         node_file = os.path.join(tmpdir, "raw_data", "nodes.txt")
-        with open(out_list, 'w') as f:
+        with open(out_list, "w") as f:
             f.write(node_file)
-        with open(node_file, 'w') as f:
+        with open(node_file, "w") as f:
             f.write("dummy")
 
         # Run cleanup
@@ -695,7 +750,9 @@ def test_tidy_up_keeps_raw_data(mock_run, mock_check):
         assert os.path.exists(os.path.join(te.store_dir, "raw_data"))
 
 
-@patch('earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability')
+@patch(
+    "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
+)
 def test_async_cleanup(mock_check):
     """Test that async cleanup waits for all tasks"""
     mock_check.return_value = None
@@ -707,7 +764,7 @@ def test_async_cleanup(mock_check):
             input_vars=["u10m"],
             batch_size=1,
             n_steps=5,
-            time_step=np.timedelta64(6, 'h'),
+            time_step=np.timedelta64(6, "h"),
             lats=np.linspace(90, -90, 721),
             lons=np.linspace(0, 360, 1440),
             store_dir=tmpdir,
@@ -716,4 +773,4 @@ def test_async_cleanup(mock_check):
 
         # Cleanup should not raise when no tasks exist
         ate.cleanup()
-        assert ate._cleanup_done
\ No newline at end of file
+        assert ate._cleanup_done

From 526e6bf47305d981b2b4660f4248d1ff60ffee65 Mon Sep 17 00:00:00 2001
From: MarsuPila <22983240+MarsuPila@users.noreply.github.com>
Date: Tue, 7 Oct 2025 15:46:02 +0000
Subject: [PATCH 17/69] passing all pre-commit tests, still need to sub-select
 tests as there are too many right now

---
 test/models/dx/test_tempest_extremes.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
index 6b0b31198..0d0d1c5a0 100644
--- a/test/models/dx/test_tempest_extremes.py
+++ b/test/models/dx/test_tempest_extremes.py
@@ -3,14 +3,13 @@
 import os
 import tempfile
 from collections import OrderedDict
-from unittest.mock import MagicMock, Mock, patch
+from unittest.mock import patch
 
 import numpy as np
 import pytest
 import torch
 from physicsnemo.distributed import DistributedManager
 
-from earth2studio.io import KVBackend
 from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 
 
@@ -373,12 +372,15 @@ def test_dump_raw_data_creates_netcdf(mock_check):
 def test_setup_files(mock_dump, mock_check):
     """Test that setup_files creates correct file structure"""
     mock_check.return_value = None
-    mock_dump.return_value = (
-        ["/tmp/data_mem_0000.nc", "/tmp/data_mem_0001.nc"],
-        np.array([0, 1]),
-    )
 
     with tempfile.TemporaryDirectory() as tmpdir:
+        mock_dump.return_value = (
+            [
+                os.path.join(tmpdir, "data_mem_0000.nc"),
+                os.path.join(tmpdir, "data_mem_0001.nc"),
+            ],
+            np.array([0, 1]),
+        )
         te = TempestExtremes(
             detect_cmd="DetectNodes",
             stitch_cmd="StitchNodes",
@@ -631,9 +633,12 @@ def test_cat_coords():
 def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check):
     """Test that providing wrong number of output file names raises error"""
     mock_check.return_value = None
-    mock_dump.return_value = (["/tmp/data1.nc", "/tmp/data2.nc"], np.array([0, 1]))
 
     with tempfile.TemporaryDirectory() as tmpdir:
+        mock_dump.return_value = (
+            [os.path.join(tmpdir, "data1.nc"), os.path.join(tmpdir, "data2.nc")],
+            np.array([0, 1]),
+        )
         te = TempestExtremes(
             detect_cmd="DetectNodes",
             stitch_cmd="StitchNodes",

From c3258d93f54fa824c69deae6ca3f767db7077e45 Mon Sep 17 00:00:00 2001
From: MarsuPila <22983240+MarsuPila@users.noreply.github.com>
Date: Tue, 7 Oct 2025 16:01:58 +0000
Subject: [PATCH 18/69] subselected tests

---
 test/models/dx/test_tempest_extremes.py | 57 -------------------------
 1 file changed, 57 deletions(-)

diff --git a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
index 0d0d1c5a0..b53f84098 100644
--- a/test/models/dx/test_tempest_extremes.py
+++ b/test/models/dx/test_tempest_extremes.py
@@ -28,28 +28,6 @@ def initialize_distributed_manager():
 # ============================================================================
 
 
-def test_tempest_extremes_initialization():
-    """Test basic initialization with required parameters"""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        te = TempestExtremes(
-            detect_cmd="DetectNodes --verbosity 0",
-            stitch_cmd="StitchNodes --verbosity 0",
-            input_vars=["u10m", "v10m", "msl"],
-            batch_size=2,
-            n_steps=10,
-            time_step=np.timedelta64(6, "h"),
-            lats=np.linspace(90, -90, 721),
-            lons=np.linspace(0, 360, 1440),
-            store_dir=tmpdir,
-            use_ram=False,
-        )
-
-        assert te.batch_size == 2
-        assert te.n_steps == 10
-        assert len(te.input_vars) == 3
-        assert os.path.exists(te.store_dir)
-
-
 def test_initialization_with_static_vars():
     """Test initialization with static variables"""
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -106,17 +84,6 @@ def test_initialization_missing_static_coords_raises_error():
 # ============================================================================
 
 
-def test_remove_arguments():
-    """Test that specified arguments are correctly removed from commands"""
-    cmd = ["DetectNodes", "--in_data_list", "input.txt", "--verbosity", "0"]
-    result = TempestExtremes.remove_arguments(cmd, ["--in_data_list"])
-
-    assert "--in_data_list" not in result
-    assert "input.txt" not in result
-    assert "--verbosity" in result
-    assert "0" in result
-
-
 def test_format_tempestextremes_commands():
     """Test command formatting removes input/output arguments"""
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -410,12 +377,6 @@ def test_setup_files(mock_dump, mock_check):
                 assert "mem_000" in content
 
 
-def test_setup_files_with_custom_names():
-    """Test setup_files with custom output file names"""
-    # Similar structure to above, but with out_file_names parameter
-    pass
-
-
 # ============================================================================
 # 7. ASYNC FUNCTIONALITY TESTS
 # ============================================================================
@@ -656,24 +617,6 @@ def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check):
             te.setup_files(out_file_names=["only_one_file.csv"])
 
 
-def test_multiple_ics_raises_error():
-    """Test that multiple initial conditions raise an error"""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        with pytest.raises(ValueError, match="only accepts multiple ensemble members"):
-            TempestExtremes(
-                detect_cmd="DetectNodes",
-                stitch_cmd="StitchNodes",
-                input_vars=["u10m"],
-                batch_size=1,
-                n_steps=5,
-                time_step=[np.timedelta64(6, "h"), np.timedelta64(12, "h")],
-                lats=np.linspace(90, -90, 721),
-                lons=np.linspace(0, 360, 1440),
-                store_dir=tmpdir,
-                use_ram=False,
-            )
-
-
 @patch(
     "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability"
 )

From 3fd145d00cd214a40e08b803b31d81ec1cc83f54 Mon Sep 17 00:00:00 2001
From: MarsuPila <22983240+MarsuPila@users.noreply.github.com>
Date: Wed, 8 Oct 2025 11:27:17 +0200
Subject: [PATCH 19/69] install doc

---
 docs/userguide/about/install.md            | 14 ++++++++++++--
 earth2studio/models/dx/tempest_extremes.py |  2 ++
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/docs/userguide/about/install.md b/docs/userguide/about/install.md
index 372920522..a07cae5e6 100644
--- a/docs/userguide/about/install.md
+++ b/docs/userguide/about/install.md
@@ -479,8 +479,8 @@ uv add earth2studio --extra corrdiff
 ::::
 :::::
 :::::{tab-item} Cyclone Trackers
-Notes: Additional dependencies for all cyclone tracking models. Only Python 3.12 and
-below support.
+Notes: Additional dependencies for cyclone tracking models `TCTrackerVitart` and `TCTrackerWuDuan`.
+Only Python 3.12 and below support.
 
 ::::{tab-set}
 :::{tab-item} uv
@@ -496,6 +496,16 @@ uv pip install earth2studio --extra cyclone
 pip install earth2studio[cyclone]
 ```
 
+`TempestExtremes` is not provided as a Python library and must be installed
+separately by the user. Installation instructions can be found on the
+[TempestExtremes GitHub page](https://github.com/ClimateGlobalChange/tempestextremes?tab=readme-ov-file#installation-via-cmake-recommended).
+
+By default, `TempestExtremes` binaries are installed to the bin folder of the
+`TempestExtremes` repository. Users can specify paths to executibles by passing
+the detect and stitch commands to the initialisation of the `TempestExtremes`
+class in earth2studio. Examples for both commands are provided in the docstring
+of this class.
+
 :::
 ::::
 :::::
diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 5eee793e3..cb24d7832 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -144,9 +144,11 @@ class generates.
     detect_cmd : str
         TempestExtremes DetectNodes command with arguments
         Note that --in_data_list and --out_file_list will be ignored if provdied
+        example: "/path/to/DetectNodes --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0"
     stitch_cmd : str
         TempestExtremes StitchNodes command with arguments
         Note that --in and --out will be ignored if provdied
+        example: "/path/to/StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10"
     input_vars :
         List of variables which are required for the tracking algorithm
     n_steps :

From c26f453b57cbd39a7b13eedaf68ab1312853fe1d Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 8 Oct 2025 01:35:51 -0700
Subject: [PATCH 20/69] throwing an error in case cleanup is not called before
 object goes out of scope

---
 earth2studio/models/dx/tempest_extremes.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index cb24d7832..0cef9e0ea 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -1194,12 +1194,11 @@ def __del__(self) -> None:
         """
         try:
             if not self._cleanup_done:
-                print(
-                    "AsyncTempestExtremes: Destructor called - cleaning up background tasks..."
-                )
                 self.cleanup(timeout_per_task=30)  # Shorter timeout in destructor
         except Exception as e:
-            print(f"Error in AsyncTempestExtremes destructor: {e}")
+            print("\033[95mError in AsyncTempestExtremes destructor: had no time to cleanup.\033[0m")
+            print("\033[95mplease call self.cleanup() from script before class goes out of scope.\033[0m")
+            print(f"\033[95mThis is resulting in the following error: {e}\033[0m")
             # Note: In destructor, we log but don't re-raise to avoid issues during interpreter shutdown
 
     def __call__(self, out_file_names: list[str] | None = None) -> Future[None]:  # type: ignore[override]

From d2a8e4a63dd5e0ebd6c88ab8e0d88d12ee399b92 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 8 Oct 2025 05:44:22 -0700
Subject: [PATCH 21/69] custom depenmdency failure message for TE

---
 earth2studio/models/dx/tempest_extremes.py | 78 +++++++++++++++++++---
 1 file changed, 67 insertions(+), 11 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 0cef9e0ea..111c0dc48 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -7,18 +7,18 @@
 from collections import OrderedDict
 from concurrent.futures import Future, ThreadPoolExecutor
 from subprocess import run
+from types import TracebackType
 from typing import Any
 
 import numpy as np
 import torch
 from physicsnemo.distributed import DistributedManager
+from rich.console import Console
+from rich.table import Table
+from rich.traceback import Traceback
 
 from earth2studio.io import KVBackend
 from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
-from earth2studio.utils.imports import (
-    OptionalDependencyFailure,
-    check_optional_dependencies,
-)
 
 
 def tile_xx_to_yy(
@@ -266,12 +266,65 @@ def check_tempest_extremes_availability(self) -> None:
         for cmd in (self.detect_cmd[0], self.stitch_cmd[0]):
             try:
                 run(cmd, capture_output=True)  # noqa: S603
-            except Exception as _:
+            except Exception as e:
                 # TODO discuss implementation of below before merging
-                OptionalDependencyFailure("tempest_extremes")
+                self.dependency_failure(e.__traceback__, e)
 
         return
 
+    def dependency_failure(
+        self, error_traceback: TracebackType | None, error_value: BaseException
+    ) -> None:
+        """Display a formatted error message for TempestExtremes dependency failures.
+
+        This method creates and prints information about TempestExtremes dependency
+        errors and raises a RuntimeError to halt execution.
+
+        Parameters
+        ----------
+        error_traceback : TracebackType | None
+            The traceback object from the exception (typically from exception.__traceback__)
+        error_value : BaseException
+            The exception object that was raised
+
+        Raises
+        ------
+        RuntimeError
+            Always raised after displaying the error message to halt execution
+        """
+        doc_url = "https://nvidia.github.io/earth2studio/userguide/about/install.html#optional-dependencies"
+
+        console = Console()
+        table = Table(show_header=False, show_lines=True)
+        table.add_row(
+            "[blue]Earth2Studio Extra Dependency Error\n"
+            + "This error typically indicates an extra dependency group is needed.\n"
+            + "Don't panic, this is usually an easy fix.[/blue]"
+        )
+        table.add_row(
+            "[yellow]The TempestExtremes class is marked needing optional dependencies'.\n\n"
+            + "unlike other dependencies, TempestExtremes has to be installed to the system by the user`\n\n"
+            + "For more information, visit the install documentation: \n"
+            + f"{doc_url}[/yellow]"
+        )
+        if error_value:
+            table.add_row(
+                Traceback(
+                    Traceback.extract(
+                        type(error_value),
+                        error_value,
+                        error_traceback,
+                        show_locals=False,
+                    )
+                )
+            )
+        console.print(table)
+
+        raise RuntimeError(
+            "TempestExtremes dependency not available. "
+            f"Please install TempestExtremes. See {doc_url}"
+        )
+
     @staticmethod
     def remove_arguments(cmd: list[str], args: list[str]) -> list[str]:
         """Remove specified arguments from command list.
@@ -333,7 +386,6 @@ def setup_output_directories(self, use_ram: bool, store_dir: str) -> None:
 
         return
 
-    @check_optional_dependencies()
     def initialise_store_coords(self) -> None:
         """Initialize coordinate system for data storage.
 
@@ -570,7 +622,6 @@ def run_te(command: list[str], print_output: bool) -> None:
 
         return
 
-    @check_optional_dependencies()
     def track_cyclones(self, out_file_names: list[str] | None = None) -> None:
         """Execute cyclone tracking using TempestExtremes.
 
@@ -1193,11 +1244,16 @@ def __del__(self) -> None:
         ensuring all background tasks complete and resources are properly released.
         """
         try:
-            if not self._cleanup_done:
+            # Use getattr to handle case where initialization failed before _cleanup_done was set
+            if not getattr(self, "_cleanup_done", True):
                 self.cleanup(timeout_per_task=30)  # Shorter timeout in destructor
         except Exception as e:
-            print("\033[95mError in AsyncTempestExtremes destructor: had no time to cleanup.\033[0m")
-            print("\033[95mplease call self.cleanup() from script before class goes out of scope.\033[0m")
+            print(
+                "\033[95mError in AsyncTempestExtremes destructor: had no time to cleanup.\033[0m"
+            )
+            print(
+                "\033[95mplease call self.cleanup() from script before class goes out of scope.\033[0m"
+            )
             print(f"\033[95mThis is resulting in the following error: {e}\033[0m")
             # Note: In destructor, we log but don't re-raise to avoid issues during interpreter shutdown
 

From 0ab6d67978f5f8200633cf643030633c1a9fcce2 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 8 Oct 2025 06:34:09 -0700
Subject: [PATCH 22/69] moved tensor tiling and concatenation to utils

---
 earth2studio/models/dx/tempest_extremes.py | 96 ++--------------------
 earth2studio/utils/coords.py               | 91 ++++++++++++++++++++
 recipes/hens/src/hens_ensemble.py          |  8 +-
 recipes/hens/src/hens_utilities.py         | 50 -----------
 test/models/dx/test_tempest_extremes.py    | 79 +-----------------
 test/utils/test_coords.py                  | 71 +++++++++++++++-
 6 files changed, 176 insertions(+), 219 deletions(-)

diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 111c0dc48..9ca4b74df 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -18,95 +18,13 @@
 from rich.traceback import Traceback
 
 from earth2studio.io import KVBackend
-from earth2studio.utils.coords import CoordSystem, map_coords, split_coords
-
-
-def tile_xx_to_yy(
-    xx: torch.Tensor, xx_coords: CoordSystem, yy: torch.Tensor, yy_coords: CoordSystem
-) -> tuple[torch.Tensor, CoordSystem]:
-    """Tile tensor xx to match the leading dimensions of tensor yy.
-
-    Parameters
-    ----------
-    xx : torch.Tensor
-        Source tensor to be tiled
-    xx_coords : CoordSystem
-        Coordinate system for xx tensor
-    yy : torch.Tensor
-        Target tensor whose shape determines tiling
-    yy_coords : CoordSystem
-        Coordinate system for yy tensor
-
-    Returns
-    -------
-    Tuple[torch.Tensor, CoordSystem]
-        Tuple containing the tiled tensor and updated coordinate system
-
-    Raises
-    ------
-    ValueError
-        If xx has more dimensions than yy
-    """
-    n_lead = len(yy.shape) - len(xx.shape)
-
-    if n_lead < 0:
-        raise ValueError("xx must have fewer dimensions than yy.")
-
-    out_shape = yy.shape[:n_lead] + tuple([-1 for _ in range(len(xx.shape))])
-    out_coords = copy.deepcopy(yy_coords)
-    for key, val in xx_coords.items():
-        out_coords[key] = val
-
-    return xx.expand(out_shape), out_coords
-
-
-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
+from earth2studio.utils.coords import (
+    CoordSystem,
+    cat_coords,
+    map_coords,
+    split_coords,
+    tile_xx_to_yy,
+)
 
 
 class TempestExtremes:
diff --git a/earth2studio/utils/coords.py b/earth2studio/utils/coords.py
index b512a243b..1b3e7e9c2 100644
--- a/earth2studio/utils/coords.py
+++ b/earth2studio/utils/coords.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 
 from collections import OrderedDict
+from copy import deepcopy
 from typing import Literal
 
 import numpy as np
@@ -429,3 +430,93 @@ def convert_multidim_to_singledim(
             i += j + 1
 
     return CoordSystem(adjusted_coords), mapping
+
+
+def tile_xx_to_yy(
+    xx: torch.Tensor, xx_coords: CoordSystem, yy: torch.Tensor, yy_coords: CoordSystem
+) -> tuple[torch.Tensor, CoordSystem]:
+    """Tile tensor xx to match the leading dimensions of tensor yy and update coords accordingly.
+       eg if xx has shape (4, 721, 1440) and yy has shape (8, 2, 35, 721, 1440),
+       xx will be tiled to shape (8, 2, 4, 721, 1440), which is useful for concatenation
+
+    Parameters
+    ----------
+    xx : torch.Tensor
+        Source tensor to be tiled
+    xx_coords : CoordSystem
+        Coordinate system for xx tensor
+    yy : torch.Tensor
+        Target tensor whose shape determines tiling
+    yy_coords : CoordSystem
+        Coordinate system for yy tensor
+
+    Returns
+    -------
+    Tuple[torch.Tensor, CoordSystem]
+        Tuple containing the tiled tensor and updated coordinate system
+
+    Raises
+    ------
+    ValueError
+        If xx has more dimensions than yy
+    """
+    n_lead = len(yy.shape) - len(xx.shape)
+
+    if n_lead < 0:
+        raise ValueError("xx must have fewer dimensions than yy.")
+
+    out_shape = yy.shape[:n_lead] + tuple([-1 for _ in range(len(xx.shape))])
+    out_coords = deepcopy(yy_coords)
+    for key, val in xx_coords.items():
+        out_coords[key] = val
+
+    return xx.expand(out_shape), out_coords
+
+
+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
diff --git a/recipes/hens/src/hens_ensemble.py b/recipes/hens/src/hens_ensemble.py
index 9ebd934a3..809636a4b 100644
--- a/recipes/hens/src/hens_ensemble.py
+++ b/recipes/hens/src/hens_ensemble.py
@@ -33,12 +33,16 @@
 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,
 )
diff --git a/recipes/hens/src/hens_utilities.py b/recipes/hens/src/hens_utilities.py
index 4d3591927..57ac7dfb6 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/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
index b53f84098..b5f893c5a 100644
--- a/test/models/dx/test_tempest_extremes.py
+++ b/test/models/dx/test_tempest_extremes.py
@@ -508,82 +508,7 @@ def record():
 
 
 # ============================================================================
-# 8. UTILITY FUNCTION TESTS
-# ============================================================================
-
-
-def test_tile_xx_to_yy():
-    """Test tiling function for expanding dimensions"""
-    from collections import OrderedDict
-
-    from earth2studio.models.dx.tempest_extremes import tile_xx_to_yy
-
-    xx = torch.randn(2, 721, 1440)
-    xx_coords = OrderedDict(
-        {
-            "variable": np.array(["z", "lsm"]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    yy = torch.randn(3, 4, 5, 721, 1440)
-    yy_coords = OrderedDict(
-        {
-            "ensemble": np.array([0, 1, 2]),
-            "time": np.array([1, 2, 3, 4]),
-            "lead_time": np.array([0, 1, 2, 3, 4]),
-            "lat": np.linspace(90, -90, 721),
-            "lon": np.linspace(0, 360, 1440),
-        }
-    )
-
-    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
-
-    # Result should have yy's leading dims + all of xx's dims
-    # yy.shape = (3, 4, 5, 721, 1440), xx.shape = (2, 721, 1440)
-    # n_lead = 5 - 3 = 2, so we prepend yy's first 2 dims to xx
-    # Result shape should be (3, 4, 2, 721, 1440)
-    assert result.shape == (3, 4, 2, 721, 1440)
-    assert "variable" in result_coords
-    assert "time" in result_coords
-
-
-def test_cat_coords():
-    """Test coordinate concatenation"""
-    from collections import OrderedDict
-
-    from earth2studio.models.dx.tempest_extremes import 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, cox, yy, 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"])
-
-
-# ============================================================================
-# 9. ERROR HANDLING TESTS
+# 8. ERROR HANDLING TESTS
 # ============================================================================
 
 
@@ -647,7 +572,7 @@ def test_async_check_for_failures_raises(mock_check):
 
 
 # ============================================================================
-# 10. CLEANUP TESTS
+# 9. CLEANUP TESTS
 # ============================================================================
 
 
diff --git a/test/utils/test_coords.py b/test/utils/test_coords.py
index e2ffa2b70..2fd10ca12 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_xx_to_yy,
+)
 
 
 @pytest.mark.parametrize(
@@ -420,3 +425,67 @@ 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_xx_to_yy():
+    """Test tiling function for expanding dimensions"""
+
+    xx = torch.randn(2, 721, 1440)
+    xx_coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(3, 4, 5, 721, 1440)
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lead_time": np.array([0, 1, 2, 3, 4]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+
+    # Result should have yy's leading dims + all of xx's dims
+    # yy.shape = (3, 4, 5, 721, 1440), xx.shape = (2, 721, 1440)
+    # n_lead = 5 - 3 = 2, so we prepend yy's first 2 dims to xx
+    # Result shape should be (3, 4, 2, 721, 1440)
+    assert result.shape == (3, 4, 2, 721, 1440)
+    assert "variable" in result_coords
+    assert "time" in result_coords
+
+
+def test_cat_coords():
+    """Test coordinate concatenation"""
+
+    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, cox, yy, 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"])

From 8ca3faebe5d1dd1846f6933cba6a6724ed8af39d Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Thu, 18 Sep 2025 22:34:47 -0700
Subject: [PATCH 23/69] enable setting fcn3 random seed

---
 earth2studio/models/px/fcn3.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 653d9ba1e..12768a863 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -173,6 +173,7 @@ def __init__(
         super().__init__()
         self.model = core_model
         self.variables = variables
+        self.seed = seed
         if "2d" in self.variables:
             self.variables[self.variables == "2d"] = "d2m"
 

From e93932ee62e1dbdcc1a8a5cc23b7e9d74f3749ce Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 00:22:13 -0700
Subject: [PATCH 24/69] add proper noise handling for fcn3

---
 earth2studio/models/px/fcn3.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 12768a863..c676ace64 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -179,6 +179,8 @@ def __init__(
 
         self.set_rng(reset=True, seed=seed)
 
+        self._internal_noise_states = []
+
     def __str__(self) -> str:
         return "fcn3"
 

From bc9e3acb7ecd9e42cb3f14850a21db4228fb816d Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 00:55:03 -0700
Subject: [PATCH 25/69] fix linting and test issues

---
 earth2studio/models/px/fcn3.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index c676ace64..12768a863 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -179,8 +179,6 @@ def __init__(
 
         self.set_rng(reset=True, seed=seed)
 
-        self._internal_noise_states = []
-
     def __str__(self) -> str:
         return "fcn3"
 

From 2685f9087f34d3fb21a4327d60ddcc35bf09b575 Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 01:02:13 -0700
Subject: [PATCH 26/69] update lockfile

---
 uv.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/uv.lock b/uv.lock
index c5b4f5437..7961ec317 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
@@ -4152,7 +4152,7 @@ wheels = [
 
 [[package]]
 name = "microsoft-aurora"
-version = "0.1.dev55+gab41cf1de"
+version = "0.1.dev55+gab41cf1"
 source = { git = "https://github.com/NickGeneva/aurora.git?rev=ab41cf1de67d5dcc723b96fc9a6219e4b548d181#ab41cf1de67d5dcc723b96fc9a6219e4b548d181" }
 dependencies = [
     { name = "azure-storage-blob" },

From e3a4e3deec168bb71ba348b4e0ba6687b194e770 Mon Sep 17 00:00:00 2001
From: Dallas Foster <dallasf@nvidia.com>
Date: Fri, 19 Sep 2025 11:40:11 -0700
Subject: [PATCH 27/69] move seed initialization and fix dxwrapper tests

---
 earth2studio/models/px/fcn3.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 12768a863..653d9ba1e 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -173,7 +173,6 @@ def __init__(
         super().__init__()
         self.model = core_model
         self.variables = variables
-        self.seed = seed
         if "2d" in self.variables:
             self.variables[self.variables == "2d"] = "d2m"
 

From 1dec990dfeeea64465e9a7dfd2070da7b419614e Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 13 Oct 2025 16:12:50 +0200
Subject: [PATCH 28/69] tc tracking pipeline

---
 recipes/tc_tracking/.gitignore                |   14 +
 recipes/tc_tracking/Dockerfile                |   39 +
 recipes/tc_tracking/README.md                 |   73 +
 recipes/tc_tracking/cfg/extract_era5.yaml     |  128 +
 recipes/tc_tracking/cfg/helene.yaml           |   38 +
 recipes/tc_tracking/cfg/reproduce_helene.yaml |   37 +
 .../post_proc/score/score_results.py          |  196 +
 .../post_proc/tc_tracking/.gitignore          |    2 +
 .../post_proc/tc_tracking/README.md           |   21 +
 .../post_proc/tc_tracking/analyse_n_plot.py   |  289 ++
 .../post_proc/tc_tracking/data_handling.py    |  362 ++
 .../tc_tracking/ensemble_slayground.ipynb     |  573 +++
 .../post_proc/tc_tracking/plotting_helpers.py |  417 ++
 .../tc_tracking/tracks_slayground.ipynb       |  223 +
 recipes/tc_tracking/pyproject.toml            |   40 +
 recipes/tc_tracking/src/__init__.py           |    0
 recipes/tc_tracking/src/data/__init__.py      |    0
 recipes/tc_tracking/src/data/file_output.py   |  154 +
 recipes/tc_tracking/src/data/localh5.py       |   88 +
 recipes/tc_tracking/src/data/utils.py         |   67 +
 .../src/extract_from_data_source/__init__.py  |    0
 .../baseline_extraction.py                    |  207 +
 recipes/tc_tracking/src/generate_ensembles.py |  249 ++
 recipes/tc_tracking/src/utils.py              |  149 +
 recipes/tc_tracking/tc_hunt.py                |   29 +
 recipes/tc_tracking/uv.lock                   | 3958 +++++++++++++++++
 26 files changed, 7353 insertions(+)
 create mode 100644 recipes/tc_tracking/.gitignore
 create mode 100644 recipes/tc_tracking/Dockerfile
 create mode 100644 recipes/tc_tracking/README.md
 create mode 100644 recipes/tc_tracking/cfg/extract_era5.yaml
 create mode 100644 recipes/tc_tracking/cfg/helene.yaml
 create mode 100644 recipes/tc_tracking/cfg/reproduce_helene.yaml
 create mode 100644 recipes/tc_tracking/post_proc/score/score_results.py
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/.gitignore
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/README.md
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
 create mode 100644 recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb
 create mode 100644 recipes/tc_tracking/pyproject.toml
 create mode 100644 recipes/tc_tracking/src/__init__.py
 create mode 100644 recipes/tc_tracking/src/data/__init__.py
 create mode 100644 recipes/tc_tracking/src/data/file_output.py
 create mode 100644 recipes/tc_tracking/src/data/localh5.py
 create mode 100644 recipes/tc_tracking/src/data/utils.py
 create mode 100644 recipes/tc_tracking/src/extract_from_data_source/__init__.py
 create mode 100644 recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
 create mode 100644 recipes/tc_tracking/src/generate_ensembles.py
 create mode 100644 recipes/tc_tracking/src/utils.py
 create mode 100644 recipes/tc_tracking/tc_hunt.py
 create mode 100644 recipes/tc_tracking/uv.lock

diff --git a/recipes/tc_tracking/.gitignore b/recipes/tc_tracking/.gitignore
new file mode 100644
index 000000000..0b695cf51
--- /dev/null
+++ b/recipes/tc_tracking/.gitignore
@@ -0,0 +1,14 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+outputs/
+*.zarr
+*.nc
+.python-version
+
+# Virtual environments
+.venv
diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile
new file mode 100644
index 000000000..49eea7cb2
--- /dev/null
+++ b/recipes/tc_tracking/Dockerfile
@@ -0,0 +1,39 @@
+FROM nvcr.io/nvidia/pytorch:25.08-py3
+
+# update repo info
+RUN apt update -y && \
+    apt install -y libibmad5 unixodbc && \
+    apt install -y netcdf-bin libnetcdf-dev
+
+# upgrade cmake
+RUN apt remove cmake -y && \
+    pip install cmake --upgrade
+
+# Install uv
+RUN wget -qO- https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV CC /usr/bin/gcc
+ENV CXX /usr/bin/g++
+
+# install TempestExtremes
+WORKDIR /
+RUN git clone https://github.com/ClimateGlobalChange/tempestextremes.git && \
+    mkdir -p /tempestextremes/build
+WORKDIR /tempestextremes/build
+RUN cmake .. && \
+    make -j && \
+    cp ./bin/DetectNodes /usr/local/bin && \
+    cp ./bin/StitchNodes /usr/local/bin
+
+
+# copy source into the container.
+RUN mkdir -p /fcn3_tcs
+COPY . /fcn3_tcs/
+
+# fcn3_tc
+WORKDIR /fcn3_tcs
+
+ENV FORCE_CUDA_EXTENSION 1
+ENV TORCH_CUDA_ARCH_LIST "8.0 8.6 9.0 10.0 12.0+PTX"
+RUN uv pip install --system --break-system-packages --no-cache-dir .
+
diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
new file mode 100644
index 000000000..95ba37830
--- /dev/null
+++ b/recipes/tc_tracking/README.md
@@ -0,0 +1,73 @@
+# FCN# cyclone tracking
+
+Baseline to acheive this is an ensembling setup. Due to the inherently stochastic nature of the model, initial condition perturbation is not required.
+
+## Setting up the environment
+
+There are two ways to set up the environment, either directly using an uv environment or using uv inside a container.
+Since torch-harmonics has to be compiled and TempestExtreme is a commandline tool, the container is the recommended way.
+
+1. **container**
+
+    There is a Dockerfile provided in the top level directory of the repository.
+
+2. **uv**
+
+    The project contains a pyproject.toml and a uv.lock file for setting up the envirnment. All you need to do is run:
+
+    ```bash
+    uv sync --frozen
+    ```
+
+    This will create a uv environment in the .venv directory. The fronzen flag will make sure the exact version specified in the lock file is used.
+    Optionally, trigger the virtual environment with:
+
+    ```bash
+    source .venv/bin/activate
+    ```
+
+    **issues with torch-harmoics**
+    if `--frozen` flag in `uv sync` cannot be used eg for updating the dependencies to the latest versions, it can happen that torch harmonics fails during dependency resolution phase. In that phase, uv has to build th to resolve dependencies, but does not yet install it. In these cases it can happen that th build fails as other dependencies are not installed yet, Installation of packages happens in a second phase, the install phase. Additionally, there is a git_lfs error that appears every now and then. To adress both or either, split the installation up in a two-step process and skip GIT_LFS:
+    ```bash
+    GIT_LFS_SKIP_SMUDGE=1 uv sync --no-install-package torch-harmonics
+    GIT_LFS_SKIP_SMUDGE=1 uv sync
+    ```
+
+
+
+## Run
+
+The project contains a script for running the code.
+
+```bash
+python tc_hunt.py --config-name=tc_selection_ens.yaml
+```
+
+## Modes
+
+### extract Reference tracks from ERA5 using IBTrACS as ground truth
+
+```yaml
+mode: 'extract_baseline'
+```
+
+IBTrACS is used as ground truth for the reference tracks. However, due to various reasons simulation data is compared against extracting the tracks directly from ERA5...
+
+### generate ensemble
+
+```yaml
+mode: 'generate_ensemble'
+```
+
+### reproduce individual members
+
+```yaml
+mode: 'reproduce_members'
+```
+
+Only works if exact same batch is reproduced, that means:
+- produces full batches
+- important to set batch size
+- ensemble size required if final batch of ensemble shall be reproduced
+- random seed has to be provided for each ensemble member, but the global random seed won't have effect here, as it mainly impacts the generation of random seeds for members
+- works only on same machine using identical environment. No guarantees otherwise
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
new file mode 100644
index 000000000..5adceb6c3
--- /dev/null
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -0,0 +1,128 @@
+project: 'reference_tracks'
+mode: 'extract_baseline'
+
+cases:
+    # erin:
+    #     year: 2025
+    #     basin: north_atlantic
+
+    # harvey:
+    #     year: 2017
+    #     basin: north_atlantic
+
+    # irma:
+    #     year: 2017
+    #     basin: north_atlantic
+
+    # maria:
+    #     year: 2017
+    #     basin: north_atlantic
+
+    # ophelia:
+    #     year: 2017
+    #     basin: north_atlantic
+
+    # michael:
+    #     year: 2018
+    #     basin: north_atlantic
+
+    # dorian:
+    #     year: 2019
+    #     basin: north_atlantic
+
+    # lorenzo:
+    #     year: 2019
+    #     basin: north_atlantic
+
+    # iota:
+    #     year: 2020
+    #     basin: north_atlantic
+
+    # ian:
+    #     year: 2022
+    #     basin: north_atlantic
+
+    # lee:
+    #     year: 2023
+    #     basin: north_atlantic
+
+    # beryl:
+    #     year: 2024
+    #     basin: north_atlantic
+
+    # milton:
+    #     year: 2024
+    #     basin: north_atlantic
+
+    helene:
+        year: 2024
+        basin: north_atlantic
+
+    # lan:
+    #     year: 2017
+    #     basin: west_pacific
+
+    # hato:
+    #     year: 2017
+    #     basin: west_pacific
+
+    # mawar:
+    #     year: 2023
+    #     basin: west_pacific
+
+    # yagi:
+    #     year: 2024
+    #     basin: west_pacific
+
+    # amphan:
+    #     year: 2020
+    #     basin: north_indian
+
+    # debbie:
+    #     year: 2017
+    #     basin: southern_pacific
+
+ibtracs_source_data: "/fcn3_cyclones/ibtracs_data/ibtracs.ALL.list.v04r01.csv"
+
+data_source:
+    h5_era5_train:
+        years: "1980-2016"
+        source:
+            _target_: src.data.localh5.LocalArchiveHDF5
+            dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/train"
+            metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
+
+    h5_era5_test:
+        years: 2017
+        source:
+            _target_: src.data.localh5.LocalArchiveHDF5
+            dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/test"
+            metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
+
+    h5_era5_oos:
+        years: "2018-2022"
+        source:
+            _target_: src.data.localh5.LocalArchiveHDF5
+            dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+            metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+
+    ncar_era5:
+        years: "2023-2024"
+        source:
+            _target_: earth2studio.data.NCAR_ERA5
+
+    gfs:
+        years: "2025"
+        source:
+            _target_: earth2studio.data.GFS
+
+orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+store_dir: "./outputs"
+
+cyclone_tracking:
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    print_te_output: False
\ No newline at end of file
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
new file mode 100644
index 000000000..c3a4537f0
--- /dev/null
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -0,0 +1,38 @@
+project: 'helene'
+mode: 'generate_ensemble'
+model: 'aifs-ens' # 'aifs-ens' 'fcn3' 'sfno'
+random_seed: 7777
+
+# ic_block_start: "2024-09-23 00:00:00" # first IC
+# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
+# ic_block_step: 24            # number of hours between individual ICs
+ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+
+n_steps: 15
+ensemble_size: 4
+batch_size: 2
+model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
+orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+
+data_source:
+    # _target_: src.data.localh5.LocalArchiveHDF5
+    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+    # _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+    _target_: earth2studio.data.CDS
+
+store_type: "netcdf"
+store_dir: "./outputs"
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+
+# cyclone_tracking:
+#     asynchronous: True
+#     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+#     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+#     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+#     keep_raw_data: False
+#     use_ram: True
+#     task_timeout_seconds: 120
+#     print_te_output: False
\ No newline at end of file
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
new file mode 100644
index 000000000..dddfbad40
--- /dev/null
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -0,0 +1,37 @@
+project: 'helene'
+mode: 'reproduce_members'
+model: 'sfno'
+random_seed: 7552
+
+reproduce_members: [
+                    # ['2024-09-24 00:00:00',  2, 4045540270],
+                    ['2024-09-24 00:00:00',  3, 4045540270],
+                    ['2024-09-24 00:00:00', 14, 3066645577],
+                    ]
+
+n_steps: 15
+ensemble_size: 15
+batch_size: 2
+model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
+orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+
+data_source:
+    # _target_: src.data.localh5.LocalArchiveHDF5
+    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+
+store_type: "netcdf"
+store_dir: "./outputs"
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
\ No newline at end of file
diff --git a/recipes/tc_tracking/post_proc/score/score_results.py b/recipes/tc_tracking/post_proc/score/score_results.py
new file mode 100644
index 000000000..a6f674786
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/score/score_results.py
@@ -0,0 +1,196 @@
+import xarray as xr
+import numpy as np
+import pandas as pd
+import sys
+import os
+import torch
+import time
+from datetime import datetime
+from typing import Any
+import sys
+from physicsnemo.distributed import DistributedManager
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from src.data.localh5 import LocalArchiveHDF5
+from earth2studio.data import fetch_data, prep_data_array
+from earth2studio.statistics import lat_weight, crps, mean, variance
+from earth2studio.utils.type import TimeArray, VariableArray
+
+
+def open_ds(fl):
+    if fl.endswith('.zarr') or (os.path.isdir(fl) and os.path.exists(os.path.join(fl, '.zgroup'))):
+        ds = xr.open_dataset(fl, engine='zarr')
+    else:
+        ds = xr.open_dataset(fl)
+
+    return ds
+
+
+def pick_vars_for_rank(vars):
+    DistributedManager.initialize()
+
+    dist = DistributedManager()
+    if dist.world_size == 1:
+        return vars
+
+    if dist.world_size != len(vars):
+        raise ValueError(f'{dist.world_size=} != {len(vars)=}')
+
+    vars = [vars[dist.rank]]
+
+    return vars
+
+
+def compute_mse(ds):
+
+    vars = list(ds.data_vars.keys())
+    vars.sort()
+    vars = pick_vars_for_rank(vars)
+    mse = torch.zeros(len(vars), len(ds.lead_time.values))
+
+    for ivar, var in enumerate(vars):
+        print(f'processing variable {var} ({ivar+1}/{len(vars)})')
+        var_ds = ds[var]
+
+
+        se = torch.zeros(len(var_ds.time.values), len(var_ds.lead_time.values),
+                         len(var_ds.lat.values),  len(var_ds.lon.values))
+
+        for ii, ic in enumerate(var_ds.time.values):
+            print(f'processing time {ic} ({ii+1}/{len(var_ds.time.values)})', end='\r', flush=True)
+
+            # fetch ground truth data
+            data_source = LocalArchiveHDF5(
+                dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
+                metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
+            )
+
+            x0, _ = fetch_data(data_source,
+                                     time=[ic],
+                                     lead_time=var_ds.lead_time.values,
+                                     variable=var_ds.name,)
+            x0 = x0.squeeze()
+
+            mean_ds = var_ds.sel(time=ic).mean(dim='ensemble')
+            se[ii, ...] = (x0 - mean_ds.values)**2
+        print('\n')
+
+        weights = torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))).unsqueeze(1).repeat(1, 1440)
+        mse[ivar, ...] = (se * weights).mean(dim=(0,2,3))
+
+    return mse
+
+
+def compute_rmse(var_ds):
+    return torch.sqrt(compute_mse(var_ds))
+
+
+def write_to_csv(rmse, ds, identifier):
+    # Get variable names and lead times for DataFrame
+    vars = list(ds.data_vars.keys())
+    vars.sort()
+    vars = pick_vars_for_rank(vars)
+
+    lead_times = ds.lead_time.values
+
+    # Convert tensor to numpy and create DataFrame
+    rmse_np = rmse.numpy()
+
+    # Create DataFrame with variables as rows and lead times as columns
+    rmse_df = pd.DataFrame(
+        rmse_np.T,
+        index=[f'{lt}' for lt in lead_times],
+        columns=vars,
+    )
+    rmse_df.index.name = 'lead_time'
+
+    # Generate output filename based on input filename
+    out_file = identifier
+    for var in vars:
+        out_file += f'_{var}'
+    out_file += '.csv'
+
+    rmse_df.to_csv(out_file)
+
+    return
+
+
+def compute_crps(ds):
+
+    vars = list(ds.data_vars.keys())
+    vars.sort()
+    vars = pick_vars_for_rank(vars)
+
+    _crps = crps(ensemble_dimension='ensemble',
+                 reduction_dimensions=['lat', 'lon'],
+                 weights=torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))).unsqueeze(1).repeat(1, 1440),
+                 fair=True)
+
+
+    # fetch ground truth data
+    _truth = LocalArchiveHDF5(
+                dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
+                metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
+            )
+
+    ccrr = torch.zeros(len(vars), len(ds.lead_time.values))
+    for ivar, var in enumerate(vars):
+        print(f'processing variable {var} ({ivar+1}/{len(vars)})')
+        ccc = torch.zeros(len(ds.time.values), len(ds.lead_time.values), device='cpu')
+        for ii, ic in enumerate(ds.time.values):
+            print(f'processing time {ic} ({ii+1}/{len(ds.time.values)})', end='\r', flush=True)
+
+            tru, tru_coords = fetch_data(_truth,
+                                         time=[ic],
+                                         lead_time=ds.lead_time.values,
+                                         variable=var,)
+            tru = tru.squeeze()
+            tru_coords.pop('time')
+            tru_coords.pop('variable')
+
+            pred, pred_coords = prep_data_array(ds[var].sel(time=ic))
+
+            _ccc, _ = _crps(x=pred.to(DistributedManager().device),
+                            x_coords=pred_coords,
+                            y=tru.to(DistributedManager().device),
+                            y_coords=tru_coords)
+            ccc[ii,:] = _ccc.to('cpu')
+
+        print('\n')
+
+        ccrr[ivar,:] = ccc.mean(dim=0)
+
+    return ccrr
+
+
+def score(fl):
+    ds = open_ds(fl)
+
+    rmse = False
+    crps = True
+
+    if False:
+        ds = ds.sel(time=ds.time[:5]).sel(ensemble=ds.ensemble[:50]).sel(lead_time=ds.lead_time[:10])
+
+    if rmse:
+        then = time.time()
+        rmse = compute_rmse(ds)
+        print(f'time taken: {time.time() - then} seconds')
+        write_to_csv(rmse, ds, 'rmse')
+
+    if crps:
+        then = time.time()
+        crps = compute_crps(ds)
+        print(f'time taken: {time.time() - then} seconds')
+        write_to_csv(crps, ds, 'crps')
+    return
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python score_results.py <netcdf_file>")
+        print("Example: python score_results.py score_2020_2020-01-01T00.00.00_mems0000-0049.nc")
+        exit()
+
+    score(sys.argv[1])
+
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/.gitignore b/recipes/tc_tracking/post_proc/tc_tracking/.gitignore
new file mode 100644
index 000000000..d2cbc61b5
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/.gitignore
@@ -0,0 +1,2 @@
+tracks_dat
+plots
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/README.md b/recipes/tc_tracking/post_proc/tc_tracking/README.md
new file mode 100644
index 000000000..56f707fc2
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/README.md
@@ -0,0 +1,21 @@
+# cyclone tracking
+
+This script is used to analyse cyclone tracks extracted using this framework from either model output or from ERA5.
+
+## Usage
+
+```bash
+python analyse_n_plot_tracks.py
+```
+
+### individual storms
+
+set the `individual_storms` flag to `True` in `analyse_n_plot_tracks.py`.
+
+### ensemble of storms
+
+set the `ensemble_of_storms` flag to `True` in `analyse_n_plot_tracks.py`.
+
+
+## Output
+...
\ No newline at end of file
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py b/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
new file mode 100644
index 000000000..f87f7009f
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
@@ -0,0 +1,289 @@
+import os
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+
+from data_handling import extract_tracks_from_file, extract_tracks, match_tracks
+from data_handling import compute_averages_of_errors_over_lead_time, get_ensemble_averages
+from plotting_helpers import plot_spaghetti, plot_over_time, plot_relative_over_time
+from plotting_helpers import plot_ib_era5, plot_extreme_extremes_histograms
+from plotting_helpers import plot_ensemble_mean_metrics, plot_errors_over_lead_time
+
+
+
+def load_tracks(case, pred_track_dir, tru_track_dir, out_dir):
+    tru_track = extract_tracks_from_file(os.path.join(tru_track_dir, f'reference_track_{case}.csv'))
+    tru_track['dist'] = np.zeros(len(tru_track))
+
+    pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir, case))
+    n_members = len(pred_tracks)
+
+    pred_tracks = match_tracks(pred_tracks, tru_track, case)
+
+    if out_dir:
+        out_dir = os.path.join(out_dir, case)
+        os.makedirs(out_dir, exist_ok=True)
+
+    ens_mean = get_ensemble_averages(pred_tracks=pred_tracks, tru_track=tru_track)
+
+    return tru_track, pred_tracks, ens_mean, n_members, out_dir
+
+
+def analyse_individual_storms(cases, pred_track_dir, tru_track_dir, out_path):
+    if isinstance(cases, str):
+        cases = [cases]
+
+    for case in tqdm(cases):
+        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,
+                                                                           pred_track_dir=pred_track_dir,
+                                                                           tru_track_dir=tru_track_dir,
+                                                                           out_dir=out_path)
+
+        # spaghetti plot
+        plot_spaghetti(true_track=tru_track,
+                       pred_tracks=pred_tracks,
+                       ensemble_mean=ens_mean['mean'],
+                       case=case,
+                       n_members=n_members,
+                       out_dir=out_dir)
+
+        # plot intensities over time
+        plot_over_time(pred_tracks=pred_tracks,
+                       tru_track=tru_track,
+                       ensemble_mean=ens_mean,
+                       case=case,
+                       n_members=n_members,
+                       out_dir=out_dir)
+
+        # plot relative intensities over time
+        plot_relative_over_time(pred_tracks=pred_tracks,
+                                tru_track=tru_track,
+                                ensemble_mean=ens_mean,
+                                case=case,
+                                n_members=n_members,
+                                out_dir=out_dir)
+
+        # plot comparison of intensities between ibtracs and era5 over time
+        plot_ib_era5(tru_track=tru_track,
+                     case=case,
+                     vars=['msl', 'wind_speed'],
+                     out_dir=out_dir)
+
+        # plot histogram of extreme values of intensities over full track
+        plot_extreme_extremes_histograms(pred_tracks=pred_tracks,
+                                        tru_track=tru_track,
+                                        ensemble_mean=ens_mean,
+                                        case=case,
+                                        out_dir=out_dir)
+
+        # plot error metrics over lead time
+        err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,
+                                                                tru_track=tru_track,
+                                                                vars=['wind_speed', 'msl', 'dist'])
+
+        plot_errors_over_lead_time(err_dict=err_dict,
+                                   case=case,
+                                   ic=pred_tracks[0]['ic'],
+                                   n_members=n_members,
+                                   n_tracks=len(pred_tracks),
+                                   out_dir=out_dir)
+
+    return
+
+
+def stack_metrics(err_dict):
+    var_errs = []
+    for var in err_dict.keys():
+        metrics = []
+        for metric in err_dict[var].keys():
+            metrics.append(err_dict[var][metric])
+
+        metrics = np.stack(metrics, axis=0)
+        var_errs.append(metrics)
+
+    return np.stack(var_errs, axis=0)
+
+
+def stack_cases(storm_metrics, max_len):
+    for ii in range(len(storm_metrics['case'])):
+        # pad storm_metrics['data'][ii].shape with nan to max_len
+        storm_metrics['data'][ii] = np.pad(storm_metrics['data'][ii],
+                                           pad_width=((0, 0), (0, 0), (0, max_len - storm_metrics['data'][ii].shape[-1])),
+                                           mode='constant',
+                                           constant_values=np.nan)
+
+    storm_metrics['data'] = np.stack(storm_metrics['data'], axis=0)
+
+    # check if shapes ok
+    should_shape = (len(storm_metrics['case']), len(storm_metrics['var']), len(storm_metrics['metric']), max_len)
+    assert storm_metrics['data'].shape == should_shape, \
+        f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
+
+    return storm_metrics
+
+
+def extract_weights(storm_metrics, max_len):
+
+    # extract weights
+    ens_idx = storm_metrics['metric'].index('n_members')
+    weights = storm_metrics['data'][:, 0, ens_idx, :]
+    weights = np.nan_to_num(weights, nan=0).astype(int)
+
+    # remove 'n_members' from metrics
+    storm_metrics['metric'].remove('n_members')
+    storm_metrics['data'] = np.delete(storm_metrics['data'], ens_idx, axis=-2)
+
+    # add weights to storm_metrics OrderedDict
+    storm_metrics['weights'] = weights
+
+    # check if shapes ok
+    should_shape = (len(storm_metrics['case']), len(storm_metrics['var']), len(storm_metrics['metric']), max_len)
+    assert storm_metrics['data'].shape == should_shape, \
+        f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
+
+    return storm_metrics
+
+
+def get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_path, vars=['wind_speed', 'msl', 'dist']):
+    storm_metrics = OrderedDict({'case': [],
+                                 'var': None,
+                                 'metric': None,
+                                 'lead time': None,
+                                 'data': []})
+    max_len, ensemble_averages, extremes = 0, {}, {}
+    for case in tqdm(cases, desc='loading storm data'):
+        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,
+                                                                           pred_track_dir=pred_track_dir,
+                                                                           tru_track_dir=tru_track_dir,
+                                                                           out_dir=out_path)
+        # record ensemble mean
+        ensemble_averages[case] = ens_mean
+
+        # compute averages of error metrics over lead time for storm
+        err_dict, _max_len = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,
+                                                                       tru_track=tru_track,
+                                                                       vars=vars)
+
+        extremes[case] = {}
+        for var in vars:
+            extremes[case][var] = {}
+            for ext, npfun in zip(['min', 'max'], [np.nanmin, np.nanmax]):
+                extremes[case][var][ext+'_pred'] = err_dict[var].pop(ext)
+                extremes[case][var][ext+'_tru'] = npfun(tru_track[var])
+
+        max_len = max(max_len, _max_len)
+        storm_metrics['case'].append(case)
+        storm_metrics['data'].append(stack_metrics(err_dict))
+
+    storm_metrics['var'] = list(err_dict.keys())
+    storm_metrics['metric'] = list(err_dict[list(err_dict.keys())[0]].keys()) # TODO remove n_members
+    storm_metrics['lead time'] = np.arange(max_len)*np.timedelta64(6, 'h')
+
+
+    return storm_metrics, max_len, ensemble_averages, extremes
+
+
+def reduce_over_all_storms(storm_metrics):
+
+    ensemble_metrics = {}
+    for var in storm_metrics['var']:
+        ensemble_metrics[var] = {}
+        var_idx = storm_metrics['var'].index(var)
+        for metric in storm_metrics['metric']:
+            met_idx = storm_metrics['metric'].index(metric)
+            ensemble_metrics[var][metric] = np.nanmean(storm_metrics['data'][:, var_idx, met_idx, :], axis=0)
+
+    ensemble_metrics['n_members'] = np.sum(storm_metrics['weights'], axis=0)
+
+    return ensemble_metrics
+
+
+def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path):
+
+    storm_metrics, max_len, ens_means = get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_path)
+
+    storm_metrics = stack_cases(storm_metrics, max_len)
+
+    storm_metrics = extract_weights(storm_metrics, max_len)
+
+    ensemble_metrics = reduce_over_all_storms(storm_metrics)
+
+    plot_ensemble_mean_metrics(ens_means, out_path)
+    # plot average dist in space and intensity over lead time (reduced over all storms)
+
+
+
+
+    # print()
+    # print(f'{storm_metrics.keys()=}')
+    # print(f'{storm_metrics['case']=}')
+    # print(f'{storm_metrics['var']=}')
+    # print(f'{storm_metrics['metric']=}')
+    # print(f'{storm_metrics['data'].shape=}')
+    # print(f'{storm_metrics['weights'].shape=}')
+
+    # print()
+    # print(f'{ensemble_metrics.keys()=}')
+    # print(f'{ensemble_metrics['msl']['mae'].shape=}')
+    # print(f'{ensemble_metrics['n_members'].shape=}')
+
+    # print()
+    # print(f'{ens_means['helene_2024_north_atlantic'].keys()=}')
+    # print(f'{ens_means['helene_2024_north_atlantic']['mean'].keys()=}')
+    # print(f'{ens_means['helene_2024_north_atlantic']['time']=}')
+    # print(f'{ens_means['helene_2024_north_atlantic']['n_members'].shape=}')
+    # print(f'{ens_means['helene_2024_north_atlantic']['mean']['dist'].shape=}')
+
+
+    return ensemble_metrics
+
+
+def analyse_n_plot_tracks():
+    cases = [
+        'amphan_2020_north_indian',      # 00
+        'beryl_2024_north_atlantic',     # 01
+        'debbie_2017_southern_pacific',  # 02
+        'dorian_2019_north_atlantic',    # 03
+        'harvey_2017_north_atlantic',    # 04
+        'hato_2017_west_pacific',        # 05
+        'helene_2024_north_atlantic',    # 06
+        'ian_2022_north_atlantic',       # 07
+        'iota_2020_north_atlantic',      # 08
+        'irma_2017_north_atlantic',      # 09
+        'lan_2017_west_pacific',         # 10
+        'lee_2023_north_atlantic',       # 11
+        'lorenzo_2019_north_atlantic',   # 12
+        'maria_2017_north_atlantic',     # 13
+        'mawar_2023_west_pacific',       # 14
+        'michael_2018_north_atlantic',   # 15
+        'milton_2024_north_atlantic',    # 16
+        'ophelia_2017_north_atlantic',   # 17
+        'yagi_2024_west_pacific',        # 18
+    ]
+
+    # case_selection = list(range(len(cases)))
+    case_selection = [6, 13]
+    individual_storms = False
+    ensemble_of_storms = True
+
+    pred_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'
+    tru_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks'
+    out_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/plots/case_studies_256_random_seed_issue'
+
+    if individual_storms:
+        analyse_individual_storms(cases=[cases[ii] for ii in case_selection],
+                                  pred_track_dir=pred_track_dir,
+                                  tru_track_dir=tru_track_dir,
+                                  out_path=out_dir)
+
+    if ensemble_of_storms:
+        analyse_ensemble_of_storms(cases=[cases[ii] for ii in case_selection],
+                                   pred_track_dir=pred_track_dir,
+                                   tru_track_dir=tru_track_dir,
+                                   out_path=out_dir)
+
+    return
+
+
+if __name__ == '__main__':
+    analyse_n_plot_tracks()
\ No newline at end of file
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py b/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
new file mode 100644
index 000000000..2cf3f5812
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
@@ -0,0 +1,362 @@
+import glob
+import numpy as np
+import pandas as pd
+
+
+def great_circle_distance(lat1, lon1, lat2, lon2, radius=6371000):
+    lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+
+    aa = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
+    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1-aa))
+
+    return radius * cc
+
+
+def merge_tracks_by_time(track, tru_track):
+    merged_track  = pd.merge(track, tru_track, on='time', how='left', suffixes=('', '_tru'))
+
+    # remove columns which are later than last time of tru_track
+    merged_track = merged_track[merged_track['time'] <= tru_track['time'].max()]
+
+    return merged_track
+
+
+def add_track_distance(track, tru_track):
+
+    merged_track = merge_tracks_by_time(track, tru_track)[['time', 'lat', 'lon', 'lat_tru', 'lon_tru']]
+
+    # compute great circle distance between each point on the track and the true track
+    dist = great_circle_distance(merged_track['lat'], merged_track['lon'], merged_track['lat_tru'], merged_track['lon_tru'])
+
+    # add to merged_track
+    merged_track['dist'] = dist
+
+    # add dist to track
+    track = pd.merge(track, merged_track[['time', 'dist']], on='time', how='left', suffixes=('', ''))
+
+    return track
+
+
+def match_tracks(pred_tracks, true_track, case):
+    # iterate over tracks and see if track exists in simulation
+    matched_tracks = []
+    min_dist, max_dist = 25371000, -1.
+
+    for _pred_track_dict in pred_tracks:
+        _pred_tracks = _pred_track_dict['tracks']
+
+        if len(_pred_tracks) == 0:
+            continue
+
+        n_tracks = _pred_tracks['track_id'].iloc[-1] + 1
+
+        for ii in range(n_tracks):
+            track = _pred_tracks.loc[_pred_tracks['track_id'] == ii].copy()
+
+            lat_pred = track['lat'].iloc[0]
+            lon_pred = track['lon'].iloc[0]
+
+            if not (true_track['time'] == track['time'].iloc[0]).any():
+                continue
+
+            lat_true = true_track.loc[true_track['time'] == track['time'].iloc[0]]['lat_ib'].item()
+            lon_true = true_track.loc[true_track['time'] == track['time'].iloc[0]]['lon_ib'].item()
+            dist = great_circle_distance(lat_pred, lon_pred, lat_true, lon_true)
+
+            if dist <= 300000:
+                min_dist, max_dist = min(min_dist, dist), max(max_dist, dist)
+
+                # add dist to track
+                track = add_track_distance(track, true_track)
+
+                matched_tracks.append({'ic': _pred_track_dict['ic'],
+                                       'member': _pred_track_dict['member'],
+                                       'first_match': track['time'].iloc[0],
+                                       'initial_dist': dist,
+                                       'tracks': track})
+                break
+
+    line = case.split("_")[:2]
+    line = f'{line[0].upper()} {line[1]}: matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, '
+    line += f'with distances ranging from {min_dist/1000:.1f} to {max_dist/1000:.1f} km'
+    print(line)
+
+    return matched_tracks
+
+
+def extract_tracks_from_file(csv_file):
+    tracks = pd.read_csv(csv_file, sep=',')
+    tracks.columns = tracks.columns.str.strip()
+
+    times = [pd.to_datetime(f'{tracks["year"].iloc[jj]}-{int(tracks.iloc[jj]["month"]):02d}-{int(tracks.iloc[jj]["day"]):02d} {int(tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(tracks))]
+
+    tracks.drop(columns=['year', 'month', 'day', 'hour'], inplace=True)
+    # if i j in tracks, drop them
+    if 'i' in tracks.columns:
+        tracks.drop(columns=['i', 'j'], inplace=True)
+
+    tracks.insert(0, 'time', times)
+
+    return tracks
+
+
+def extract_tracks(in_dir):
+    tracks = []
+    files = glob.glob(f'{in_dir}/*.csv')
+    files.sort()
+
+    for csv_file in files:
+        # read csv, strip whitespace from column names
+        _tracks = extract_tracks_from_file(csv_file)
+
+        mem = int(csv_file.split('_mems')[-1].split('.csv')[0])
+        ic = pd.to_datetime(csv_file.split('_mems')[0][-19:])
+
+        tracks.append({'ic': ic, 'member': mem, 'tracks': _tracks})
+
+    return tracks
+
+
+def merge_tracks_by_lead_time(track_dict, tru_track):
+
+    merged_track = merge_tracks_by_time(track_dict['tracks'], tru_track)
+
+    # clip leading and trailing rows in which only predicted or only true track are present
+    t_max = max(track_dict['tracks']['time'].max(), tru_track['time'].max())
+    t_min = min(track_dict['tracks']['time'].min(), tru_track['time'].min())
+    merged_track = merged_track[merged_track['time'] >= t_min]
+    merged_track = merged_track[merged_track['time'] <= t_max]
+
+    return merged_track
+
+
+# function computing MAE
+def compute_mae(tru_vars, pred_vars):
+    return np.nanmean(np.abs(tru_vars - pred_vars), axis=0)
+
+
+# function computing mse
+def compute_mse(tru_vars, pred_vars):
+    return np.nanmean((tru_vars - pred_vars)**2, axis=0)
+
+
+# function computing RMSE
+def compute_rmse(tru_vars, pred_vars):
+    return np.sqrt(compute_mse(tru_vars, pred_vars))
+
+
+# function computing variance
+def compute_variance(arr):
+    return np.nanvar(arr, axis=0)
+
+
+def compute_std(arr):
+    return np.sqrt(compute_variance(arr))
+
+
+def remove_trailing_nans(merged_track, var):
+    either_nans = np.logical_or(merged_track[var+'_tru'].isna(), merged_track[var].isna())
+    cut_off = np.where(~either_nans)[0][-1]
+
+    return merged_track.iloc[:cut_off+1]
+
+
+def rebase_by_lead_time(pred_tracks, tru_track, vars):
+    err_dict = {}
+    for var in vars:
+        err_dict[var] = {'pred': [], 'tru': []}
+
+    max_len = 0
+    ii = 0
+    for track in pred_tracks:
+        ii += 1
+        merged_track = merge_tracks_by_lead_time(track, tru_track)
+        if merged_track is None:
+            continue
+
+        # remove trailing rows in which both true and predicted track are nan
+        merged_track = remove_trailing_nans(merged_track, 'msl')
+
+        max_len = max(max_len, len(merged_track))
+
+        for var in err_dict.keys():
+            err_dict[var]['pred'].append(merged_track[var])
+            err_dict[var]['tru'].append(merged_track[var+'_tru'])
+
+    return err_dict, max_len
+
+
+def compute_error_metrics(err_dict, max_len):
+    for var in err_dict.keys():
+        pred_vars = err_dict[var]['pred']
+        tru_vars = err_dict[var]['tru']
+
+        # pad with nans to make all arrays the same length
+        counts = np.zeros(max_len, dtype=int)
+        for ii in range(len(pred_vars)):
+            counts[:len(pred_vars[ii])] += 1
+
+            pred_vars[ii] = np.pad(pred_vars[ii], (0, max_len - len(pred_vars[ii])), mode='constant', constant_values=np.nan)
+            tru_vars[ii] = np.pad(tru_vars[ii], (0, max_len - len(tru_vars[ii])), mode='constant', constant_values=np.nan)
+
+
+        # merge to single array
+        pred_vars, tru_vars = np.array(pred_vars), np.array(tru_vars)
+
+        err_dict[var] = {'mae': compute_mae(tru_vars, pred_vars),
+                         'mse': compute_mse(tru_vars, pred_vars),
+                         'variance': compute_variance(pred_vars),
+                         'max': np.nanmax(pred_vars, axis=-1),
+                         'min': np.nanmin(pred_vars, axis=-1),
+                         'n_members': counts}
+
+    return err_dict
+
+
+def compute_averages_of_errors_over_lead_time(pred_tracks, tru_track, vars):
+
+    err_dict, max_len = rebase_by_lead_time(pred_tracks, tru_track, vars)
+
+    err_dict = compute_error_metrics(err_dict, max_len)
+
+    return err_dict, max_len
+
+
+def lat_lon_to_xyz(lat, lon, radius=6371000):
+    """
+    Converts longitude and latitude coordinates to 3D Cartesian (XYZ) coordinates using numpy.
+    This function can handle single values or numpy arrays as input.
+
+    Args:
+        lon (float or np.ndarray): Longitude(s) in degrees (range [0, 360)).
+        lat (float or np.ndarray): Latitude(s) in degrees (range [-90, 90]).
+        radius (float, optional): The radius of the sphere (e.g., Earth's radius).
+                                  Defaults to 1.0 for a unit sphere.
+
+    Returns:
+        np.ndarray: A numpy array containing the X, Y, and Z coordinates.
+                    - If inputs are scalars, returns a (3,) array.
+                    - If inputs are arrays of length N, returns an (N, 3) array.
+    """
+    # Convert degrees to radians using numpy's vectorized function
+    lat_rad, lon_rad = np.radians(lat), np.radians(lon)
+
+    # Apply the conversion formulas using numpy's trigonometric functions
+    xx = radius * np.cos(lat_rad) * np.cos(lon_rad)
+    yy = radius * np.cos(lat_rad) * np.sin(lon_rad)
+    zz = radius * np.sin(lat_rad)
+
+    # Stack the coordinates into a single numpy array
+    return xx, yy, zz
+
+
+def xyz_to_lat_lon(xx, yy, zz):
+    """
+    Converts 3D Cartesian (XYZ) coordinates back to longitude and latitude using numpy.
+    This function can handle single values or numpy arrays as input.
+
+    Args:
+        x (float or np.ndarray): X coordinate(s).
+        y (float or np.ndarray): Y coordinate(s).
+        z (float or np.ndarray): Z coordinate(s).
+
+    Returns:
+        tuple: A tuple containing longitude and latitude (in degrees).
+               - Longitude is in the range [0, 360).
+               - Latitude is in the range [-90, 90].
+    """
+    # Calculate the radius. This is needed to normalize the vector for latitude calculation.
+    radius = np.sqrt(xx**2 + yy**2 + zz**2)
+
+    # Latitude can be calculated using arcsin.
+    # We add a small epsilon to the denominator to avoid division by zero if the input is (0,0,0).
+    lat_rad = np.arcsin(zz / (radius + 1e-9))
+
+    # Longitude is calculated using arctan2(y, x) for quadrant-correct results.
+    lon_rad = np.arctan2(yy, xx)
+
+    # Convert radians to degrees and adjust longitude to [0, 360)
+    lat = np.degrees(lat_rad)
+    lon = (np.degrees(lon_rad) + 360) % 360
+
+    return lat, lon
+
+
+def cartesian_to_spherical_track(stats, tru_track, frame_of_reference):
+
+    mean_lat, mean_lon = xyz_to_lat_lon(stats['mean']['x'], stats['mean']['y'], stats['mean']['z'])
+
+    # delete cartesian mean/variance
+    for var in ['x', 'y', 'z']:
+        for metric in ['mean', 'variance']:
+            del stats[metric][var]
+
+    # add spherical mean
+    stats['mean']['lat'] = mean_lat
+    stats['mean']['lon'] = mean_lon
+
+    # compute distance
+    tru_cont = pd.merge(frame_of_reference, tru_track, on='time', how='left')
+
+    # compute great circle distance between each point on the track and the true track
+    dist = great_circle_distance(tru_cont['lat'], tru_cont['lon'], stats['mean']['lat'], stats['mean']['lon'])
+
+    # add to merged_track
+    stats['mean']['dist'] = dist.to_numpy(dtype=float)
+
+    for var in ['msl', 'wind_speed']:
+        stats['mean'][var+'_err_of_mean'] = stats['mean'][var] - tru_cont[var]
+
+    return stats
+
+
+def get_ensemble_averages(pred_tracks, tru_track, vars: list[str]=['msl', 'wind_speed', 'x', 'y', 'z']):
+
+    stats = {'time': None,
+             'n_members': None,
+             'mean': {var: [] for var in vars},
+             'variance': {var: [] for var in vars}}
+
+    # loop over all tracks, match with
+    this_is_the_last_time = pred_tracks[0]['ic']
+
+    # iterate over all tracks to get set of all times present
+    for track in pred_tracks:
+        this_is_the_last_time = max(this_is_the_last_time, track['tracks']['time'].values[-1])
+
+    all_times = np.arange(pred_tracks[0]['ic'], this_is_the_last_time, np.timedelta64(6, 'h'))
+    stats["time"] = all_times
+
+    # start data frame single column called time which holds all_times
+    frame_of_reference = pd.DataFrame(data=all_times, index=np.arange(len(all_times)), columns=['time'])
+
+    for track in pred_tracks:
+        # convert lat/lon to xyz
+        xx, yy, zz = lat_lon_to_xyz(track['tracks']['lat'], track['tracks']['lon'])
+        track['tracks']['x'] = xx
+        track['tracks']['y'] = yy
+        track['tracks']['z'] = zz
+
+        contextualised = pd.merge(frame_of_reference, track['tracks'], on='time', how='left')
+
+        for var in vars:
+            stats['mean'][var].append(contextualised[var])
+
+    for var in vars:
+        # get number of ensemble members contributing to each time step
+        if stats["n_members"] is None:
+            stats["n_members"] = np.count_nonzero(~np.isnan(stats['mean'][var]), axis=0)
+        else:
+            assert np.all(stats["n_members"] == np.count_nonzero(~np.isnan(stats['mean'][var]), axis=0)), \
+                f'n_members is not the same for all variables but should be....'
+
+        help = np.stack(stats['mean'][var])
+        stats['variance'][var] = np.nanvar(help, axis=0)
+        stats['mean'][var] = np.nanmean(help, axis=0)
+
+    # convert xyz back to lat/lon
+    stats = cartesian_to_spherical_track(stats, tru_track, frame_of_reference)
+
+    return stats
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb b/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
new file mode 100644
index 000000000..c192c783d
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## read csv file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:   0%|          | 0/19 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AMPHAN 2020: matched 256 out of 256 tracks, with distances ranging from 35.2 to 35.2 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:   5%|▌         | 1/19 [00:06<01:59,  6.63s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BERYL 2024: matched 242 out of 256 tracks, with distances ranging from 114.5 to 285.1 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  11%|█         | 2/19 [00:12<01:41,  6.00s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBBIE 2017: matched 256 out of 256 tracks, with distances ranging from 7.7 to 7.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  16%|█▌        | 3/19 [00:20<01:54,  7.16s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DORIAN 2019: matched 70 out of 256 tracks, with distances ranging from 28.3 to 295.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  21%|██        | 4/19 [00:33<02:20,  9.38s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HARVEY 2017: matched 256 out of 256 tracks, with distances ranging from 42.2 to 42.2 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  26%|██▋       | 5/19 [00:47<02:33, 11.00s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HATO 2017: matched 253 out of 256 tracks, with distances ranging from 72.8 to 72.8 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:  32%|███▏      | 6/19 [01:01<02:36, 12.07s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HELENE 2024: matched 256 out of 256 tracks, with distances ranging from 21.7 to 21.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  37%|███▋      | 7/19 [01:15<02:32, 12.73s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IAN 2022: matched 256 out of 256 tracks, with distances ranging from 29.8 to 29.8 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:  42%|████▏     | 8/19 [01:29<02:22, 12.97s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IOTA 2020: matched 256 out of 256 tracks, with distances ranging from 73.7 to 73.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  47%|████▋     | 9/19 [01:37<01:53, 11.39s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IRMA 2017: matched 243 out of 256 tracks, with distances ranging from 7.7 to 7.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  53%|█████▎    | 10/19 [01:53<01:56, 13.00s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAN 2017: matched 256 out of 256 tracks, with distances ranging from 15.6 to 15.6 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:  58%|█████▊    | 11/19 [02:02<01:33, 11.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LEE 2023: matched 256 out of 256 tracks, with distances ranging from 46.9 to 46.9 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  63%|██████▎   | 12/19 [02:20<01:35, 13.57s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LORENZO 2019: matched 256 out of 256 tracks, with distances ranging from 33.8 to 116.7 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:  68%|██████▊   | 13/19 [02:33<01:21, 13.60s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MARIA 2017: matched 256 out of 256 tracks, with distances ranging from 38.4 to 38.4 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  74%|███████▎  | 14/19 [02:46<01:06, 13.39s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAWAR 2023: matched 256 out of 256 tracks, with distances ranging from 85.0 to 85.0 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  79%|███████▉  | 15/19 [02:54<00:46, 11.55s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MICHAEL 2018: matched 256 out of 256 tracks, with distances ranging from 50.0 to 50.0 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  84%|████████▍ | 16/19 [03:08<00:37, 12.56s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MILTON 2024: matched 256 out of 256 tracks, with distances ranging from 44.8 to 44.8 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading storm data:  89%|████████▉ | 17/19 [03:22<00:25, 12.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OPHELIA 2017: matched 227 out of 256 tracks, with distances ranging from 11.1 to 101.6 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data:  95%|█████████▍| 18/19 [03:33<00:12, 12.20s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "YAGI 2024: matched 256 out of 256 tracks, with distances ranging from 17.5 to 17.5 km\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
+      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
+      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
+      "loading storm data: 100%|██████████| 19/19 [03:44<00:00, 11.80s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from analyse_n_plot import get_individual_storm_metrics, stack_cases, extract_weights, reduce_over_all_storms\n",
+    "\n",
+    "cases = [\n",
+    "    'amphan_2020_north_indian',      # 00\n",
+    "    'beryl_2024_north_atlantic',     # 01\n",
+    "    'debbie_2017_southern_pacific',  # 02\n",
+    "    'dorian_2019_north_atlantic',    # 03\n",
+    "    'harvey_2017_north_atlantic',    # 04\n",
+    "    'hato_2017_west_pacific',        # 05\n",
+    "    'helene_2024_north_atlantic',    # 06\n",
+    "    'ian_2022_north_atlantic',       # 07\n",
+    "    'iota_2020_north_atlantic',      # 08\n",
+    "    'irma_2017_north_atlantic',      # 09\n",
+    "    'lan_2017_west_pacific',         # 10\n",
+    "    'lee_2023_north_atlantic',       # 11\n",
+    "    'lorenzo_2019_north_atlantic',   # 12\n",
+    "    'maria_2017_north_atlantic',     # 13\n",
+    "    'mawar_2023_west_pacific',       # 14\n",
+    "    'michael_2018_north_atlantic',   # 15\n",
+    "    'milton_2024_north_atlantic',    # 16\n",
+    "    'ophelia_2017_north_atlantic',   # 17\n",
+    "    'yagi_2024_west_pacific',        # 18\n",
+    "]\n",
+    "\n",
+    "# case_selection = [7, 13]\n",
+    "case_selection = list(range(len(cases)))\n",
+    "\n",
+    "pred_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'\n",
+    "tru_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks'\n",
+    "out_dir = f'./plots'\n",
+    "\n",
+    "cases = [cases[ii] for ii in case_selection]\n",
+    "\n",
+    "storm_metrics, max_paired_len, ens_means, extremes = get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_dir)\n",
+    "\n",
+    "storm_metrics = stack_cases(storm_metrics, max_paired_len)\n",
+    "\n",
+    "storm_metrics = extract_weights(storm_metrics, max_paired_len)\n",
+    "\n",
+    "ensemble_metrics = reduce_over_all_storms(storm_metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot average error of mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def bring_to_common_length(arr, max_val):\n",
+    "    if max_paired_len - len(arr) <= 0:\n",
+    "        return arr[:max_paired_len]\n",
+    "    else:\n",
+    "        return np.pad(arr, (0, max_paired_len - len(arr)), 'constant' , constant_values=0)\n",
+    "\n",
+    "\n",
+    "def average_lead_time_errors_over_cases(ens_means):\n",
+    "    err_lt = {}\n",
+    "    vars = ['dist', 'msl_err_of_mean', 'wind_speed_err_of_mean']\n",
+    "    for var in vars:\n",
+    "        errs, weights = [], []\n",
+    "        for _, means in ens_means.items():\n",
+    "            errs.append(bring_to_common_length(means['mean'][var], max_paired_len))\n",
+    "            weights.append(bring_to_common_length( means['n_members'], max_paired_len))\n",
+    "\n",
+    "        errs = np.stack(errs)\n",
+    "        weights = np.stack(weights)\n",
+    "\n",
+    "        # set nans in err to 0 and their associated weights also to 0\n",
+    "        weights[np.isnan(errs)] = 0\n",
+    "        errs[np.isnan(errs)] = 0\n",
+    "\n",
+    "        # compute weighted average\n",
+    "        err_lt[var] = np.average(errs, axis=-2, weights=weights)\n",
+    "\n",
+    "    return err_lt\n",
+    "\n",
+    "\n",
+    "def plot_ensemble_mean_errors_over_lead_time(err_lt, lead_times, n_cases,\n",
+    "                               norm_dict = {'msl_err_of_mean': 100, 'wind_speed_err_of_mean': 1, 'dist': 1000},\n",
+    "                               unit_dict = {'msl_err_of_mean': 'hPa', 'wind_speed_err_of_mean': 'm/s', 'dist': 'km'},\n",
+    "                               out_path: str | None = None,\n",
+    "                               max_lt: np.timedelta64 | None = None):\n",
+    "\n",
+    "    \"\"\"\n",
+    "    plot mean absolute error of ensemble mean weighted by matched ensemble size\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # find last index in lead_times that is equal to or below max_lt (xlim doesn't work on timedelta)\n",
+    "    if max_lt:\n",
+    "        max_lt_idx = np.where(lead_times <= max_lt)[0][-1]\n",
+    "    else:\n",
+    "        max_lt_idx = len(lead_times)\n",
+    "\n",
+    "    fig, ax = plt.subplots(len(err_lt), figsize=((len(err_lt)+1)*2, 8), sharex=True)\n",
+    "\n",
+    "    # Convert lead_times to hours for plotting\n",
+    "    lead_times_hours = lead_times / np.timedelta64(1, 'h')\n",
+    "\n",
+    "    for ivar, var in enumerate(err_lt.keys()):\n",
+    "        ax[ivar].plot(lead_times_hours[:max_lt_idx], err_lt[var][:max_lt_idx]/norm_dict[var])\n",
+    "        ax[ivar].set_ylabel(f'{var} [{unit_dict[var]}]', fontsize=12, weight='bold')\n",
+    "\n",
+    "        if ivar == len(err_lt) - 1:\n",
+    "            ax[ivar].set_xlabel('lead time [h]', fontsize=12)\n",
+    "        ax[ivar].grid(True)\n",
+    "\n",
+    "    sup_title = f'average error of ensemble means over {n_cases} cases \\n'\n",
+    "    sup_title += '(weighted by number of matched tracks at lead time)'\n",
+    "    fig.suptitle(sup_title, fontsize=16)\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    if out_path:\n",
+    "        plt.savefig(os.path.join(out_path, f'error_metrics_of_ens_mean_over_lead_time.png'))\n",
+    "\n",
+    "    return\n",
+    "\n",
+    "\n",
+    "def plot_ensemble_mean_metrics(ens_means, out_path=None):\n",
+    "\n",
+    "    err_lt = average_lead_time_errors_over_cases(ens_means)\n",
+    "\n",
+    "    plot_ensemble_mean_errors_over_lead_time(err_lt=err_lt,\n",
+    "                                    lead_times=np.arange(max_paired_len)*np.timedelta64(6, 'h'),\n",
+    "                                    n_cases=len(ens_means),\n",
+    "                                    out_path=out_path,\n",
+    "                                    max_lt=np.timedelta64(192, 'h'))\n",
+    "\n",
+    "    return\n",
+    "\n",
+    "plot_ensemble_mean_metrics(ens_means, out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scatter plot of maximum intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "plt.close('all')\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 8))\n",
+    "\n",
+    "for ii, (var, npfun, minmax) in enumerate(zip(['msl', 'wind_speed'], [np.nanmin, np.nanmax], ['min', 'max'])):\n",
+    "    tick_pos, tick_label = [], []\n",
+    "    for case, ext in extremes.items():\n",
+    "        yy = ext[var][minmax+'_pred']\n",
+    "        xx = ext[var][minmax+'_tru']*np.ones_like(yy)\n",
+    "        ax[ii].plot(xx, yy, marker='o', linestyle='None', color='black', alpha=.1)\n",
+    "\n",
+    "    legend = False\n",
+    "    for case, ext in extremes.items():\n",
+    "        yy = ext[var][minmax+'_pred']\n",
+    "        if not legend:\n",
+    "            ax[ii].plot(ext[var][minmax+'_tru'], npfun(ens_means[case]['mean'][var]), marker='o', linestyle='None', color='lime', alpha=1, label=f'{minmax} of mean')\n",
+    "            ax[ii].plot(ext[var][minmax+'_tru'], np.nanmean(yy), marker='o', linestyle='None', color='red', alpha=1, label=f'mean of {minmax}')\n",
+    "            legend = True\n",
+    "        else:\n",
+    "            ax[ii].plot(ext[var][minmax+'_tru'], npfun(ens_means[case]['mean'][var]), marker='o', linestyle='None', color='lime', alpha=1)\n",
+    "            ax[ii].plot(ext[var][minmax+'_tru'], np.nanmean(yy), marker='o', linestyle='None', color='red', alpha=1)\n",
+    "\n",
+    "        tick_pos.append(ext[var][minmax+'_tru'])\n",
+    "        tick_label.append(case)\n",
+    "\n",
+    "    # ax[ii].legend()\n",
+    "    ax[ii].set_aspect('equal')\n",
+    "    ax[ii].legend()\n",
+    "    tick_pos_dflt = ax[ii].get_xticks()\n",
+    "    labels_dflt = [f'{xx:.0f}' for xx in tick_pos_dflt]\n",
+    "\n",
+    "    tick_pos = np.append(tick_pos_dflt, tick_pos)\n",
+    "    tick_label = np.append(labels_dflt, tick_label)\n",
+    "\n",
+    "    ax[ii].set_xticks(tick_pos)\n",
+    "    ax[ii].set_xticklabels(tick_label, rotation=90, ha='right')\n",
+    "\n",
+    "    # 2. Get the limits of the axes\n",
+    "    low_x, high_x = ax[ii].get_xlim()\n",
+    "    low_y, high_y = ax[ii].get_ylim()\n",
+    "\n",
+    "    # 3. Calculate the range for the diagonal line\n",
+    "    # This ensures the line is drawn only within the plotted area\n",
+    "    low = max(low_x, low_y)\n",
+    "    high = min(high_x, high_y)\n",
+    "    ax[ii].set_xlim(low, high)\n",
+    "    ax[ii].set_ylim(low, high)\n",
+    "\n",
+    "    # 4. Plot the diagonal line\n",
+    "    ax[ii].plot([low, high], [low, high], ls='--', color='k', label='f(x) = x', zorder=0, alpha=.6)\n",
+    "\n",
+    "fig.tight_layout()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py b/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
new file mode 100644
index 000000000..169615749
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
@@ -0,0 +1,417 @@
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+import pandas as pd
+from data_handling import merge_tracks_by_time
+
+def add_some_gap(lat_min, lat_max, lon_min, lon_max):
+    gap_fac = .1
+    lat_gap = (lat_max - lat_min) * gap_fac
+    lon_gap = (lon_max - lon_min) * gap_fac
+
+    lat_min, lat_max = lat_min - lat_gap, lat_max + lat_gap
+    lon_min, lon_max = lon_min - lon_gap, lon_max + lon_gap
+
+    if lat_gap/lon_gap > 2:
+        d_lon = .5*(lat_max - lat_min)
+        med_lon = .5*(lon_min + lon_max)
+        lon_min, lon_max = med_lon - d_lon/2, med_lon + d_lon/2
+
+    elif lon_gap/lat_gap > 2:
+        d_lat = .5*(lon_max - lon_min)
+        med_lat = .5*(lat_min + lat_max)
+        lat_min, lat_max = med_lat - d_lat/2, med_lat + d_lat/2
+
+    return lat_min, lat_max, lon_min, lon_max
+
+
+def get_central_coords(track):
+    lat_cen = track['lat'].median()
+    lon_cen = track['lon'].median()
+
+    return lat_cen, lon_cen
+
+
+def plot_spaghetti(true_track,
+                   pred_tracks,
+                   ensemble_mean,
+                   case,
+                   n_members,
+                   out_dir: str | None = None,
+                   alpha: float=.2,
+                   line_width: float=2,
+                   ic = None):
+    plt.close('all')
+
+    lat_cen, lon_cen = get_central_coords(true_track)
+
+    # Create figure and axis
+    fig = plt.figure(figsize=(22,10))
+    sup_title = f'{(case.split("_")[0]).upper()} '
+    sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
+    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    fig.suptitle(sup_title, fontsize=16)
+
+    projection = ccrs.LambertAzimuthalEqualArea(central_longitude=lon_cen, central_latitude=lat_cen)
+    ax = fig.add_subplot(1, 1, 1, projection=projection)
+
+    ax.add_feature(cfeature.COASTLINE,lw=.5)
+    ax.add_feature(cfeature.RIVERS,lw=.5)
+    if case != 'debbie_2017_southern_pacific': # cartopy issues with small islands
+        ax.add_feature(cfeature.OCEAN, facecolor='#b0c4de')
+        ax.add_feature(cfeature.LAND, facecolor='#C4B9A3')
+
+    lat_min, lat_max, lon_min, lon_max = 90, -90, 360, -.1
+
+    # Plot the line in white
+    for _track in pred_tracks:
+        track = _track['tracks']
+        if ic is not None and _track['ic'] not in ic:
+            continue
+
+        lat_min, lat_max = min(lat_min, track['lat'].min()), max(lat_max, track['lat'].max())
+        lon_min, lon_max = min(lon_min, track['lon'].min()), max(lon_max, track['lon'].max())
+
+        ax.plot(track['lon'], track['lat'], transform=ccrs.PlateCarree(),
+                color='black', linewidth=line_width, alpha=alpha)
+
+    ax.plot(true_track['lon'], true_track['lat'], transform=ccrs.PlateCarree(),
+            color='red', linewidth=line_width, alpha=1.)
+
+    ax.plot(ensemble_mean['lon'], ensemble_mean['lat'], transform=ccrs.PlateCarree(),
+            color='lime', linewidth=line_width, alpha=1.)
+
+    lat_min, lat_max = min(lat_min, true_track['lat'].min()), max(lat_max, true_track['lat'].max())
+    lon_min, lon_max = min(lon_min, true_track['lon'].min()), max(lon_max, true_track['lon'].max())
+
+    lat_min, lat_max, lon_min, lon_max = add_some_gap(lat_min, lat_max, lon_min, lon_max)
+
+    # Remove extra space around the plot
+    plt.tight_layout()
+
+    ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
+    ax.gridlines(draw_labels=False, dms=False, x_inline=False, y_inline=False)
+
+
+    if out_dir:
+        fig.savefig(os.path.join(out_dir, f'{case}_tracks.png'))
+
+    return
+
+
+def normalised_intensities(track, tru_track, var):
+
+    merged_track = merge_tracks_by_time(track, tru_track)
+
+    if var == 'msl':
+        merged_track[var] = (merged_track[var] - merged_track[var+'_tru']) / (101325 - merged_track[var+'_tru'])
+    else:
+        merged_track[var] = (merged_track[var] - merged_track[var+'_tru']) / merged_track[var+'_tru']
+
+    return merged_track
+
+
+def plot_relative_over_time(pred_tracks, tru_track, ensemble_mean, case, n_members, ics=None, out_dir: str | None = None):
+
+    fig, _ax = plt.subplots(2,1, figsize=(11, 11), sharex=True)
+    sup_title = f'{(case.split("_")[0]).upper()} '
+    sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
+    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    fig.suptitle(sup_title, fontsize=16)
+
+    vars = ['msl', 'wind_speed']
+    labels = ['(msl - msl_ref)/(101325Pa - msl_ref)', 'max_wind/max_wind_ref - 1']
+
+    ic, end = pred_tracks[0]['ic'], tru_track['time'].max()
+    rel_steps = int(((end - ic) / np.timedelta64(6, 'h') + 1)*.75)
+
+
+    for ii in range(_ax.shape[0]):
+        vmin, vmax = 1000, -1000
+        for _track in pred_tracks:
+
+            track = _track['tracks']
+            if ics is not None and _track['ic'] not in ics:
+                continue
+
+            track = normalised_intensities(track, tru_track, vars[ii])
+
+            vmin, vmax = min(vmin, track[vars[ii]][:rel_steps].min()), max(vmax, track[vars[ii]][:rel_steps].max())
+
+            ax = _ax[ii]
+            ax.plot(track['time'], track[vars[ii]], color='black', alpha=.1)
+
+        _ax[ii].set_ylabel(labels[ii])
+        _ax[ii].grid(True)
+        _ax[ii].set_ylim(vmin, vmax)
+
+        ax.plot(tru_track['time'], [0 for _ in range(len(tru_track))], color='orangered', linewidth=2.5, label='era5 comparison')
+
+        mean = pd.DataFrame({'time': ensemble_mean['time'],
+                             vars[ii]: ensemble_mean['mean'][vars[ii]]})
+        _track = normalised_intensities(mean, tru_track, vars[ii])
+        ax.plot(_track['time'], _track[vars[ii]], color='lime', linewidth=2.5, label='ensemble mean', linestyle='--')
+
+
+        ax.legend()
+
+    # Set x-axis label to indicate hours
+    _ax[-1].set_xlabel('time [UTC]')
+
+    plt.xlim(track['time'].min()-np.timedelta64(6, 'h'), tru_track['time'].max()+np.timedelta64(6, 'h'))
+
+    if out_dir:
+        plt.savefig(os.path.join(out_dir, f'{case}_rel_intensities.png'))
+
+    return
+
+
+def plot_over_time(pred_tracks,
+                   tru_track,
+                   ensemble_mean,
+                   case,
+                   n_members,
+                   vars=['msl', 'wind_speed', 'dist'],
+                   labels=['msl [hPa]', 'maximum instantaneous wind speed [m/s]', 'distance [km]'],
+                   ics=None,
+                   out_dir: str | None = None):
+
+    fig, _ax = plt.subplots(len(vars),1, figsize=(11, 15), sharex=True)
+    sup_title = f'{(case.split("_")[0]).upper()} '
+    sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
+    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    fig.suptitle(sup_title, fontsize=16)
+
+    t_min, t_max = np.datetime64('2120-05-16 12:00:00'), np.datetime64('1820-05-16 12:00:00')
+
+    for ii in range(_ax.shape[0]):
+        for _track in pred_tracks:
+            track = _track['tracks']
+            if ics is not None and _track['ic'] not in ics:
+                continue
+
+            if vars[ii] == 'dist':
+                yy = track[vars[ii]]/1000
+            elif vars[ii] == 'msl':
+                yy = track[vars[ii]]/100
+            else:
+                yy = track[vars[ii]]
+
+            _ax[ii].plot(track['time'], yy, color='black', alpha=.1)
+
+            t_min, t_max = min(t_min, track['time'].min()), max(t_max, track['time'].max())
+
+        _ax[ii].set_xlim(t_min-np.timedelta64(6, 'h'), t_max+np.timedelta64(6, 'h'))
+        _ax[ii].set_ylabel(labels[ii])
+        _ax[ii].grid(True)
+
+        # if vars[ii] != 'dist':
+        yy = tru_track[vars[ii]]/100 if vars[ii] == 'msl' else tru_track[vars[ii]]
+        _ax[ii].plot(tru_track['time'], yy, color='orangered', linewidth=2.5, label='era5 comparison')
+
+
+        if vars[ii] == 'dist':
+            yy = ensemble_mean['mean'][vars[ii]]/1000
+        elif vars[ii] == 'msl':
+            yy = ensemble_mean['mean'][vars[ii]]/100
+        else:
+            yy = ensemble_mean['mean'][vars[ii]]
+        _ax[ii].plot(ensemble_mean['time'], yy, color='lime', linewidth=2.5, label='ensemble mean', linestyle='--')
+        _ax[ii].legend()
+
+
+    # Set x-axis label to indicate hours
+    _ax[-1].set_xlabel('time [UTC]')
+
+    if out_dir:
+        plt.savefig(os.path.join(out_dir, f'{case}_abs_intensities.png'))
+
+    return
+
+
+def plot_ib_era5(tru_track, case, vars=['msl', 'wind_speed'], out_dir: str | None = None):
+    plt.close('all')
+
+    fig, ax1 = plt.subplots(1, 1, figsize=(8, 5))
+    sup_title = f'{(case.split("_")[0]).upper()} '
+    fig.suptitle(sup_title, fontsize=16)
+
+    ax2 = ax1.twinx()
+
+    if 'msl' in vars:
+        p_norm = 101325
+        ax1.plot(tru_track['time'], (p_norm-tru_track['msl'])/(p_norm-tru_track['msl_ib']), 'black')
+        ax1.set_ylabel('(1013hPa-msl_era5)/(1013hPa-msl_ib)', color='black')
+
+    if 'wind_speed' in vars:
+        ax2.plot(tru_track['time'], tru_track['wind_speed']/tru_track['wind_speed_ib'], 'orangered')
+        ax2.set_ylabel('wind_speed_era5/wind_speed_ib', color='orangered')
+
+    fig.tight_layout()
+    if out_dir:
+        plt.savefig(os.path.join(out_dir, f'{case}_ib_era5_wind_speed.png'))
+
+    return
+
+
+def root_metrics(err_dict):
+    for var in err_dict.keys():
+        mse = err_dict[var].pop('mse')
+        err_dict[var]['rmse'] = np.sqrt(mse)
+        variance = err_dict[var].pop('variance')
+        err_dict[var]['standard_deviation'] = np.sqrt(variance)
+        n_mems_lt = err_dict[var].pop('n_members')
+
+    return err_dict, n_mems_lt
+
+
+def plot_errors_over_lead_time(err_dict,
+                               case,
+                               ic,
+                               n_members,
+                               n_tracks,
+                               norm_dict = {'msl': 100, 'wind_speed': 1, 'dist': 1000},
+                               unit_dict = {'msl': 'hPa', 'wind_speed': 'm/s', 'dist': 'km'},
+                               out_dir: str | None = None):
+
+    if 'mse' in err_dict[list(err_dict.keys())[0]].keys():
+        err_dict, n_mems_lt = root_metrics(err_dict)
+
+    vars = list(err_dict.keys())
+    metrics = list(err_dict[vars[0]].keys())
+
+    # remove min, max as not over lead time but per member
+    for extreme in ['min', 'max']:
+        if extreme in metrics:
+            metrics.remove(extreme)
+
+    print(metrics)
+
+    lead_time = np.arange(err_dict[vars[0]][metrics[0]].shape[0])*np.timedelta64(6, 'h')
+
+    fig, ax = plt.subplots(len(vars), len(metrics), figsize=((len(metrics)+1)*2, (len(vars)+1)*2), sharex=True)
+
+    for ivar, var in enumerate(err_dict.keys()):
+        for imet, metric in enumerate(metrics):
+
+            ax[ivar, imet].plot(lead_time, err_dict[var][metric]/norm_dict[var])
+
+            if ivar == 0:
+                ax[ivar, imet].set_title(metric, fontsize=12, weight='bold')
+
+            if imet == 0:
+                ax[ivar, imet].set_ylabel(f'{var} [{unit_dict[var]}]', fontsize=12, weight='bold')
+
+            if ivar == len(vars) - 1:
+                ax[ivar, imet].set_xlabel('lead time [h]', fontsize=12)
+
+
+    sup_title = f'{(case.split("_")[0]).upper()} '
+    sup_title += f'\n initialised on {ic}'
+    sup_title += f'\n {n_tracks} tracks in {n_members} ensemble members'
+    fig.suptitle(sup_title, fontsize=16)
+
+    fig.tight_layout()
+    if out_dir:
+        plt.savefig(os.path.join(out_dir, f'{case}_error_metrics_over_lead_time.png'))
+
+    return
+
+
+def extract_reference_extremes(tru_track, pred_tracks, ens_mean, vars):
+
+    extreme_dict = {}
+    for var in vars:
+        extreme_dict[var] = {'pred': np.zeros(len(pred_tracks))}
+        for ii, track in enumerate(pred_tracks):
+            if var in ['wind_speed']:
+                extreme_dict[var]['pred'][ii] = np.nanmax(track['tracks'][var])
+                extreme_dict[var]['tru'] = np.nanmax(tru_track[var])
+                extreme_dict[var]['ens_mean'] = np.nanmax(ens_mean['mean'][var])
+            elif var in ['msl']:
+                extreme_dict[var]['pred'][ii] = np.nanmin(track['tracks'][var])
+                extreme_dict[var]['tru'] = np.nanmin(tru_track[var])
+                extreme_dict[var]['ens_mean'] = np.nanmin(ens_mean['mean'][var])
+
+    return extreme_dict
+
+
+def add_stats_box(ax, pred_var, tru_var, var, reduction, unit):
+
+    # add text box below plot with number of members exceeding the threshold
+    n_exceed_spd = len(pred_var[pred_var > tru_var])
+    n_total = len(pred_var)
+
+    # Create table-like format with aligned colons for wind speed
+    comp = 'exceeding' if var in ['wind_speed'] else 'below'
+    stats = [
+        ('era5 reference:', f"{tru_var:.1f} {unit}"),
+        (f'members {comp} ref:', f'{n_exceed_spd} of {n_total} ({(n_exceed_spd/n_total)*100:.1f}%)'),
+        (f'max {reduction} {var}:', f'{pred_var.max():.1f} {unit}'),
+        (f'min {reduction} {var}:', f'{pred_var.min():.1f} {unit}'),
+        (f'avg {reduction} {var}:', f'{pred_var.mean():.1f} {unit}'),
+        (f'std {reduction} {var}:', f'{pred_var.std():.1f} {unit}')
+    ]
+
+    # Format as table with aligned colons
+    max_label_width = max(len(label) for label, _ in stats)
+    text = '\n'.join([f'{label:<{max_label_width}}  {value}' for label, value in stats])
+
+    ax.text(.01, -0.25, text, transform=ax.transAxes, ha='left', va='top',
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.8),
+            fontfamily='monospace')
+
+    return
+
+
+def plot_extreme_extremes_histograms(pred_tracks, tru_track, ensemble_mean, case, vars=['wind_speed', 'msl'], out_dir: str | None = None, nbins: int=12):
+
+    extreme_dict = extract_reference_extremes(tru_track, pred_tracks, ensemble_mean, vars)
+
+    # plot histogram of spd and msl
+    fig, ax = plt.subplots(1, len(vars), figsize=(3*(len(vars)+1), 6), sharey=True)
+    fig.suptitle(f'{(case.split("_")[0]).upper()} initialised on {pred_tracks[0]["ic"]}', fontsize=16)
+    ax[0].set_ylabel('count')
+
+    for ii, var in enumerate(vars):
+
+        reduction = 'max' if var in ['wind_speed'] else 'min'
+        unit = 'm/s' if var in ['wind_speed'] else 'hPa'
+        scale = 100 if var in ['msl'] else 1
+
+        pred_var = extreme_dict[var]['pred']/scale
+        tru_var = extreme_dict[var]['tru']/scale
+        mean_var = extreme_dict[var]['ens_mean']/scale
+
+        # plot histo and vertical line at tru_var
+        ax[ii].hist(pred_var, bins=nbins)
+        ax[ii].axvline(tru_var, color='orangered', linestyle='--', label='era5 reference')
+        ax[ii].axvline(mean_var, color='lime', linestyle='--', label='ensemble mean')
+
+        ax[ii].set_title(f'{reduction} {var} (x, t)')
+        ax[ii].set_xlabel(f'{var} [{unit}]')
+        ax[ii].legend()
+
+        add_stats_box(ax[ii], pred_var, tru_var, var, reduction, unit)
+
+    fig.tight_layout()
+    if out_dir:
+        fig.savefig(os.path.join(out_dir, f'{case}_histograms.png'))
+
+    plt.show()
+
+
+
+def plot_ensemble_mean_metrics(ens_means, out_path):
+
+    errs = []
+    for _, means in ens_means.items():
+        errs.append(means)
+
+    errs = np.stack(errs)
+    print(errs.shape)
+    exit()
+
+    return
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb b/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb
new file mode 100644
index 000000000..6e9eef56a
--- /dev/null
+++ b/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb
@@ -0,0 +1,223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## read csv file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os.path import join\n",
+    "from analyse_n_plot import load_tracks\n",
+    "\n",
+    "# case = 'amphan_2020_north_indian'\n",
+    "# case = 'beryl_2024_north_atlantic'\n",
+    "# case = 'debbie_2017_southern_pacific'\n",
+    "# case = 'dorian_2019_north_atlantic'\n",
+    "# case = 'harvey_2017_north_atlantic'\n",
+    "# case = 'hato_2017_west_pacific'\n",
+    "case = 'helene_2024_north_atlantic'\n",
+    "# case = 'ian_2022_north_atlantic'\n",
+    "# case = 'iota_2020_north_atlantic'\n",
+    "# case = 'irma_2017_north_atlantic'\n",
+    "# case = 'lan_2017_west_pacific'\n",
+    "# case = 'lee_2023_north_atlantic'\n",
+    "# case = 'lorenzo_2019_north_atlantic'\n",
+    "# case = 'maria_2017_north_atlantic'\n",
+    "# case = 'mawar_2023_west_pacific'\n",
+    "# case = 'michael_2018_north_atlantic'\n",
+    "# case = 'milton_2024_north_atlantic'\n",
+    "# case = 'ophelia_2017_north_atlantic'\n",
+    "# case = 'yagi_2024_west_pacific'\n",
+    "\n",
+    "# case = 'erin_2025_north_atlantic'\n",
+    "\n",
+    "base_path_colibri = '/home/mkoch/Documents/projects/fcn3_tcs/track_data/'\n",
+    "base_path_eos = '/home/mkoch/coreai/fcn3_cyclones/results/'\n",
+    "\n",
+    "base_path = base_path_eos\n",
+    "\n",
+    "pred_track_dir = f'predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'\n",
+    "tru_track_dir = f'reference_tracks'\n",
+    "# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\n",
+    "# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\n",
+    "# pred_track_dir = 'track_data/predictions/erin_lagged'\n",
+    "out_dir = f'./plots'\n",
+    "\n",
+    "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",
+    "                                                                   pred_track_dir=join(base_path, pred_track_dir),\n",
+    "                                                                   tru_track_dir=join(base_path, tru_track_dir),\n",
+    "                                                                   out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot tracks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_spaghetti\n",
+    "\n",
+    "plot_spaghetti(true_track=tru_track,\n",
+    "               pred_tracks=pred_tracks,\n",
+    "               ensemble_mean=ens_mean['mean'],\n",
+    "               case=case,\n",
+    "               n_members=n_members,\n",
+    "               out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot intensities over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_over_time\n",
+    "\n",
+    "plot_over_time(pred_tracks=pred_tracks,\n",
+    "               tru_track=tru_track,\n",
+    "               ensemble_mean=ens_mean,\n",
+    "               case=case,\n",
+    "               n_members=n_members,\n",
+    "               out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot relative intensities over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_relative_over_time\n",
+    "\n",
+    "plot_relative_over_time(pred_tracks=pred_tracks,\n",
+    "                        tru_track=tru_track,\n",
+    "                        ensemble_mean=ens_mean,\n",
+    "                        case=case,\n",
+    "                        n_members=n_members,\n",
+    "                        out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot ERA5 over IB vars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_ib_era5\n",
+    "\n",
+    "plot_ib_era5(tru_track=tru_track,\n",
+    "             case=case,\n",
+    "             vars=['msl', 'wind_speed'],\n",
+    "             out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extreme values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_extreme_extremes_histograms\n",
+    "\n",
+    "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n",
+    "                                 tru_track=tru_track,\n",
+    "                                 ensemble_mean=ens_mean,\n",
+    "                                 case=case,\n",
+    "                                 out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## moments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from data_handling import compute_averages_of_errors_over_lead_time\n",
+    "from plotting_helpers import plot_errors_over_lead_time\n",
+    "\n",
+    "vars = ['wind_speed', 'msl', 'dist']\n",
+    "\n",
+    "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n",
+    "                                                        tru_track=tru_track,\n",
+    "                                                        vars=vars)\n",
+    "\n",
+    "plot_errors_over_lead_time(err_dict=err_dict,\n",
+    "                           case=case,\n",
+    "                           ic=pred_tracks[0]['ic'],\n",
+    "                           n_members=n_members,\n",
+    "                           n_tracks=len(pred_tracks),\n",
+    "                           out_dir=out_dir)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
new file mode 100644
index 000000000..9642ee916
--- /dev/null
+++ b/recipes/tc_tracking/pyproject.toml
@@ -0,0 +1,40 @@
+[project]
+name = "fcn3-tcs"
+version = "0.1.0"
+description = "no, i won't"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "anemoi-graphs==0.6.2",
+    "anemoi-models==0.5.1",
+    "cartopy>=0.24.1",
+    "earth2studio[data]>=0.10.0",
+    "earthkit-regrid==0.4.0",
+    "hydra-core>=1.3.2",
+    "importlib-metadata>=8.7.0",
+    "ipykernel>=6.30.1",
+    "makani>=0.2.0",
+    "matplotlib>=3.10.3",
+    "more-itertools>=10.7.0",
+    "moviepy>=2.2.1",
+    "ninja>=1.13.0",
+    "omegaconf>=2.3.0",
+    "ruamel-yaml>=0.18.14",
+    "scipy>=1.16.0",
+    "setuptools>=80.9.0",
+    "torch-harmonics>=0.8.1",
+    "tqdm>=4.67.1",
+    "tropycal>=1.4",
+    "zarrdump>=0.6.0",
+]
+
+[tool.uv.sources]
+makani = { git = "https://github.com/NVIDIA/makani" }
+torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" }
+earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" }
+
+[tool.uv]
+no-build-isolation-package = ["torch-harmonics"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
diff --git a/recipes/tc_tracking/src/__init__.py b/recipes/tc_tracking/src/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/recipes/tc_tracking/src/data/__init__.py b/recipes/tc_tracking/src/data/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/recipes/tc_tracking/src/data/file_output.py b/recipes/tc_tracking/src/data/file_output.py
new file mode 100644
index 000000000..2b2e5e090
--- /dev/null
+++ b/recipes/tc_tracking/src/data/file_output.py
@@ -0,0 +1,154 @@
+from collections import OrderedDict
+import copy
+import os
+import shutil
+from torch import from_numpy
+import numpy as np
+
+from earth2studio.io import NetCDF4Backend, ZarrBackend
+from earth2studio.utils.coords import map_coords, split_coords
+
+
+def initialise_output_coords(
+    cfg,
+    model,
+    ics,
+    out_vars,
+) -> dict:
+    """Initialize output coordinates
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        Hydra config object
+    lon_coords: np.ndarray[np.float64]
+        a 1d array containing the longitude values of the grid. ordered ascending
+    lat_coords: np.ndarray[np.float64]
+        a 1d array containing the latitude values of the grid. ordered descending
+
+    Returns
+    -------
+    output_coords: dict
+        output coordinates
+    """
+    out_coords = OrderedDict(
+    {
+        k: v for k, v in model.output_coords(model.input_coords()).items() if
+        (k != "batch") and (v.shape != 0)
+    }
+    )
+
+    out_coords["time"] = ics
+    out_coords["lead_time"] = np.asarray(
+        [out_coords["lead_time"] * i for i in range(cfg.n_steps + 1)]
+    ).flatten()
+    out_coords["variable"] = np.array(out_vars)
+
+    out_coords.move_to_end("lead_time", last=False)
+    out_coords.move_to_end("time", last=False)
+
+    return out_coords
+
+
+def add_arrays_to_store(store, out_coords, mems, add_arrays, ic=None, array_kwargs={}):
+    oco = copy.deepcopy(out_coords)
+    oco["ensemble"] = mems
+    oco.move_to_end("ensemble", last=False)
+    out_vars = oco.pop("variable")
+
+    if ic is not None:
+        oco["time"] = np.array([ic])
+
+    if add_arrays:
+        store.add_array(coords=oco,
+                        array_name=out_vars,
+                        **array_kwargs)
+
+    return
+
+
+def setup_output(cfg, model, ics, add_arrays):
+
+    out_vars = list(dict.fromkeys(cfg.out_vars))
+    out_coords = initialise_output_coords(cfg, model, ics, out_vars)
+    chunks = {
+        "ensemble": 1,
+        "time": 1,
+        "lead_time": 1,
+        "variable": 1,
+    }
+    array_kwargs = {}
+
+    # TODO: let proc zero create dir and add barrier
+    os.makedirs(cfg.store_dir, exist_ok=True)
+    file_name = os.path.join(cfg.store_dir, cfg.project)
+
+    if cfg.store_type == "netcdf" or cfg.store_type == "none":
+        store = None
+
+    elif cfg.store_type == "zarr":
+        # TODO: wrap in ordered_execution function
+        if not file_name.endswith(".zarr"):
+            file_name += ".zarr"
+
+        if os.path.exists(file_name) and add_arrays:
+            shutil.rmtree(file_name)
+
+        store = ZarrBackend(
+            file_name=file_name,
+            chunks=chunks,
+            backend_kwargs={"overwrite": False},
+        )
+
+        add_arrays_to_store(store=store,
+                            out_coords=out_coords,
+                            mems=np.asarray(list(range(cfg.ensemble_size))).flatten(),
+                            add_arrays=add_arrays,
+                            array_kwargs=array_kwargs)
+
+    else:
+        raise ValueError(f"Invalid store type: {cfg.store_type}")
+
+    return store, out_coords
+
+
+def initialise_netcdf_output(cfg, out_coords, ic, ic_mems):
+    mems = np.concatenate([mem for iic, mem, _ in ic_mems if iic == ic]).flatten()
+    seeds = np.concatenate([np.array([seed]*len(mem), dtype=int) for iic, mem, seed in ic_mems if iic == ic]).flatten()
+
+    # setup filename
+    file_name = os.path.join(cfg.store_dir, cfg.project)
+    if file_name.endswith(".nc"):
+        file_name = file_name.replace(".nc", "")
+    file_name = file_name + f"_{np.datetime_as_string(ic, unit='s')}_mems{mems[0]:04d}-{mems[-1]:04d}.nc"
+    file_name = file_name.replace(":", ".")
+
+    # create store
+    chunks = {
+        "ensemble": 1,
+        "time": 1,
+        "lead_time": 1,
+        "variable": 1,
+    }
+    store = NetCDF4Backend(
+        file_name=file_name,
+        backend_kwargs={"mode": "w", "diskless": False, "chunks": chunks},
+    )
+
+    # add random seed to store
+    store.add_array(coords={"ensemble": mems}, array_name='random_seed')
+    store.write(from_numpy(seeds), {"ensemble": mems}, 'random_seed')
+
+    # add arrays to the store
+    add_arrays_to_store(store=store,
+                        out_coords=out_coords,
+                        mems=mems,
+                        ic=ic,
+                        add_arrays=True)
+
+    return store
+
+def write_to_store(store, xx, coords, out_coords):
+    if store is not None:
+        xx_sub, coords_sub = map_coords(xx, coords, out_coords)
+        store.write(*split_coords(xx_sub, coords_sub, dim="variable"))
diff --git a/recipes/tc_tracking/src/data/localh5.py b/recipes/tc_tracking/src/data/localh5.py
new file mode 100644
index 000000000..7a31c0779
--- /dev/null
+++ b/recipes/tc_tracking/src/data/localh5.py
@@ -0,0 +1,88 @@
+from datetime import datetime, timedelta
+import glob
+import json
+import os
+from typing import Iterable, Tuple
+
+import h5py
+import numpy as np
+import xarray as xr
+
+
+from earth2studio.utils.type import TimeArray, VariableArray
+
+
+DIMS = ["time", "variable", "lat", "lon"]
+
+
+class LocalArchiveHDF5:
+    def __init__(
+        self,
+        dirs: str | Iterable,
+        metadata_file: str,
+        latlon_box: Tuple[Tuple[float, float], Tuple[float, int]] | None = None,
+    ):
+        if isinstance(dirs, str):
+            dirs = [dirs]
+
+        self.files_by_year = {}
+        for dir in dirs:
+            for path in glob.glob(os.path.join(dir, "????.h5")):
+                year = int(os.path.basename(path).split(".")[0])
+                self.files_by_year[year] = path
+
+        with open(metadata_file, 'r') as f:
+            metadata = json.load(f)
+        self.lat = np.array(metadata["coords"]["lat"])
+        self.lon = np.array(metadata["coords"]["lon"])
+        if latlon_box is not None:
+            (self.lat, self.lat_slice) = _find_index_range(self.lat, *latlon_box[0])
+            (self.lon, self.lon_slice) = _find_index_range(self.lon, *latlon_box[1])
+        else:
+            self.lat_slice = self.lon_slice = slice(None)
+        self.dt = timedelta(hours=metadata["dhours"])
+        self.variables = metadata["coords"]["channel"]
+        self.field_shape = (len(self.lat), len(self.lon))
+
+    def __call__(
+        self,
+        time: datetime | list[datetime] | TimeArray,
+        variable: str | list[str] | VariableArray,
+    ) -> xr.DataArray:
+        if isinstance(time, datetime):
+            time = [time]
+        time = [
+            datetime.fromisoformat(str(t)[:19]) if isinstance(t, np.datetime64) else t
+            for t in time
+        ]
+        if isinstance(variable, str):
+            variable = [variable]
+
+        var_indices = [self.variables.index(v) for v in variable]
+
+        x = np.zeros((len(time), len(variable)) + self.field_shape, dtype=np.float32)
+
+        for (k, t) in enumerate(time):
+            time_idx = int((t - datetime(t.year, 1, 1)) / self.dt)
+            with h5py.File(self.files_by_year[t.year], 'r') as ds:
+                for (i, var_idx) in enumerate(var_indices):
+                    x[k, i, :, :] = ds["fields"][time_idx, var_idx, self.lat_slice, self.lon_slice]
+
+        coords = {
+            "time": np.array(time, copy=False),
+            "variable": np.array(variable, copy=False),
+            "lat": self.lat,
+            "lon": self.lon
+        }
+
+        return xr.DataArray(data=x, coords=coords, dims=DIMS)
+
+
+def _find_index_range(seq, x0, x1):
+    in_range = (x0 <= seq) & (seq <= x1)
+    subseq = np.array(seq[in_range])
+    ind = np.arange(len(seq))[in_range]
+    i0 = ind[0]
+    i1 = ind[-1] + 1
+
+    return (subseq, slice(i0, i1))
diff --git a/recipes/tc_tracking/src/data/utils.py b/recipes/tc_tracking/src/data/utils.py
new file mode 100644
index 000000000..30841642b
--- /dev/null
+++ b/recipes/tc_tracking/src/data/utils.py
@@ -0,0 +1,67 @@
+import hydra
+import torch
+import numpy as np
+import xarray as xr
+from collections import OrderedDict
+
+
+def load_heights(oro_path):
+    oro = xr.load_dataset(oro_path)
+
+    coords = OrderedDict({'variable': np.array(['height']), 'lat': oro.latitude, 'lon': oro.longitude})
+    geop = torch.Tensor(oro['Z'].to_numpy())/9.80665 # devide by gravi to get height
+
+    return geop, coords
+
+class DataSourceManager:
+    # TODO: catch case in which ther's only single data source
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+        # build dict of data sources
+        self.data_sources = {}
+        self.data_source_mapping = {}
+        self.single_source = False
+        for name, source in self.cfg.data_source.items():
+            # catch classic case of single source
+            if name == '_target_':
+                self.single_source = True
+                self.data_source = hydra.utils.instantiate(self.cfg.data_source)
+                return
+
+            # instantiate data source
+            self.data_sources[name] = hydra.utils.instantiate(source.source)
+
+            # extract valid years for source and build mapping
+            years = source.years
+            if isinstance(years, str):
+                if not '-' in years:
+                    years = int(years)
+
+            if isinstance(years, int):
+                years = [years]
+            else:
+                years = list(range(int(years.split('-')[0]), int(years.split('-')[1])+1))
+
+            for year in years:
+                self.data_source_mapping[year] = name
+
+        return
+
+
+    def select_data_source(self, time_stamps):
+        if self.single_source:
+            return self.data_source
+
+        year = np.unique(time_stamps.astype('datetime64[Y]').astype(int) + 1970)
+
+        if len(year) > 1:
+            raise ValueError(f'track spans multiple years, which is not yet supported')
+        else:
+            year = year[0]
+
+        if year not in self.data_source_mapping:
+            raise ValueError(f'no data source provided for year {year}')
+
+        return self.data_sources[self.data_source_mapping[year]]
\ No newline at end of file
diff --git a/recipes/tc_tracking/src/extract_from_data_source/__init__.py b/recipes/tc_tracking/src/extract_from_data_source/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
new file mode 100644
index 000000000..cca3cd795
--- /dev/null
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -0,0 +1,207 @@
+import copy
+import numpy as np
+import os
+import tropycal.tracks as tropytracks
+import pandas as pd
+
+from physicsnemo.distributed import DistributedManager
+
+from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
+from earth2studio.data import fetch_data
+from earth2studio.utils.coords import split_coords
+from earth2studio.io import KVBackend
+
+from src.data.utils import DataSourceManager, load_heights
+from src.utils import great_circle_distance
+
+
+def extract_from_historic_data(cfg,
+                               ic,
+                               n_steps,
+                               time_step: np.timedelta64 = np.timedelta64(6, 'h'),
+                               vars: list[str] = ['msl', 'z300', 'z500', 'u10m', 'v10m'],
+                               data_source_mngr: DataSourceManager = None):
+
+    times = np.arange(np.timedelta64(0, 'h'), n_steps*time_step, time_step)
+    data_source = data_source_mngr.select_data_source(ic+times)
+
+    # fetch data on which TC tracking is executed and add ensemble dim, so that it works with the diagnostic
+    xx, coords = fetch_data(data_source,
+                            time=[ic],
+                            lead_time=times,
+                            variable=vars,
+                            device='cpu')
+    xx = xx.unsqueeze(0)
+    coords["ensemble"] = np.array([0])
+    coords.move_to_end("ensemble", last=False)
+
+    heights, height_coords = load_heights(cfg.orography_path)
+
+    # set up TC tracking, add downloaded data and execute tracking algorithm
+    cyclone_tracking = TempestExtremes(detect_cmd=cfg.cyclone_tracking.detect_cmd,
+                                       stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
+                                       input_vars=cfg.cyclone_tracking.vars,
+                                       n_steps=n_steps-1, # assumes prediction steps
+                                       time_step=time_step,
+                                       lats=coords['lat'],
+                                       lons=coords['lon'],
+                                       static_vars=heights,
+                                       static_coords=height_coords,
+                                       store_dir=cfg.store_dir,
+                                       keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
+                                       print_te_output=cfg.cyclone_tracking.print_te_output,
+                                       use_ram=cfg.cyclone_tracking.use_ram)
+    cyclone_tracking.record_state(xx, coords)
+    cyclone_tracking()
+
+    # load track_file into dataFrame
+    hist_tracks = pd.read_csv(cyclone_tracking.track_file)
+    hist_tracks.columns = hist_tracks.columns.str.strip()
+    os.remove(cyclone_tracking.track_file)
+
+    return hist_tracks
+
+
+def extract_from_ibtracs(cfg, ibtracs, case, time_step):
+    print(f'extracting storm {case} ({cfg.cases[case].year}, {cfg.cases[case].basin}) from ibtracs')
+    storm = ibtracs.get_storm((case, cfg.cases[case].year))
+    storm.time = storm.time.astype(np.datetime64)
+
+    # convert to pd.DataFrame, add day year month hour for easier merging later on
+    ib_storm = pd.DataFrame({
+        'time': storm.time,
+        'year': storm.time.astype('datetime64[Y]').astype(int) + 1970,
+        'month': storm.time.astype('datetime64[M]').astype(int) % 12 + 1,
+        'day': np.array([int(pd.Timestamp(tt).day) for tt in storm.time]),
+        'hour': storm.time.astype('datetime64[h]').astype(int) % 24,
+        'lat': storm.lat,
+        'lon': storm.lon,
+        'wind_speed': storm.vmax,
+        'mslp': storm.mslp,
+        'type': storm.type,
+    })
+
+    # get time window to extract from historic data
+    ic = storm.time[0]
+
+    # Ensure ic is at 00h, 06h, 12h, or 18h. if not, set to previous 6-hourly time step
+    ic_datetime = pd.to_datetime(ic)
+    hour = ic_datetime.hour
+    if hour % 6 != 0:
+        # Round down to the previous 6-hourly time step
+        hours_to_subtract = hour % 6
+        ic = ic - np.timedelta64(hours_to_subtract, 'h')
+        ic = ic.astype('datetime64[h]')
+
+    # calculate number of steps to extract from historic data
+    n_steps = (storm.time[-1] - ic) // time_step
+    if (storm.time[-1] - ic) % time_step != 0:
+        n_steps += 1
+
+    # select ib_storm times that are in query_times
+    ib_storm = ib_storm[ib_storm['time'].isin(np.arange(ic, ic + n_steps*time_step, time_step))]
+
+    return ib_storm, ic, n_steps
+
+
+def match_tracks(ib_storm, hist_tracks, case):
+    # add time as np.datetime64
+    times = np.array([pd.to_datetime(f'{hist_tracks["year"].iloc[jj]}-{int(hist_tracks.iloc[jj]["month"]):02d}-{int(hist_tracks.iloc[jj]["day"]):02d} {int(hist_tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(hist_tracks))])#.astype('datetime64[h]')
+    # hist_tracks.drop(columns=['year', 'month', 'day', 'hour', 'i', 'j', 'track_id'], inplace=True)
+    hist_tracks.insert(0, 'time', times)
+
+    n_tracks = hist_tracks['track_id'].nunique()
+
+    # go over tracks and see if they match ibtracks
+    matched_track = None
+    for time in ib_storm['time']:
+        lat_ib = ib_storm.loc[ib_storm['time'] == time]['lat'].item()
+        lon_ib = ib_storm.loc[ib_storm['time'] == time]['lon'].item()
+
+        for track_id in range(n_tracks):
+            track = hist_tracks[hist_tracks['track_id'] == track_id]
+            # check if time is in track
+            # if time in track['time']:
+            if (track['time'] == time).any():
+                lat_track = track.loc[track['time'] == time]['lat'].item()
+                lon_track = track.loc[track['time'] == time]['lon'].item()
+                dist = great_circle_distance(lat_ib, lon_ib, lat_track, lon_track)
+                if dist < 300000:
+                    matched_track = track_id
+                    print(f'matched track {matched_track} for strom {case} with distance {dist/1000:.2f}km')
+                    break
+
+        if matched_track is not None:
+            break
+
+    return hist_tracks[hist_tracks['track_id'] == matched_track].reset_index(drop=True)
+
+
+def add_ibtracs_data(matched_track, ib_storm):
+    # add ib_storm data to matched_track, only at times in matched_track
+    matched_track = pd.merge(matched_track, ib_storm, on='time', how='right', suffixes=('', '_ib')).reset_index(drop=True)
+    matched_track.rename(columns={'mslp': 'msl_ib'}, inplace=True)
+
+    # Preserve integer data types for datetime components
+    # Convert back to integers, handling any NaN values appropriately
+    datetime_cols = ['year', 'month', 'day', 'hour']
+    for col in datetime_cols:
+        if col in matched_track.columns:
+            # Use 'Int64' (nullable integer) to handle NaN values if they exist
+            matched_track[col] = matched_track[col].astype('Int64')
+
+    # set track_id to 0, keep as dummy value to have data format consistent with simulation output
+    matched_track['track_id'] = 0
+
+    # if values at lon_ib are negative, set to 360 + lon_ib
+    matched_track.loc[matched_track['lon_ib'] < 0, 'lon_ib'] = 360 + matched_track.loc[matched_track['lon_ib'] < 0, 'lon_ib']
+
+    return matched_track
+
+
+def write_track_to_csv(matched_track, case, store_dir, basin):
+    # remove columns i, j, time
+    matched_track.drop(columns=['i', 'j', 'time'], inplace=True)
+
+    # re-order columns
+    matched_track = matched_track[['track_id', 'year_ib', 'month_ib', 'day_ib', 'hour_ib', 'lon', 'lat', 'msl', 'wind_speed', 'lon_ib', 'lat_ib', 'msl_ib', 'wind_speed_ib', 'type']]
+
+    # rename columns
+    matched_track.rename(columns={'year_ib': 'year', 'month_ib': 'month', 'day_ib': 'day', 'hour_ib': 'hour'}, inplace=True)
+
+    # multiply msl_ib by 100 to get Pa
+    matched_track['msl_ib'] = matched_track['msl_ib'] * 100
+
+    # write to csv
+    csv_name = f'reference_track_{case}_{matched_track['year'].iloc[0]}_{basin}.csv'
+    matched_track.to_csv(os.path.join(store_dir, csv_name), index=False)
+
+    return
+
+
+def extract_baseline(cfg,
+                     time_step: np.timedelta64 = np.timedelta64(6, 'h'),
+                     vars:list[str] = ['msl', 'z300', 'z500', 'u10m', 'v10m'],):
+
+    DistributedManager.initialize()
+
+    ibtracs = tropytracks.TrackDataset(basin='all',
+                                       source='ibtracs',
+                                       ibtracs_mode='jtwc_neumann',
+                                       ibtracs_url=cfg.ibtracs_source_data)
+
+    data_source_mngr = DataSourceManager(cfg)
+
+    for case in cfg.cases:
+
+        ib_storm, ic, n_steps = extract_from_ibtracs(cfg, ibtracs, case, time_step)
+
+        hist_tracks = extract_from_historic_data(cfg=cfg, ic=ic, n_steps=n_steps, vars=vars, data_source_mngr=data_source_mngr)
+
+        matched_track = match_tracks(ib_storm, hist_tracks, case)
+
+        matched_track = add_ibtracs_data(matched_track, ib_storm)
+
+        write_track_to_csv(matched_track, case, cfg.store_dir, cfg.cases[case].basin)
+
+    return
\ No newline at end of file
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
new file mode 100644
index 000000000..51d63d641
--- /dev/null
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -0,0 +1,249 @@
+import os
+import torch
+import numpy as np
+from tqdm import tqdm
+from math import ceil
+from zarr import consolidate_metadata
+from omegaconf import OmegaConf
+import random
+
+from physicsnemo.distributed import DistributedManager
+
+from earth2studio.models.auto import Package
+from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
+from earth2studio.data import fetch_data
+from earth2studio.perturbation import SphericalGaussian
+
+from src.data.utils import DataSourceManager, load_heights
+from src.data.file_output import setup_output, initialise_netcdf_output, write_to_store
+from src.utils import run_with_rank_ordered_execution, set_initial_times, remove_duplicates, get_set_of_random_seeds
+
+
+def initialise(cfg):
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"]="expandable_segments:True"
+
+    DistributedManager.initialize()
+
+    # set seed
+    if 'random_seed' in cfg:
+        torch.manual_seed(cfg.random_seed)
+        np.random.seed(cfg.random_seed)
+        random.seed(cfg.random_seed)
+        torch.cuda.manual_seed(cfg.random_seed)
+
+    return
+
+
+def load_model(cfg):
+    model = 'fcn3'
+    if 'model' in cfg:
+        model = cfg.model
+
+    if model == 'fcn3':
+        from earth2studio.models.px import FCN3
+        package = Package(cfg.model_package)
+        model = FCN3.load_model(package)
+    elif model == 'sfno':
+        from earth2studio.models.px import SFNO
+        package = Package('/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72')
+        model = SFNO.load_model(package)
+    elif model == 'aifs-ens':
+        from earth2studio.models.px import AIFSENS
+        model = AIFSENS.load_model(AIFSENS.load_default_package())
+    else:
+        raise ValueError(f'model {model} not supported')
+
+    return model.to(DistributedManager().device)
+
+
+def run_inference(model, cfg, store, out_coords, ic_mems):
+    dist = DistributedManager()
+
+    # data_source = hydra.utils.instantiate(cfg.data_source)
+    data_source_mngr = DataSourceManager(cfg)
+
+    #iterate over initial conditions
+    ic_prev = None
+
+    cyclone_tracking = None
+    if "cyclone_tracking" in cfg:
+        oco = model.output_coords(model.input_coords())
+
+        heights, height_coords = load_heights(cfg.orography_path) if "orography_path" in cfg else (None, None)
+
+        tracker = AsyncTempestExtremes if cfg.cyclone_tracking.asynchronous else TempestExtremes
+        cyclone_tracking = tracker(detect_cmd=cfg.cyclone_tracking.detect_cmd,
+                                   stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
+                                   input_vars=cfg.cyclone_tracking.vars,
+                                   batch_size=cfg.batch_size,
+                                   n_steps=cfg.n_steps,
+                                   time_step=oco['lead_time'][0],
+                                   lats=oco['lat'],
+                                   lons=oco['lon'],
+                                   static_vars=heights,
+                                   static_coords=height_coords,
+                                   store_dir=cfg.store_dir,
+                                   keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
+                                   print_te_output=cfg.cyclone_tracking.print_te_output,
+                                   use_ram=cfg.cyclone_tracking.use_ram,
+                                   timeout=cfg.cyclone_tracking.task_timeout_seconds)
+
+    for ic, mems, seed in ic_mems:
+        mini_batch_size = len(mems)
+
+        data_source = data_source_mngr.select_data_source(ic)
+
+        # if new IC, fetch data, create iterator
+        if ic != ic_prev:
+            print(model.variables)
+            # exit()
+            if cfg.store_type == "netcdf":
+                store = initialise_netcdf_output(cfg, out_coords, ic, ic_mems)
+            x0, coords0 = fetch_data(data_source,
+                                     time=[np.datetime64(ic)],
+                                     lead_time=model.input_coords()['lead_time'],
+                                     variable=model.variables,
+                                     device=dist.device)
+            ic_prev = ic
+            exit()
+
+        coords = {"ensemble": np.array(mems)} | coords0.copy()
+        xx = x0.unsqueeze(0).repeat(mini_batch_size, *([1] * x0.ndim))
+
+        # set random state or apply perturbation
+        if (not 'model' in cfg) or (cfg.model == 'fcn3'):
+            model.set_rng(seed=seed)
+        else:
+            sg = SphericalGaussian(noise_amplitude=0.0005)
+            xx, coords = sg(xx, coords)
+
+
+        iterator = model.create_iterator(xx, coords)
+
+        # roll out the model and record data as desired
+        for _, (xx, coords) in tqdm(zip(range(cfg.n_steps+1), iterator), total=cfg.n_steps+1):
+            write_to_store(store, xx, coords, out_coords)
+            if cyclone_tracking:
+                cyclone_tracking.record_state(xx, coords)
+
+        if cyclone_tracking:
+            cyclone_tracking(out_file_names=[f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv" for mem in mems])
+
+    # Consolidate metadata in zarr files
+    if dist.rank == 0 and cfg.store_type == "zarr":
+        # TODO add barrier such that rank 0 finsihes last
+        consolidate_metadata(store.store)
+
+    return store
+
+
+def distribute_runs(ic_mems):
+    dist = DistributedManager()
+
+    # get the number of initial conditions
+    ic_mems_per_rank = len(ic_mems) // dist.world_size
+    if len(ic_mems) % dist.world_size != 0:
+        ic_mems_per_rank += 1
+
+    # get the initial conditions for this rank
+    ic_mems = ic_mems[dist.rank * ic_mems_per_rank : (dist.rank + 1) * ic_mems_per_rank]
+
+    if len(ic_mems) == 0:
+        print(f'nothing to do for rank {dist.rank}, exiting')
+        ic_mems = None
+
+    return ic_mems
+
+
+def configure_runs(cfg):
+    ic_mems = []
+
+    ics = set_initial_times(cfg)
+
+    seeds = get_set_of_random_seeds(n_ics=len(ics),
+                                    ensemble_size=cfg.ensemble_size,
+                                    batch_size=cfg.batch_size,
+                                    seed=cfg.random_seed if 'random_seed' in cfg else None)
+
+    ii = 0
+    for ic in ics:
+        for mem in range(0, cfg.ensemble_size, cfg.batch_size):
+            mems = np.arange(mem, min(mem+cfg.batch_size, cfg.ensemble_size))
+            ic_mems.append((ic, mems, int(seeds[ii])))
+            ii += 1
+
+    if not DistributedManager().distributed:
+        return ic_mems, ics
+
+    ic_mems = distribute_runs(ic_mems)
+
+    return ic_mems, ics
+
+
+def set_reproduction_configs(cfg):
+    ic_mems = OmegaConf.to_container(cfg.reproduce_members)
+
+    ics = []
+    for ii in range(len(ic_mems)):
+        # time to numpy object
+        ic_mems[ii][0] = np.datetime64(ic_mems[ii][0])
+        ics.append(ic_mems[ii][0])
+
+        # get full batches that include members which shall be reproduced
+        batch_id = ic_mems[ii][1]//cfg.batch_size
+        ic_mems[ii][1] = np.arange(batch_id*cfg.batch_size, min((batch_id+1)*cfg.batch_size, cfg.ensemble_size))
+
+    # remove duplicates
+    ic_mems = remove_duplicates(ic_mems)
+    ics = list(set(ics))
+
+    if not DistributedManager().distributed:
+        return ic_mems, ics
+
+    ic_mems = distribute_runs(ic_mems)
+
+    return ic_mems, ics
+
+
+def generate_ensemble(cfg):
+
+    initialise(cfg)
+
+    ic_mems, ics = configure_runs(cfg)
+
+    model = load_model(cfg)
+
+    # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+
+    if ic_mems is None:
+        DistributedManager().cleanup()
+        exit()
+
+    store = run_inference(model, cfg, store, out_coords, ic_mems)
+
+    return
+
+
+def reproduce_members(cfg):
+    if cfg.store_type == 'zarr':
+        raise ValueError('Zarr output not suported for reproducing ensemble members')
+
+    initialise(cfg)
+
+    ic_mems, ics = set_reproduction_configs(cfg)
+
+    model = load_model(cfg)
+
+    # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+
+    if ic_mems is None:
+        DistributedManager().cleanup()
+        exit()
+
+    store = run_inference(model, cfg, store, out_coords, ic_mems)
+
+    return
\ No newline at end of file
diff --git a/recipes/tc_tracking/src/utils.py b/recipes/tc_tracking/src/utils.py
new file mode 100644
index 000000000..9c42727a8
--- /dev/null
+++ b/recipes/tc_tracking/src/utils.py
@@ -0,0 +1,149 @@
+from math import ceil
+from typing import Callable, Any
+from omegaconf import DictConfig
+import numpy as np
+import torch
+
+from physicsnemo.distributed import DistributedManager
+from earth2studio.utils.time import to_time_array
+
+
+def set_initial_times(cfg: DictConfig) -> list[np.datetime64]:
+    """Build list of IC times.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        Hydra config object
+
+    Returns
+    -------
+    list[np.datetime64]
+        Dictionary containing model, model class and model package.
+    """
+    # list of ICs
+    if "ics" in cfg:
+        if "ic_block_start" in cfg:
+            raise ValueError(
+                "either provide a list of start times or define a block, not both"
+            )
+        ics = to_time_array(sorted(cfg.ics))
+
+    # block of ICs
+    else:
+        ics = to_time_array([cfg.ic_block_start, cfg.ic_block_end])
+        ics = np.arange(
+            ics[0],
+            ics[1] + np.timedelta64(cfg.ic_block_step, "h"),
+            np.timedelta64(cfg.ic_block_step, "h"),
+        )
+
+    return ics
+
+
+def remove_duplicates(data_list):
+    """
+    Remove duplicates while preserving numpy dtype distinctions.
+    Arrays with same values but different dtypes are considered different.
+    """
+    def to_hashable(item):
+        if isinstance(item, np.ndarray):
+            # Include dtype and shape information
+            return (tuple(item.tolist()), str(item.dtype), item.shape)
+        elif isinstance(item, np.datetime64):
+            return str(item)
+        return item
+
+    seen = set()
+    result = []
+
+    for sublist in data_list:
+        hashable_key = tuple(to_hashable(item) for item in sublist)
+        if hashable_key not in seen:
+            seen.add(hashable_key)
+            result.append(sublist)
+
+    return result
+
+
+def get_set_of_random_seeds(n_ics, ensemble_size, batch_size, seed):
+    n_batches = ceil(ensemble_size/batch_size)
+    rng = np.random.default_rng(seed=seed)
+
+    seeds = np.array([])
+    ii = 0
+    while len(np.unique(seeds)) != n_batches*n_ics:
+        ii += 1
+        if ii > 1000:
+            raise RecursionError(f'failed to generate unique set of {n_batches*n_ics} random seeds after 1000 iterations. giving up :(')
+        seeds = rng.integers(low=0, high=2**32, size=n_batches*n_ics, dtype=np.uint32)
+
+    return seeds
+
+
+def run_with_rank_ordered_execution(
+    func: Callable, *args: Any, first_rank: int = 0, **kwargs: Any
+) -> Any:
+    """Executes `func(*args, **kwargs)` safely in a distributed setting:
+    - First on the specified `rank`
+    - Then, after synchronization, on the other ranks
+
+    Args:
+        func (Callable): Function to execute
+        args (tuple, optional): Positional arguments for the function. Defaults to ().
+        first_rank (int, optional): Rank to run the function first. Defaults to 0.
+        kwargs (dict, optional): Keyword arguments for the function. Defaults to None.
+
+    Returns:
+        The return value of func(*args, **kwargs)
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    dist = DistributedManager()
+    current_rank = dist.rank
+
+    if current_rank == first_rank:
+        result = func(*args, **kwargs)
+    else:
+        result = None
+
+    # Synchronize all processes after the first rank runs the function
+    # Skip the barrier if single-process (no distributed process group)
+    if dist.distributed:
+        torch.distributed.barrier()
+
+    if current_rank != first_rank:
+        result = func(*args, **kwargs)
+
+    if dist.distributed:
+        torch.distributed.barrier()
+
+    return result
+
+
+def squeeze_coords(xx, coords, squeeze_dim):
+    if isinstance(squeeze_dim, str):
+        squeeze_dim = [squeeze_dim]
+
+    for dim in squeeze_dim:
+        idx = list(coords.keys()).index(dim)
+
+        assert coords[dim].shape == (1,), f"can only squeeze dims of length 1, coords[{dim}] has shape {coords[dim].shape}"
+        assert xx.shape[idx] == 1, f"can only squeeze dims of length 1, xx[{dim}] has shape {xx.shape[idx]}"
+
+        xx = xx.squeeze(idx)
+        coords.pop(dim)
+
+    return xx, coords
+
+
+def great_circle_distance(lat1, lon1, lat2, lon2):
+    lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+
+    aa = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
+    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1-aa))
+
+    return 6371000 * cc
diff --git a/recipes/tc_tracking/tc_hunt.py b/recipes/tc_tracking/tc_hunt.py
new file mode 100644
index 000000000..7cd04a538
--- /dev/null
+++ b/recipes/tc_tracking/tc_hunt.py
@@ -0,0 +1,29 @@
+import hydra
+from omegaconf import DictConfig
+
+from src.extract_from_data_source.baseline_extraction import extract_baseline
+from src.generate_ensembles import generate_ensemble, reproduce_members
+
+
+@hydra.main(version_base=None, config_path="cfg", config_name="none")
+def main(cfg: DictConfig) -> None:
+
+    if cfg.mode == 'extract_baseline':
+        extract_baseline(cfg)
+
+    elif cfg.mode == 'generate_ensemble':
+        generate_ensemble(cfg)
+
+    elif cfg.mode == 'reproduce_members':
+        reproduce_members(cfg)
+
+    else:
+        raise ValueError(f'invalid mode: {cfg.mode}, choose from "extract_baseline" or "generate_ensemble"')
+
+    print(f"finished **yaaayyyy**")
+
+    return
+
+
+if __name__ == "__main__":
+    main()
diff --git a/recipes/tc_tracking/uv.lock b/recipes/tc_tracking/uv.lock
new file mode 100644
index 000000000..2e3bfa2a7
--- /dev/null
+++ b/recipes/tc_tracking/uv.lock
@@ -0,0 +1,3958 @@
+version = 1
+revision = 2
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and sys_platform == 'darwin'",
+]
+
+[[package]]
+name = "aiobotocore"
+version = "2.23.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "aioitertools" },
+    { name = "botocore" },
+    { name = "jmespath" },
+    { name = "multidict" },
+    { name = "python-dateutil" },
+    { name = "wrapt" },
+]
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From 02945f18d0c48f98c1c2e9d7465df402110a99d9 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 13 Oct 2025 16:33:31 +0200
Subject: [PATCH 29/69] update

---
 recipes/tc_tracking/Dockerfile                |   1 -
 recipes/tc_tracking/cfg/extract_era5.yaml     |   2 +-
 recipes/tc_tracking/cfg/helene.yaml           |   2 +-
 recipes/tc_tracking/cfg/reproduce_helene.yaml |   2 +-
 .../post_proc/score/score_results.py          | 157 +++---
 .../post_proc/tc_tracking/README.md           |   2 +-
 .../post_proc/tc_tracking/analyse_n_plot.py   | 322 +++++++-----
 .../post_proc/tc_tracking/data_handling.py    | 238 +++++----
 .../post_proc/tc_tracking/plotting_helpers.py | 487 +++++++++++-------
 recipes/tc_tracking/src/data/file_output.py   |  56 +-
 recipes/tc_tracking/src/data/localh5.py       |  23 +-
 recipes/tc_tracking/src/data/utils.py         |  29 +-
 .../baseline_extraction.py                    |  13 +-
 recipes/tc_tracking/src/generate_ensembles.py | 172 ++++---
 recipes/tc_tracking/src/utils.py              |  31 +-
 recipes/tc_tracking/tc_hunt.py                |  11 +-
 16 files changed, 943 insertions(+), 605 deletions(-)

diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile
index 49eea7cb2..3d5de337d 100644
--- a/recipes/tc_tracking/Dockerfile
+++ b/recipes/tc_tracking/Dockerfile
@@ -36,4 +36,3 @@ WORKDIR /fcn3_tcs
 ENV FORCE_CUDA_EXTENSION 1
 ENV TORCH_CUDA_ARCH_LIST "8.0 8.6 9.0 10.0 12.0+PTX"
 RUN uv pip install --system --break-system-packages --no-cache-dir .
-
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 5adceb6c3..536d946ec 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -125,4 +125,4 @@ cyclone_tracking:
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
     keep_raw_data: False
     use_ram: True
-    print_te_output: False
\ No newline at end of file
+    print_te_output: False
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index c3a4537f0..778b0f19c 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -35,4 +35,4 @@ out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 #     keep_raw_data: False
 #     use_ram: True
 #     task_timeout_seconds: 120
-#     print_te_output: False
\ No newline at end of file
+#     print_te_output: False
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index dddfbad40..9430727de 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -34,4 +34,4 @@ cyclone_tracking:
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
-    print_te_output: False
\ No newline at end of file
+    print_te_output: False
diff --git a/recipes/tc_tracking/post_proc/score/score_results.py b/recipes/tc_tracking/post_proc/score/score_results.py
index a6f674786..667d914b5 100644
--- a/recipes/tc_tracking/post_proc/score/score_results.py
+++ b/recipes/tc_tracking/post_proc/score/score_results.py
@@ -1,26 +1,29 @@
-import xarray as xr
-import numpy as np
-import pandas as pd
-import sys
 import os
-import torch
+import sys
 import time
 from datetime import datetime
 from typing import Any
-import sys
+
+import numpy as np
+import pandas as pd
+import torch
+import xarray as xr
 from physicsnemo.distributed import DistributedManager
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from src.data.localh5 import LocalArchiveHDF5
+
 from earth2studio.data import fetch_data, prep_data_array
-from earth2studio.statistics import lat_weight, crps, mean, variance
+from earth2studio.statistics import crps, lat_weight, mean, variance
 from earth2studio.utils.type import TimeArray, VariableArray
 
 
 def open_ds(fl):
-    if fl.endswith('.zarr') or (os.path.isdir(fl) and os.path.exists(os.path.join(fl, '.zgroup'))):
-        ds = xr.open_dataset(fl, engine='zarr')
+    if fl.endswith(".zarr") or (
+        os.path.isdir(fl) and os.path.exists(os.path.join(fl, ".zgroup"))
+    ):
+        ds = xr.open_dataset(fl, engine="zarr")
     else:
         ds = xr.open_dataset(fl)
 
@@ -35,7 +38,7 @@ def pick_vars_for_rank(vars):
         return vars
 
     if dist.world_size != len(vars):
-        raise ValueError(f'{dist.world_size=} != {len(vars)=}')
+        raise ValueError(f"{dist.world_size=} != {len(vars)=}")
 
     vars = [vars[dist.rank]]
 
@@ -50,34 +53,49 @@ def compute_mse(ds):
     mse = torch.zeros(len(vars), len(ds.lead_time.values))
 
     for ivar, var in enumerate(vars):
-        print(f'processing variable {var} ({ivar+1}/{len(vars)})')
+        print(f"processing variable {var} ({ivar+1}/{len(vars)})")
         var_ds = ds[var]
 
-
-        se = torch.zeros(len(var_ds.time.values), len(var_ds.lead_time.values),
-                         len(var_ds.lat.values),  len(var_ds.lon.values))
+        se = torch.zeros(
+            len(var_ds.time.values),
+            len(var_ds.lead_time.values),
+            len(var_ds.lat.values),
+            len(var_ds.lon.values),
+        )
 
         for ii, ic in enumerate(var_ds.time.values):
-            print(f'processing time {ic} ({ii+1}/{len(var_ds.time.values)})', end='\r', flush=True)
+            print(
+                f"processing time {ic} ({ii+1}/{len(var_ds.time.values)})",
+                end="\r",
+                flush=True,
+            )
 
             # fetch ground truth data
             data_source = LocalArchiveHDF5(
-                dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
+                dirs=[
+                    "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+                ],
                 metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
             )
 
-            x0, _ = fetch_data(data_source,
-                                     time=[ic],
-                                     lead_time=var_ds.lead_time.values,
-                                     variable=var_ds.name,)
+            x0, _ = fetch_data(
+                data_source,
+                time=[ic],
+                lead_time=var_ds.lead_time.values,
+                variable=var_ds.name,
+            )
             x0 = x0.squeeze()
 
-            mean_ds = var_ds.sel(time=ic).mean(dim='ensemble')
-            se[ii, ...] = (x0 - mean_ds.values)**2
-        print('\n')
+            mean_ds = var_ds.sel(time=ic).mean(dim="ensemble")
+            se[ii, ...] = (x0 - mean_ds.values) ** 2
+        print("\n")
 
-        weights = torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))).unsqueeze(1).repeat(1, 1440)
-        mse[ivar, ...] = (se * weights).mean(dim=(0,2,3))
+        weights = (
+            torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat))))
+            .unsqueeze(1)
+            .repeat(1, 1440)
+        )
+        mse[ivar, ...] = (se * weights).mean(dim=(0, 2, 3))
 
     return mse
 
@@ -100,16 +118,16 @@ def write_to_csv(rmse, ds, identifier):
     # Create DataFrame with variables as rows and lead times as columns
     rmse_df = pd.DataFrame(
         rmse_np.T,
-        index=[f'{lt}' for lt in lead_times],
+        index=[f"{lt}" for lt in lead_times],
         columns=vars,
     )
-    rmse_df.index.name = 'lead_time'
+    rmse_df.index.name = "lead_time"
 
     # Generate output filename based on input filename
     out_file = identifier
     for var in vars:
-        out_file += f'_{var}'
-    out_file += '.csv'
+        out_file += f"_{var}"
+    out_file += ".csv"
 
     rmse_df.to_csv(out_file)
 
@@ -122,44 +140,57 @@ def compute_crps(ds):
     vars.sort()
     vars = pick_vars_for_rank(vars)
 
-    _crps = crps(ensemble_dimension='ensemble',
-                 reduction_dimensions=['lat', 'lon'],
-                 weights=torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))).unsqueeze(1).repeat(1, 1440),
-                 fair=True)
-
+    _crps = crps(
+        ensemble_dimension="ensemble",
+        reduction_dimensions=["lat", "lon"],
+        weights=torch.as_tensor(
+            lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))
+        )
+        .unsqueeze(1)
+        .repeat(1, 1440),
+        fair=True,
+    )
 
     # fetch ground truth data
     _truth = LocalArchiveHDF5(
-                dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
-                metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
-            )
+        dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
+        metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
+    )
 
     ccrr = torch.zeros(len(vars), len(ds.lead_time.values))
     for ivar, var in enumerate(vars):
-        print(f'processing variable {var} ({ivar+1}/{len(vars)})')
-        ccc = torch.zeros(len(ds.time.values), len(ds.lead_time.values), device='cpu')
+        print(f"processing variable {var} ({ivar+1}/{len(vars)})")
+        ccc = torch.zeros(len(ds.time.values), len(ds.lead_time.values), device="cpu")
         for ii, ic in enumerate(ds.time.values):
-            print(f'processing time {ic} ({ii+1}/{len(ds.time.values)})', end='\r', flush=True)
+            print(
+                f"processing time {ic} ({ii+1}/{len(ds.time.values)})",
+                end="\r",
+                flush=True,
+            )
 
-            tru, tru_coords = fetch_data(_truth,
-                                         time=[ic],
-                                         lead_time=ds.lead_time.values,
-                                         variable=var,)
+            tru, tru_coords = fetch_data(
+                _truth,
+                time=[ic],
+                lead_time=ds.lead_time.values,
+                variable=var,
+            )
             tru = tru.squeeze()
-            tru_coords.pop('time')
-            tru_coords.pop('variable')
+            tru_coords.pop("time")
+            tru_coords.pop("variable")
 
             pred, pred_coords = prep_data_array(ds[var].sel(time=ic))
 
-            _ccc, _ = _crps(x=pred.to(DistributedManager().device),
-                            x_coords=pred_coords,
-                            y=tru.to(DistributedManager().device),
-                            y_coords=tru_coords)
-            ccc[ii,:] = _ccc.to('cpu')
+            _ccc, _ = _crps(
+                x=pred.to(DistributedManager().device),
+                x_coords=pred_coords,
+                y=tru.to(DistributedManager().device),
+                y_coords=tru_coords,
+            )
+            ccc[ii, :] = _ccc.to("cpu")
 
-        print('\n')
+        print("\n")
 
-        ccrr[ivar,:] = ccc.mean(dim=0)
+        ccrr[ivar, :] = ccc.mean(dim=0)
 
     return ccrr
 
@@ -171,26 +202,32 @@ def score(fl):
     crps = True
 
     if False:
-        ds = ds.sel(time=ds.time[:5]).sel(ensemble=ds.ensemble[:50]).sel(lead_time=ds.lead_time[:10])
+        ds = (
+            ds.sel(time=ds.time[:5])
+            .sel(ensemble=ds.ensemble[:50])
+            .sel(lead_time=ds.lead_time[:10])
+        )
 
     if rmse:
         then = time.time()
         rmse = compute_rmse(ds)
-        print(f'time taken: {time.time() - then} seconds')
-        write_to_csv(rmse, ds, 'rmse')
+        print(f"time taken: {time.time() - then} seconds")
+        write_to_csv(rmse, ds, "rmse")
 
     if crps:
         then = time.time()
         crps = compute_crps(ds)
-        print(f'time taken: {time.time() - then} seconds')
-        write_to_csv(crps, ds, 'crps')
+        print(f"time taken: {time.time() - then} seconds")
+        write_to_csv(crps, ds, "crps")
     return
 
+
 if __name__ == "__main__":
     if len(sys.argv) < 2:
         print("Usage: python score_results.py <netcdf_file>")
-        print("Example: python score_results.py score_2020_2020-01-01T00.00.00_mems0000-0049.nc")
+        print(
+            "Example: python score_results.py score_2020_2020-01-01T00.00.00_mems0000-0049.nc"
+        )
         exit()
 
     score(sys.argv[1])
-
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/README.md b/recipes/tc_tracking/post_proc/tc_tracking/README.md
index 56f707fc2..7e420da9e 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/README.md
+++ b/recipes/tc_tracking/post_proc/tc_tracking/README.md
@@ -18,4 +18,4 @@ set the `ensemble_of_storms` flag to `True` in `analyse_n_plot_tracks.py`.
 
 
 ## Output
-...
\ No newline at end of file
+...
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py b/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
index f87f7009f..6d758b4fa 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
+++ b/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
@@ -1,19 +1,31 @@
 import os
-import numpy as np
-from tqdm import tqdm
 from collections import OrderedDict
 
-from data_handling import extract_tracks_from_file, extract_tracks, match_tracks
-from data_handling import compute_averages_of_errors_over_lead_time, get_ensemble_averages
-from plotting_helpers import plot_spaghetti, plot_over_time, plot_relative_over_time
-from plotting_helpers import plot_ib_era5, plot_extreme_extremes_histograms
-from plotting_helpers import plot_ensemble_mean_metrics, plot_errors_over_lead_time
-
+import numpy as np
+from data_handling import (
+    compute_averages_of_errors_over_lead_time,
+    extract_tracks,
+    extract_tracks_from_file,
+    get_ensemble_averages,
+    match_tracks,
+)
+from plotting_helpers import (
+    plot_ensemble_mean_metrics,
+    plot_errors_over_lead_time,
+    plot_extreme_extremes_histograms,
+    plot_ib_era5,
+    plot_over_time,
+    plot_relative_over_time,
+    plot_spaghetti,
+)
+from tqdm import tqdm
 
 
 def load_tracks(case, pred_track_dir, tru_track_dir, out_dir):
-    tru_track = extract_tracks_from_file(os.path.join(tru_track_dir, f'reference_track_{case}.csv'))
-    tru_track['dist'] = np.zeros(len(tru_track))
+    tru_track = extract_tracks_from_file(
+        os.path.join(tru_track_dir, f"reference_track_{case}.csv")
+    )
+    tru_track["dist"] = np.zeros(len(tru_track))
 
     pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir, case))
     n_members = len(pred_tracks)
@@ -34,59 +46,72 @@ def analyse_individual_storms(cases, pred_track_dir, tru_track_dir, out_path):
         cases = [cases]
 
     for case in tqdm(cases):
-        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,
-                                                                           pred_track_dir=pred_track_dir,
-                                                                           tru_track_dir=tru_track_dir,
-                                                                           out_dir=out_path)
+        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(
+            case=case,
+            pred_track_dir=pred_track_dir,
+            tru_track_dir=tru_track_dir,
+            out_dir=out_path,
+        )
 
         # spaghetti plot
-        plot_spaghetti(true_track=tru_track,
-                       pred_tracks=pred_tracks,
-                       ensemble_mean=ens_mean['mean'],
-                       case=case,
-                       n_members=n_members,
-                       out_dir=out_dir)
+        plot_spaghetti(
+            true_track=tru_track,
+            pred_tracks=pred_tracks,
+            ensemble_mean=ens_mean["mean"],
+            case=case,
+            n_members=n_members,
+            out_dir=out_dir,
+        )
 
         # plot intensities over time
-        plot_over_time(pred_tracks=pred_tracks,
-                       tru_track=tru_track,
-                       ensemble_mean=ens_mean,
-                       case=case,
-                       n_members=n_members,
-                       out_dir=out_dir)
+        plot_over_time(
+            pred_tracks=pred_tracks,
+            tru_track=tru_track,
+            ensemble_mean=ens_mean,
+            case=case,
+            n_members=n_members,
+            out_dir=out_dir,
+        )
 
         # plot relative intensities over time
-        plot_relative_over_time(pred_tracks=pred_tracks,
-                                tru_track=tru_track,
-                                ensemble_mean=ens_mean,
-                                case=case,
-                                n_members=n_members,
-                                out_dir=out_dir)
+        plot_relative_over_time(
+            pred_tracks=pred_tracks,
+            tru_track=tru_track,
+            ensemble_mean=ens_mean,
+            case=case,
+            n_members=n_members,
+            out_dir=out_dir,
+        )
 
         # plot comparison of intensities between ibtracs and era5 over time
-        plot_ib_era5(tru_track=tru_track,
-                     case=case,
-                     vars=['msl', 'wind_speed'],
-                     out_dir=out_dir)
+        plot_ib_era5(
+            tru_track=tru_track, case=case, vars=["msl", "wind_speed"], out_dir=out_dir
+        )
 
         # plot histogram of extreme values of intensities over full track
-        plot_extreme_extremes_histograms(pred_tracks=pred_tracks,
-                                        tru_track=tru_track,
-                                        ensemble_mean=ens_mean,
-                                        case=case,
-                                        out_dir=out_dir)
+        plot_extreme_extremes_histograms(
+            pred_tracks=pred_tracks,
+            tru_track=tru_track,
+            ensemble_mean=ens_mean,
+            case=case,
+            out_dir=out_dir,
+        )
 
         # plot error metrics over lead time
-        err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,
-                                                                tru_track=tru_track,
-                                                                vars=['wind_speed', 'msl', 'dist'])
-
-        plot_errors_over_lead_time(err_dict=err_dict,
-                                   case=case,
-                                   ic=pred_tracks[0]['ic'],
-                                   n_members=n_members,
-                                   n_tracks=len(pred_tracks),
-                                   out_dir=out_dir)
+        err_dict, _ = compute_averages_of_errors_over_lead_time(
+            pred_tracks=pred_tracks,
+            tru_track=tru_track,
+            vars=["wind_speed", "msl", "dist"],
+        )
+
+        plot_errors_over_lead_time(
+            err_dict=err_dict,
+            case=case,
+            ic=pred_tracks[0]["ic"],
+            n_members=n_members,
+            n_tracks=len(pred_tracks),
+            out_dir=out_dir,
+        )
 
     return
 
@@ -105,19 +130,31 @@ def stack_metrics(err_dict):
 
 
 def stack_cases(storm_metrics, max_len):
-    for ii in range(len(storm_metrics['case'])):
+    for ii in range(len(storm_metrics["case"])):
         # pad storm_metrics['data'][ii].shape with nan to max_len
-        storm_metrics['data'][ii] = np.pad(storm_metrics['data'][ii],
-                                           pad_width=((0, 0), (0, 0), (0, max_len - storm_metrics['data'][ii].shape[-1])),
-                                           mode='constant',
-                                           constant_values=np.nan)
-
-    storm_metrics['data'] = np.stack(storm_metrics['data'], axis=0)
+        storm_metrics["data"][ii] = np.pad(
+            storm_metrics["data"][ii],
+            pad_width=(
+                (0, 0),
+                (0, 0),
+                (0, max_len - storm_metrics["data"][ii].shape[-1]),
+            ),
+            mode="constant",
+            constant_values=np.nan,
+        )
+
+    storm_metrics["data"] = np.stack(storm_metrics["data"], axis=0)
 
     # check if shapes ok
-    should_shape = (len(storm_metrics['case']), len(storm_metrics['var']), len(storm_metrics['metric']), max_len)
-    assert storm_metrics['data'].shape == should_shape, \
-        f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
+    should_shape = (
+        len(storm_metrics["case"]),
+        len(storm_metrics["var"]),
+        len(storm_metrics["metric"]),
+        max_len,
+    )
+    assert (
+        storm_metrics["data"].shape == should_shape
+    ), f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
 
     return storm_metrics
 
@@ -125,60 +162,69 @@ def stack_cases(storm_metrics, max_len):
 def extract_weights(storm_metrics, max_len):
 
     # extract weights
-    ens_idx = storm_metrics['metric'].index('n_members')
-    weights = storm_metrics['data'][:, 0, ens_idx, :]
+    ens_idx = storm_metrics["metric"].index("n_members")
+    weights = storm_metrics["data"][:, 0, ens_idx, :]
     weights = np.nan_to_num(weights, nan=0).astype(int)
 
     # remove 'n_members' from metrics
-    storm_metrics['metric'].remove('n_members')
-    storm_metrics['data'] = np.delete(storm_metrics['data'], ens_idx, axis=-2)
+    storm_metrics["metric"].remove("n_members")
+    storm_metrics["data"] = np.delete(storm_metrics["data"], ens_idx, axis=-2)
 
     # add weights to storm_metrics OrderedDict
-    storm_metrics['weights'] = weights
+    storm_metrics["weights"] = weights
 
     # check if shapes ok
-    should_shape = (len(storm_metrics['case']), len(storm_metrics['var']), len(storm_metrics['metric']), max_len)
-    assert storm_metrics['data'].shape == should_shape, \
-        f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
+    should_shape = (
+        len(storm_metrics["case"]),
+        len(storm_metrics["var"]),
+        len(storm_metrics["metric"]),
+        max_len,
+    )
+    assert (
+        storm_metrics["data"].shape == should_shape
+    ), f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}'
 
     return storm_metrics
 
 
-def get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_path, vars=['wind_speed', 'msl', 'dist']):
-    storm_metrics = OrderedDict({'case': [],
-                                 'var': None,
-                                 'metric': None,
-                                 'lead time': None,
-                                 'data': []})
+def get_individual_storm_metrics(
+    cases, pred_track_dir, tru_track_dir, out_path, vars=["wind_speed", "msl", "dist"]
+):
+    storm_metrics = OrderedDict(
+        {"case": [], "var": None, "metric": None, "lead time": None, "data": []}
+    )
     max_len, ensemble_averages, extremes = 0, {}, {}
-    for case in tqdm(cases, desc='loading storm data'):
-        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,
-                                                                           pred_track_dir=pred_track_dir,
-                                                                           tru_track_dir=tru_track_dir,
-                                                                           out_dir=out_path)
+    for case in tqdm(cases, desc="loading storm data"):
+        tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(
+            case=case,
+            pred_track_dir=pred_track_dir,
+            tru_track_dir=tru_track_dir,
+            out_dir=out_path,
+        )
         # record ensemble mean
         ensemble_averages[case] = ens_mean
 
         # compute averages of error metrics over lead time for storm
-        err_dict, _max_len = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,
-                                                                       tru_track=tru_track,
-                                                                       vars=vars)
+        err_dict, _max_len = compute_averages_of_errors_over_lead_time(
+            pred_tracks=pred_tracks, tru_track=tru_track, vars=vars
+        )
 
         extremes[case] = {}
         for var in vars:
             extremes[case][var] = {}
-            for ext, npfun in zip(['min', 'max'], [np.nanmin, np.nanmax]):
-                extremes[case][var][ext+'_pred'] = err_dict[var].pop(ext)
-                extremes[case][var][ext+'_tru'] = npfun(tru_track[var])
+            for ext, npfun in zip(["min", "max"], [np.nanmin, np.nanmax]):
+                extremes[case][var][ext + "_pred"] = err_dict[var].pop(ext)
+                extremes[case][var][ext + "_tru"] = npfun(tru_track[var])
 
         max_len = max(max_len, _max_len)
-        storm_metrics['case'].append(case)
-        storm_metrics['data'].append(stack_metrics(err_dict))
-
-    storm_metrics['var'] = list(err_dict.keys())
-    storm_metrics['metric'] = list(err_dict[list(err_dict.keys())[0]].keys()) # TODO remove n_members
-    storm_metrics['lead time'] = np.arange(max_len)*np.timedelta64(6, 'h')
+        storm_metrics["case"].append(case)
+        storm_metrics["data"].append(stack_metrics(err_dict))
 
+    storm_metrics["var"] = list(err_dict.keys())
+    storm_metrics["metric"] = list(
+        err_dict[list(err_dict.keys())[0]].keys()
+    )  # TODO remove n_members
+    storm_metrics["lead time"] = np.arange(max_len) * np.timedelta64(6, "h")
 
     return storm_metrics, max_len, ensemble_averages, extremes
 
@@ -186,21 +232,25 @@ def get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_path,
 def reduce_over_all_storms(storm_metrics):
 
     ensemble_metrics = {}
-    for var in storm_metrics['var']:
+    for var in storm_metrics["var"]:
         ensemble_metrics[var] = {}
-        var_idx = storm_metrics['var'].index(var)
-        for metric in storm_metrics['metric']:
-            met_idx = storm_metrics['metric'].index(metric)
-            ensemble_metrics[var][metric] = np.nanmean(storm_metrics['data'][:, var_idx, met_idx, :], axis=0)
+        var_idx = storm_metrics["var"].index(var)
+        for metric in storm_metrics["metric"]:
+            met_idx = storm_metrics["metric"].index(metric)
+            ensemble_metrics[var][metric] = np.nanmean(
+                storm_metrics["data"][:, var_idx, met_idx, :], axis=0
+            )
 
-    ensemble_metrics['n_members'] = np.sum(storm_metrics['weights'], axis=0)
+    ensemble_metrics["n_members"] = np.sum(storm_metrics["weights"], axis=0)
 
     return ensemble_metrics
 
 
 def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path):
 
-    storm_metrics, max_len, ens_means = get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_path)
+    storm_metrics, max_len, ens_means = get_individual_storm_metrics(
+        cases, pred_track_dir, tru_track_dir, out_path
+    )
 
     storm_metrics = stack_cases(storm_metrics, max_len)
 
@@ -211,9 +261,6 @@ def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path):
     plot_ensemble_mean_metrics(ens_means, out_path)
     # plot average dist in space and intensity over lead time (reduced over all storms)
 
-
-
-
     # print()
     # print(f'{storm_metrics.keys()=}')
     # print(f'{storm_metrics['case']=}')
@@ -234,31 +281,30 @@ def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path):
     # print(f'{ens_means['helene_2024_north_atlantic']['n_members'].shape=}')
     # print(f'{ens_means['helene_2024_north_atlantic']['mean']['dist'].shape=}')
 
-
     return ensemble_metrics
 
 
 def analyse_n_plot_tracks():
     cases = [
-        'amphan_2020_north_indian',      # 00
-        'beryl_2024_north_atlantic',     # 01
-        'debbie_2017_southern_pacific',  # 02
-        'dorian_2019_north_atlantic',    # 03
-        'harvey_2017_north_atlantic',    # 04
-        'hato_2017_west_pacific',        # 05
-        'helene_2024_north_atlantic',    # 06
-        'ian_2022_north_atlantic',       # 07
-        'iota_2020_north_atlantic',      # 08
-        'irma_2017_north_atlantic',      # 09
-        'lan_2017_west_pacific',         # 10
-        'lee_2023_north_atlantic',       # 11
-        'lorenzo_2019_north_atlantic',   # 12
-        'maria_2017_north_atlantic',     # 13
-        'mawar_2023_west_pacific',       # 14
-        'michael_2018_north_atlantic',   # 15
-        'milton_2024_north_atlantic',    # 16
-        'ophelia_2017_north_atlantic',   # 17
-        'yagi_2024_west_pacific',        # 18
+        "amphan_2020_north_indian",  # 00
+        "beryl_2024_north_atlantic",  # 01
+        "debbie_2017_southern_pacific",  # 02
+        "dorian_2019_north_atlantic",  # 03
+        "harvey_2017_north_atlantic",  # 04
+        "hato_2017_west_pacific",  # 05
+        "helene_2024_north_atlantic",  # 06
+        "ian_2022_north_atlantic",  # 07
+        "iota_2020_north_atlantic",  # 08
+        "irma_2017_north_atlantic",  # 09
+        "lan_2017_west_pacific",  # 10
+        "lee_2023_north_atlantic",  # 11
+        "lorenzo_2019_north_atlantic",  # 12
+        "maria_2017_north_atlantic",  # 13
+        "mawar_2023_west_pacific",  # 14
+        "michael_2018_north_atlantic",  # 15
+        "milton_2024_north_atlantic",  # 16
+        "ophelia_2017_north_atlantic",  # 17
+        "yagi_2024_west_pacific",  # 18
     ]
 
     # case_selection = list(range(len(cases)))
@@ -266,24 +312,30 @@ def analyse_n_plot_tracks():
     individual_storms = False
     ensemble_of_storms = True
 
-    pred_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'
-    tru_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks'
-    out_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/plots/case_studies_256_random_seed_issue'
+    pred_track_dir = f"/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te"
+    tru_track_dir = (
+        f"/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks"
+    )
+    out_dir = f"/home/mkoch/Documents/projects/fcn3_tcs/plots/case_studies_256_random_seed_issue"
 
     if individual_storms:
-        analyse_individual_storms(cases=[cases[ii] for ii in case_selection],
-                                  pred_track_dir=pred_track_dir,
-                                  tru_track_dir=tru_track_dir,
-                                  out_path=out_dir)
+        analyse_individual_storms(
+            cases=[cases[ii] for ii in case_selection],
+            pred_track_dir=pred_track_dir,
+            tru_track_dir=tru_track_dir,
+            out_path=out_dir,
+        )
 
     if ensemble_of_storms:
-        analyse_ensemble_of_storms(cases=[cases[ii] for ii in case_selection],
-                                   pred_track_dir=pred_track_dir,
-                                   tru_track_dir=tru_track_dir,
-                                   out_path=out_dir)
+        analyse_ensemble_of_storms(
+            cases=[cases[ii] for ii in case_selection],
+            pred_track_dir=pred_track_dir,
+            tru_track_dir=tru_track_dir,
+            out_path=out_dir,
+        )
 
     return
 
 
-if __name__ == '__main__':
-    analyse_n_plot_tracks()
\ No newline at end of file
+if __name__ == "__main__":
+    analyse_n_plot_tracks()
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py b/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
index 2cf3f5812..c6cf5fb7d 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
+++ b/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
@@ -1,4 +1,5 @@
 import glob
+
 import numpy as np
 import pandas as pd
 
@@ -8,33 +9,44 @@ def great_circle_distance(lat1, lon1, lat2, lon2, radius=6371000):
     dlon = lon2 - lon1
     dlat = lat2 - lat1
 
-    aa = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
-    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1-aa))
+    aa = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
+    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1 - aa))
 
     return radius * cc
 
 
 def merge_tracks_by_time(track, tru_track):
-    merged_track  = pd.merge(track, tru_track, on='time', how='left', suffixes=('', '_tru'))
+    merged_track = pd.merge(
+        track, tru_track, on="time", how="left", suffixes=("", "_tru")
+    )
 
     # remove columns which are later than last time of tru_track
-    merged_track = merged_track[merged_track['time'] <= tru_track['time'].max()]
+    merged_track = merged_track[merged_track["time"] <= tru_track["time"].max()]
 
     return merged_track
 
 
 def add_track_distance(track, tru_track):
 
-    merged_track = merge_tracks_by_time(track, tru_track)[['time', 'lat', 'lon', 'lat_tru', 'lon_tru']]
+    merged_track = merge_tracks_by_time(track, tru_track)[
+        ["time", "lat", "lon", "lat_tru", "lon_tru"]
+    ]
 
     # compute great circle distance between each point on the track and the true track
-    dist = great_circle_distance(merged_track['lat'], merged_track['lon'], merged_track['lat_tru'], merged_track['lon_tru'])
+    dist = great_circle_distance(
+        merged_track["lat"],
+        merged_track["lon"],
+        merged_track["lat_tru"],
+        merged_track["lon_tru"],
+    )
 
     # add to merged_track
-    merged_track['dist'] = dist
+    merged_track["dist"] = dist
 
     # add dist to track
-    track = pd.merge(track, merged_track[['time', 'dist']], on='time', how='left', suffixes=('', ''))
+    track = pd.merge(
+        track, merged_track[["time", "dist"]], on="time", how="left", suffixes=("", "")
+    )
 
     return track
 
@@ -42,27 +54,31 @@ def add_track_distance(track, tru_track):
 def match_tracks(pred_tracks, true_track, case):
     # iterate over tracks and see if track exists in simulation
     matched_tracks = []
-    min_dist, max_dist = 25371000, -1.
+    min_dist, max_dist = 25371000, -1.0
 
     for _pred_track_dict in pred_tracks:
-        _pred_tracks = _pred_track_dict['tracks']
+        _pred_tracks = _pred_track_dict["tracks"]
 
         if len(_pred_tracks) == 0:
             continue
 
-        n_tracks = _pred_tracks['track_id'].iloc[-1] + 1
+        n_tracks = _pred_tracks["track_id"].iloc[-1] + 1
 
         for ii in range(n_tracks):
-            track = _pred_tracks.loc[_pred_tracks['track_id'] == ii].copy()
+            track = _pred_tracks.loc[_pred_tracks["track_id"] == ii].copy()
 
-            lat_pred = track['lat'].iloc[0]
-            lon_pred = track['lon'].iloc[0]
+            lat_pred = track["lat"].iloc[0]
+            lon_pred = track["lon"].iloc[0]
 
-            if not (true_track['time'] == track['time'].iloc[0]).any():
+            if not (true_track["time"] == track["time"].iloc[0]).any():
                 continue
 
-            lat_true = true_track.loc[true_track['time'] == track['time'].iloc[0]]['lat_ib'].item()
-            lon_true = true_track.loc[true_track['time'] == track['time'].iloc[0]]['lon_ib'].item()
+            lat_true = true_track.loc[true_track["time"] == track["time"].iloc[0]][
+                "lat_ib"
+            ].item()
+            lon_true = true_track.loc[true_track["time"] == track["time"].iloc[0]][
+                "lon_ib"
+            ].item()
             dist = great_circle_distance(lat_pred, lon_pred, lat_true, lon_true)
 
             if dist <= 300000:
@@ -71,63 +87,72 @@ def match_tracks(pred_tracks, true_track, case):
                 # add dist to track
                 track = add_track_distance(track, true_track)
 
-                matched_tracks.append({'ic': _pred_track_dict['ic'],
-                                       'member': _pred_track_dict['member'],
-                                       'first_match': track['time'].iloc[0],
-                                       'initial_dist': dist,
-                                       'tracks': track})
+                matched_tracks.append(
+                    {
+                        "ic": _pred_track_dict["ic"],
+                        "member": _pred_track_dict["member"],
+                        "first_match": track["time"].iloc[0],
+                        "initial_dist": dist,
+                        "tracks": track,
+                    }
+                )
                 break
 
     line = case.split("_")[:2]
-    line = f'{line[0].upper()} {line[1]}: matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, '
-    line += f'with distances ranging from {min_dist/1000:.1f} to {max_dist/1000:.1f} km'
+    line = f"{line[0].upper()} {line[1]}: matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, "
+    line += f"with distances ranging from {min_dist/1000:.1f} to {max_dist/1000:.1f} km"
     print(line)
 
     return matched_tracks
 
 
 def extract_tracks_from_file(csv_file):
-    tracks = pd.read_csv(csv_file, sep=',')
+    tracks = pd.read_csv(csv_file, sep=",")
     tracks.columns = tracks.columns.str.strip()
 
-    times = [pd.to_datetime(f'{tracks["year"].iloc[jj]}-{int(tracks.iloc[jj]["month"]):02d}-{int(tracks.iloc[jj]["day"]):02d} {int(tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(tracks))]
+    times = [
+        pd.to_datetime(
+            f'{tracks["year"].iloc[jj]}-{int(tracks.iloc[jj]["month"]):02d}-{int(tracks.iloc[jj]["day"]):02d} {int(tracks.iloc[jj]["hour"]):02d}:00:00'
+        )
+        for jj in range(len(tracks))
+    ]
 
-    tracks.drop(columns=['year', 'month', 'day', 'hour'], inplace=True)
+    tracks.drop(columns=["year", "month", "day", "hour"], inplace=True)
     # if i j in tracks, drop them
-    if 'i' in tracks.columns:
-        tracks.drop(columns=['i', 'j'], inplace=True)
+    if "i" in tracks.columns:
+        tracks.drop(columns=["i", "j"], inplace=True)
 
-    tracks.insert(0, 'time', times)
+    tracks.insert(0, "time", times)
 
     return tracks
 
 
 def extract_tracks(in_dir):
     tracks = []
-    files = glob.glob(f'{in_dir}/*.csv')
+    files = glob.glob(f"{in_dir}/*.csv")
     files.sort()
 
     for csv_file in files:
         # read csv, strip whitespace from column names
         _tracks = extract_tracks_from_file(csv_file)
 
-        mem = int(csv_file.split('_mems')[-1].split('.csv')[0])
-        ic = pd.to_datetime(csv_file.split('_mems')[0][-19:])
+        mem = int(csv_file.split("_mems")[-1].split(".csv")[0])
+        ic = pd.to_datetime(csv_file.split("_mems")[0][-19:])
 
-        tracks.append({'ic': ic, 'member': mem, 'tracks': _tracks})
+        tracks.append({"ic": ic, "member": mem, "tracks": _tracks})
 
     return tracks
 
 
 def merge_tracks_by_lead_time(track_dict, tru_track):
 
-    merged_track = merge_tracks_by_time(track_dict['tracks'], tru_track)
+    merged_track = merge_tracks_by_time(track_dict["tracks"], tru_track)
 
     # clip leading and trailing rows in which only predicted or only true track are present
-    t_max = max(track_dict['tracks']['time'].max(), tru_track['time'].max())
-    t_min = min(track_dict['tracks']['time'].min(), tru_track['time'].min())
-    merged_track = merged_track[merged_track['time'] >= t_min]
-    merged_track = merged_track[merged_track['time'] <= t_max]
+    t_max = max(track_dict["tracks"]["time"].max(), tru_track["time"].max())
+    t_min = min(track_dict["tracks"]["time"].min(), tru_track["time"].min())
+    merged_track = merged_track[merged_track["time"] >= t_min]
+    merged_track = merged_track[merged_track["time"] <= t_max]
 
     return merged_track
 
@@ -139,7 +164,7 @@ def compute_mae(tru_vars, pred_vars):
 
 # function computing mse
 def compute_mse(tru_vars, pred_vars):
-    return np.nanmean((tru_vars - pred_vars)**2, axis=0)
+    return np.nanmean((tru_vars - pred_vars) ** 2, axis=0)
 
 
 # function computing RMSE
@@ -157,16 +182,18 @@ def compute_std(arr):
 
 
 def remove_trailing_nans(merged_track, var):
-    either_nans = np.logical_or(merged_track[var+'_tru'].isna(), merged_track[var].isna())
+    either_nans = np.logical_or(
+        merged_track[var + "_tru"].isna(), merged_track[var].isna()
+    )
     cut_off = np.where(~either_nans)[0][-1]
 
-    return merged_track.iloc[:cut_off+1]
+    return merged_track.iloc[: cut_off + 1]
 
 
 def rebase_by_lead_time(pred_tracks, tru_track, vars):
     err_dict = {}
     for var in vars:
-        err_dict[var] = {'pred': [], 'tru': []}
+        err_dict[var] = {"pred": [], "tru": []}
 
     max_len = 0
     ii = 0
@@ -177,40 +204,51 @@ def rebase_by_lead_time(pred_tracks, tru_track, vars):
             continue
 
         # remove trailing rows in which both true and predicted track are nan
-        merged_track = remove_trailing_nans(merged_track, 'msl')
+        merged_track = remove_trailing_nans(merged_track, "msl")
 
         max_len = max(max_len, len(merged_track))
 
         for var in err_dict.keys():
-            err_dict[var]['pred'].append(merged_track[var])
-            err_dict[var]['tru'].append(merged_track[var+'_tru'])
+            err_dict[var]["pred"].append(merged_track[var])
+            err_dict[var]["tru"].append(merged_track[var + "_tru"])
 
     return err_dict, max_len
 
 
 def compute_error_metrics(err_dict, max_len):
     for var in err_dict.keys():
-        pred_vars = err_dict[var]['pred']
-        tru_vars = err_dict[var]['tru']
+        pred_vars = err_dict[var]["pred"]
+        tru_vars = err_dict[var]["tru"]
 
         # pad with nans to make all arrays the same length
         counts = np.zeros(max_len, dtype=int)
         for ii in range(len(pred_vars)):
-            counts[:len(pred_vars[ii])] += 1
-
-            pred_vars[ii] = np.pad(pred_vars[ii], (0, max_len - len(pred_vars[ii])), mode='constant', constant_values=np.nan)
-            tru_vars[ii] = np.pad(tru_vars[ii], (0, max_len - len(tru_vars[ii])), mode='constant', constant_values=np.nan)
-
+            counts[: len(pred_vars[ii])] += 1
+
+            pred_vars[ii] = np.pad(
+                pred_vars[ii],
+                (0, max_len - len(pred_vars[ii])),
+                mode="constant",
+                constant_values=np.nan,
+            )
+            tru_vars[ii] = np.pad(
+                tru_vars[ii],
+                (0, max_len - len(tru_vars[ii])),
+                mode="constant",
+                constant_values=np.nan,
+            )
 
         # merge to single array
         pred_vars, tru_vars = np.array(pred_vars), np.array(tru_vars)
 
-        err_dict[var] = {'mae': compute_mae(tru_vars, pred_vars),
-                         'mse': compute_mse(tru_vars, pred_vars),
-                         'variance': compute_variance(pred_vars),
-                         'max': np.nanmax(pred_vars, axis=-1),
-                         'min': np.nanmin(pred_vars, axis=-1),
-                         'n_members': counts}
+        err_dict[var] = {
+            "mae": compute_mae(tru_vars, pred_vars),
+            "mse": compute_mse(tru_vars, pred_vars),
+            "variance": compute_variance(pred_vars),
+            "max": np.nanmax(pred_vars, axis=-1),
+            "min": np.nanmin(pred_vars, axis=-1),
+            "n_members": counts,
+        }
 
     return err_dict
 
@@ -286,75 +324,93 @@ def xyz_to_lat_lon(xx, yy, zz):
 
 def cartesian_to_spherical_track(stats, tru_track, frame_of_reference):
 
-    mean_lat, mean_lon = xyz_to_lat_lon(stats['mean']['x'], stats['mean']['y'], stats['mean']['z'])
+    mean_lat, mean_lon = xyz_to_lat_lon(
+        stats["mean"]["x"], stats["mean"]["y"], stats["mean"]["z"]
+    )
 
     # delete cartesian mean/variance
-    for var in ['x', 'y', 'z']:
-        for metric in ['mean', 'variance']:
+    for var in ["x", "y", "z"]:
+        for metric in ["mean", "variance"]:
             del stats[metric][var]
 
     # add spherical mean
-    stats['mean']['lat'] = mean_lat
-    stats['mean']['lon'] = mean_lon
+    stats["mean"]["lat"] = mean_lat
+    stats["mean"]["lon"] = mean_lon
 
     # compute distance
-    tru_cont = pd.merge(frame_of_reference, tru_track, on='time', how='left')
+    tru_cont = pd.merge(frame_of_reference, tru_track, on="time", how="left")
 
     # compute great circle distance between each point on the track and the true track
-    dist = great_circle_distance(tru_cont['lat'], tru_cont['lon'], stats['mean']['lat'], stats['mean']['lon'])
+    dist = great_circle_distance(
+        tru_cont["lat"], tru_cont["lon"], stats["mean"]["lat"], stats["mean"]["lon"]
+    )
 
     # add to merged_track
-    stats['mean']['dist'] = dist.to_numpy(dtype=float)
+    stats["mean"]["dist"] = dist.to_numpy(dtype=float)
 
-    for var in ['msl', 'wind_speed']:
-        stats['mean'][var+'_err_of_mean'] = stats['mean'][var] - tru_cont[var]
+    for var in ["msl", "wind_speed"]:
+        stats["mean"][var + "_err_of_mean"] = stats["mean"][var] - tru_cont[var]
 
     return stats
 
 
-def get_ensemble_averages(pred_tracks, tru_track, vars: list[str]=['msl', 'wind_speed', 'x', 'y', 'z']):
+def get_ensemble_averages(
+    pred_tracks, tru_track, vars: list[str] = ["msl", "wind_speed", "x", "y", "z"]
+):
 
-    stats = {'time': None,
-             'n_members': None,
-             'mean': {var: [] for var in vars},
-             'variance': {var: [] for var in vars}}
+    stats = {
+        "time": None,
+        "n_members": None,
+        "mean": {var: [] for var in vars},
+        "variance": {var: [] for var in vars},
+    }
 
     # loop over all tracks, match with
-    this_is_the_last_time = pred_tracks[0]['ic']
+    this_is_the_last_time = pred_tracks[0]["ic"]
 
     # iterate over all tracks to get set of all times present
     for track in pred_tracks:
-        this_is_the_last_time = max(this_is_the_last_time, track['tracks']['time'].values[-1])
+        this_is_the_last_time = max(
+            this_is_the_last_time, track["tracks"]["time"].values[-1]
+        )
 
-    all_times = np.arange(pred_tracks[0]['ic'], this_is_the_last_time, np.timedelta64(6, 'h'))
+    all_times = np.arange(
+        pred_tracks[0]["ic"], this_is_the_last_time, np.timedelta64(6, "h")
+    )
     stats["time"] = all_times
 
     # start data frame single column called time which holds all_times
-    frame_of_reference = pd.DataFrame(data=all_times, index=np.arange(len(all_times)), columns=['time'])
+    frame_of_reference = pd.DataFrame(
+        data=all_times, index=np.arange(len(all_times)), columns=["time"]
+    )
 
     for track in pred_tracks:
         # convert lat/lon to xyz
-        xx, yy, zz = lat_lon_to_xyz(track['tracks']['lat'], track['tracks']['lon'])
-        track['tracks']['x'] = xx
-        track['tracks']['y'] = yy
-        track['tracks']['z'] = zz
+        xx, yy, zz = lat_lon_to_xyz(track["tracks"]["lat"], track["tracks"]["lon"])
+        track["tracks"]["x"] = xx
+        track["tracks"]["y"] = yy
+        track["tracks"]["z"] = zz
 
-        contextualised = pd.merge(frame_of_reference, track['tracks'], on='time', how='left')
+        contextualised = pd.merge(
+            frame_of_reference, track["tracks"], on="time", how="left"
+        )
 
         for var in vars:
-            stats['mean'][var].append(contextualised[var])
+            stats["mean"][var].append(contextualised[var])
 
     for var in vars:
         # get number of ensemble members contributing to each time step
         if stats["n_members"] is None:
-            stats["n_members"] = np.count_nonzero(~np.isnan(stats['mean'][var]), axis=0)
+            stats["n_members"] = np.count_nonzero(~np.isnan(stats["mean"][var]), axis=0)
         else:
-            assert np.all(stats["n_members"] == np.count_nonzero(~np.isnan(stats['mean'][var]), axis=0)), \
-                f'n_members is not the same for all variables but should be....'
-
-        help = np.stack(stats['mean'][var])
-        stats['variance'][var] = np.nanvar(help, axis=0)
-        stats['mean'][var] = np.nanmean(help, axis=0)
+            assert np.all(
+                stats["n_members"]
+                == np.count_nonzero(~np.isnan(stats["mean"][var]), axis=0)
+            ), f"n_members is not the same for all variables but should be...."
+
+        help = np.stack(stats["mean"][var])
+        stats["variance"][var] = np.nanvar(help, axis=0)
+        stats["mean"][var] = np.nanmean(help, axis=0)
 
     # convert xyz back to lat/lon
     stats = cartesian_to_spherical_track(stats, tru_track, frame_of_reference)
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py b/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
index 169615749..19dda8b1e 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
+++ b/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
@@ -1,92 +1,126 @@
+import os
+
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
 import matplotlib.pyplot as plt
-import os
 import numpy as np
 import pandas as pd
 from data_handling import merge_tracks_by_time
 
+
 def add_some_gap(lat_min, lat_max, lon_min, lon_max):
-    gap_fac = .1
+    gap_fac = 0.1
     lat_gap = (lat_max - lat_min) * gap_fac
     lon_gap = (lon_max - lon_min) * gap_fac
 
     lat_min, lat_max = lat_min - lat_gap, lat_max + lat_gap
     lon_min, lon_max = lon_min - lon_gap, lon_max + lon_gap
 
-    if lat_gap/lon_gap > 2:
-        d_lon = .5*(lat_max - lat_min)
-        med_lon = .5*(lon_min + lon_max)
-        lon_min, lon_max = med_lon - d_lon/2, med_lon + d_lon/2
+    if lat_gap / lon_gap > 2:
+        d_lon = 0.5 * (lat_max - lat_min)
+        med_lon = 0.5 * (lon_min + lon_max)
+        lon_min, lon_max = med_lon - d_lon / 2, med_lon + d_lon / 2
 
-    elif lon_gap/lat_gap > 2:
-        d_lat = .5*(lon_max - lon_min)
-        med_lat = .5*(lat_min + lat_max)
-        lat_min, lat_max = med_lat - d_lat/2, med_lat + d_lat/2
+    elif lon_gap / lat_gap > 2:
+        d_lat = 0.5 * (lon_max - lon_min)
+        med_lat = 0.5 * (lat_min + lat_max)
+        lat_min, lat_max = med_lat - d_lat / 2, med_lat + d_lat / 2
 
     return lat_min, lat_max, lon_min, lon_max
 
 
 def get_central_coords(track):
-    lat_cen = track['lat'].median()
-    lon_cen = track['lon'].median()
+    lat_cen = track["lat"].median()
+    lon_cen = track["lon"].median()
 
     return lat_cen, lon_cen
 
 
-def plot_spaghetti(true_track,
-                   pred_tracks,
-                   ensemble_mean,
-                   case,
-                   n_members,
-                   out_dir: str | None = None,
-                   alpha: float=.2,
-                   line_width: float=2,
-                   ic = None):
-    plt.close('all')
+def plot_spaghetti(
+    true_track,
+    pred_tracks,
+    ensemble_mean,
+    case,
+    n_members,
+    out_dir: str | None = None,
+    alpha: float = 0.2,
+    line_width: float = 2,
+    ic=None,
+):
+    plt.close("all")
 
     lat_cen, lon_cen = get_central_coords(true_track)
 
     # Create figure and axis
-    fig = plt.figure(figsize=(22,10))
+    fig = plt.figure(figsize=(22, 10))
     sup_title = f'{(case.split("_")[0]).upper()} '
     sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
-    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members"
     fig.suptitle(sup_title, fontsize=16)
 
-    projection = ccrs.LambertAzimuthalEqualArea(central_longitude=lon_cen, central_latitude=lat_cen)
+    projection = ccrs.LambertAzimuthalEqualArea(
+        central_longitude=lon_cen, central_latitude=lat_cen
+    )
     ax = fig.add_subplot(1, 1, 1, projection=projection)
 
-    ax.add_feature(cfeature.COASTLINE,lw=.5)
-    ax.add_feature(cfeature.RIVERS,lw=.5)
-    if case != 'debbie_2017_southern_pacific': # cartopy issues with small islands
-        ax.add_feature(cfeature.OCEAN, facecolor='#b0c4de')
-        ax.add_feature(cfeature.LAND, facecolor='#C4B9A3')
+    ax.add_feature(cfeature.COASTLINE, lw=0.5)
+    ax.add_feature(cfeature.RIVERS, lw=0.5)
+    if case != "debbie_2017_southern_pacific":  # cartopy issues with small islands
+        ax.add_feature(cfeature.OCEAN, facecolor="#b0c4de")
+        ax.add_feature(cfeature.LAND, facecolor="#C4B9A3")
 
-    lat_min, lat_max, lon_min, lon_max = 90, -90, 360, -.1
+    lat_min, lat_max, lon_min, lon_max = 90, -90, 360, -0.1
 
     # Plot the line in white
     for _track in pred_tracks:
-        track = _track['tracks']
-        if ic is not None and _track['ic'] not in ic:
+        track = _track["tracks"]
+        if ic is not None and _track["ic"] not in ic:
             continue
 
-        lat_min, lat_max = min(lat_min, track['lat'].min()), max(lat_max, track['lat'].max())
-        lon_min, lon_max = min(lon_min, track['lon'].min()), max(lon_max, track['lon'].max())
-
-        ax.plot(track['lon'], track['lat'], transform=ccrs.PlateCarree(),
-                color='black', linewidth=line_width, alpha=alpha)
-
-    ax.plot(true_track['lon'], true_track['lat'], transform=ccrs.PlateCarree(),
-            color='red', linewidth=line_width, alpha=1.)
-
-    ax.plot(ensemble_mean['lon'], ensemble_mean['lat'], transform=ccrs.PlateCarree(),
-            color='lime', linewidth=line_width, alpha=1.)
-
-    lat_min, lat_max = min(lat_min, true_track['lat'].min()), max(lat_max, true_track['lat'].max())
-    lon_min, lon_max = min(lon_min, true_track['lon'].min()), max(lon_max, true_track['lon'].max())
-
-    lat_min, lat_max, lon_min, lon_max = add_some_gap(lat_min, lat_max, lon_min, lon_max)
+        lat_min, lat_max = min(lat_min, track["lat"].min()), max(
+            lat_max, track["lat"].max()
+        )
+        lon_min, lon_max = min(lon_min, track["lon"].min()), max(
+            lon_max, track["lon"].max()
+        )
+
+        ax.plot(
+            track["lon"],
+            track["lat"],
+            transform=ccrs.PlateCarree(),
+            color="black",
+            linewidth=line_width,
+            alpha=alpha,
+        )
+
+    ax.plot(
+        true_track["lon"],
+        true_track["lat"],
+        transform=ccrs.PlateCarree(),
+        color="red",
+        linewidth=line_width,
+        alpha=1.0,
+    )
+
+    ax.plot(
+        ensemble_mean["lon"],
+        ensemble_mean["lat"],
+        transform=ccrs.PlateCarree(),
+        color="lime",
+        linewidth=line_width,
+        alpha=1.0,
+    )
+
+    lat_min, lat_max = min(lat_min, true_track["lat"].min()), max(
+        lat_max, true_track["lat"].max()
+    )
+    lon_min, lon_max = min(lon_min, true_track["lon"].min()), max(
+        lon_max, true_track["lon"].max()
+    )
+
+    lat_min, lat_max, lon_min, lon_max = add_some_gap(
+        lat_min, lat_max, lon_min, lon_max
+    )
 
     # Remove extra space around the plot
     plt.tight_layout()
@@ -94,9 +128,8 @@ def plot_spaghetti(true_track,
     ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
     ax.gridlines(draw_labels=False, dms=False, x_inline=False, y_inline=False)
 
-
     if out_dir:
-        fig.savefig(os.path.join(out_dir, f'{case}_tracks.png'))
+        fig.savefig(os.path.join(out_dir, f"{case}_tracks.png"))
 
     return
 
@@ -105,134 +138,182 @@ def normalised_intensities(track, tru_track, var):
 
     merged_track = merge_tracks_by_time(track, tru_track)
 
-    if var == 'msl':
-        merged_track[var] = (merged_track[var] - merged_track[var+'_tru']) / (101325 - merged_track[var+'_tru'])
+    if var == "msl":
+        merged_track[var] = (merged_track[var] - merged_track[var + "_tru"]) / (
+            101325 - merged_track[var + "_tru"]
+        )
     else:
-        merged_track[var] = (merged_track[var] - merged_track[var+'_tru']) / merged_track[var+'_tru']
+        merged_track[var] = (
+            merged_track[var] - merged_track[var + "_tru"]
+        ) / merged_track[var + "_tru"]
 
     return merged_track
 
 
-def plot_relative_over_time(pred_tracks, tru_track, ensemble_mean, case, n_members, ics=None, out_dir: str | None = None):
+def plot_relative_over_time(
+    pred_tracks,
+    tru_track,
+    ensemble_mean,
+    case,
+    n_members,
+    ics=None,
+    out_dir: str | None = None,
+):
 
-    fig, _ax = plt.subplots(2,1, figsize=(11, 11), sharex=True)
+    fig, _ax = plt.subplots(2, 1, figsize=(11, 11), sharex=True)
     sup_title = f'{(case.split("_")[0]).upper()} '
     sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
-    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members"
     fig.suptitle(sup_title, fontsize=16)
 
-    vars = ['msl', 'wind_speed']
-    labels = ['(msl - msl_ref)/(101325Pa - msl_ref)', 'max_wind/max_wind_ref - 1']
-
-    ic, end = pred_tracks[0]['ic'], tru_track['time'].max()
-    rel_steps = int(((end - ic) / np.timedelta64(6, 'h') + 1)*.75)
+    vars = ["msl", "wind_speed"]
+    labels = ["(msl - msl_ref)/(101325Pa - msl_ref)", "max_wind/max_wind_ref - 1"]
 
+    ic, end = pred_tracks[0]["ic"], tru_track["time"].max()
+    rel_steps = int(((end - ic) / np.timedelta64(6, "h") + 1) * 0.75)
 
     for ii in range(_ax.shape[0]):
         vmin, vmax = 1000, -1000
         for _track in pred_tracks:
 
-            track = _track['tracks']
-            if ics is not None and _track['ic'] not in ics:
+            track = _track["tracks"]
+            if ics is not None and _track["ic"] not in ics:
                 continue
 
             track = normalised_intensities(track, tru_track, vars[ii])
 
-            vmin, vmax = min(vmin, track[vars[ii]][:rel_steps].min()), max(vmax, track[vars[ii]][:rel_steps].max())
+            vmin, vmax = min(vmin, track[vars[ii]][:rel_steps].min()), max(
+                vmax, track[vars[ii]][:rel_steps].max()
+            )
 
             ax = _ax[ii]
-            ax.plot(track['time'], track[vars[ii]], color='black', alpha=.1)
+            ax.plot(track["time"], track[vars[ii]], color="black", alpha=0.1)
 
         _ax[ii].set_ylabel(labels[ii])
         _ax[ii].grid(True)
         _ax[ii].set_ylim(vmin, vmax)
 
-        ax.plot(tru_track['time'], [0 for _ in range(len(tru_track))], color='orangered', linewidth=2.5, label='era5 comparison')
-
-        mean = pd.DataFrame({'time': ensemble_mean['time'],
-                             vars[ii]: ensemble_mean['mean'][vars[ii]]})
+        ax.plot(
+            tru_track["time"],
+            [0 for _ in range(len(tru_track))],
+            color="orangered",
+            linewidth=2.5,
+            label="era5 comparison",
+        )
+
+        mean = pd.DataFrame(
+            {"time": ensemble_mean["time"], vars[ii]: ensemble_mean["mean"][vars[ii]]}
+        )
         _track = normalised_intensities(mean, tru_track, vars[ii])
-        ax.plot(_track['time'], _track[vars[ii]], color='lime', linewidth=2.5, label='ensemble mean', linestyle='--')
-
+        ax.plot(
+            _track["time"],
+            _track[vars[ii]],
+            color="lime",
+            linewidth=2.5,
+            label="ensemble mean",
+            linestyle="--",
+        )
 
         ax.legend()
 
     # Set x-axis label to indicate hours
-    _ax[-1].set_xlabel('time [UTC]')
+    _ax[-1].set_xlabel("time [UTC]")
 
-    plt.xlim(track['time'].min()-np.timedelta64(6, 'h'), tru_track['time'].max()+np.timedelta64(6, 'h'))
+    plt.xlim(
+        track["time"].min() - np.timedelta64(6, "h"),
+        tru_track["time"].max() + np.timedelta64(6, "h"),
+    )
 
     if out_dir:
-        plt.savefig(os.path.join(out_dir, f'{case}_rel_intensities.png'))
+        plt.savefig(os.path.join(out_dir, f"{case}_rel_intensities.png"))
 
     return
 
 
-def plot_over_time(pred_tracks,
-                   tru_track,
-                   ensemble_mean,
-                   case,
-                   n_members,
-                   vars=['msl', 'wind_speed', 'dist'],
-                   labels=['msl [hPa]', 'maximum instantaneous wind speed [m/s]', 'distance [km]'],
-                   ics=None,
-                   out_dir: str | None = None):
+def plot_over_time(
+    pred_tracks,
+    tru_track,
+    ensemble_mean,
+    case,
+    n_members,
+    vars=["msl", "wind_speed", "dist"],
+    labels=["msl [hPa]", "maximum instantaneous wind speed [m/s]", "distance [km]"],
+    ics=None,
+    out_dir: str | None = None,
+):
 
-    fig, _ax = plt.subplots(len(vars),1, figsize=(11, 15), sharex=True)
+    fig, _ax = plt.subplots(len(vars), 1, figsize=(11, 15), sharex=True)
     sup_title = f'{(case.split("_")[0]).upper()} '
     sup_title += f'\n initialised on {pred_tracks[0]["ic"]}'
-    sup_title += f'\n {len(pred_tracks)} tracks in {n_members} ensemble members'
+    sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members"
     fig.suptitle(sup_title, fontsize=16)
 
-    t_min, t_max = np.datetime64('2120-05-16 12:00:00'), np.datetime64('1820-05-16 12:00:00')
+    t_min, t_max = np.datetime64("2120-05-16 12:00:00"), np.datetime64(
+        "1820-05-16 12:00:00"
+    )
 
     for ii in range(_ax.shape[0]):
         for _track in pred_tracks:
-            track = _track['tracks']
-            if ics is not None and _track['ic'] not in ics:
+            track = _track["tracks"]
+            if ics is not None and _track["ic"] not in ics:
                 continue
 
-            if vars[ii] == 'dist':
-                yy = track[vars[ii]]/1000
-            elif vars[ii] == 'msl':
-                yy = track[vars[ii]]/100
+            if vars[ii] == "dist":
+                yy = track[vars[ii]] / 1000
+            elif vars[ii] == "msl":
+                yy = track[vars[ii]] / 100
             else:
                 yy = track[vars[ii]]
 
-            _ax[ii].plot(track['time'], yy, color='black', alpha=.1)
+            _ax[ii].plot(track["time"], yy, color="black", alpha=0.1)
 
-            t_min, t_max = min(t_min, track['time'].min()), max(t_max, track['time'].max())
+            t_min, t_max = min(t_min, track["time"].min()), max(
+                t_max, track["time"].max()
+            )
 
-        _ax[ii].set_xlim(t_min-np.timedelta64(6, 'h'), t_max+np.timedelta64(6, 'h'))
+        _ax[ii].set_xlim(t_min - np.timedelta64(6, "h"), t_max + np.timedelta64(6, "h"))
         _ax[ii].set_ylabel(labels[ii])
         _ax[ii].grid(True)
 
         # if vars[ii] != 'dist':
-        yy = tru_track[vars[ii]]/100 if vars[ii] == 'msl' else tru_track[vars[ii]]
-        _ax[ii].plot(tru_track['time'], yy, color='orangered', linewidth=2.5, label='era5 comparison')
-
-
-        if vars[ii] == 'dist':
-            yy = ensemble_mean['mean'][vars[ii]]/1000
-        elif vars[ii] == 'msl':
-            yy = ensemble_mean['mean'][vars[ii]]/100
+        yy = tru_track[vars[ii]] / 100 if vars[ii] == "msl" else tru_track[vars[ii]]
+        _ax[ii].plot(
+            tru_track["time"],
+            yy,
+            color="orangered",
+            linewidth=2.5,
+            label="era5 comparison",
+        )
+
+        if vars[ii] == "dist":
+            yy = ensemble_mean["mean"][vars[ii]] / 1000
+        elif vars[ii] == "msl":
+            yy = ensemble_mean["mean"][vars[ii]] / 100
         else:
-            yy = ensemble_mean['mean'][vars[ii]]
-        _ax[ii].plot(ensemble_mean['time'], yy, color='lime', linewidth=2.5, label='ensemble mean', linestyle='--')
+            yy = ensemble_mean["mean"][vars[ii]]
+        _ax[ii].plot(
+            ensemble_mean["time"],
+            yy,
+            color="lime",
+            linewidth=2.5,
+            label="ensemble mean",
+            linestyle="--",
+        )
         _ax[ii].legend()
 
-
     # Set x-axis label to indicate hours
-    _ax[-1].set_xlabel('time [UTC]')
+    _ax[-1].set_xlabel("time [UTC]")
 
     if out_dir:
-        plt.savefig(os.path.join(out_dir, f'{case}_abs_intensities.png'))
+        plt.savefig(os.path.join(out_dir, f"{case}_abs_intensities.png"))
 
     return
 
 
-def plot_ib_era5(tru_track, case, vars=['msl', 'wind_speed'], out_dir: str | None = None):
-    plt.close('all')
+def plot_ib_era5(
+    tru_track, case, vars=["msl", "wind_speed"], out_dir: str | None = None
+):
+    plt.close("all")
 
     fig, ax1 = plt.subplots(1, 1, figsize=(8, 5))
     sup_title = f'{(case.split("_")[0]).upper()} '
@@ -240,82 +321,100 @@ def plot_ib_era5(tru_track, case, vars=['msl', 'wind_speed'], out_dir: str | Non
 
     ax2 = ax1.twinx()
 
-    if 'msl' in vars:
+    if "msl" in vars:
         p_norm = 101325
-        ax1.plot(tru_track['time'], (p_norm-tru_track['msl'])/(p_norm-tru_track['msl_ib']), 'black')
-        ax1.set_ylabel('(1013hPa-msl_era5)/(1013hPa-msl_ib)', color='black')
-
-    if 'wind_speed' in vars:
-        ax2.plot(tru_track['time'], tru_track['wind_speed']/tru_track['wind_speed_ib'], 'orangered')
-        ax2.set_ylabel('wind_speed_era5/wind_speed_ib', color='orangered')
+        ax1.plot(
+            tru_track["time"],
+            (p_norm - tru_track["msl"]) / (p_norm - tru_track["msl_ib"]),
+            "black",
+        )
+        ax1.set_ylabel("(1013hPa-msl_era5)/(1013hPa-msl_ib)", color="black")
+
+    if "wind_speed" in vars:
+        ax2.plot(
+            tru_track["time"],
+            tru_track["wind_speed"] / tru_track["wind_speed_ib"],
+            "orangered",
+        )
+        ax2.set_ylabel("wind_speed_era5/wind_speed_ib", color="orangered")
 
     fig.tight_layout()
     if out_dir:
-        plt.savefig(os.path.join(out_dir, f'{case}_ib_era5_wind_speed.png'))
+        plt.savefig(os.path.join(out_dir, f"{case}_ib_era5_wind_speed.png"))
 
     return
 
 
 def root_metrics(err_dict):
     for var in err_dict.keys():
-        mse = err_dict[var].pop('mse')
-        err_dict[var]['rmse'] = np.sqrt(mse)
-        variance = err_dict[var].pop('variance')
-        err_dict[var]['standard_deviation'] = np.sqrt(variance)
-        n_mems_lt = err_dict[var].pop('n_members')
+        mse = err_dict[var].pop("mse")
+        err_dict[var]["rmse"] = np.sqrt(mse)
+        variance = err_dict[var].pop("variance")
+        err_dict[var]["standard_deviation"] = np.sqrt(variance)
+        n_mems_lt = err_dict[var].pop("n_members")
 
     return err_dict, n_mems_lt
 
 
-def plot_errors_over_lead_time(err_dict,
-                               case,
-                               ic,
-                               n_members,
-                               n_tracks,
-                               norm_dict = {'msl': 100, 'wind_speed': 1, 'dist': 1000},
-                               unit_dict = {'msl': 'hPa', 'wind_speed': 'm/s', 'dist': 'km'},
-                               out_dir: str | None = None):
+def plot_errors_over_lead_time(
+    err_dict,
+    case,
+    ic,
+    n_members,
+    n_tracks,
+    norm_dict={"msl": 100, "wind_speed": 1, "dist": 1000},
+    unit_dict={"msl": "hPa", "wind_speed": "m/s", "dist": "km"},
+    out_dir: str | None = None,
+):
 
-    if 'mse' in err_dict[list(err_dict.keys())[0]].keys():
+    if "mse" in err_dict[list(err_dict.keys())[0]].keys():
         err_dict, n_mems_lt = root_metrics(err_dict)
 
     vars = list(err_dict.keys())
     metrics = list(err_dict[vars[0]].keys())
 
     # remove min, max as not over lead time but per member
-    for extreme in ['min', 'max']:
+    for extreme in ["min", "max"]:
         if extreme in metrics:
             metrics.remove(extreme)
 
     print(metrics)
 
-    lead_time = np.arange(err_dict[vars[0]][metrics[0]].shape[0])*np.timedelta64(6, 'h')
+    lead_time = np.arange(err_dict[vars[0]][metrics[0]].shape[0]) * np.timedelta64(
+        6, "h"
+    )
 
-    fig, ax = plt.subplots(len(vars), len(metrics), figsize=((len(metrics)+1)*2, (len(vars)+1)*2), sharex=True)
+    fig, ax = plt.subplots(
+        len(vars),
+        len(metrics),
+        figsize=((len(metrics) + 1) * 2, (len(vars) + 1) * 2),
+        sharex=True,
+    )
 
     for ivar, var in enumerate(err_dict.keys()):
         for imet, metric in enumerate(metrics):
 
-            ax[ivar, imet].plot(lead_time, err_dict[var][metric]/norm_dict[var])
+            ax[ivar, imet].plot(lead_time, err_dict[var][metric] / norm_dict[var])
 
             if ivar == 0:
-                ax[ivar, imet].set_title(metric, fontsize=12, weight='bold')
+                ax[ivar, imet].set_title(metric, fontsize=12, weight="bold")
 
             if imet == 0:
-                ax[ivar, imet].set_ylabel(f'{var} [{unit_dict[var]}]', fontsize=12, weight='bold')
+                ax[ivar, imet].set_ylabel(
+                    f"{var} [{unit_dict[var]}]", fontsize=12, weight="bold"
+                )
 
             if ivar == len(vars) - 1:
-                ax[ivar, imet].set_xlabel('lead time [h]', fontsize=12)
-
+                ax[ivar, imet].set_xlabel("lead time [h]", fontsize=12)
 
     sup_title = f'{(case.split("_")[0]).upper()} '
-    sup_title += f'\n initialised on {ic}'
-    sup_title += f'\n {n_tracks} tracks in {n_members} ensemble members'
+    sup_title += f"\n initialised on {ic}"
+    sup_title += f"\n {n_tracks} tracks in {n_members} ensemble members"
     fig.suptitle(sup_title, fontsize=16)
 
     fig.tight_layout()
     if out_dir:
-        plt.savefig(os.path.join(out_dir, f'{case}_error_metrics_over_lead_time.png'))
+        plt.savefig(os.path.join(out_dir, f"{case}_error_metrics_over_lead_time.png"))
 
     return
 
@@ -324,16 +423,16 @@ def extract_reference_extremes(tru_track, pred_tracks, ens_mean, vars):
 
     extreme_dict = {}
     for var in vars:
-        extreme_dict[var] = {'pred': np.zeros(len(pred_tracks))}
+        extreme_dict[var] = {"pred": np.zeros(len(pred_tracks))}
         for ii, track in enumerate(pred_tracks):
-            if var in ['wind_speed']:
-                extreme_dict[var]['pred'][ii] = np.nanmax(track['tracks'][var])
-                extreme_dict[var]['tru'] = np.nanmax(tru_track[var])
-                extreme_dict[var]['ens_mean'] = np.nanmax(ens_mean['mean'][var])
-            elif var in ['msl']:
-                extreme_dict[var]['pred'][ii] = np.nanmin(track['tracks'][var])
-                extreme_dict[var]['tru'] = np.nanmin(tru_track[var])
-                extreme_dict[var]['ens_mean'] = np.nanmin(ens_mean['mean'][var])
+            if var in ["wind_speed"]:
+                extreme_dict[var]["pred"][ii] = np.nanmax(track["tracks"][var])
+                extreme_dict[var]["tru"] = np.nanmax(tru_track[var])
+                extreme_dict[var]["ens_mean"] = np.nanmax(ens_mean["mean"][var])
+            elif var in ["msl"]:
+                extreme_dict[var]["pred"][ii] = np.nanmin(track["tracks"][var])
+                extreme_dict[var]["tru"] = np.nanmin(tru_track[var])
+                extreme_dict[var]["ens_mean"] = np.nanmin(ens_mean["mean"][var])
 
     return extreme_dict
 
@@ -345,65 +444,89 @@ def add_stats_box(ax, pred_var, tru_var, var, reduction, unit):
     n_total = len(pred_var)
 
     # Create table-like format with aligned colons for wind speed
-    comp = 'exceeding' if var in ['wind_speed'] else 'below'
+    comp = "exceeding" if var in ["wind_speed"] else "below"
     stats = [
-        ('era5 reference:', f"{tru_var:.1f} {unit}"),
-        (f'members {comp} ref:', f'{n_exceed_spd} of {n_total} ({(n_exceed_spd/n_total)*100:.1f}%)'),
-        (f'max {reduction} {var}:', f'{pred_var.max():.1f} {unit}'),
-        (f'min {reduction} {var}:', f'{pred_var.min():.1f} {unit}'),
-        (f'avg {reduction} {var}:', f'{pred_var.mean():.1f} {unit}'),
-        (f'std {reduction} {var}:', f'{pred_var.std():.1f} {unit}')
+        ("era5 reference:", f"{tru_var:.1f} {unit}"),
+        (
+            f"members {comp} ref:",
+            f"{n_exceed_spd} of {n_total} ({(n_exceed_spd/n_total)*100:.1f}%)",
+        ),
+        (f"max {reduction} {var}:", f"{pred_var.max():.1f} {unit}"),
+        (f"min {reduction} {var}:", f"{pred_var.min():.1f} {unit}"),
+        (f"avg {reduction} {var}:", f"{pred_var.mean():.1f} {unit}"),
+        (f"std {reduction} {var}:", f"{pred_var.std():.1f} {unit}"),
     ]
 
     # Format as table with aligned colons
     max_label_width = max(len(label) for label, _ in stats)
-    text = '\n'.join([f'{label:<{max_label_width}}  {value}' for label, value in stats])
-
-    ax.text(.01, -0.25, text, transform=ax.transAxes, ha='left', va='top',
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.8),
-            fontfamily='monospace')
+    text = "\n".join([f"{label:<{max_label_width}}  {value}" for label, value in stats])
+
+    ax.text(
+        0.01,
+        -0.25,
+        text,
+        transform=ax.transAxes,
+        ha="left",
+        va="top",
+        bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.8),
+        fontfamily="monospace",
+    )
 
     return
 
 
-def plot_extreme_extremes_histograms(pred_tracks, tru_track, ensemble_mean, case, vars=['wind_speed', 'msl'], out_dir: str | None = None, nbins: int=12):
+def plot_extreme_extremes_histograms(
+    pred_tracks,
+    tru_track,
+    ensemble_mean,
+    case,
+    vars=["wind_speed", "msl"],
+    out_dir: str | None = None,
+    nbins: int = 12,
+):
 
-    extreme_dict = extract_reference_extremes(tru_track, pred_tracks, ensemble_mean, vars)
+    extreme_dict = extract_reference_extremes(
+        tru_track, pred_tracks, ensemble_mean, vars
+    )
 
     # plot histogram of spd and msl
-    fig, ax = plt.subplots(1, len(vars), figsize=(3*(len(vars)+1), 6), sharey=True)
-    fig.suptitle(f'{(case.split("_")[0]).upper()} initialised on {pred_tracks[0]["ic"]}', fontsize=16)
-    ax[0].set_ylabel('count')
+    fig, ax = plt.subplots(1, len(vars), figsize=(3 * (len(vars) + 1), 6), sharey=True)
+    fig.suptitle(
+        f'{(case.split("_")[0]).upper()} initialised on {pred_tracks[0]["ic"]}',
+        fontsize=16,
+    )
+    ax[0].set_ylabel("count")
 
     for ii, var in enumerate(vars):
 
-        reduction = 'max' if var in ['wind_speed'] else 'min'
-        unit = 'm/s' if var in ['wind_speed'] else 'hPa'
-        scale = 100 if var in ['msl'] else 1
+        reduction = "max" if var in ["wind_speed"] else "min"
+        unit = "m/s" if var in ["wind_speed"] else "hPa"
+        scale = 100 if var in ["msl"] else 1
 
-        pred_var = extreme_dict[var]['pred']/scale
-        tru_var = extreme_dict[var]['tru']/scale
-        mean_var = extreme_dict[var]['ens_mean']/scale
+        pred_var = extreme_dict[var]["pred"] / scale
+        tru_var = extreme_dict[var]["tru"] / scale
+        mean_var = extreme_dict[var]["ens_mean"] / scale
 
         # plot histo and vertical line at tru_var
         ax[ii].hist(pred_var, bins=nbins)
-        ax[ii].axvline(tru_var, color='orangered', linestyle='--', label='era5 reference')
-        ax[ii].axvline(mean_var, color='lime', linestyle='--', label='ensemble mean')
+        ax[ii].axvline(
+            tru_var, color="orangered", linestyle="--", label="era5 reference"
+        )
+        ax[ii].axvline(mean_var, color="lime", linestyle="--", label="ensemble mean")
 
-        ax[ii].set_title(f'{reduction} {var} (x, t)')
-        ax[ii].set_xlabel(f'{var} [{unit}]')
+        ax[ii].set_title(f"{reduction} {var} (x, t)")
+        ax[ii].set_xlabel(f"{var} [{unit}]")
         ax[ii].legend()
 
         add_stats_box(ax[ii], pred_var, tru_var, var, reduction, unit)
 
     fig.tight_layout()
     if out_dir:
-        fig.savefig(os.path.join(out_dir, f'{case}_histograms.png'))
+        fig.savefig(os.path.join(out_dir, f"{case}_histograms.png"))
 
     plt.show()
 
 
-
 def plot_ensemble_mean_metrics(ens_means, out_path):
 
     errs = []
diff --git a/recipes/tc_tracking/src/data/file_output.py b/recipes/tc_tracking/src/data/file_output.py
index 2b2e5e090..9e18ad59e 100644
--- a/recipes/tc_tracking/src/data/file_output.py
+++ b/recipes/tc_tracking/src/data/file_output.py
@@ -1,9 +1,10 @@
-from collections import OrderedDict
 import copy
 import os
 import shutil
-from torch import from_numpy
+from collections import OrderedDict
+
 import numpy as np
+from torch import from_numpy
 
 from earth2studio.io import NetCDF4Backend, ZarrBackend
 from earth2studio.utils.coords import map_coords, split_coords
@@ -32,10 +33,11 @@ def initialise_output_coords(
         output coordinates
     """
     out_coords = OrderedDict(
-    {
-        k: v for k, v in model.output_coords(model.input_coords()).items() if
-        (k != "batch") and (v.shape != 0)
-    }
+        {
+            k: v
+            for k, v in model.output_coords(model.input_coords()).items()
+            if (k != "batch") and (v.shape != 0)
+        }
     )
 
     out_coords["time"] = ics
@@ -60,9 +62,7 @@ def add_arrays_to_store(store, out_coords, mems, add_arrays, ic=None, array_kwar
         oco["time"] = np.array([ic])
 
     if add_arrays:
-        store.add_array(coords=oco,
-                        array_name=out_vars,
-                        **array_kwargs)
+        store.add_array(coords=oco, array_name=out_vars, **array_kwargs)
 
     return
 
@@ -100,11 +100,13 @@ def setup_output(cfg, model, ics, add_arrays):
             backend_kwargs={"overwrite": False},
         )
 
-        add_arrays_to_store(store=store,
-                            out_coords=out_coords,
-                            mems=np.asarray(list(range(cfg.ensemble_size))).flatten(),
-                            add_arrays=add_arrays,
-                            array_kwargs=array_kwargs)
+        add_arrays_to_store(
+            store=store,
+            out_coords=out_coords,
+            mems=np.asarray(list(range(cfg.ensemble_size))).flatten(),
+            add_arrays=add_arrays,
+            array_kwargs=array_kwargs,
+        )
 
     else:
         raise ValueError(f"Invalid store type: {cfg.store_type}")
@@ -114,13 +116,22 @@ def setup_output(cfg, model, ics, add_arrays):
 
 def initialise_netcdf_output(cfg, out_coords, ic, ic_mems):
     mems = np.concatenate([mem for iic, mem, _ in ic_mems if iic == ic]).flatten()
-    seeds = np.concatenate([np.array([seed]*len(mem), dtype=int) for iic, mem, seed in ic_mems if iic == ic]).flatten()
+    seeds = np.concatenate(
+        [
+            np.array([seed] * len(mem), dtype=int)
+            for iic, mem, seed in ic_mems
+            if iic == ic
+        ]
+    ).flatten()
 
     # setup filename
     file_name = os.path.join(cfg.store_dir, cfg.project)
     if file_name.endswith(".nc"):
         file_name = file_name.replace(".nc", "")
-    file_name = file_name + f"_{np.datetime_as_string(ic, unit='s')}_mems{mems[0]:04d}-{mems[-1]:04d}.nc"
+    file_name = (
+        file_name
+        + f"_{np.datetime_as_string(ic, unit='s')}_mems{mems[0]:04d}-{mems[-1]:04d}.nc"
+    )
     file_name = file_name.replace(":", ".")
 
     # create store
@@ -136,18 +147,17 @@ def initialise_netcdf_output(cfg, out_coords, ic, ic_mems):
     )
 
     # add random seed to store
-    store.add_array(coords={"ensemble": mems}, array_name='random_seed')
-    store.write(from_numpy(seeds), {"ensemble": mems}, 'random_seed')
+    store.add_array(coords={"ensemble": mems}, array_name="random_seed")
+    store.write(from_numpy(seeds), {"ensemble": mems}, "random_seed")
 
     # add arrays to the store
-    add_arrays_to_store(store=store,
-                        out_coords=out_coords,
-                        mems=mems,
-                        ic=ic,
-                        add_arrays=True)
+    add_arrays_to_store(
+        store=store, out_coords=out_coords, mems=mems, ic=ic, add_arrays=True
+    )
 
     return store
 
+
 def write_to_store(store, xx, coords, out_coords):
     if store is not None:
         xx_sub, coords_sub = map_coords(xx, coords, out_coords)
diff --git a/recipes/tc_tracking/src/data/localh5.py b/recipes/tc_tracking/src/data/localh5.py
index 7a31c0779..76a371e41 100644
--- a/recipes/tc_tracking/src/data/localh5.py
+++ b/recipes/tc_tracking/src/data/localh5.py
@@ -1,17 +1,16 @@
-from datetime import datetime, timedelta
 import glob
 import json
 import os
-from typing import Iterable, Tuple
+from collections.abc import Iterable
+from datetime import datetime, timedelta
+from typing import Tuple
 
 import h5py
 import numpy as np
 import xarray as xr
 
-
 from earth2studio.utils.type import TimeArray, VariableArray
 
-
 DIMS = ["time", "variable", "lat", "lon"]
 
 
@@ -20,7 +19,7 @@ def __init__(
         self,
         dirs: str | Iterable,
         metadata_file: str,
-        latlon_box: Tuple[Tuple[float, float], Tuple[float, int]] | None = None,
+        latlon_box: tuple[tuple[float, float], tuple[float, int]] | None = None,
     ):
         if isinstance(dirs, str):
             dirs = [dirs]
@@ -31,7 +30,7 @@ def __init__(
                 year = int(os.path.basename(path).split(".")[0])
                 self.files_by_year[year] = path
 
-        with open(metadata_file, 'r') as f:
+        with open(metadata_file) as f:
             metadata = json.load(f)
         self.lat = np.array(metadata["coords"]["lat"])
         self.lon = np.array(metadata["coords"]["lon"])
@@ -62,17 +61,19 @@ def __call__(
 
         x = np.zeros((len(time), len(variable)) + self.field_shape, dtype=np.float32)
 
-        for (k, t) in enumerate(time):
+        for k, t in enumerate(time):
             time_idx = int((t - datetime(t.year, 1, 1)) / self.dt)
-            with h5py.File(self.files_by_year[t.year], 'r') as ds:
-                for (i, var_idx) in enumerate(var_indices):
-                    x[k, i, :, :] = ds["fields"][time_idx, var_idx, self.lat_slice, self.lon_slice]
+            with h5py.File(self.files_by_year[t.year], "r") as ds:
+                for i, var_idx in enumerate(var_indices):
+                    x[k, i, :, :] = ds["fields"][
+                        time_idx, var_idx, self.lat_slice, self.lon_slice
+                    ]
 
         coords = {
             "time": np.array(time, copy=False),
             "variable": np.array(variable, copy=False),
             "lat": self.lat,
-            "lon": self.lon
+            "lon": self.lon,
         }
 
         return xr.DataArray(data=x, coords=coords, dims=DIMS)
diff --git a/recipes/tc_tracking/src/data/utils.py b/recipes/tc_tracking/src/data/utils.py
index 30841642b..c60905c5e 100644
--- a/recipes/tc_tracking/src/data/utils.py
+++ b/recipes/tc_tracking/src/data/utils.py
@@ -1,18 +1,22 @@
+from collections import OrderedDict
+
 import hydra
-import torch
 import numpy as np
+import torch
 import xarray as xr
-from collections import OrderedDict
 
 
 def load_heights(oro_path):
     oro = xr.load_dataset(oro_path)
 
-    coords = OrderedDict({'variable': np.array(['height']), 'lat': oro.latitude, 'lon': oro.longitude})
-    geop = torch.Tensor(oro['Z'].to_numpy())/9.80665 # devide by gravi to get height
+    coords = OrderedDict(
+        {"variable": np.array(["height"]), "lat": oro.latitude, "lon": oro.longitude}
+    )
+    geop = torch.Tensor(oro["Z"].to_numpy()) / 9.80665  # devide by gravi to get height
 
     return geop, coords
 
+
 class DataSourceManager:
     # TODO: catch case in which ther's only single data source
 
@@ -25,7 +29,7 @@ def __init__(self, cfg):
         self.single_source = False
         for name, source in self.cfg.data_source.items():
             # catch classic case of single source
-            if name == '_target_':
+            if name == "_target_":
                 self.single_source = True
                 self.data_source = hydra.utils.instantiate(self.cfg.data_source)
                 return
@@ -36,32 +40,33 @@ def __init__(self, cfg):
             # extract valid years for source and build mapping
             years = source.years
             if isinstance(years, str):
-                if not '-' in years:
+                if not "-" in years:
                     years = int(years)
 
             if isinstance(years, int):
                 years = [years]
             else:
-                years = list(range(int(years.split('-')[0]), int(years.split('-')[1])+1))
+                years = list(
+                    range(int(years.split("-")[0]), int(years.split("-")[1]) + 1)
+                )
 
             for year in years:
                 self.data_source_mapping[year] = name
 
         return
 
-
     def select_data_source(self, time_stamps):
         if self.single_source:
             return self.data_source
 
-        year = np.unique(time_stamps.astype('datetime64[Y]').astype(int) + 1970)
+        year = np.unique(time_stamps.astype("datetime64[Y]").astype(int) + 1970)
 
         if len(year) > 1:
-            raise ValueError(f'track spans multiple years, which is not yet supported')
+            raise ValueError(f"track spans multiple years, which is not yet supported")
         else:
             year = year[0]
 
         if year not in self.data_source_mapping:
-            raise ValueError(f'no data source provided for year {year}')
+            raise ValueError(f"no data source provided for year {year}")
 
-        return self.data_sources[self.data_source_mapping[year]]
\ No newline at end of file
+        return self.data_sources[self.data_source_mapping[year]]
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index cca3cd795..6d4c04117 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -1,16 +1,15 @@
 import copy
-import numpy as np
 import os
-import tropycal.tracks as tropytracks
-import pandas as pd
 
+import numpy as np
+import pandas as pd
+import tropycal.tracks as tropytracks
 from physicsnemo.distributed import DistributedManager
 
-from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
 from earth2studio.data import fetch_data
-from earth2studio.utils.coords import split_coords
 from earth2studio.io import KVBackend
-
+from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
+from earth2studio.utils.coords import split_coords
 from src.data.utils import DataSourceManager, load_heights
 from src.utils import great_circle_distance
 
@@ -204,4 +203,4 @@ def extract_baseline(cfg,
 
         write_track_to_csv(matched_track, case, cfg.store_dir, cfg.cases[case].basin)
 
-    return
\ No newline at end of file
+    return
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 51d63d641..ee14a74e9 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -1,31 +1,35 @@
 import os
-import torch
-import numpy as np
-from tqdm import tqdm
-from math import ceil
-from zarr import consolidate_metadata
-from omegaconf import OmegaConf
 import random
+from math import ceil
 
+import numpy as np
+import torch
+from omegaconf import OmegaConf
 from physicsnemo.distributed import DistributedManager
+from tqdm import tqdm
+from zarr import consolidate_metadata
 
-from earth2studio.models.auto import Package
-from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
 from earth2studio.data import fetch_data
+from earth2studio.models.auto import Package
+from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 from earth2studio.perturbation import SphericalGaussian
-
+from src.data.file_output import initialise_netcdf_output, setup_output, write_to_store
 from src.data.utils import DataSourceManager, load_heights
-from src.data.file_output import setup_output, initialise_netcdf_output, write_to_store
-from src.utils import run_with_rank_ordered_execution, set_initial_times, remove_duplicates, get_set_of_random_seeds
+from src.utils import (
+    get_set_of_random_seeds,
+    remove_duplicates,
+    run_with_rank_ordered_execution,
+    set_initial_times,
+)
 
 
 def initialise(cfg):
-    os.environ["PYTORCH_CUDA_ALLOC_CONF"]="expandable_segments:True"
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 
     DistributedManager.initialize()
 
     # set seed
-    if 'random_seed' in cfg:
+    if "random_seed" in cfg:
         torch.manual_seed(cfg.random_seed)
         np.random.seed(cfg.random_seed)
         random.seed(cfg.random_seed)
@@ -35,23 +39,28 @@ def initialise(cfg):
 
 
 def load_model(cfg):
-    model = 'fcn3'
-    if 'model' in cfg:
+    model = "fcn3"
+    if "model" in cfg:
         model = cfg.model
 
-    if model == 'fcn3':
+    if model == "fcn3":
         from earth2studio.models.px import FCN3
+
         package = Package(cfg.model_package)
         model = FCN3.load_model(package)
-    elif model == 'sfno':
+    elif model == "sfno":
         from earth2studio.models.px import SFNO
-        package = Package('/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72')
+
+        package = Package(
+            "/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72"
+        )
         model = SFNO.load_model(package)
-    elif model == 'aifs-ens':
+    elif model == "aifs-ens":
         from earth2studio.models.px import AIFSENS
+
         model = AIFSENS.load_model(AIFSENS.load_default_package())
     else:
-        raise ValueError(f'model {model} not supported')
+        raise ValueError(f"model {model} not supported")
 
     return model.to(DistributedManager().device)
 
@@ -62,31 +71,41 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
     # data_source = hydra.utils.instantiate(cfg.data_source)
     data_source_mngr = DataSourceManager(cfg)
 
-    #iterate over initial conditions
+    # iterate over initial conditions
     ic_prev = None
 
     cyclone_tracking = None
     if "cyclone_tracking" in cfg:
         oco = model.output_coords(model.input_coords())
 
-        heights, height_coords = load_heights(cfg.orography_path) if "orography_path" in cfg else (None, None)
-
-        tracker = AsyncTempestExtremes if cfg.cyclone_tracking.asynchronous else TempestExtremes
-        cyclone_tracking = tracker(detect_cmd=cfg.cyclone_tracking.detect_cmd,
-                                   stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
-                                   input_vars=cfg.cyclone_tracking.vars,
-                                   batch_size=cfg.batch_size,
-                                   n_steps=cfg.n_steps,
-                                   time_step=oco['lead_time'][0],
-                                   lats=oco['lat'],
-                                   lons=oco['lon'],
-                                   static_vars=heights,
-                                   static_coords=height_coords,
-                                   store_dir=cfg.store_dir,
-                                   keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
-                                   print_te_output=cfg.cyclone_tracking.print_te_output,
-                                   use_ram=cfg.cyclone_tracking.use_ram,
-                                   timeout=cfg.cyclone_tracking.task_timeout_seconds)
+        heights, height_coords = (
+            load_heights(cfg.orography_path)
+            if "orography_path" in cfg
+            else (None, None)
+        )
+
+        tracker = (
+            AsyncTempestExtremes
+            if cfg.cyclone_tracking.asynchronous
+            else TempestExtremes
+        )
+        cyclone_tracking = tracker(
+            detect_cmd=cfg.cyclone_tracking.detect_cmd,
+            stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
+            input_vars=cfg.cyclone_tracking.vars,
+            batch_size=cfg.batch_size,
+            n_steps=cfg.n_steps,
+            time_step=oco["lead_time"][0],
+            lats=oco["lat"],
+            lons=oco["lon"],
+            static_vars=heights,
+            static_coords=height_coords,
+            store_dir=cfg.store_dir,
+            keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
+            print_te_output=cfg.cyclone_tracking.print_te_output,
+            use_ram=cfg.cyclone_tracking.use_ram,
+            timeout=cfg.cyclone_tracking.task_timeout_seconds,
+        )
 
     for ic, mems, seed in ic_mems:
         mini_batch_size = len(mems)
@@ -99,11 +118,13 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
             # exit()
             if cfg.store_type == "netcdf":
                 store = initialise_netcdf_output(cfg, out_coords, ic, ic_mems)
-            x0, coords0 = fetch_data(data_source,
-                                     time=[np.datetime64(ic)],
-                                     lead_time=model.input_coords()['lead_time'],
-                                     variable=model.variables,
-                                     device=dist.device)
+            x0, coords0 = fetch_data(
+                data_source,
+                time=[np.datetime64(ic)],
+                lead_time=model.input_coords()["lead_time"],
+                variable=model.variables,
+                device=dist.device,
+            )
             ic_prev = ic
             exit()
 
@@ -111,23 +132,29 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
         xx = x0.unsqueeze(0).repeat(mini_batch_size, *([1] * x0.ndim))
 
         # set random state or apply perturbation
-        if (not 'model' in cfg) or (cfg.model == 'fcn3'):
+        if (not "model" in cfg) or (cfg.model == "fcn3"):
             model.set_rng(seed=seed)
         else:
             sg = SphericalGaussian(noise_amplitude=0.0005)
             xx, coords = sg(xx, coords)
 
-
         iterator = model.create_iterator(xx, coords)
 
         # roll out the model and record data as desired
-        for _, (xx, coords) in tqdm(zip(range(cfg.n_steps+1), iterator), total=cfg.n_steps+1):
+        for _, (xx, coords) in tqdm(
+            zip(range(cfg.n_steps + 1), iterator), total=cfg.n_steps + 1
+        ):
             write_to_store(store, xx, coords, out_coords)
             if cyclone_tracking:
                 cyclone_tracking.record_state(xx, coords)
 
         if cyclone_tracking:
-            cyclone_tracking(out_file_names=[f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv" for mem in mems])
+            cyclone_tracking(
+                out_file_names=[
+                    f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv"
+                    for mem in mems
+                ]
+            )
 
     # Consolidate metadata in zarr files
     if dist.rank == 0 and cfg.store_type == "zarr":
@@ -149,7 +176,7 @@ def distribute_runs(ic_mems):
     ic_mems = ic_mems[dist.rank * ic_mems_per_rank : (dist.rank + 1) * ic_mems_per_rank]
 
     if len(ic_mems) == 0:
-        print(f'nothing to do for rank {dist.rank}, exiting')
+        print(f"nothing to do for rank {dist.rank}, exiting")
         ic_mems = None
 
     return ic_mems
@@ -160,15 +187,17 @@ def configure_runs(cfg):
 
     ics = set_initial_times(cfg)
 
-    seeds = get_set_of_random_seeds(n_ics=len(ics),
-                                    ensemble_size=cfg.ensemble_size,
-                                    batch_size=cfg.batch_size,
-                                    seed=cfg.random_seed if 'random_seed' in cfg else None)
+    seeds = get_set_of_random_seeds(
+        n_ics=len(ics),
+        ensemble_size=cfg.ensemble_size,
+        batch_size=cfg.batch_size,
+        seed=cfg.random_seed if "random_seed" in cfg else None,
+    )
 
     ii = 0
     for ic in ics:
         for mem in range(0, cfg.ensemble_size, cfg.batch_size):
-            mems = np.arange(mem, min(mem+cfg.batch_size, cfg.ensemble_size))
+            mems = np.arange(mem, min(mem + cfg.batch_size, cfg.ensemble_size))
             ic_mems.append((ic, mems, int(seeds[ii])))
             ii += 1
 
@@ -190,8 +219,11 @@ def set_reproduction_configs(cfg):
         ics.append(ic_mems[ii][0])
 
         # get full batches that include members which shall be reproduced
-        batch_id = ic_mems[ii][1]//cfg.batch_size
-        ic_mems[ii][1] = np.arange(batch_id*cfg.batch_size, min((batch_id+1)*cfg.batch_size, cfg.ensemble_size))
+        batch_id = ic_mems[ii][1] // cfg.batch_size
+        ic_mems[ii][1] = np.arange(
+            batch_id * cfg.batch_size,
+            min((batch_id + 1) * cfg.batch_size, cfg.ensemble_size),
+        )
 
     # remove duplicates
     ic_mems = remove_duplicates(ic_mems)
@@ -214,8 +246,15 @@ def generate_ensemble(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = (
+        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+            setup_output,
+            cfg=cfg,
+            model=model,
+            ics=ics,
+            add_arrays=DistributedManager().rank == 0,
+        )
+    )
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -227,8 +266,8 @@ def generate_ensemble(cfg):
 
 
 def reproduce_members(cfg):
-    if cfg.store_type == 'zarr':
-        raise ValueError('Zarr output not suported for reproducing ensemble members')
+    if cfg.store_type == "zarr":
+        raise ValueError("Zarr output not suported for reproducing ensemble members")
 
     initialise(cfg)
 
@@ -237,8 +276,15 @@ def reproduce_members(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = (
+        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+            setup_output,
+            cfg=cfg,
+            model=model,
+            ics=ics,
+            add_arrays=DistributedManager().rank == 0,
+        )
+    )
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -246,4 +292,4 @@ def reproduce_members(cfg):
 
     store = run_inference(model, cfg, store, out_coords, ic_mems)
 
-    return
\ No newline at end of file
+    return
diff --git a/recipes/tc_tracking/src/utils.py b/recipes/tc_tracking/src/utils.py
index 9c42727a8..99676eb1f 100644
--- a/recipes/tc_tracking/src/utils.py
+++ b/recipes/tc_tracking/src/utils.py
@@ -1,10 +1,12 @@
+from collections.abc import Callable
 from math import ceil
-from typing import Callable, Any
-from omegaconf import DictConfig
+from typing import Any
+
 import numpy as np
 import torch
-
+from omegaconf import DictConfig
 from physicsnemo.distributed import DistributedManager
+
 from earth2studio.utils.time import to_time_array
 
 
@@ -46,6 +48,7 @@ def remove_duplicates(data_list):
     Remove duplicates while preserving numpy dtype distinctions.
     Arrays with same values but different dtypes are considered different.
     """
+
     def to_hashable(item):
         if isinstance(item, np.ndarray):
             # Include dtype and shape information
@@ -67,16 +70,18 @@ def to_hashable(item):
 
 
 def get_set_of_random_seeds(n_ics, ensemble_size, batch_size, seed):
-    n_batches = ceil(ensemble_size/batch_size)
+    n_batches = ceil(ensemble_size / batch_size)
     rng = np.random.default_rng(seed=seed)
 
     seeds = np.array([])
     ii = 0
-    while len(np.unique(seeds)) != n_batches*n_ics:
+    while len(np.unique(seeds)) != n_batches * n_ics:
         ii += 1
         if ii > 1000:
-            raise RecursionError(f'failed to generate unique set of {n_batches*n_ics} random seeds after 1000 iterations. giving up :(')
-        seeds = rng.integers(low=0, high=2**32, size=n_batches*n_ics, dtype=np.uint32)
+            raise RecursionError(
+                f"failed to generate unique set of {n_batches*n_ics} random seeds after 1000 iterations. giving up :("
+            )
+        seeds = rng.integers(low=0, high=2**32, size=n_batches * n_ics, dtype=np.uint32)
 
     return seeds
 
@@ -129,8 +134,12 @@ def squeeze_coords(xx, coords, squeeze_dim):
     for dim in squeeze_dim:
         idx = list(coords.keys()).index(dim)
 
-        assert coords[dim].shape == (1,), f"can only squeeze dims of length 1, coords[{dim}] has shape {coords[dim].shape}"
-        assert xx.shape[idx] == 1, f"can only squeeze dims of length 1, xx[{dim}] has shape {xx.shape[idx]}"
+        assert coords[dim].shape == (
+            1,
+        ), f"can only squeeze dims of length 1, coords[{dim}] has shape {coords[dim].shape}"
+        assert (
+            xx.shape[idx] == 1
+        ), f"can only squeeze dims of length 1, xx[{dim}] has shape {xx.shape[idx]}"
 
         xx = xx.squeeze(idx)
         coords.pop(dim)
@@ -143,7 +152,7 @@ def great_circle_distance(lat1, lon1, lat2, lon2):
     dlon = lon2 - lon1
     dlat = lat2 - lat1
 
-    aa = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
-    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1-aa))
+    aa = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
+    cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1 - aa))
 
     return 6371000 * cc
diff --git a/recipes/tc_tracking/tc_hunt.py b/recipes/tc_tracking/tc_hunt.py
index 7cd04a538..028a66074 100644
--- a/recipes/tc_tracking/tc_hunt.py
+++ b/recipes/tc_tracking/tc_hunt.py
@@ -1,6 +1,5 @@
 import hydra
 from omegaconf import DictConfig
-
 from src.extract_from_data_source.baseline_extraction import extract_baseline
 from src.generate_ensembles import generate_ensemble, reproduce_members
 
@@ -8,17 +7,19 @@
 @hydra.main(version_base=None, config_path="cfg", config_name="none")
 def main(cfg: DictConfig) -> None:
 
-    if cfg.mode == 'extract_baseline':
+    if cfg.mode == "extract_baseline":
         extract_baseline(cfg)
 
-    elif cfg.mode == 'generate_ensemble':
+    elif cfg.mode == "generate_ensemble":
         generate_ensemble(cfg)
 
-    elif cfg.mode == 'reproduce_members':
+    elif cfg.mode == "reproduce_members":
         reproduce_members(cfg)
 
     else:
-        raise ValueError(f'invalid mode: {cfg.mode}, choose from "extract_baseline" or "generate_ensemble"')
+        raise ValueError(
+            f'invalid mode: {cfg.mode}, choose from "extract_baseline" or "generate_ensemble"'
+        )
 
     print(f"finished **yaaayyyy**")
 

From f89efe3e2b6af55ea308cc9a32603d03f5582e8c Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 13 Oct 2025 08:03:21 -0700
Subject: [PATCH 30/69] updated uv.lock

---
 recipes/tc_tracking/uv.lock | 70 ++++++++++++++++++-------------------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/recipes/tc_tracking/uv.lock b/recipes/tc_tracking/uv.lock
index 2e3bfa2a7..8f7ead6c7 100644
--- a/recipes/tc_tracking/uv.lock
+++ b/recipes/tc_tracking/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
@@ -129,8 +129,8 @@ dependencies = [
     { name = "numpy" },
     { name = "plotly" },
     { name = "scikit-learn" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "torch-geometric" },
     { name = "typeguard" },
@@ -148,8 +148,8 @@ dependencies = [
     { name = "anemoi-utils" },
     { name = "einops" },
     { name = "hydra-core" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "torch-geometric" },
 ]
@@ -785,7 +785,7 @@ wheels = [
 [[package]]
 name = "earth2studio"
 version = "0.10.0a0"
-source = { git = "https://github.com/mariusaurus/earth2studio.git?branch=mkoch%2Fte-aifs-ens#fc9e8da2c2dcd1392b51970604961186e5938e68" }
+source = { git = "https://github.com/mariusaurus/earth2studio.git?branch=mkoch%2Ftc_tracking#a4d59708fba9884b79e08a76819d5e9fef84396d" }
 dependencies = [
     { name = "cfgrib" },
     { name = "cftime" },
@@ -801,8 +801,8 @@ dependencies = [
     { name = "python-dotenv" },
     { name = "rich" },
     { name = "s3fs" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "tqdm" },
     { name = "xarray", extra = ["parallel"] },
@@ -952,7 +952,7 @@ requires-dist = [
     { name = "anemoi-graphs", specifier = "==0.6.2" },
     { name = "anemoi-models", specifier = "==0.5.1" },
     { name = "cartopy", specifier = ">=0.24.1" },
-    { name = "earth2studio", extras = ["data"], git = "https://github.com/mariusaurus/earth2studio.git?branch=mkoch%2Fte-aifs-ens" },
+    { name = "earth2studio", extras = ["data"], git = "https://github.com/mariusaurus/earth2studio.git?branch=mkoch%2Ftc_tracking" },
     { name = "earthkit-regrid", specifier = "==0.4.0" },
     { name = "hydra-core", specifier = ">=1.3.2" },
     { name = "importlib-metadata", specifier = ">=8.7.0" },
@@ -1706,8 +1706,8 @@ dependencies = [
     { name = "numpy" },
     { name = "nvidia-physicsnemo" },
     { name = "pynvml" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "torch-harmonics" },
     { name = "tqdm" },
@@ -2250,8 +2250,8 @@ dependencies = [
     { name = "s3fs" },
     { name = "setuptools" },
     { name = "timm" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "tqdm" },
     { name = "treelib" },
@@ -3412,8 +3412,8 @@ dependencies = [
     { name = "huggingface-hub" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
     { name = "torchvision" },
 ]
@@ -3436,19 +3436,17 @@ name = "torch"
 version = "2.7.1"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
-    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
     "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
     "python_full_version < '3.13' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "filelock", marker = "sys_platform == 'darwin'" },
-    { name = "fsspec", marker = "sys_platform == 'darwin'" },
-    { name = "jinja2", marker = "sys_platform == 'darwin'" },
-    { name = "networkx", marker = "sys_platform == 'darwin'" },
-    { name = "setuptools", marker = "sys_platform == 'darwin'" },
-    { name = "sympy", marker = "sys_platform == 'darwin'" },
-    { name = "typing-extensions", marker = "sys_platform == 'darwin'" },
+    { name = "filelock", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "fsspec", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "jinja2", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "networkx", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "setuptools", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "sympy", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "typing-extensions", marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
 ]
 wheels = [
     { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:7b4f8b2b83bd08f7d399025a9a7b323bdbb53d20566f1e0d584689bb92d82f9a" },
@@ -3465,15 +3463,17 @@ resolution-markers = [
     "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux'",
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux'",
     "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "filelock", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "fsspec", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "jinja2", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "networkx", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "setuptools", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "sympy", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "filelock", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "fsspec", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "jinja2", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "networkx", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "setuptools", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "sympy", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "typing-extensions", marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:0bc887068772233f532b51a3e8c8cfc682ae62bef74bf4e0c53526c8b9e4138f" },
@@ -3550,8 +3550,8 @@ version = "0.8.1"
 source = { git = "https://github.com/NVIDIA/torch-harmonics.git#6b8301cc6e9d04af114f5d87debd184b7810a33d" }
 dependencies = [
     { name = "numpy" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
 ]
 
@@ -3562,8 +3562,8 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "pillow" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin' and sys_platform != 'linux'" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
 ]
 wheels = [

From 92896eb9e45b1ead494bab8af2938822c2855e7d Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 13 Oct 2025 08:31:45 -0700
Subject: [PATCH 31/69] seems to work now

---
 recipes/tc_tracking/cfg/helene.yaml           |  27 ++-
 recipes/tc_tracking/cfg/reproduce_helene.yaml |   5 +-
 recipes/tc_tracking/src/generate_ensembles.py | 178 +++++++-----------
 3 files changed, 79 insertions(+), 131 deletions(-)

diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index 778b0f19c..bf867f1f6 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -1,6 +1,6 @@
 project: 'helene'
 mode: 'generate_ensemble'
-model: 'aifs-ens' # 'aifs-ens' 'fcn3' 'sfno'
+model: 'fcn3' # 'fcn3' 'sfno'
 random_seed: 7777
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
@@ -15,24 +15,21 @@ model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_201
 orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
-    # _target_: src.data.localh5.LocalArchiveHDF5
-    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
-    # _target_: earth2studio.data.NCAR_ERA5
+    _target_: earth2studio.data.NCAR_ERA5
     # _target_: earth2studio.data.GFS
     # _target_: earth2studio.data.IFS
-    _target_: earth2studio.data.CDS
+    # _target_: earth2studio.data.CDS
 
 store_type: "netcdf"
 store_dir: "./outputs"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
-# cyclone_tracking:
-#     asynchronous: True
-#     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
-#     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
-#     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-#     keep_raw_data: False
-#     use_ram: True
-#     task_timeout_seconds: 120
-#     print_te_output: False
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index 9430727de..5be6f8f96 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -1,6 +1,6 @@
 project: 'helene'
 mode: 'reproduce_members'
-model: 'sfno'
+model: 'fcn3'
 random_seed: 7552
 
 reproduce_members: [
@@ -16,9 +16,6 @@ model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_201
 orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
-    # _target_: src.data.localh5.LocalArchiveHDF5
-    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
     _target_: earth2studio.data.NCAR_ERA5
     # _target_: earth2studio.data.GFS
 
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index ee14a74e9..fc27af8e6 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -1,35 +1,30 @@
 import os
-import random
-from math import ceil
-
-import numpy as np
 import torch
-from omegaconf import OmegaConf
-from physicsnemo.distributed import DistributedManager
+import numpy as np
 from tqdm import tqdm
 from zarr import consolidate_metadata
+from omegaconf import OmegaConf
+import random
+
+from physicsnemo.distributed import DistributedManager
 
-from earth2studio.data import fetch_data
 from earth2studio.models.auto import Package
-from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
+from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
+from earth2studio.data import fetch_data
 from earth2studio.perturbation import SphericalGaussian
-from src.data.file_output import initialise_netcdf_output, setup_output, write_to_store
+
 from src.data.utils import DataSourceManager, load_heights
-from src.utils import (
-    get_set_of_random_seeds,
-    remove_duplicates,
-    run_with_rank_ordered_execution,
-    set_initial_times,
-)
+from src.data.file_output import setup_output, initialise_netcdf_output, write_to_store
+from src.utils import run_with_rank_ordered_execution, set_initial_times, remove_duplicates, get_set_of_random_seeds
 
 
 def initialise(cfg):
-    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"]="expandable_segments:True"
 
     DistributedManager.initialize()
 
     # set seed
-    if "random_seed" in cfg:
+    if 'random_seed' in cfg:
         torch.manual_seed(cfg.random_seed)
         np.random.seed(cfg.random_seed)
         random.seed(cfg.random_seed)
@@ -39,28 +34,23 @@ def initialise(cfg):
 
 
 def load_model(cfg):
-    model = "fcn3"
-    if "model" in cfg:
+    model = 'fcn3'
+    if 'model' in cfg:
         model = cfg.model
 
-    if model == "fcn3":
+    if model == 'fcn3':
         from earth2studio.models.px import FCN3
-
         package = Package(cfg.model_package)
         model = FCN3.load_model(package)
-    elif model == "sfno":
+    elif model == 'sfno':
         from earth2studio.models.px import SFNO
-
-        package = Package(
-            "/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72"
-        )
+        package = Package('/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72')
         model = SFNO.load_model(package)
-    elif model == "aifs-ens":
+    elif model == 'aifs-ens':
         from earth2studio.models.px import AIFSENS
-
         model = AIFSENS.load_model(AIFSENS.load_default_package())
     else:
-        raise ValueError(f"model {model} not supported")
+        raise ValueError(f'model {model} not supported')
 
     return model.to(DistributedManager().device)
 
@@ -71,41 +61,31 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
     # data_source = hydra.utils.instantiate(cfg.data_source)
     data_source_mngr = DataSourceManager(cfg)
 
-    # iterate over initial conditions
+    #iterate over initial conditions
     ic_prev = None
 
     cyclone_tracking = None
     if "cyclone_tracking" in cfg:
         oco = model.output_coords(model.input_coords())
 
-        heights, height_coords = (
-            load_heights(cfg.orography_path)
-            if "orography_path" in cfg
-            else (None, None)
-        )
-
-        tracker = (
-            AsyncTempestExtremes
-            if cfg.cyclone_tracking.asynchronous
-            else TempestExtremes
-        )
-        cyclone_tracking = tracker(
-            detect_cmd=cfg.cyclone_tracking.detect_cmd,
-            stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
-            input_vars=cfg.cyclone_tracking.vars,
-            batch_size=cfg.batch_size,
-            n_steps=cfg.n_steps,
-            time_step=oco["lead_time"][0],
-            lats=oco["lat"],
-            lons=oco["lon"],
-            static_vars=heights,
-            static_coords=height_coords,
-            store_dir=cfg.store_dir,
-            keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
-            print_te_output=cfg.cyclone_tracking.print_te_output,
-            use_ram=cfg.cyclone_tracking.use_ram,
-            timeout=cfg.cyclone_tracking.task_timeout_seconds,
-        )
+        heights, height_coords = load_heights(cfg.orography_path) if "orography_path" in cfg else (None, None)
+
+        tracker = AsyncTempestExtremes if cfg.cyclone_tracking.asynchronous else TempestExtremes
+        cyclone_tracking = tracker(detect_cmd=cfg.cyclone_tracking.detect_cmd,
+                                   stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
+                                   input_vars=cfg.cyclone_tracking.vars,
+                                   batch_size=cfg.batch_size,
+                                   n_steps=cfg.n_steps,
+                                   time_step=oco['lead_time'][0],
+                                   lats=oco['lat'],
+                                   lons=oco['lon'],
+                                   static_vars=heights,
+                                   static_coords=height_coords,
+                                   store_dir=cfg.store_dir,
+                                   keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
+                                   print_te_output=cfg.cyclone_tracking.print_te_output,
+                                   use_ram=cfg.cyclone_tracking.use_ram,
+                                   timeout=cfg.cyclone_tracking.task_timeout_seconds)
 
     for ic, mems, seed in ic_mems:
         mini_batch_size = len(mems)
@@ -114,47 +94,40 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
 
         # if new IC, fetch data, create iterator
         if ic != ic_prev:
-            print(model.variables)
-            # exit()
             if cfg.store_type == "netcdf":
                 store = initialise_netcdf_output(cfg, out_coords, ic, ic_mems)
-            x0, coords0 = fetch_data(
-                data_source,
-                time=[np.datetime64(ic)],
-                lead_time=model.input_coords()["lead_time"],
-                variable=model.variables,
-                device=dist.device,
-            )
+            x0, coords0 = fetch_data(data_source,
+                                     time=[np.datetime64(ic)],
+                                     lead_time=model.input_coords()['lead_time'],
+                                     variable=model.variables,
+                                     device=dist.device)
             ic_prev = ic
-            exit()
 
         coords = {"ensemble": np.array(mems)} | coords0.copy()
         xx = x0.unsqueeze(0).repeat(mini_batch_size, *([1] * x0.ndim))
 
         # set random state or apply perturbation
-        if (not "model" in cfg) or (cfg.model == "fcn3"):
+        if (not 'model' in cfg) or (cfg.model == 'fcn3'):
             model.set_rng(seed=seed)
         else:
             sg = SphericalGaussian(noise_amplitude=0.0005)
             xx, coords = sg(xx, coords)
 
+
         iterator = model.create_iterator(xx, coords)
 
         # roll out the model and record data as desired
-        for _, (xx, coords) in tqdm(
-            zip(range(cfg.n_steps + 1), iterator), total=cfg.n_steps + 1
-        ):
+        for _, (xx, coords) in tqdm(zip(range(cfg.n_steps+1), iterator), total=cfg.n_steps+1):
             write_to_store(store, xx, coords, out_coords)
             if cyclone_tracking:
                 cyclone_tracking.record_state(xx, coords)
 
         if cyclone_tracking:
-            cyclone_tracking(
-                out_file_names=[
-                    f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv"
-                    for mem in mems
-                ]
-            )
+            cyclone_tracking(out_file_names=[f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv" for mem in mems])
+
+    # Wait for all async cyclone tracking tasks to complete before continuing
+    if cyclone_tracking and isinstance(cyclone_tracking, AsyncTempestExtremes):
+        cyclone_tracking.cleanup()
 
     # Consolidate metadata in zarr files
     if dist.rank == 0 and cfg.store_type == "zarr":
@@ -176,7 +149,7 @@ def distribute_runs(ic_mems):
     ic_mems = ic_mems[dist.rank * ic_mems_per_rank : (dist.rank + 1) * ic_mems_per_rank]
 
     if len(ic_mems) == 0:
-        print(f"nothing to do for rank {dist.rank}, exiting")
+        print(f'nothing to do for rank {dist.rank}, exiting')
         ic_mems = None
 
     return ic_mems
@@ -187,17 +160,15 @@ def configure_runs(cfg):
 
     ics = set_initial_times(cfg)
 
-    seeds = get_set_of_random_seeds(
-        n_ics=len(ics),
-        ensemble_size=cfg.ensemble_size,
-        batch_size=cfg.batch_size,
-        seed=cfg.random_seed if "random_seed" in cfg else None,
-    )
+    seeds = get_set_of_random_seeds(n_ics=len(ics),
+                                    ensemble_size=cfg.ensemble_size,
+                                    batch_size=cfg.batch_size,
+                                    seed=cfg.random_seed if 'random_seed' in cfg else None)
 
     ii = 0
     for ic in ics:
         for mem in range(0, cfg.ensemble_size, cfg.batch_size):
-            mems = np.arange(mem, min(mem + cfg.batch_size, cfg.ensemble_size))
+            mems = np.arange(mem, min(mem+cfg.batch_size, cfg.ensemble_size))
             ic_mems.append((ic, mems, int(seeds[ii])))
             ii += 1
 
@@ -219,11 +190,8 @@ def set_reproduction_configs(cfg):
         ics.append(ic_mems[ii][0])
 
         # get full batches that include members which shall be reproduced
-        batch_id = ic_mems[ii][1] // cfg.batch_size
-        ic_mems[ii][1] = np.arange(
-            batch_id * cfg.batch_size,
-            min((batch_id + 1) * cfg.batch_size, cfg.ensemble_size),
-        )
+        batch_id = ic_mems[ii][1]//cfg.batch_size
+        ic_mems[ii][1] = np.arange(batch_id*cfg.batch_size, min((batch_id+1)*cfg.batch_size, cfg.ensemble_size))
 
     # remove duplicates
     ic_mems = remove_duplicates(ic_mems)
@@ -246,15 +214,8 @@ def generate_ensemble(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = (
-        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-            setup_output,
-            cfg=cfg,
-            model=model,
-            ics=ics,
-            add_arrays=DistributedManager().rank == 0,
-        )
-    )
+    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -266,8 +227,8 @@ def generate_ensemble(cfg):
 
 
 def reproduce_members(cfg):
-    if cfg.store_type == "zarr":
-        raise ValueError("Zarr output not suported for reproducing ensemble members")
+    if cfg.store_type == 'zarr':
+        raise ValueError('Zarr output not suported for reproducing ensemble members')
 
     initialise(cfg)
 
@@ -276,15 +237,8 @@ def reproduce_members(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = (
-        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-            setup_output,
-            cfg=cfg,
-            model=model,
-            ics=ics,
-            add_arrays=DistributedManager().rank == 0,
-        )
-    )
+    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -292,4 +246,4 @@ def reproduce_members(cfg):
 
     store = run_inference(model, cfg, store, out_coords, ic_mems)
 
-    return
+    return
\ No newline at end of file

From 9e0e106feac974f9e2e1f52ecff205721ef23976 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 14 Oct 2025 10:26:36 +0200
Subject: [PATCH 32/69] wind gust from HRRR analysis

---
 earth2studio/lexicon/hrrr.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/earth2studio/lexicon/hrrr.py b/earth2studio/lexicon/hrrr.py
index 2c691131e..29fd8ff87 100644
--- a/earth2studio/lexicon/hrrr.py
+++ b/earth2studio/lexicon/hrrr.py
@@ -55,6 +55,7 @@ def build_vocab() -> dict[str, str]:
             "cicep": "wrfsfc::CICEP::surface::anl",
             "cfrzr": "wrfsfc::CFRZR::surface::anl",
             "crain": "wrfsfc::CRAIN::surface::anl",
+            "fg10m": "wrfsfc::GUST::surface::anl",
         }
         prs_levels = [
             50,

From 5550ad9e3c24033060ba7268fe7e9bc9b6c50451 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 14 Oct 2025 03:39:08 -0700
Subject: [PATCH 33/69] minor updates

---
 recipes/tc_tracking/Dockerfile                |  8 +++-----
 recipes/tc_tracking/README.md                 |  2 +-
 recipes/tc_tracking/cfg/helene.yaml           |  1 -
 recipes/tc_tracking/cfg/reproduce_helene.yaml |  1 -
 recipes/tc_tracking/src/generate_ensembles.py | 18 ++++++++++--------
 5 files changed, 14 insertions(+), 16 deletions(-)

diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile
index 3d5de337d..d27d9bb3a 100644
--- a/recipes/tc_tracking/Dockerfile
+++ b/recipes/tc_tracking/Dockerfile
@@ -27,11 +27,9 @@ RUN cmake .. && \
 
 
 # copy source into the container.
-RUN mkdir -p /fcn3_tcs
-COPY . /fcn3_tcs/
-
-# fcn3_tc
-WORKDIR /fcn3_tcs
+RUN mkdir -p /tc_tracking
+COPY . /tc_tracking
+WORKDIR /tc_tracking
 
 ENV FORCE_CUDA_EXTENSION 1
 ENV TORCH_CUDA_ARCH_LIST "8.0 8.6 9.0 10.0 12.0+PTX"
diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 95ba37830..0a8c811b5 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -1,4 +1,4 @@
-# FCN# cyclone tracking
+# cyclone tracking
 
 Baseline to acheive this is an ensembling setup. Due to the inherently stochastic nature of the model, initial condition perturbation is not required.
 
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index bf867f1f6..f8f4153e8 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -11,7 +11,6 @@ ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 n_steps: 15
 ensemble_size: 4
 batch_size: 2
-model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
 orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index 5be6f8f96..555b428e9 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -12,7 +12,6 @@ reproduce_members: [
 n_steps: 15
 ensemble_size: 15
 batch_size: 2
-model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
 orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index fc27af8e6..5a724eb7e 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -38,21 +38,23 @@ def load_model(cfg):
     if 'model' in cfg:
         model = cfg.model
 
+    # select model
     if model == 'fcn3':
         from earth2studio.models.px import FCN3
-        package = Package(cfg.model_package)
-        model = FCN3.load_model(package)
+        model = FCN3
     elif model == 'sfno':
         from earth2studio.models.px import SFNO
-        package = Package('/lustre/fsw/coreai_climate_earth2/mkoch/hens/hens_checkpoints/sfno_linear_74chq_sc2_layers8_edim620_wstgl2-epoch70_seed72')
-        model = SFNO.load_model(package)
-    elif model == 'aifs-ens':
-        from earth2studio.models.px import AIFSENS
-        model = AIFSENS.load_model(AIFSENS.load_default_package())
+        model = SFNO
     else:
         raise ValueError(f'model {model} not supported')
 
-    return model.to(DistributedManager().device)
+    # load weights
+    pkg = Package(cfg.model_package) if 'model_package' in cfg else model.load_default_package()
+
+    # load full model
+    model = model.load_model(pkg).to(DistributedManager().device)
+
+    return model
 
 
 def run_inference(model, cfg, store, out_coords, ic_mems):

From 869b8fe34bfb9534ac7540da02cdbace1314e326 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 24 Oct 2025 02:24:39 -0700
Subject: [PATCH 34/69] stability test

---
 .../cfg/reproduce_stability_test.yaml         |  42 ++++
 recipes/tc_tracking/cfg/stability_test.yaml   |  38 ++++
 recipes/tc_tracking/set_envs.sh               |   3 +
 recipes/tc_tracking/src/generate_ensembles.py | 196 ++++++++++++------
 recipes/tc_tracking/src/utils.py              |  68 ++++++
 5 files changed, 285 insertions(+), 62 deletions(-)
 create mode 100644 recipes/tc_tracking/cfg/reproduce_stability_test.yaml
 create mode 100644 recipes/tc_tracking/cfg/stability_test.yaml
 create mode 100644 recipes/tc_tracking/set_envs.sh

diff --git a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
new file mode 100644
index 000000000..5412f4b57
--- /dev/null
+++ b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
@@ -0,0 +1,42 @@
+project: 'stability_test'
+mode: 'reproduce_members'
+model: 'fcn3' # 'aifs-ens' 'fcn3' 'sfno'
+random_seed: 7778
+
+reproduce_members: [
+                    ['2023-05-01 00:00:00',  84, 481776871],
+                    ['2023-05-01 00:00:00',  85, 481776871],
+                    ]
+
+n_steps: 300
+ensemble_size: 96
+batch_size: 2
+# model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
+orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+
+data_source:
+    # _target_: src.data.localh5.LocalArchiveHDF5
+    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+
+
+store_type: "none"
+store_dir:  "./outputs"
+out_vars: ['t1000', 'u250', 'v850']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
+
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
diff --git a/recipes/tc_tracking/cfg/stability_test.yaml b/recipes/tc_tracking/cfg/stability_test.yaml
new file mode 100644
index 000000000..5df45e927
--- /dev/null
+++ b/recipes/tc_tracking/cfg/stability_test.yaml
@@ -0,0 +1,38 @@
+project: 'stability_test'
+mode: 'generate_ensemble'
+model: 'fcn3' # 'aifs-ens' 'fcn3' 'sfno'
+random_seed: 7778
+
+# ic_block_start: "2025-08-29 06:00:00" # first IC
+# ic_block_end: "2025-10-01 00:00:00" # upper boundary for ICs
+# ic_block_step: 6            # number of hours between individual ICs
+ics: ["2023-05-01 00:00:00"]
+
+n_steps: 300 #280
+ensemble_size: 96 # 1024 # 43
+batch_size: 2
+# model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
+orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+
+data_source:
+    # _target_: src.data.localh5.LocalArchiveHDF5
+    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+
+
+store_type: "netcdf"
+store_dir:  "./outputs"
+out_vars: ['t1000', 'u250', 'v850']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
diff --git a/recipes/tc_tracking/set_envs.sh b/recipes/tc_tracking/set_envs.sh
new file mode 100644
index 000000000..e324a0ddc
--- /dev/null
+++ b/recipes/tc_tracking/set_envs.sh
@@ -0,0 +1,3 @@
+export LOGURU_LEVEL=INFO
+export PYTHONWARNINGS=ignore
+export HYDRA_FULL_ERROR=1
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 5a724eb7e..7445806fc 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -1,30 +1,37 @@
 import os
-import torch
-import numpy as np
-from tqdm import tqdm
-from zarr import consolidate_metadata
-from omegaconf import OmegaConf
 import random
+from copy import deepcopy
 
+import numpy as np
+import torch
+from omegaconf import OmegaConf
 from physicsnemo.distributed import DistributedManager
+from tqdm import tqdm
+from zarr import consolidate_metadata
 
-from earth2studio.models.auto import Package
-from earth2studio.models.dx import TempestExtremes, AsyncTempestExtremes
 from earth2studio.data import fetch_data
+from earth2studio.models.auto import Package
+from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 from earth2studio.perturbation import SphericalGaussian
-
+from earth2studio.utils.coords import map_coords
+from src.data.file_output import initialise_netcdf_output, setup_output, write_to_store
 from src.data.utils import DataSourceManager, load_heights
-from src.data.file_output import setup_output, initialise_netcdf_output, write_to_store
-from src.utils import run_with_rank_ordered_execution, set_initial_times, remove_duplicates, get_set_of_random_seeds
+from src.utils import (
+    InstabilityDetection,
+    get_set_of_random_seeds,
+    remove_duplicates,
+    run_with_rank_ordered_execution,
+    set_initial_times,
+)
 
 
 def initialise(cfg):
-    os.environ["PYTORCH_CUDA_ALLOC_CONF"]="expandable_segments:True"
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 
     DistributedManager.initialize()
 
     # set seed
-    if 'random_seed' in cfg:
+    if "random_seed" in cfg:
         torch.manual_seed(cfg.random_seed)
         np.random.seed(cfg.random_seed)
         random.seed(cfg.random_seed)
@@ -34,22 +41,28 @@ def initialise(cfg):
 
 
 def load_model(cfg):
-    model = 'fcn3'
-    if 'model' in cfg:
+    model = "fcn3"
+    if "model" in cfg:
         model = cfg.model
 
     # select model
-    if model == 'fcn3':
+    if model == "fcn3":
         from earth2studio.models.px import FCN3
+
         model = FCN3
-    elif model == 'sfno':
+    elif model == "sfno":
         from earth2studio.models.px import SFNO
+
         model = SFNO
     else:
-        raise ValueError(f'model {model} not supported')
+        raise ValueError(f"model {model} not supported")
 
     # load weights
-    pkg = Package(cfg.model_package) if 'model_package' in cfg else model.load_default_package()
+    pkg = (
+        Package(cfg.model_package)
+        if "model_package" in cfg
+        else model.load_default_package()
+    )
 
     # load full model
     model = model.load_model(pkg).to(DistributedManager().device)
@@ -63,31 +76,48 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
     # data_source = hydra.utils.instantiate(cfg.data_source)
     data_source_mngr = DataSourceManager(cfg)
 
-    #iterate over initial conditions
+    # iterate over initial conditions
     ic_prev = None
 
     cyclone_tracking = None
     if "cyclone_tracking" in cfg:
         oco = model.output_coords(model.input_coords())
 
-        heights, height_coords = load_heights(cfg.orography_path) if "orography_path" in cfg else (None, None)
-
-        tracker = AsyncTempestExtremes if cfg.cyclone_tracking.asynchronous else TempestExtremes
-        cyclone_tracking = tracker(detect_cmd=cfg.cyclone_tracking.detect_cmd,
-                                   stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
-                                   input_vars=cfg.cyclone_tracking.vars,
-                                   batch_size=cfg.batch_size,
-                                   n_steps=cfg.n_steps,
-                                   time_step=oco['lead_time'][0],
-                                   lats=oco['lat'],
-                                   lons=oco['lon'],
-                                   static_vars=heights,
-                                   static_coords=height_coords,
-                                   store_dir=cfg.store_dir,
-                                   keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
-                                   print_te_output=cfg.cyclone_tracking.print_te_output,
-                                   use_ram=cfg.cyclone_tracking.use_ram,
-                                   timeout=cfg.cyclone_tracking.task_timeout_seconds)
+        heights, height_coords = (
+            load_heights(cfg.orography_path)
+            if "orography_path" in cfg
+            else (None, None)
+        )
+
+        tracker = (
+            AsyncTempestExtremes
+            if cfg.cyclone_tracking.asynchronous
+            else TempestExtremes
+        )
+        cyclone_tracking = tracker(
+            detect_cmd=cfg.cyclone_tracking.detect_cmd,
+            stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
+            input_vars=cfg.cyclone_tracking.vars,
+            batch_size=cfg.batch_size,
+            n_steps=cfg.n_steps,
+            time_step=oco["lead_time"][0],
+            lats=oco["lat"],
+            lons=oco["lon"],
+            static_vars=heights,
+            static_coords=height_coords,
+            store_dir=cfg.store_dir,
+            keep_raw_data=cfg.cyclone_tracking.keep_raw_data,
+            print_te_output=cfg.cyclone_tracking.print_te_output,
+            use_ram=cfg.cyclone_tracking.use_ram,
+            timeout=cfg.cyclone_tracking.task_timeout_seconds,
+        )
+
+    stability_check = None
+    if "stability_check" in cfg:
+        stability_check = InstabilityDetection(
+            vars=np.array(cfg.stability_check.variables),
+            thresholds=np.array(cfg.stability_check.thresholds),
+        )
 
     for ic, mems, seed in ic_mems:
         mini_batch_size = len(mems)
@@ -98,34 +128,57 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
         if ic != ic_prev:
             if cfg.store_type == "netcdf":
                 store = initialise_netcdf_output(cfg, out_coords, ic, ic_mems)
-            x0, coords0 = fetch_data(data_source,
-                                     time=[np.datetime64(ic)],
-                                     lead_time=model.input_coords()['lead_time'],
-                                     variable=model.variables,
-                                     device=dist.device)
+            x0, coords0 = fetch_data(
+                data_source,
+                time=[np.datetime64(ic)],
+                lead_time=model.input_coords()["lead_time"],
+                variable=model.variables,
+                device=dist.device,
+            )
             ic_prev = ic
 
         coords = {"ensemble": np.array(mems)} | coords0.copy()
         xx = x0.unsqueeze(0).repeat(mini_batch_size, *([1] * x0.ndim))
 
+        if stability_check:
+            stability_check.reset(deepcopy(coords))
+        # print(stability_check.input_coords)
+        # exit()
+
         # set random state or apply perturbation
-        if (not 'model' in cfg) or (cfg.model == 'fcn3'):
+        if (not "model" in cfg) or (cfg.model == "fcn3"):
             model.set_rng(seed=seed)
         else:
             sg = SphericalGaussian(noise_amplitude=0.0005)
             xx, coords = sg(xx, coords)
 
-
         iterator = model.create_iterator(xx, coords)
 
         # roll out the model and record data as desired
-        for _, (xx, coords) in tqdm(zip(range(cfg.n_steps+1), iterator), total=cfg.n_steps+1):
+        for _, (xx, coords) in tqdm(
+            zip(range(cfg.n_steps + 1), iterator), total=cfg.n_steps + 1
+        ):
             write_to_store(store, xx, coords, out_coords)
             if cyclone_tracking:
                 cyclone_tracking.record_state(xx, coords)
 
-        if cyclone_tracking:
-            cyclone_tracking(out_file_names=[f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv" for mem in mems])
+            if stability_check:
+                yy, coy = map_coords(xx, coords, stability_check.input_coords)
+                stab, _ = stability_check(yy, coy)
+                if not stab.all():
+                    ic_mems.append((ic, mems, seed + 1))
+                    print(
+                        f"CAUTION: one of members {mems} became unstable. will re-create with new seed."
+                    )
+                    break
+
+        if cyclone_tracking and stab.all():
+            cyclone_tracking(
+                out_file_names=[
+                    f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv"
+                    for mem in mems
+                ]
+            )
 
     # Wait for all async cyclone tracking tasks to complete before continuing
     if cyclone_tracking and isinstance(cyclone_tracking, AsyncTempestExtremes):
@@ -151,7 +204,7 @@ def distribute_runs(ic_mems):
     ic_mems = ic_mems[dist.rank * ic_mems_per_rank : (dist.rank + 1) * ic_mems_per_rank]
 
     if len(ic_mems) == 0:
-        print(f'nothing to do for rank {dist.rank}, exiting')
+        print(f"nothing to do for rank {dist.rank}, exiting")
         ic_mems = None
 
     return ic_mems
@@ -162,15 +215,17 @@ def configure_runs(cfg):
 
     ics = set_initial_times(cfg)
 
-    seeds = get_set_of_random_seeds(n_ics=len(ics),
-                                    ensemble_size=cfg.ensemble_size,
-                                    batch_size=cfg.batch_size,
-                                    seed=cfg.random_seed if 'random_seed' in cfg else None)
+    seeds = get_set_of_random_seeds(
+        n_ics=len(ics),
+        ensemble_size=cfg.ensemble_size,
+        batch_size=cfg.batch_size,
+        seed=cfg.random_seed if "random_seed" in cfg else None,
+    )
 
     ii = 0
     for ic in ics:
         for mem in range(0, cfg.ensemble_size, cfg.batch_size):
-            mems = np.arange(mem, min(mem+cfg.batch_size, cfg.ensemble_size))
+            mems = np.arange(mem, min(mem + cfg.batch_size, cfg.ensemble_size))
             ic_mems.append((ic, mems, int(seeds[ii])))
             ii += 1
 
@@ -192,8 +247,11 @@ def set_reproduction_configs(cfg):
         ics.append(ic_mems[ii][0])
 
         # get full batches that include members which shall be reproduced
-        batch_id = ic_mems[ii][1]//cfg.batch_size
-        ic_mems[ii][1] = np.arange(batch_id*cfg.batch_size, min((batch_id+1)*cfg.batch_size, cfg.ensemble_size))
+        batch_id = ic_mems[ii][1] // cfg.batch_size
+        ic_mems[ii][1] = np.arange(
+            batch_id * cfg.batch_size,
+            min((batch_id + 1) * cfg.batch_size, cfg.ensemble_size),
+        )
 
     # remove duplicates
     ic_mems = remove_duplicates(ic_mems)
@@ -216,8 +274,15 @@ def generate_ensemble(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = (
+        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+            setup_output,
+            cfg=cfg,
+            model=model,
+            ics=ics,
+            add_arrays=DistributedManager().rank == 0,
+        )
+    )
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -229,8 +294,8 @@ def generate_ensemble(cfg):
 
 
 def reproduce_members(cfg):
-    if cfg.store_type == 'zarr':
-        raise ValueError('Zarr output not suported for reproducing ensemble members')
+    if cfg.store_type == "zarr":
+        raise ValueError("Zarr output not suported for reproducing ensemble members")
 
     initialise(cfg)
 
@@ -239,8 +304,15 @@ def reproduce_members(cfg):
     model = load_model(cfg)
 
     # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
-    store, out_coords = run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
-        setup_output, cfg=cfg, model=model, ics=ics, add_arrays=DistributedManager().rank == 0)
+    store, out_coords = (
+        run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
+            setup_output,
+            cfg=cfg,
+            model=model,
+            ics=ics,
+            add_arrays=DistributedManager().rank == 0,
+        )
+    )
 
     if ic_mems is None:
         DistributedManager().cleanup()
@@ -248,4 +320,4 @@ def reproduce_members(cfg):
 
     store = run_inference(model, cfg, store, out_coords, ic_mems)
 
-    return
\ No newline at end of file
+    return
diff --git a/recipes/tc_tracking/src/utils.py b/recipes/tc_tracking/src/utils.py
index 99676eb1f..bdf50acac 100644
--- a/recipes/tc_tracking/src/utils.py
+++ b/recipes/tc_tracking/src/utils.py
@@ -1,3 +1,4 @@
+from collections import OrderedDict
 from collections.abc import Callable
 from math import ceil
 from typing import Any
@@ -156,3 +157,70 @@ def great_circle_distance(lat1, lon1, lat2, lon2):
     cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1 - aa))
 
     return 6371000 * cc
+
+
+class InstabilityDetection:
+    def __init__(
+        self,
+        vars: list[str],
+        thresholds: list[float],
+        input_coords: OrderedDict | None = None,
+    ) -> None:
+
+        self.vars = vars
+        self.thresh = torch.Tensor(thresholds)
+        self.reset(input_coords)
+
+        if not len(vars) == len(thresholds):
+            raise ValueError("please provide exactly one threshold per variable")
+
+    def reset(self, coords: OrderedDict = None):
+        self.baseline = None
+        self._input_coords = None
+        self._output_coords = None
+
+        if coords:
+            self.update_coords(coords)
+
+    def update_coords(self, coords):
+        # scrub time and lead_time dimensions if exist
+        coords.pop("time", None)
+        coords.pop("lead_time", None)
+        self._output_coords = OrderedDict({"ensemble": coords.pop("ensemble", None)})
+
+        self._input_coords = coords
+        self._input_coords["variable"] = self.vars
+
+    @property
+    def input_coords(self):
+        return self._input_coords
+
+    @property
+    def output_coords(self):
+        return self._output_coords
+
+    def __call__(self, xx: torch.Tensor, coords: OrderedDict):
+
+        var_dim = list(coords).index("variable")
+        batch_dim = list(coords).index("ensemble") if "ensemble" in coords else None
+        if "ensemble" in coords:
+            batch_dim = list(coords).index("ensemble")
+
+        if self.baseline is None:
+            self.baseline = xx.mean(
+                dim=tuple(
+                    [ii for ii in range(len(coords)) if not ii in (var_dim, batch_dim)]
+                )
+            )
+            comp = self.baseline
+            self.update_coords(coords)
+        else:
+            comp = xx.mean(
+                dim=tuple(
+                    [ii for ii in range(len(coords)) if not ii in (var_dim, batch_dim)]
+                )
+            )
+
+        return (torch.abs(comp - self.baseline) < self.thresh.to(xx.device)).all(
+            dim=-1
+        ), self._output_coords

From 343d0353a79f3d1c9ddaf6eb3f865fbfe9af88a3 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 24 Oct 2025 03:03:18 -0700
Subject: [PATCH 35/69] version check for torch-harmonics import

---
 earth2studio/models/px/fcn3.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py
index 653d9ba1e..33aec462c 100644
--- a/earth2studio/models/px/fcn3.py
+++ b/earth2studio/models/px/fcn3.py
@@ -21,6 +21,7 @@
 import numpy as np
 import torch
 from loguru import logger
+from packaging.version import Version
 
 from earth2studio.models.auto import AutoModelMixin, Package
 from earth2studio.models.batch import batch_coords, batch_func
@@ -52,6 +53,15 @@
     load_model_package = None
     _cuda_extension_available = False
 
+if Version(torch_harmonics.__version__) >= Version("0.8.1"):
+    from torch_harmonics.disco import cuda_kernels_is_available
+
+    _cuda_extension_available = cuda_kernels_is_available()
+else:
+    from importlib.util import find_spec
+
+    _cuda_extension_available = find_spec("disco_cuda_extension") is not None
+
 
 VARIABLES = [
     "u10m",

From 037a5a779b36d36cf7592372be23817f060f0795 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 30 Oct 2025 11:37:51 +0100
Subject: [PATCH 36/69] addressed greptile comments

---
 docs/userguide/about/install.md            |   2 +-
 earth2studio/models/dx/__init__.py         |   5 +-
 earth2studio/models/dx/tempest_extremes.py |  32 +--
 earth2studio/utils/coords.py               |   8 +
 test/utils/test_coords.py                  | 224 +++++++++++++++++++++
 5 files changed, 254 insertions(+), 17 deletions(-)

diff --git a/docs/userguide/about/install.md b/docs/userguide/about/install.md
index a07cae5e6..abef51442 100644
--- a/docs/userguide/about/install.md
+++ b/docs/userguide/about/install.md
@@ -501,7 +501,7 @@ separately by the user. Installation instructions can be found on the
 [TempestExtremes GitHub page](https://github.com/ClimateGlobalChange/tempestextremes?tab=readme-ov-file#installation-via-cmake-recommended).
 
 By default, `TempestExtremes` binaries are installed to the bin folder of the
-`TempestExtremes` repository. Users can specify paths to executibles by passing
+`TempestExtremes` repository. Users can specify paths to executables by passing
 the detect and stitch commands to the initialisation of the `TempestExtremes`
 class in earth2studio. Examples for both commands are provided in the docstring
 of this class.
diff --git a/earth2studio/models/dx/__init__.py b/earth2studio/models/dx/__init__.py
index 7625f367c..509d56476 100644
--- a/earth2studio/models/dx/__init__.py
+++ b/earth2studio/models/dx/__init__.py
@@ -39,18 +39,19 @@
     TCTrackerWuDuan,
 )
 from earth2studio.models.dx.tempest_extremes import (
-    TempestExtremes,
     AsyncTempestExtremes,
+    TempestExtremes,
 )
 from earth2studio.models.dx.wind_gust import WindgustAFNO  # noqa
 
 __all__ = [
-    "ClimateNet",
+    "AsyncTempestExtremes" "ClimateNet",
     "CorrDiff",
     "CorrDiffTaiwan",
     "PrecipitationAFNO",
     "PrecipitationAFNOv2",
     "SolarRadiationAFNO1H",
     "SolarRadiationAFNO6H",
+    "TempestExtremes",
     "WindgustAFNO",
 ]
diff --git a/earth2studio/models/dx/tempest_extremes.py b/earth2studio/models/dx/tempest_extremes.py
index 9ca4b74df..db5e59257 100644
--- a/earth2studio/models/dx/tempest_extremes.py
+++ b/earth2studio/models/dx/tempest_extremes.py
@@ -35,19 +35,19 @@ class TempestExtremes:
     To be compatible with TE, model outputs are buffered as a netcdf file with the
     coordinates (time, lat, lon). Optionally, this netcdf file can be stored in RAM to
     avoid writing large amount of data to disk.
-    Since TE natively only provides a command-line interface, a subporcess is spawned
+    Since TE natively only provides a command-line interface, a subprocess is spawned
     calling TE. To provide users with as much flexibility as possible, the command to
-    execude TE has to be specified by the user and passed to the class during
+    execute TE has to be specified by the user and passed to the class during
     initialisation. Any arguments regarding input and output files will be ignored
     and automatically populated with the files holding the model output.
-    Additionally, this apporach allows the user to point the class to TE executibles
+    Additionally, this approach allows the user to point the class to TE executables
     at any location of the system.
-    TE is purely CPU-based, but for most appliations still faster than producing the
+    TE is purely CPU-based, but for most applications still faster than producing the
     atmospheric data on the GPU. AsyncTempestExtremes provides an implementation in
-    which a CPU thread applies TE to the gerneated data, while the GPU continues
+    which a CPU thread applies TE to the generated data, while the GPU continues
     producing the next prediction.
 
-    The result will be a TE-genereated track file, stored in a user-defined location
+    The result will be a TE-generated track file, stored in a user-defined location
 
     NOTE:
         - currently works on batch size 1 only (currently worked on)
@@ -61,11 +61,11 @@ class generates.
     ----------
     detect_cmd : str
         TempestExtremes DetectNodes command with arguments
-        Note that --in_data_list and --out_file_list will be ignored if provdied
+        Note that --in_data_list and --out_file_list will be ignored if provided
         example: "/path/to/DetectNodes --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0"
     stitch_cmd : str
         TempestExtremes StitchNodes command with arguments
-        Note that --in and --out will be ignored if provdied
+        Note that --in and --out will be ignored if provided
         example: "/path/to/StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10"
     input_vars :
         List of variables which are required for the tracking algorithm
@@ -220,8 +220,8 @@ def dependency_failure(
             + "Don't panic, this is usually an easy fix.[/blue]"
         )
         table.add_row(
-            "[yellow]The TempestExtremes class is marked needing optional dependencies'.\n\n"
-            + "unlike other dependencies, TempestExtremes has to be installed to the system by the user`\n\n"
+            "[yellow]The TempestExtremes class is marked needing optional dependencies.\n\n"
+            + "unlike other dependencies, TempestExtremes has to be installed to the system by the user.\n\n"
             + "For more information, visit the install documentation: \n"
             + f"{doc_url}[/yellow]"
         )
@@ -290,7 +290,12 @@ def setup_output_directories(self, use_ram: bool, store_dir: str) -> None:
                 )  # output for raw data
 
         if use_ram:
-            self.ram_dir = os.path.join("/dev/shm", "cyclone_tracking")  # noqa: S108
+            ram_path = "/dev/shm"  # noqa: S108
+            if not os.path.exists(ram_path):
+                raise ValueError(
+                    "/dev/shm not available. Set use_ram=False and use store_dir to specify a different location."  # noqa: S108
+                )
+            self.ram_dir = os.path.join(ram_path, "cyclone_tracking")
             if DistributedManager().local_rank == 0:
                 os.makedirs(
                     self.ram_dir, exist_ok=True
@@ -373,7 +378,7 @@ def dump_raw_data(self) -> tuple[list[str], np.ndarray]:
         """Dump raw data from store to NetCDF file.
 
         Morphs the store to TempestExtremes-compatible [time, lat, lon] structure
-        and writes to a (optianlly in-RAM) NetCDF file, which can be ingested by TE.
+        and writes to a (optionally in-RAM) NetCDF file, which can be ingested by TE.
 
         Returns
         -------
@@ -388,7 +393,7 @@ def dump_raw_data(self) -> tuple[list[str], np.ndarray]:
         ic = self.store.coords["time"][0]
         mems = self.store.coords["ensemble"]
 
-        # morph store to tempes extremes-compatible [time, lat, lon] structure
+        # morph store to TempestExtremes-compatible [time, lat, lon] structure
         lead_times = self.store.coords.pop("lead_time")
         self.store.coords["time"] = [ic + lt for lt in lead_times]
 
@@ -662,7 +667,6 @@ def cleanup_tempest_executor(timeout_per_task: int = 360) -> None:
     ChildProcessError
         If one or more background TempestExtremes tasks failed
     """
-    """Clean up the thread pool executor."""
     global _tempest_executor, _tempest_background_tasks
     with _tempest_executor_lock:
         if _tempest_executor is not None:
diff --git a/earth2studio/utils/coords.py b/earth2studio/utils/coords.py
index 1b3e7e9c2..46429eac7 100644
--- a/earth2studio/utils/coords.py
+++ b/earth2studio/utils/coords.py
@@ -459,12 +459,20 @@ def tile_xx_to_yy(
     ------
     ValueError
         If xx has more dimensions than yy
+    ValueError
+        If trailing coordinate keys of yy_coords do not match xx_coords keys
     """
     n_lead = len(yy.shape) - len(xx.shape)
 
     if n_lead < 0:
         raise ValueError("xx must have fewer dimensions than yy.")
 
+    if list(xx_coords.keys()) != list(yy_coords.keys())[-len(xx_coords) :]:
+        raise ValueError(
+            f"Trailing coordinate keys must match: xx_coords keys {list(xx_coords.keys())} "
+            f"!= yy_coords trailing keys {list(yy_coords.keys())[-len(xx_coords):]}"
+        )
+
     out_shape = yy.shape[:n_lead] + tuple([-1 for _ in range(len(xx.shape))])
     out_coords = deepcopy(yy_coords)
     for key, val in xx_coords.items():
diff --git a/test/utils/test_coords.py b/test/utils/test_coords.py
index 2fd10ca12..29a899cd4 100644
--- a/test/utils/test_coords.py
+++ b/test/utils/test_coords.py
@@ -445,6 +445,7 @@ def test_tile_xx_to_yy():
             "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(["u10m", "v10m"]),
             "lat": np.linspace(90, -90, 721),
             "lon": np.linspace(0, 360, 1440),
         }
@@ -461,6 +462,140 @@ def test_tile_xx_to_yy():
     assert "time" in result_coords
 
 
+def test_tile_xx_to_yy_failure():
+    """Test that tile_xx_to_yy fails when trailing coordinate keys don't match"""
+    xx = torch.randn(2, 721, 1440)
+    xx_coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    yy = torch.randn(3, 4, 5, 721, 1440)
+    # Trailing keys are ["lead_time", "lat", "lon"] but xx_coords has ["variable", "lat", "lon"]
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lead_time": np.array([0, 1, 2, 3, 4]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+
+    with pytest.raises(ValueError, match="Trailing coordinate keys must match"):
+        tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+
+
+def test_tile_xx_to_yy_edge_cases():
+    """Test edge cases for tile_xx_to_yy"""
+    # Test: xx has more dimensions than yy (should fail)
+    xx = torch.randn(2, 3, 4)
+    xx_coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.array([0, 1, 2]),
+            "c": np.array([0, 1, 2, 3]),
+        }
+    )
+    yy = torch.randn(2, 3)
+    yy_coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.array([0, 1, 2]),
+        }
+    )
+    with pytest.raises(ValueError, match="xx must have fewer dimensions than yy"):
+        tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+
+    # Test: dimension size mismatch for trailing dimensions (same names, different sizes)
+    xx = torch.randn(2, 721, 1440)
+    xx_coords = OrderedDict(
+        {
+            "variable": np.array(["z", "lsm"]),
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    yy = torch.randn(3, 4, 2, 361, 1440)  # Different lat size: 361 vs 721
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "variable": np.array(["a", "b"]),
+            "lat": np.linspace(90, -90, 361),  # Different size
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    # This should work because trailing keys match, but coordinate values might differ
+    # The function overwrites yy_coords values with xx_coords values
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+    assert result.shape == (3, 4, 2, 721, 1440)
+    assert np.array_equal(result_coords["lat"], xx_coords["lat"])
+
+    # Test: single dimension xx
+    xx = torch.randn(721)
+    xx_coords = OrderedDict({"lat": np.linspace(90, -90, 721)})
+    yy = torch.randn(3, 4, 721)
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "lat": np.linspace(90, -90, 721),
+        }
+    )
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+    assert result.shape == (3, 4, 721)
+    assert "lat" in result_coords
+
+    # Test: empty tensors (0-sized dimensions)
+    xx = torch.randn(0, 721, 1440)
+    xx_coords = OrderedDict(
+        {
+            "variable": np.array([]),  # Empty array
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    yy = torch.randn(3, 4, 0, 721, 1440)
+    yy_coords = OrderedDict(
+        {
+            "ensemble": np.array([0, 1, 2]),
+            "time": np.array([1, 2, 3, 4]),
+            "variable": np.array([]),  # Empty array
+            "lat": np.linspace(90, -90, 721),
+            "lon": np.linspace(0, 360, 1440),
+        }
+    )
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+    assert result.shape == (3, 4, 0, 721, 1440)
+    assert len(result_coords["variable"]) == 0
+
+    # Test: mismatched coordinate values (same names and sizes, different values)
+    # This should work - it just overwrites with xx_coords values
+    xx = torch.randn(2, 10)
+    xx_coords = OrderedDict(
+        {
+            "a": np.array([0, 1]),
+            "b": np.arange(10),
+        }
+    )
+    yy = torch.randn(3, 2, 10)
+    yy_coords = OrderedDict(
+        {
+            "c": np.array([0, 1, 2]),
+            "a": np.array([10, 20]),  # Different values
+            "b": np.arange(10, 20),  # Different values
+        }
+    )
+    result, result_coords = tile_xx_to_yy(xx, xx_coords, yy, yy_coords)
+    assert result.shape == (3, 2, 10)
+    assert np.array_equal(result_coords["a"], xx_coords["a"])
+    assert np.array_equal(result_coords["b"], xx_coords["b"])
+
+
 def test_cat_coords():
     """Test coordinate concatenation"""
 
@@ -489,3 +624,92 @@ def test_cat_coords():
     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, cox, yy, 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, cox, yy, 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_errors():
+    """Test error cases for 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),
+        }
+    )
+
+    # Test missing dimension in first coords
+    with pytest.raises(ValueError, match="dim.*is not in coords"):
+        cat_coords(xx, cox, yy, coy, dim="nonexistent")
+
+    # Test missing dimension in second coords
+    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(ValueError, match="dim.*is not in coords"):
+        cat_coords(xx_extra, cox_with_extra, yy, coy, dim="extra_dim")

From 608315be1e1e7eef618b9567728af7ca5ab9b290 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 30 Oct 2025 11:40:20 +0100
Subject: [PATCH 37/69] time import

---
 test/models/dx/test_tempest_extremes.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py
index b5f893c5a..d10d9e5f0 100644
--- a/test/models/dx/test_tempest_extremes.py
+++ b/test/models/dx/test_tempest_extremes.py
@@ -2,6 +2,7 @@
 
 import os
 import tempfile
+import time
 from collections import OrderedDict
 from unittest.mock import patch
 
@@ -494,8 +495,6 @@ def record():
 
         # Wait a bit and verify record hasn't completed
         record_started.wait(timeout=1)
-        import time
-
         time.sleep(0.1)
         assert not record_completed.is_set()
 

From 3f55702976556430d0ac55c38a276c09fa11711e Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 30 Oct 2025 11:41:54 +0100
Subject: [PATCH 38/69] comma

---
 earth2studio/models/dx/__init__.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/earth2studio/models/dx/__init__.py b/earth2studio/models/dx/__init__.py
index 509d56476..3217d3488 100644
--- a/earth2studio/models/dx/__init__.py
+++ b/earth2studio/models/dx/__init__.py
@@ -45,7 +45,8 @@
 from earth2studio.models.dx.wind_gust import WindgustAFNO  # noqa
 
 __all__ = [
-    "AsyncTempestExtremes" "ClimateNet",
+    "AsyncTempestExtremes",
+    "ClimateNet",
     "CorrDiff",
     "CorrDiffTaiwan",
     "PrecipitationAFNO",

From dc0cd799c7357aff140482ba90e0cc92a1b72c1d Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 9 Jan 2026 16:53:45 +0100
Subject: [PATCH 39/69] updated env

---
 recipes/tc_tracking/Dockerfile     | 14 +++++++-------
 recipes/tc_tracking/pyproject.toml |  9 ++-------
 2 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile
index d27d9bb3a..4d5dbf31d 100644
--- a/recipes/tc_tracking/Dockerfile
+++ b/recipes/tc_tracking/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvcr.io/nvidia/pytorch:25.08-py3
+FROM nvcr.io/nvidia/pytorch:25.11-py3
 
 # update repo info
 RUN apt update -y && \
@@ -12,8 +12,8 @@ RUN apt remove cmake -y && \
 # Install uv
 RUN wget -qO- https://astral.sh/uv/install.sh | sh
 ENV PATH="/root/.local/bin:$PATH"
-ENV CC /usr/bin/gcc
-ENV CXX /usr/bin/g++
+ENV CC=/usr/bin/gcc
+ENV CXX=/usr/bin/g++
 
 # install TempestExtremes
 WORKDIR /
@@ -28,9 +28,9 @@ RUN cmake .. && \
 
 # copy source into the container.
 RUN mkdir -p /tc_tracking
-COPY . /tc_tracking
-WORKDIR /tc_tracking
+COPY . /tc_tracking_src
+WORKDIR /tc_tracking_src
 
-ENV FORCE_CUDA_EXTENSION 1
-ENV TORCH_CUDA_ARCH_LIST "8.0 8.6 9.0 10.0 12.0+PTX"
+ENV FORCE_CUDA_EXTENSION=1
+ENV TORCH_CUDA_ARCH_LIST="8.0 8.6 9.0 10.0 12.0+PTX"
 RUN uv pip install --system --break-system-packages --no-cache-dir .
diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index 9642ee916..f8165afda 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -5,15 +5,11 @@ description = "no, i won't"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "anemoi-graphs==0.6.2",
-    "anemoi-models==0.5.1",
     "cartopy>=0.24.1",
-    "earth2studio[data]>=0.10.0",
-    "earthkit-regrid==0.4.0",
+    "earth2studio[data,aifsens,fcn3]>=0.11.0",
     "hydra-core>=1.3.2",
     "importlib-metadata>=8.7.0",
     "ipykernel>=6.30.1",
-    "makani>=0.2.0",
     "matplotlib>=3.10.3",
     "more-itertools>=10.7.0",
     "moviepy>=2.2.1",
@@ -29,9 +25,8 @@ dependencies = [
 ]
 
 [tool.uv.sources]
-makani = { git = "https://github.com/NVIDIA/makani" }
 torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" }
-earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" }
+earth2studio = { git = "https://github.com/NVIDIA/earth2studio.git" }
 
 [tool.uv]
 no-build-isolation-package = ["torch-harmonics"]

From 70361a79ae226d1fc5cc09d958707b90e7fcfc69 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 9 Jan 2026 08:11:16 -0800
Subject: [PATCH 40/69] moved tempest_extremes

---
 earth2studio/models/dx/__init__.py                            | 4 ----
 .../src/extract_from_data_source/baseline_extraction.py       | 2 +-
 recipes/tc_tracking/src/generate_ensembles.py                 | 2 +-
 .../models/dx => recipes/tc_tracking/src}/tempest_extremes.py | 0
 4 files changed, 2 insertions(+), 6 deletions(-)
 rename {earth2studio/models/dx => recipes/tc_tracking/src}/tempest_extremes.py (100%)

diff --git a/earth2studio/models/dx/__init__.py b/earth2studio/models/dx/__init__.py
index 3217d3488..ca90e2f2b 100644
--- a/earth2studio/models/dx/__init__.py
+++ b/earth2studio/models/dx/__init__.py
@@ -38,10 +38,6 @@
     TCTrackerVitart,
     TCTrackerWuDuan,
 )
-from earth2studio.models.dx.tempest_extremes import (
-    AsyncTempestExtremes,
-    TempestExtremes,
-)
 from earth2studio.models.dx.wind_gust import WindgustAFNO  # noqa
 
 __all__ = [
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index 6d4c04117..8c69cbfa9 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -8,8 +8,8 @@
 
 from earth2studio.data import fetch_data
 from earth2studio.io import KVBackend
-from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 from earth2studio.utils.coords import split_coords
+from src.tempest_extremes import AsyncTempestExtremes, TempestExtremes
 from src.data.utils import DataSourceManager, load_heights
 from src.utils import great_circle_distance
 
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 7445806fc..eea1828b2 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -11,9 +11,9 @@
 
 from earth2studio.data import fetch_data
 from earth2studio.models.auto import Package
-from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes
 from earth2studio.perturbation import SphericalGaussian
 from earth2studio.utils.coords import map_coords
+from src.tempest_extremes import AsyncTempestExtremes, TempestExtremes
 from src.data.file_output import initialise_netcdf_output, setup_output, write_to_store
 from src.data.utils import DataSourceManager, load_heights
 from src.utils import (
diff --git a/earth2studio/models/dx/tempest_extremes.py b/recipes/tc_tracking/src/tempest_extremes.py
similarity index 100%
rename from earth2studio/models/dx/tempest_extremes.py
rename to recipes/tc_tracking/src/tempest_extremes.py

From e172e50d263f07cb2b8e62040148428c3309fee3 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 9 Jan 2026 08:44:28 -0800
Subject: [PATCH 41/69] wip

---
 recipes/tc_tracking/cfg/slay_helena.yaml      |  38 ++++++++++++++++++
 recipes/tc_tracking/orography.nc              | Bin 0 -> 2618598 bytes
 recipes/tc_tracking/pyproject.toml            |   2 +-
 recipes/tc_tracking/src/generate_ensembles.py |   8 +++-
 4 files changed, 45 insertions(+), 3 deletions(-)
 create mode 100644 recipes/tc_tracking/cfg/slay_helena.yaml
 create mode 100644 recipes/tc_tracking/orography.nc

diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
new file mode 100644
index 000000000..95bdfe989
--- /dev/null
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -0,0 +1,38 @@
+project: 'helene'
+mode: 'generate_ensemble'
+model: 'aifs' # 'fcn3' 'sfno' 'aifs'
+random_seed: 7777
+
+# ic_block_start: "2024-09-23 00:00:00" # first IC
+# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
+# ic_block_step: 24            # number of hours between individual ICs
+ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+
+n_steps: 15
+ensemble_size: 2
+batch_size: 2
+orography_path: './orography.nc'
+
+data_source:
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+    # _target_: earth2studio.data.CDS
+
+store_type: "netcdf"
+store_dir: "./outputs"
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
+
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
\ No newline at end of file
diff --git a/recipes/tc_tracking/orography.nc b/recipes/tc_tracking/orography.nc
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index f8165afda..d9d759d3f 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -26,7 +26,7 @@ dependencies = [
 
 [tool.uv.sources]
 torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" }
-earth2studio = { git = "https://github.com/NVIDIA/earth2studio.git" }
+earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" }
 
 [tool.uv]
 no-build-isolation-package = ["torch-harmonics"]
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index eea1828b2..d3632fbbc 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -46,7 +46,11 @@ def load_model(cfg):
         model = cfg.model
 
     # select model
-    if model == "fcn3":
+    if model[:4] == "aifs":
+        from earth2studio.models.px import AIFSENS
+
+        model = AIFSENS
+    elif model == "fcn3":
         from earth2studio.models.px import FCN3
 
         model = FCN3
@@ -172,7 +176,7 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
                     )
                     break
 
-        if cyclone_tracking and stab.all():
+        if cyclone_tracking and (not stability_check or stab.all()):
             cyclone_tracking(
                 out_file_names=[
                     f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv"

From edb197815e23d7aef78d0f253bb4c313340349b7 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 13 Jan 2026 03:11:01 -0800
Subject: [PATCH 42/69] exploring aifs ensemble capability

---
 earth2studio/models/px/aifsens.py             | 2 +-
 recipes/tc_tracking/cfg/slay_helena.yaml      | 2 +-
 recipes/tc_tracking/pyproject.toml            | 3 +--
 recipes/tc_tracking/src/generate_ensembles.py | 4 +++-
 uv.lock                                       | 5 -----
 5 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/earth2studio/models/px/aifsens.py b/earth2studio/models/px/aifsens.py
index 3f8244702..e4854b7a6 100644
--- a/earth2studio/models/px/aifsens.py
+++ b/earth2studio/models/px/aifsens.py
@@ -778,7 +778,7 @@ def _forward(
                 :,
                 1,
             ]
-            out[..., 1, :, self.model.data_indices.data.output.full] = y
+            out[..., 1, :, self.model.data_indices.data.output.full] = y.squeeze(1)
             out[..., 1, :, self.model.data_indices.data.input.forcing] = x[
                 :, 1, :, self.model.data_indices.model.input.forcing
             ]
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index 95bdfe989..55a25478f 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -1,6 +1,6 @@
 project: 'helene'
 mode: 'generate_ensemble'
-model: 'aifs' # 'fcn3' 'sfno' 'aifs'
+model: 'aifsens' # 'fcn3' 'sfno' 'aifs'
 random_seed: 7777
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index d9d759d3f..105d8bd0b 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -18,7 +18,6 @@ dependencies = [
     "ruamel-yaml>=0.18.14",
     "scipy>=1.16.0",
     "setuptools>=80.9.0",
-    "torch-harmonics>=0.8.1",
     "tqdm>=4.67.1",
     "tropycal>=1.4",
     "zarrdump>=0.6.0",
@@ -26,7 +25,7 @@ dependencies = [
 
 [tool.uv.sources]
 torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" }
-earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" }
+earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" } # CAUTION: replace with NV main branch
 
 [tool.uv]
 no-build-isolation-package = ["torch-harmonics"]
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index d3632fbbc..139cade9c 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -136,7 +136,7 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
                 data_source,
                 time=[np.datetime64(ic)],
                 lead_time=model.input_coords()["lead_time"],
-                variable=model.variables,
+                variable=model.input_coords()["variable"],
                 device=dist.device,
             )
             ic_prev = ic
@@ -152,6 +152,8 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
         # set random state or apply perturbation
         if (not "model" in cfg) or (cfg.model == "fcn3"):
             model.set_rng(seed=seed)
+        elif cfg.model[:4] == 'aifs': # no need for perturbation, but also cannot set internal noise state
+            pass
         else:
             sg = SphericalGaussian(noise_amplitude=0.0005)
             xx, coords = sg(xx, coords)
diff --git a/uv.lock b/uv.lock
index 9255d358b..57afb71f0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4124,13 +4124,8 @@ wheels = [
 
 [[package]]
 name = "microsoft-aurora"
-<<<<<<< HEAD
-version = "0.1.dev55+gab41cf1"
-source = { git = "https://github.com/NickGeneva/aurora.git?rev=ab41cf1de67d5dcc723b96fc9a6219e4b548d181#ab41cf1de67d5dcc723b96fc9a6219e4b548d181" }
-=======
 version = "1.8.0"
 source = { registry = "https://pypi.org/simple" }
->>>>>>> main
 dependencies = [
     { name = "azure-storage-blob" },
     { name = "einops" },

From 8c3c848a02d962b849799c2bb7639a5f6b7f36ac Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 13 Jan 2026 09:23:25 -0800
Subject: [PATCH 43/69] thread issue with writing to netcdf in threads

---
 recipes/tc_tracking/cfg/slay_helena.yaml      |   4 +-
 .../post_proc/score/score_results.py          | 233 ------------------
 recipes/tc_tracking/pyproject.toml            |   1 +
 recipes/tc_tracking/src/tempest_extremes.py   | 125 +++++++---
 recipes/tc_tracking/test/.gitignore           |   0
 recipes/tc_tracking/test/baseline_helene.yaml |  38 +++
 6 files changed, 129 insertions(+), 272 deletions(-)
 delete mode 100644 recipes/tc_tracking/post_proc/score/score_results.py
 create mode 100644 recipes/tc_tracking/test/.gitignore
 create mode 100644 recipes/tc_tracking/test/baseline_helene.yaml

diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index 55a25478f..f8907fd0f 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -1,6 +1,6 @@
 project: 'helene'
 mode: 'generate_ensemble'
-model: 'aifsens' # 'fcn3' 'sfno' 'aifs'
+model: 'fcn3' # 'fcn3' 'sfno' 'aifs'
 random_seed: 7777
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
@@ -9,7 +9,7 @@ random_seed: 7777
 ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 
 n_steps: 15
-ensemble_size: 2
+ensemble_size: 6
 batch_size: 2
 orography_path: './orography.nc'
 
diff --git a/recipes/tc_tracking/post_proc/score/score_results.py b/recipes/tc_tracking/post_proc/score/score_results.py
deleted file mode 100644
index 667d914b5..000000000
--- a/recipes/tc_tracking/post_proc/score/score_results.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import os
-import sys
-import time
-from datetime import datetime
-from typing import Any
-
-import numpy as np
-import pandas as pd
-import torch
-import xarray as xr
-from physicsnemo.distributed import DistributedManager
-
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from src.data.localh5 import LocalArchiveHDF5
-
-from earth2studio.data import fetch_data, prep_data_array
-from earth2studio.statistics import crps, lat_weight, mean, variance
-from earth2studio.utils.type import TimeArray, VariableArray
-
-
-def open_ds(fl):
-    if fl.endswith(".zarr") or (
-        os.path.isdir(fl) and os.path.exists(os.path.join(fl, ".zgroup"))
-    ):
-        ds = xr.open_dataset(fl, engine="zarr")
-    else:
-        ds = xr.open_dataset(fl)
-
-    return ds
-
-
-def pick_vars_for_rank(vars):
-    DistributedManager.initialize()
-
-    dist = DistributedManager()
-    if dist.world_size == 1:
-        return vars
-
-    if dist.world_size != len(vars):
-        raise ValueError(f"{dist.world_size=} != {len(vars)=}")
-
-    vars = [vars[dist.rank]]
-
-    return vars
-
-
-def compute_mse(ds):
-
-    vars = list(ds.data_vars.keys())
-    vars.sort()
-    vars = pick_vars_for_rank(vars)
-    mse = torch.zeros(len(vars), len(ds.lead_time.values))
-
-    for ivar, var in enumerate(vars):
-        print(f"processing variable {var} ({ivar+1}/{len(vars)})")
-        var_ds = ds[var]
-
-        se = torch.zeros(
-            len(var_ds.time.values),
-            len(var_ds.lead_time.values),
-            len(var_ds.lat.values),
-            len(var_ds.lon.values),
-        )
-
-        for ii, ic in enumerate(var_ds.time.values):
-            print(
-                f"processing time {ic} ({ii+1}/{len(var_ds.time.values)})",
-                end="\r",
-                flush=True,
-            )
-
-            # fetch ground truth data
-            data_source = LocalArchiveHDF5(
-                dirs=[
-                    "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-                ],
-                metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
-            )
-
-            x0, _ = fetch_data(
-                data_source,
-                time=[ic],
-                lead_time=var_ds.lead_time.values,
-                variable=var_ds.name,
-            )
-            x0 = x0.squeeze()
-
-            mean_ds = var_ds.sel(time=ic).mean(dim="ensemble")
-            se[ii, ...] = (x0 - mean_ds.values) ** 2
-        print("\n")
-
-        weights = (
-            torch.as_tensor(lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat))))
-            .unsqueeze(1)
-            .repeat(1, 1440)
-        )
-        mse[ivar, ...] = (se * weights).mean(dim=(0, 2, 3))
-
-    return mse
-
-
-def compute_rmse(var_ds):
-    return torch.sqrt(compute_mse(var_ds))
-
-
-def write_to_csv(rmse, ds, identifier):
-    # Get variable names and lead times for DataFrame
-    vars = list(ds.data_vars.keys())
-    vars.sort()
-    vars = pick_vars_for_rank(vars)
-
-    lead_times = ds.lead_time.values
-
-    # Convert tensor to numpy and create DataFrame
-    rmse_np = rmse.numpy()
-
-    # Create DataFrame with variables as rows and lead times as columns
-    rmse_df = pd.DataFrame(
-        rmse_np.T,
-        index=[f"{lt}" for lt in lead_times],
-        columns=vars,
-    )
-    rmse_df.index.name = "lead_time"
-
-    # Generate output filename based on input filename
-    out_file = identifier
-    for var in vars:
-        out_file += f"_{var}"
-    out_file += ".csv"
-
-    rmse_df.to_csv(out_file)
-
-    return
-
-
-def compute_crps(ds):
-
-    vars = list(ds.data_vars.keys())
-    vars.sort()
-    vars = pick_vars_for_rank(vars)
-
-    _crps = crps(
-        ensemble_dimension="ensemble",
-        reduction_dimensions=["lat", "lon"],
-        weights=torch.as_tensor(
-            lat_weight(np.linspace(ds.lat[-1], ds.lat[0], len(ds.lat)))
-        )
-        .unsqueeze(1)
-        .repeat(1, 1440),
-        fair=True,
-    )
-
-    # fetch ground truth data
-    _truth = LocalArchiveHDF5(
-        dirs=["/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"],
-        metadata_file="/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json",
-    )
-
-    ccrr = torch.zeros(len(vars), len(ds.lead_time.values))
-    for ivar, var in enumerate(vars):
-        print(f"processing variable {var} ({ivar+1}/{len(vars)})")
-        ccc = torch.zeros(len(ds.time.values), len(ds.lead_time.values), device="cpu")
-        for ii, ic in enumerate(ds.time.values):
-            print(
-                f"processing time {ic} ({ii+1}/{len(ds.time.values)})",
-                end="\r",
-                flush=True,
-            )
-
-            tru, tru_coords = fetch_data(
-                _truth,
-                time=[ic],
-                lead_time=ds.lead_time.values,
-                variable=var,
-            )
-            tru = tru.squeeze()
-            tru_coords.pop("time")
-            tru_coords.pop("variable")
-
-            pred, pred_coords = prep_data_array(ds[var].sel(time=ic))
-
-            _ccc, _ = _crps(
-                x=pred.to(DistributedManager().device),
-                x_coords=pred_coords,
-                y=tru.to(DistributedManager().device),
-                y_coords=tru_coords,
-            )
-            ccc[ii, :] = _ccc.to("cpu")
-
-        print("\n")
-
-        ccrr[ivar, :] = ccc.mean(dim=0)
-
-    return ccrr
-
-
-def score(fl):
-    ds = open_ds(fl)
-
-    rmse = False
-    crps = True
-
-    if False:
-        ds = (
-            ds.sel(time=ds.time[:5])
-            .sel(ensemble=ds.ensemble[:50])
-            .sel(lead_time=ds.lead_time[:10])
-        )
-
-    if rmse:
-        then = time.time()
-        rmse = compute_rmse(ds)
-        print(f"time taken: {time.time() - then} seconds")
-        write_to_csv(rmse, ds, "rmse")
-
-    if crps:
-        then = time.time()
-        crps = compute_crps(ds)
-        print(f"time taken: {time.time() - then} seconds")
-        write_to_csv(crps, ds, "crps")
-    return
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python score_results.py <netcdf_file>")
-        print(
-            "Example: python score_results.py score_2020_2020-01-01T00.00.00_mems0000-0049.nc"
-        )
-        exit()
-
-    score(sys.argv[1])
diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index 105d8bd0b..ae90a0822 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -15,6 +15,7 @@ dependencies = [
     "moviepy>=2.2.1",
     "ninja>=1.13.0",
     "omegaconf>=2.3.0",
+    "pytest",
     "ruamel-yaml>=0.18.14",
     "scipy>=1.16.0",
     "setuptools>=80.9.0",
diff --git a/recipes/tc_tracking/src/tempest_extremes.py b/recipes/tc_tracking/src/tempest_extremes.py
index db5e59257..cc18cc39d 100644
--- a/recipes/tc_tracking/src/tempest_extremes.py
+++ b/recipes/tc_tracking/src/tempest_extremes.py
@@ -730,16 +730,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         # Track background tasks for this instance
         self._instance_tasks: list[Future] = []
         self._instance_lock = threading.Lock()
-        self._dump_in_progress = threading.Event()
-        self._dump_in_progress.set()  # Initially no dump in progress
         self._has_failed = False  # Track if any previous task failed
         self._cleanup_done = False  # Track if cleanup has been performed
 
     def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
-        """Record state but ensure dump_raw_data has finished successfully before proceeding.
-
-        This method ensures data integrity by waiting for any ongoing dump operations
-        to complete before recording new state data.
+        """Record state data, checking for any previous task failures.
 
         Parameters
         ----------
@@ -756,34 +751,9 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         # Check if any previous task failed and raise error immediately
         self._check_for_failures()
 
-        # Wait for any ongoing dump_raw_data to complete
-        self._dump_in_progress.wait()
-
         # Call parent's record_state method
         super().record_state(xx, coords)
 
-    def dump_raw_data(self) -> tuple[list[str], np.ndarray]:
-        """Dump raw data with synchronization to prevent concurrent access.
-
-        Uses threading events to ensure only one dump operation occurs at a time
-        to maintain data integrity.
-
-        Returns
-        -------
-        tuple[list[str], np.ndarray]
-            Tuple containing list of raw file paths and array of member indices
-        """
-        # Signal that dump is in progress
-        self._dump_in_progress.clear()
-
-        try:
-            # Call parent's dump_raw_data method
-            result = super().dump_raw_data()
-            return result
-        finally:
-            # Signal that dump is complete
-            self._dump_in_progress.set()
-
     def _check_for_failures(self) -> None:
         """Check if any previous tasks failed with ChildProcessError and raise immediately.
 
@@ -821,6 +791,10 @@ def track_cyclones_async(self, out_file_names: list[str] | None = None) -> Futur
         This method submits cyclone tracking operations to a background thread pool
         for asynchronous execution, allowing the main thread to continue processing.
 
+        IMPORTANT: The netCDF file writing (dump_raw_data) is done SYNCHRONOUSLY
+        in the main thread before submitting, because HDF5/netCDF4 is not thread-safe.
+        Only the CPU-intensive TempestExtremes detect/stitch runs in the background.
+
         Returns
         -------
         Future
@@ -853,9 +827,17 @@ def track_cyclones_async(self, out_file_names: list[str] | None = None) -> Futur
                     f"Warning: {running_tasks} TempestExtremes tasks already running. Consider waiting for some to complete."
                 )
 
-        # Submit the task
+        # CRITICAL: Run setup_files() (which includes dump_raw_data with netCDF writes)
+        # SYNCHRONOUSLY in the main thread. HDF5/netCDF4 is not thread-safe, and
+        # concurrent writes from multiple threads cause segmentation faults.
+        insies, outsies, node_files, track_files = self.setup_files(out_file_names)
+        self.track_files = track_files
+
+        # Submit only the TE processing (detect/stitch) to the background thread
         executor = get_tempest_executor()
-        future = executor.submit(self.track_cyclones, out_file_names)
+        future = executor.submit(
+            self._run_te_and_cleanup, insies, outsies, node_files, track_files
+        )
 
         # Add to both global and instance task lists
         with _tempest_executor_lock:
@@ -1027,6 +1009,79 @@ def _process_single_member(
         if self.print_te_output:
             print(f"took {(time.time() - then):.1f}s to track cyclones for one member")
 
+    def _run_te_and_cleanup(
+        self,
+        insies: list[str],
+        outsies: list[str],
+        node_files: list[str],
+        track_files: list[str],
+    ) -> None:
+        """Run TempestExtremes processing and cleanup (no netCDF writing).
+
+        This method is designed to run in a background thread after the netCDF
+        files have already been written by the main thread.
+
+        Parameters
+        ----------
+        insies : list[str]
+            List of input file list paths
+        outsies : list[str]
+            List of output file list paths
+        node_files : list[str]
+            List of node file paths
+        track_files : list[str]
+            List of track file paths
+
+        Raises
+        ------
+        ChildProcessError
+            If TempestExtremes detect or stitch operations fail for any member
+        """
+        then = time.time()
+
+        # Create a dedicated local thread pool for member processing
+        n_members = len(insies)
+        max_workers = min(n_members, os.cpu_count() or 1)
+        if self.max_workers is not None:
+            max_workers = min(self.max_workers, max_workers)
+
+        with ThreadPoolExecutor(
+            max_workers=max_workers, thread_name_prefix="te_members"
+        ) as executor:
+            # Submit one task per member to process in parallel
+            member_futures = []
+
+            for ins, outs, node_file, track_file in zip(
+                insies, outsies, node_files, track_files
+            ):
+                future = executor.submit(
+                    self._process_single_member, ins, outs, node_file, track_file
+                )
+                member_futures.append(future)
+
+            # Wait for all members to complete
+            exceptions = []
+            for i, future in enumerate(member_futures):
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"Member {i+1} processing failed: {e}")
+                    exceptions.append((i, e))
+
+        # Raise if any failed
+        if exceptions:
+            error_msg = (
+                f"Processing failed for {len(exceptions)} member(s): {exceptions}"
+            )
+            raise ChildProcessError(error_msg)
+
+        # Clean up after all are done
+        self.tidy_up(insies, outsies)
+
+        print(
+            f"took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members"
+        )
+
     def track_cyclones(self, out_file_names: list[str] | None = None) -> None:
         """Execute cyclone tracking with parallel processing per ensemble member.
 
@@ -1119,10 +1174,6 @@ def cleanup(self, timeout_per_task: int = 360) -> None:
             return
 
         try:
-            # Wait for any ongoing dump to complete
-            if hasattr(self, "_dump_in_progress"):
-                self._dump_in_progress.wait(timeout=60)
-
             # Wait for all instance tasks to complete
             if hasattr(self, "_instance_tasks") and hasattr(self, "_instance_lock"):
                 with self._instance_lock:
diff --git a/recipes/tc_tracking/test/.gitignore b/recipes/tc_tracking/test/.gitignore
new file mode 100644
index 000000000..e69de29bb
diff --git a/recipes/tc_tracking/test/baseline_helene.yaml b/recipes/tc_tracking/test/baseline_helene.yaml
new file mode 100644
index 000000000..3918f2a37
--- /dev/null
+++ b/recipes/tc_tracking/test/baseline_helene.yaml
@@ -0,0 +1,38 @@
+project: 'helene'
+mode: 'generate_ensemble'
+model: 'fcn3' # 'fcn3' 'sfno' 'aifs'
+random_seed: 7777
+
+# ic_block_start: "2024-09-23 00:00:00" # first IC
+# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
+# ic_block_step: 24            # number of hours between individual ICs
+ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+
+n_steps: 15
+ensemble_size: 6
+batch_size: 2
+orography_path: '../orography.nc'
+
+data_source:
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+    # _target_: earth2studio.data.CDS
+
+store_type: "netcdf"
+store_dir: "./outputs"
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
+
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
\ No newline at end of file

From d41351e52e247b09e3f0871aa301209da9d91462 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 14 Jan 2026 07:01:59 -0800
Subject: [PATCH 44/69] automated testing of writing TE files and their
 reproducibility. bug fix for mk race condition

---
 recipes/tc_tracking/src/data/file_output.py   |  7 ++-
 recipes/tc_tracking/tc_hunt.py                |  9 +++
 recipes/tc_tracking/test/.gitignore           |  1 +
 recipes/tc_tracking/test/baseline_helene.yaml | 24 ++++----
 .../tc_tracking/test/reproduce_helene.yaml    | 36 ++++++++++++
 recipes/tc_tracking/test/test_tc_hunt.sh      | 57 +++++++++++++++++++
 6 files changed, 121 insertions(+), 13 deletions(-)
 create mode 100644 recipes/tc_tracking/test/reproduce_helene.yaml
 create mode 100644 recipes/tc_tracking/test/test_tc_hunt.sh

diff --git a/recipes/tc_tracking/src/data/file_output.py b/recipes/tc_tracking/src/data/file_output.py
index 9e18ad59e..1d171df61 100644
--- a/recipes/tc_tracking/src/data/file_output.py
+++ b/recipes/tc_tracking/src/data/file_output.py
@@ -69,7 +69,12 @@ def add_arrays_to_store(store, out_coords, mems, add_arrays, ic=None, array_kwar
 
 def setup_output(cfg, model, ics, add_arrays):
 
-    out_vars = list(dict.fromkeys(cfg.out_vars))
+    if 'out_vars' in cfg:
+        out_vars = list(dict.fromkeys(cfg.out_vars))
+    else:
+        out_vars = []
+        if not cfg.store_type == 'none':
+            raise ValueError("out_vars must be specified if store_type is not none.")
     out_coords = initialise_output_coords(cfg, model, ics, out_vars)
     chunks = {
         "ensemble": 1,
diff --git a/recipes/tc_tracking/tc_hunt.py b/recipes/tc_tracking/tc_hunt.py
index 028a66074..cb042d11a 100644
--- a/recipes/tc_tracking/tc_hunt.py
+++ b/recipes/tc_tracking/tc_hunt.py
@@ -1,3 +1,12 @@
+import os
+
+# Set MKL/OMP threading environment variables BEFORE any other imports
+# This prevents MKL initialization race conditions that cause
+# divide-by-zero crashes when running FCN3 with NCCL/UCX
+os.environ.setdefault("MKL_NUM_THREADS", "1")
+os.environ.setdefault("OMP_NUM_THREADS", "1")
+os.environ.setdefault("MKL_DYNAMIC", "FALSE")
+
 import hydra
 from omegaconf import DictConfig
 from src.extract_from_data_source.baseline_extraction import extract_baseline
diff --git a/recipes/tc_tracking/test/.gitignore b/recipes/tc_tracking/test/.gitignore
index e69de29bb..c6db2a46f 100644
--- a/recipes/tc_tracking/test/.gitignore
+++ b/recipes/tc_tracking/test/.gitignore
@@ -0,0 +1 @@
+outputs_*
diff --git a/recipes/tc_tracking/test/baseline_helene.yaml b/recipes/tc_tracking/test/baseline_helene.yaml
index 3918f2a37..8b18bc713 100644
--- a/recipes/tc_tracking/test/baseline_helene.yaml
+++ b/recipes/tc_tracking/test/baseline_helene.yaml
@@ -1,27 +1,20 @@
-project: 'helene'
+project: 'baseline_helene'
 mode: 'generate_ensemble'
 model: 'fcn3' # 'fcn3' 'sfno' 'aifs'
 random_seed: 7777
 
-# ic_block_start: "2024-09-23 00:00:00" # first IC
-# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
-# ic_block_step: 24            # number of hours between individual ICs
 ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 
 n_steps: 15
-ensemble_size: 6
+ensemble_size: 5
 batch_size: 2
 orography_path: '../orography.nc'
 
 data_source:
     _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
-    # _target_: earth2studio.data.CDS
 
-store_type: "netcdf"
-store_dir: "./outputs"
-out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+store_type: "none"
+store_dir: ./outputs_${project}
 
 cyclone_tracking:
     asynchronous: True
@@ -35,4 +28,11 @@ cyclone_tracking:
 
 stability_check:
     variables: ['t1000', 'u250', 'v850']
-    thresholds: [10, 10, 10]
\ No newline at end of file
+    thresholds: [10, 10, 10]
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
diff --git a/recipes/tc_tracking/test/reproduce_helene.yaml b/recipes/tc_tracking/test/reproduce_helene.yaml
new file mode 100644
index 000000000..4e6ab7e0b
--- /dev/null
+++ b/recipes/tc_tracking/test/reproduce_helene.yaml
@@ -0,0 +1,36 @@
+project: 'reproduce_helene'
+mode: 'reproduce_members'
+model: 'fcn3'
+
+reproduce_members: [
+                    ['2024-09-29 00:00:00', 3, 4045540270],
+                    ['2024-09-29 00:00:00', 4, 2135091646],
+                   ]
+
+n_steps: 15
+ensemble_size: 5
+batch_size: 2
+orography_path: '../orography.nc'
+
+data_source:
+    _target_: earth2studio.data.NCAR_ERA5
+
+store_type: "none"
+store_dir: "./outputs_${project}"
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
diff --git a/recipes/tc_tracking/test/test_tc_hunt.sh b/recipes/tc_tracking/test/test_tc_hunt.sh
new file mode 100644
index 000000000..a837564e2
--- /dev/null
+++ b/recipes/tc_tracking/test/test_tc_hunt.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+set -e
+
+baseline_dir="outputs_baseline_helene/cyclone_tracks_te"
+reproduce_dir="outputs_reproduce_helene/cyclone_tracks_te"
+
+# Run baseline
+echo ""
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo " >>> running baseline. might take a couple of minutes..."
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo ""
+python ../tc_hunt.py --config-path=$(pwd) --config-name=baseline_helene.yaml
+
+# Get track files for members 2, 3, 4
+mapfile -t track_files < <(ls "$baseline_dir"/tracks_*.csv | sort | grep -E 'mem_000[234]' | xargs -n1 basename)
+
+# Extract seeds for members 3 and 4 from filenames
+seed_mem3=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0003' | grep -oP 'seed_\K\d+')
+seed_mem4=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0004' | grep -oP 'seed_\K\d+')
+
+# Check if seeds in reproduce_helene.yaml match, update if not
+yaml_seed3=$(grep -oP "\['2024-09-29 00:00:00', 3, \K\d+" reproduce_helene.yaml)
+yaml_seed4=$(grep -oP "\['2024-09-29 00:00:00', 4, \K\d+" reproduce_helene.yaml)
+
+if [ "$seed_mem3" != "$yaml_seed3" ] || [ "$seed_mem4" != "$yaml_seed4" ]; then
+    echo "WARNING: Updating seeds in reproduce_helene.yaml (mem3: $yaml_seed3->$seed_mem3, mem4: $yaml_seed4->$seed_mem4)"
+    sed -i "s/\['2024-09-29 00:00:00', 3, [0-9]*\]/['2024-09-29 00:00:00', 3, $seed_mem3]/" reproduce_helene.yaml
+    sed -i "s/\['2024-09-29 00:00:00', 4, [0-9]*\]/['2024-09-29 00:00:00', 4, $seed_mem4]/" reproduce_helene.yaml
+fi
+
+# Run reproduce
+echo ""
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo " >>> reproducing members 2, 3 and 4. might take a couple of minutes..."
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo ""
+python ../tc_hunt.py --config-path=$(pwd) --config-name=reproduce_helene.yaml
+
+# Compare track files for members 3 and 4, error if any differ
+for f in "${track_files[@]}"; do
+    [[ $f =~ mem_000[34] ]] || continue
+    if ! diff -q "$baseline_dir/$f" "$reproduce_dir/$f" > /dev/null 2>&1; then
+        echo ""
+        echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+        echo " >>> oh no!!! ERROR: $f differs between baseline and reproduce"
+        echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+        echo ""
+        diff "$baseline_dir/$f" "$reproduce_dir/$f"
+        exit 1
+    fi
+done
+echo ""
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo " >>> all good, yay (: track files have been reproduced successfully"
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo ""
\ No newline at end of file

From 780e45897afb3f35848559c16999f872216330b2 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 14 Jan 2026 07:51:07 -0800
Subject: [PATCH 45/69] README for tc_hunt test

---
 recipes/tc_tracking/test/README.md | 53 ++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 recipes/tc_tracking/test/README.md

diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md
new file mode 100644
index 000000000..30d944bf8
--- /dev/null
+++ b/recipes/tc_tracking/test/README.md
@@ -0,0 +1,53 @@
+# Tests
+
+## Test 1: Reproducibility of Helene Ensemble Members
+
+> [!Note]
+> The test can be done manually following the steps below. Alternatively,
+> `test_tc_hunt.sh` provides an automated way for running the full test.
+
+This case will test the whether the pipeline can genreate an ensemble forecast
+using FCN3 and track tropical cylones using TempestExtremes.
+Furthermore, it is tested if those exact tracks can be reproduced.
+Note that reproducing with AIFS-ENS won't work as the model does not provide
+access to its internal random state.
+
+First, let us produce a five member ensemble of a 90h prediction of Hurricane Helene
+```bash
+cd earth2studio/recipes/tc_tracking/test
+python ../tc_hunt.py --config-path=$(pwd) --config-name=baseline_helene.yaml
+```
+
+You can find the track files in `outputs_baseline_helene/cyclone_tracks_te`.
+The directory should contain five csv files with naming pattern accrding to
+`tracks_2024-09-29T00:00:00_mem_000X_seed_YYYYYYYYY_bs_2.csv`
+This pattern provides information about initial condition, ensemble member ID
+and batch size.
+
+Next, check if the random seeds for members three and four provided in `reproduce_helene.yaml`
+are identical to the seeds in the csv file names. If not, replace the seeds for both members
+in the yaml file with the seeds from the csv file names.
+
+Having made sure that the configurations for reproduicing the ensemble members
+is correct, let us now reproduce members three and four of the previous run:
+```bash
+python ../tc_hunt.py --config-path=$(pwd) --config-name=reproduce_helene.yaml
+```
+Note that the baseline run was produced with batch size two. For reproducibility,
+we need to apply the same batch size in the reproduction run and reproduce the full
+batch that contained the ensemble member in question. For the present case, this means
+that member two will also be reproduced, as it is in the same batch as member three.
+
+### Expected Result
+
+There should now be three track files in `outputs_reproduce_helene/cyclone_tracks_te`,
+with file names identical to the associated files in `outputs_baseline_helene/cyclone_tracks_te`.
+If also the content of the files, ie the genereted tracks, is identical can be tested trhough
+a simple `diff` call:
+
+```bash
+diff outputs_baseline_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_0002_seed_4045540270_bs_2.csv \
+     outputs_reproduce_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_0002_seed_4045540270_bs_2.csv
+```
+
+Repeat the diff call for all reproduced ensemble members, the return should always be empty.

From 4360ea4a9979e09faeee0bf5944248e9ed9f6f54 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 14 Jan 2026 09:07:52 -0800
Subject: [PATCH 46/69] second test for extracting historic data

---
 .../baseline_extraction.py                    |  5 +-
 recipes/tc_tracking/test/README.md            |  5 +
 recipes/tc_tracking/test/baseline_helene.yaml |  2 +-
 recipes/tc_tracking/test/extract_era5.yaml    | 37 ++++++++
 .../test/ibtracs.ALL.list.v04r01.csv          | 92 +++++++++++++++++++
 .../tc_tracking/test/reproduce_helene.yaml    |  4 +-
 recipes/tc_tracking/test/test_tc_hunt.sh      |  8 +-
 7 files changed, 144 insertions(+), 9 deletions(-)
 create mode 100644 recipes/tc_tracking/test/extract_era5.yaml
 create mode 100644 recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv

diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index 8c69cbfa9..e2e8056b9 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -40,6 +40,7 @@ def extract_from_historic_data(cfg,
     cyclone_tracking = TempestExtremes(detect_cmd=cfg.cyclone_tracking.detect_cmd,
                                        stitch_cmd=cfg.cyclone_tracking.stitch_cmd,
                                        input_vars=cfg.cyclone_tracking.vars,
+                                       batch_size=1,
                                        n_steps=n_steps-1, # assumes prediction steps
                                        time_step=time_step,
                                        lats=coords['lat'],
@@ -54,9 +55,9 @@ def extract_from_historic_data(cfg,
     cyclone_tracking()
 
     # load track_file into dataFrame
-    hist_tracks = pd.read_csv(cyclone_tracking.track_file)
+    hist_tracks = pd.read_csv(cyclone_tracking.track_files[0])
     hist_tracks.columns = hist_tracks.columns.str.strip()
-    os.remove(cyclone_tracking.track_file)
+    os.remove(cyclone_tracking.track_files[0])
 
     return hist_tracks
 
diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md
index 30d944bf8..ae5808372 100644
--- a/recipes/tc_tracking/test/README.md
+++ b/recipes/tc_tracking/test/README.md
@@ -5,6 +5,8 @@
 > [!Note]
 > The test can be done manually following the steps below. Alternatively,
 > `test_tc_hunt.sh` provides an automated way for running the full test.
+> If, at the end you see a message containing `all good, yay (:`, the test
+> finished successfully.
 
 This case will test the whether the pipeline can genreate an ensemble forecast
 using FCN3 and track tropical cylones using TempestExtremes.
@@ -51,3 +53,6 @@ diff outputs_baseline_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_00
 ```
 
 Repeat the diff call for all reproduced ensemble members, the return should always be empty.
+
+
+## Test 2: Extracting indivdual storms from historic data
diff --git a/recipes/tc_tracking/test/baseline_helene.yaml b/recipes/tc_tracking/test/baseline_helene.yaml
index 8b18bc713..8574640c4 100644
--- a/recipes/tc_tracking/test/baseline_helene.yaml
+++ b/recipes/tc_tracking/test/baseline_helene.yaml
@@ -3,7 +3,7 @@ mode: 'generate_ensemble'
 model: 'fcn3' # 'fcn3' 'sfno' 'aifs'
 random_seed: 7777
 
-ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 
 n_steps: 15
 ensemble_size: 5
diff --git a/recipes/tc_tracking/test/extract_era5.yaml b/recipes/tc_tracking/test/extract_era5.yaml
new file mode 100644
index 000000000..343568c8f
--- /dev/null
+++ b/recipes/tc_tracking/test/extract_era5.yaml
@@ -0,0 +1,37 @@
+project: 'reference_tracks'
+mode: 'extract_baseline'
+
+cases:
+    helene:
+        year: 2024
+        basin: north_atlantic
+
+    hato:
+        year: 2017
+        basin: west_pacific
+
+ibtracs_source_data: "ibtracs.ALL.list.v04r01.csv"
+
+data_source:
+    ncar_era5:
+        years: "2017-2024"
+        source:
+            _target_: earth2studio.data.NCAR_ERA5
+
+orography_path: '../orography.nc'
+store_dir: "./outputs_${project}"
+
+cyclone_tracking:
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv b/recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv
new file mode 100644
index 000000000..235107e4f
--- /dev/null
+++ b/recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv
@@ -0,0 +1,92 @@
+SID,SEASON,NUMBER,BASIN,SUBBASIN,NAME,ISO_TIME,NATURE,LAT,LON,WMO_WIND,WMO_PRES,WMO_AGENCY,TRACK_TYPE,DIST2LAND,LANDFALL,IFLAG,USA_AGENCY,USA_ATCF_ID,USA_LAT,USA_LON,USA_RECORD,USA_STATUS,USA_WIND,USA_PRES,USA_SSHS,USA_R34_NE,USA_R34_SE,USA_R34_SW,USA_R34_NW,USA_R50_NE,USA_R50_SE,USA_R50_SW,USA_R50_NW,USA_R64_NE,USA_R64_SE,USA_R64_SW,USA_R64_NW,USA_POCI,USA_ROCI,USA_RMW,USA_EYE,TOKYO_LAT,TOKYO_LON,TOKYO_GRADE,TOKYO_WIND,TOKYO_PRES,TOKYO_R50_DIR,TOKYO_R50_LONG,TOKYO_R50_SHORT,TOKYO_R30_DIR,TOKYO_R30_LONG,TOKYO_R30_SHORT,TOKYO_LAND,CMA_LAT,CMA_LON,CMA_CAT,CMA_WIND,CMA_PRES,HKO_LAT,HKO_LON,HKO_CAT,HKO_WIND,HKO_PRES,KMA_LAT,KMA_LON,KMA_CAT,KMA_WIND,KMA_PRES,KMA_R50_DIR,KMA_R50_LONG,KMA_R50_SHORT,KMA_R30_DIR,KMA_R30_LONG,KMA_R30_SHORT,NEWDELHI_LAT,NEWDELHI_LON,NEWDELHI_GRADE,NEWDELHI_WIND,NEWDELHI_PRES,NEWDELHI_CI,NEWDELHI_DP,NEWDELHI_POCI,REUNION_LAT,REUNION_LON,REUNION_TYPE,REUNION_WIND,REUNION_PRES,REUNION_TNUM,REUNION_CI,REUNION_RMW,REUNION_R34_NE,REUNION_R34_SE,REUNION_R34_SW,REUNION_R34_NW,REUNION_R50_NE,REUNION_R50_SE,REUNION_R50_SW,REUNION_R50_NW,REUNION_R64_NE,REUNION_R64_SE,REUNION_R64_SW,REUNION_R64_NW,BOM_LAT,BOM_LON,BOM_TYPE,BOM_WIND,BOM_PRES,BOM_TNUM,BOM_CI,BOM_RMW,BOM_R34_NE,BOM_R34_SE,BOM_R34_SW,BOM_R34_NW,BOM_R50_NE,BOM_R50_SE,BOM_R50_SW,BOM_R50_NW,BOM_R64_NE,BOM_R64_SE,BOM_R64_SW,BOM_R64_NW,BOM_ROCI,BOM_POCI,BOM_EYE,BOM_POS_METHOD,BOM_PRES_METHOD,NADI_LAT,NADI_LON,NADI_CAT,NADI_WIND,NADI_PRES,WELLINGTON_LAT,WELLINGTON_LON,WELLINGTON_WIND,WELLINGTON_PRES,DS824_LAT,DS824_LON,DS824_STAGE,DS824_WIND,DS824_PRES,TD9636_LAT,TD9636_LON,TD9636_STAGE,TD9636_WIND,TD9636_PRES,TD9635_LAT,TD9635_LON,TD9635_WIND,TD9635_PRES,TD9635_ROCI,NEUMANN_LAT,NEUMANN_LON,NEUMANN_CLASS,NEUMANN_WIND,NEUMANN_PRES,MLC_LAT,MLC_LON,MLC_CLASS,MLC_WIND,MLC_PRES,USA_GUST,BOM_GUST,BOM_GUST_PER,REUNION_GUST,REUNION_GUST_PER,USA_SEAHGT,USA_SEARAD_NE,USA_SEARAD_SE,USA_SEARAD_SW,USA_SEARAD_NW,STORM_SPEED,STORM_DIR
+ ,Year, , , , , , ,degrees_north,degrees_east,kts,mb, , ,km,km, , , ,degrees_north,degrees_east, , ,kts,mb,1,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,mb,nmile,nmile,nmile,degrees_north,degrees_east,1,kts,mb, ,nmile,nmile, ,nmile,nmile,1,degrees_north,degrees_east,1,kts,mb,degrees_north,degrees_east, ,kts,mb,degrees_north,degrees_east, ,kts,mb, ,nmile,nmile, ,nmile,nmile,degrees_north,degrees_east, ,kts,mb,1,mb,mb,degrees_north,degrees_east, ,kts,mb,1,1,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,degrees_north,degrees_east, ,kts,mb,1,1,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,nmile,mb,nmile, , ,degrees_north,degrees_east,1,kts,mb,degrees_north,degrees_east,kts,mb,degrees_north,degrees_east, ,kts,mb,degrees_north,degrees_east, ,kts,mb,degrees_north,degrees_east,kts,mb,nmile,degrees_north,degrees_east, ,kts,mb,degrees_north,degrees_east, ,kts,mb,kts,kts,second,kts,second,ft,nmile,nmile,nmile,nmile,kts,degrees
+2017232N19130,2017,62,WP,MM,HATO,2017-08-19 12:00:00,TS,19.1,130.1, ,1006,tokyo,main,825,774,_O_____________, , , , , , , , ,-5, , , , , , , , , , , , , , , , ,19.1,130.1,2, ,1006,0, , ,0, , ,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,10,290
+2017232N19130,2017,62,WP,MM,HATO,2017-08-19 15:00:00,TS,19.3,129.6, , , ,main,774,771,_P_____________, , , , , , , , ,-5, , , , , , , , , , , , , , , , ,19.3,129.6,2, ,1005,0, , ,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,5,260
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+2024268N17278,2024,63,NA,NA,HELENE,2024-09-28 12:00:00,DS,36.6,-87.6,25,997,hurdat_atl,main,0,0,O______________,hurdat_atl,AL092024,36.6,-87.6, ,LO,25,997,-3, , , , , , , , , , , , ,1009,320,160, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,6,110
+2024268N17278,2024,63,NA,NA,HELENE,2024-09-28 15:00:00,DS,36.7,-87.3, , , ,main,0,0,P______________, ,AL092024,36.7,-87.3, ,LO,23,999,-3, , , , , , , , , , , , ,1010,320,160, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,7,45
+2024268N17278,2024,63,NA,NA,HELENE,2024-09-28 18:00:00,DS,37.1,-87.0,20,1000,hurdat_atl,main,0, ,O______________,hurdat_atl,AL092024,37.1,-87.0, ,LO,20,1000,-3, , , , , , , , , , , , ,1011,320,160, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,9,30
diff --git a/recipes/tc_tracking/test/reproduce_helene.yaml b/recipes/tc_tracking/test/reproduce_helene.yaml
index 4e6ab7e0b..d420e8cf5 100644
--- a/recipes/tc_tracking/test/reproduce_helene.yaml
+++ b/recipes/tc_tracking/test/reproduce_helene.yaml
@@ -3,8 +3,8 @@ mode: 'reproduce_members'
 model: 'fcn3'
 
 reproduce_members: [
-                    ['2024-09-29 00:00:00', 3, 4045540270],
-                    ['2024-09-29 00:00:00', 4, 2135091646],
+                    ['2024-09-24 00:00:00', 3, 4045540270],
+                    ['2024-09-24 00:00:00', 4, 2135091646],
                    ]
 
 n_steps: 15
diff --git a/recipes/tc_tracking/test/test_tc_hunt.sh b/recipes/tc_tracking/test/test_tc_hunt.sh
index a837564e2..226d7e4ba 100644
--- a/recipes/tc_tracking/test/test_tc_hunt.sh
+++ b/recipes/tc_tracking/test/test_tc_hunt.sh
@@ -20,13 +20,13 @@ seed_mem3=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0003' | grep -oP 'seed
 seed_mem4=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0004' | grep -oP 'seed_\K\d+')
 
 # Check if seeds in reproduce_helene.yaml match, update if not
-yaml_seed3=$(grep -oP "\['2024-09-29 00:00:00', 3, \K\d+" reproduce_helene.yaml)
-yaml_seed4=$(grep -oP "\['2024-09-29 00:00:00', 4, \K\d+" reproduce_helene.yaml)
+yaml_seed3=$(grep -oP "\['2024-09-24 00:00:00', 3, \K\d+" reproduce_helene.yaml)
+yaml_seed4=$(grep -oP "\['2024-09-24 00:00:00', 4, \K\d+" reproduce_helene.yaml)
 
 if [ "$seed_mem3" != "$yaml_seed3" ] || [ "$seed_mem4" != "$yaml_seed4" ]; then
     echo "WARNING: Updating seeds in reproduce_helene.yaml (mem3: $yaml_seed3->$seed_mem3, mem4: $yaml_seed4->$seed_mem4)"
-    sed -i "s/\['2024-09-29 00:00:00', 3, [0-9]*\]/['2024-09-29 00:00:00', 3, $seed_mem3]/" reproduce_helene.yaml
-    sed -i "s/\['2024-09-29 00:00:00', 4, [0-9]*\]/['2024-09-29 00:00:00', 4, $seed_mem4]/" reproduce_helene.yaml
+    sed -i "s/\['2024-09-24 00:00:00', 3, [0-9]*\]/['2024-09-24 00:00:00', 3, $seed_mem3]/" reproduce_helene.yaml
+    sed -i "s/\['2024-09-24 00:00:00', 4, [0-9]*\]/['2024-09-24 00:00:00', 4, $seed_mem4]/" reproduce_helene.yaml
 fi
 
 # Run reproduce

From 8f766f52ab00f461ffbc31c966515a831ec4fcd8 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 14 Jan 2026 09:18:52 -0800
Subject: [PATCH 47/69] added aux data for tests

---
 .../ibtracs.ALL.list.v04r01.csv               |  0
 ...reference_track_hato_2017_west_pacific.csv | 18 +++++++++++++++
 ...rence_track_helene_2024_north_atlantic.csv | 22 +++++++++++++++++++
 recipes/tc_tracking/test/extract_era5.yaml    |  2 +-
 4 files changed, 41 insertions(+), 1 deletion(-)
 rename recipes/tc_tracking/test/{ => aux_data}/ibtracs.ALL.list.v04r01.csv (100%)
 create mode 100644 recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv
 create mode 100644 recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv

diff --git a/recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv b/recipes/tc_tracking/test/aux_data/ibtracs.ALL.list.v04r01.csv
similarity index 100%
rename from recipes/tc_tracking/test/ibtracs.ALL.list.v04r01.csv
rename to recipes/tc_tracking/test/aux_data/ibtracs.ALL.list.v04r01.csv
diff --git a/recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv b/recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv
new file mode 100644
index 000000000..1b059ddf2
--- /dev/null
+++ b/recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv
@@ -0,0 +1,18 @@
+track_id,year,month,day,hour,lon,lat,msl,wind_speed,lon_ib,lat_ib,msl_ib,wind_speed_ib,type
+0,2017,8,20,6,127.5,20.25,100042.6,11.14135,127.0,19.8,100700,20,LO
+0,2017,8,20,12,126.25,20.0,99920.69,13.37682,126.5,19.5,100400,25,LO
+0,2017,8,20,18,124.75,19.75,99411.19,14.71875,125.6,19.6,100000,30,LO
+0,2017,8,21,0,124.75,20.0,99353.5,15.51507,125.0,20.0,100000,30,TD
+0,2017,8,21,6,124.0,20.25,99140.69,17.40358,124.0,20.4,99600,35,TS
+0,2017,8,21,12,123.0,20.25,99114.25,19.19828,122.7,20.1,98900,45,TS
+0,2017,8,21,18,121.75,20.5,98904.0,20.54659,121.3,20.0,98900,45,TS
+0,2017,8,22,0,120.25,20.0,99026.25,20.57231,119.9,20.1,98500,50,TS
+0,2017,8,22,6,118.75,20.5,98515.25,21.76146,118.6,20.4,98100,60,TS
+0,2017,8,22,12,117.5,20.75,98860.81,19.70913,117.3,20.6,96700,75,TY
+0,2017,8,22,18,115.75,21.0,98629.56,20.3585,115.9,21.0,96300,80,TY
+0,2017,8,23,0,114.5,21.5,98447.94,23.96312,114.5,21.5,95600,90,TY
+0,2017,8,23,6,113.25,22.5,98561.62,21.51315,112.8,22.2,96400,85,TY
+0,2017,8,23,12,111.0,23.0,99105.94,11.8202,111.0,22.7,97800,60,TS
+0,2017,8,23,18,109.0,23.25,99294.94,12.52222,109.0,23.3,98900,45,TS
+0,2017,8,24,0,107.75,23.0,99549.06,14.2229,107.6,23.5,99600,35,TS
+0,2017,8,24,6,106.5,23.75,99834.75,10.76084,106.0,23.8,100000,30,TD
diff --git a/recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv b/recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv
new file mode 100644
index 000000000..097025b5f
--- /dev/null
+++ b/recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv
@@ -0,0 +1,22 @@
+track_id,year,month,day,hour,lon,lat,msl,wind_speed,lon_ib,lat_ib,msl_ib,wind_speed_ib,type
+0,2024,9,23,12,,,,,278.3,17.2,100400,30,DB
+0,2024,9,23,18,,,,,278.1,17.8,100400,35,DB
+0,2024,9,24,0,278.75,18.75,100360.6,15.62581,277.8,18.2,100200,35,DB
+0,2024,9,24,6,276.75,19.5,100281.8,15.10542,277.2,18.6,100100,35,DB
+0,2024,9,24,12,276.0,19.25,100305.4,16.96963,276.3,19.2,99900,40,TS
+0,2024,9,24,18,275.75,19.5,100204.3,17.21835,275.4,19.4,99700,45,TS
+0,2024,9,25,0,275.0,19.75,99562.31,19.14207,274.8,19.7,99000,50,TS
+0,2024,9,25,6,274.25,20.5,99200.88,20.83507,274.1,20.3,98500,55,TS
+0,2024,9,25,12,274.0,21.5,98551.31,22.38866,273.8,21.1,97900,65,HU
+0,2024,9,25,18,273.5,22.0,98219.5,22.58122,273.5,22.0,97800,70,HU
+0,2024,9,26,0,273.25,22.75,97705.0,24.20595,273.3,22.8,97300,70,HU
+0,2024,9,26,6,273.5,23.5,97704.31,25.95841,273.5,23.6,97000,75,HU
+0,2024,9,26,12,274.25,24.75,96791.25,27.48642,274.2,24.7,96300,85,HU
+0,2024,9,26,18,275.25,26.25,96992.0,31.93279,275.0,26.6,95800,105,HU
+0,2024,9,27,0,275.75,28.5,96418.94,31.27935,275.7,28.7,94100,120,HU
+0,2024,9,27,6,276.75,31.25,97122.88,24.10372,276.7,31.3,95700,80,HU
+0,2024,9,27,12,276.75,34.5,97658.5,18.11959,276.8,34.4,97300,45,TS
+0,2024,9,27,18,275.5,37.5,98334.94,14.60408,275.1,36.8,98300,40,LO
+0,2024,9,28,0,273.0,38.0,98724.5,14.04134,273.4,38.1,98500,40,LO
+0,2024,9,28,6,271.75,37.25,99192.81,10.94264,271.9,37.4,99200,30,LO
+0,2024,9,28,12,272.0,36.5,99795.0,6.417272,272.4,36.6,99700,25,LO
diff --git a/recipes/tc_tracking/test/extract_era5.yaml b/recipes/tc_tracking/test/extract_era5.yaml
index 343568c8f..3ab736fa1 100644
--- a/recipes/tc_tracking/test/extract_era5.yaml
+++ b/recipes/tc_tracking/test/extract_era5.yaml
@@ -10,7 +10,7 @@ cases:
         year: 2017
         basin: west_pacific
 
-ibtracs_source_data: "ibtracs.ALL.list.v04r01.csv"
+ibtracs_source_data: "aux_data_ibtracs.ALL.list.v04r01.csv"
 
 data_source:
     ncar_era5:

From f3286fbc20b3da87bef3e2b0fd01a7b2a2f854cf Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 15 Jan 2026 07:03:10 -0800
Subject: [PATCH 48/69] test for reference track extraction

---
 recipes/tc_tracking/test/README.md            | 28 +++++++++++++++
 .../test/{ => cfg}/baseline_helene.yaml       |  0
 .../test/{ => cfg}/extract_era5.yaml          |  2 +-
 .../test/{ => cfg}/reproduce_helene.yaml      |  0
 .../test/test_historic_tc_extraction.sh       | 36 +++++++++++++++++++
 recipes/tc_tracking/test/test_tc_hunt.sh      |  4 +--
 6 files changed, 67 insertions(+), 3 deletions(-)
 rename recipes/tc_tracking/test/{ => cfg}/baseline_helene.yaml (100%)
 rename recipes/tc_tracking/test/{ => cfg}/extract_era5.yaml (94%)
 rename recipes/tc_tracking/test/{ => cfg}/reproduce_helene.yaml (100%)
 create mode 100644 recipes/tc_tracking/test/test_historic_tc_extraction.sh

diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md
index ae5808372..1ab5a7b85 100644
--- a/recipes/tc_tracking/test/README.md
+++ b/recipes/tc_tracking/test/README.md
@@ -56,3 +56,31 @@ Repeat the diff call for all reproduced ensemble members, the return should alwa
 
 
 ## Test 2: Extracting indivdual storms from historic data
+
+> [!Note]
+> The test can be done manually following the steps below. Alternatively,
+> `test_historic_tc_extraction.sh` provides an automated way for running
+> the full test. If, at the end you see a message containing `all good, yay (:`,
+> the test finished successfully.
+
+This mode lets users extract stroms from historic data sets. The user can choose
+a storm, the script will look for that storm in the IBTrACS data base, obtain data
+around the storm's life time from a data source (on-prem or online) and then extract
+that storm using TempestExtremes.
+
+For the current test, let us extract Typhoon Hato (2017) and Hurricane Helene (2024)
+from ERA5:
+```bash
+python ../tc_hunt.py --config-path=$(pwd) --config-name=extract_era5.yaml
+```
+
+### Expected Result
+
+The run should produce two reference tracks in `outputs_reference_tracks/`.
+Now, let us compare the extracted tracks with the baseline:
+```bash
+diff outputs_reference_tracks/reference_track_hato_2017_west_pacific.csv \
+     aux_data/reference_track_hato_2017_west_pacific.csv
+diff outputs_reference_tracks/reference_track_helene_2024_north_atlantic.csv \
+     aux_data/reference_track_helene_2024_north_atlantic.csv
+```
diff --git a/recipes/tc_tracking/test/baseline_helene.yaml b/recipes/tc_tracking/test/cfg/baseline_helene.yaml
similarity index 100%
rename from recipes/tc_tracking/test/baseline_helene.yaml
rename to recipes/tc_tracking/test/cfg/baseline_helene.yaml
diff --git a/recipes/tc_tracking/test/extract_era5.yaml b/recipes/tc_tracking/test/cfg/extract_era5.yaml
similarity index 94%
rename from recipes/tc_tracking/test/extract_era5.yaml
rename to recipes/tc_tracking/test/cfg/extract_era5.yaml
index 3ab736fa1..b7e1d3265 100644
--- a/recipes/tc_tracking/test/extract_era5.yaml
+++ b/recipes/tc_tracking/test/cfg/extract_era5.yaml
@@ -10,7 +10,7 @@ cases:
         year: 2017
         basin: west_pacific
 
-ibtracs_source_data: "aux_data_ibtracs.ALL.list.v04r01.csv"
+ibtracs_source_data: "aux_data/ibtracs.ALL.list.v04r01.csv"
 
 data_source:
     ncar_era5:
diff --git a/recipes/tc_tracking/test/reproduce_helene.yaml b/recipes/tc_tracking/test/cfg/reproduce_helene.yaml
similarity index 100%
rename from recipes/tc_tracking/test/reproduce_helene.yaml
rename to recipes/tc_tracking/test/cfg/reproduce_helene.yaml
diff --git a/recipes/tc_tracking/test/test_historic_tc_extraction.sh b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
new file mode 100644
index 000000000..c0a15e294
--- /dev/null
+++ b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+set -e
+
+
+# Run extraction
+echo ""
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo " >>> running baseline. might take a couple of minutes to download data..."
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo ""
+python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=extract_era5.yaml
+
+
+# Compare reference track files between ground truth and produced output
+reference_tracks=(
+    "reference_track_hato_2017_west_pacific.csv"
+    "reference_track_helene_2024_north_atlantic.csv"
+)
+for f in "${reference_tracks[@]}"; do
+    if ! diff -q "aux_data/$f" "outputs_reference_tracks/$f" > /dev/null 2>&1; then
+        echo ""
+        echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+        echo " >>> oh no!!! ERROR: $f differs between baseline and reproduce"
+        echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+        echo ""
+        diff "aux_data/$f" "outputs_reference_tracks/$f"
+        exit 1
+    fi
+done
+
+
+echo ""
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo " >>> all good, yay (: track files have been reproduced successfully"
+echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
+echo ""
diff --git a/recipes/tc_tracking/test/test_tc_hunt.sh b/recipes/tc_tracking/test/test_tc_hunt.sh
index 226d7e4ba..6dd299683 100644
--- a/recipes/tc_tracking/test/test_tc_hunt.sh
+++ b/recipes/tc_tracking/test/test_tc_hunt.sh
@@ -10,7 +10,7 @@ echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 echo " >>> running baseline. might take a couple of minutes..."
 echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
 echo ""
-python ../tc_hunt.py --config-path=$(pwd) --config-name=baseline_helene.yaml
+python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=baseline_helene.yaml
 
 # Get track files for members 2, 3, 4
 mapfile -t track_files < <(ls "$baseline_dir"/tracks_*.csv | sort | grep -E 'mem_000[234]' | xargs -n1 basename)
@@ -35,7 +35,7 @@ echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 echo " >>> reproducing members 2, 3 and 4. might take a couple of minutes..."
 echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
 echo ""
-python ../tc_hunt.py --config-path=$(pwd) --config-name=reproduce_helene.yaml
+python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=reproduce_helene.yaml
 
 # Compare track files for members 3 and 4, error if any differ
 for f in "${track_files[@]}"; do

From 3638bcca3d90a513728bed1f6ddd7598d96d9ca9 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 16 Jan 2026 00:21:50 -0800
Subject: [PATCH 49/69] wip

---
 recipes/tc_tracking/.gitignore                |  2 +-
 recipes/tc_tracking/README.md                 | 35 +++++++++++++++++--
 recipes/tc_tracking/cfg/extract_era5.yaml     | 11 ++++--
 recipes/tc_tracking/cfg/helene.yaml           | 17 ++++++---
 recipes/tc_tracking/cfg/reproduce_helene.yaml | 15 +++++---
 .../cfg/reproduce_stability_test.yaml         |  9 ++++-
 recipes/tc_tracking/cfg/slay_helena.yaml      | 11 ++++--
 recipes/tc_tracking/cfg/stability_test.yaml   |  9 ++++-
 recipes/tc_tracking/src/generate_ensembles.py |  3 +-
 recipes/tc_tracking/test/README.md            | 16 +++++----
 ...sv => ibtracs.HATO_HELENE.list.v04r01.csv} |  0
 .../tc_tracking/test/cfg/extract_era5.yaml    |  2 +-
 .../test/test_historic_tc_extraction.sh       |  2 +-
 recipes/tc_tracking/test/test_tc_hunt.sh      | 10 +++---
 14 files changed, 107 insertions(+), 35 deletions(-)
 rename recipes/tc_tracking/test/aux_data/{ibtracs.ALL.list.v04r01.csv => ibtracs.HATO_HELENE.list.v04r01.csv} (100%)

diff --git a/recipes/tc_tracking/.gitignore b/recipes/tc_tracking/.gitignore
index 0b695cf51..45e978e37 100644
--- a/recipes/tc_tracking/.gitignore
+++ b/recipes/tc_tracking/.gitignore
@@ -5,7 +5,7 @@ build/
 dist/
 wheels/
 *.egg-info
-outputs/
+outputs*/
 *.zarr
 *.nc
 .python-version
diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 0a8c811b5..07fa81f1c 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -34,8 +34,7 @@ Since torch-harmonics has to be compiled and TempestExtreme is a commandline too
     ```
 
 
-
-## Run
+## run
 
 The project contains a script for running the code.
 
@@ -43,13 +42,15 @@ The project contains a script for running the code.
 python tc_hunt.py --config-name=tc_selection_ens.yaml
 ```
 
-## Modes
+## modes
 
 ### extract Reference tracks from ERA5 using IBTrACS as ground truth
 
 ```yaml
 mode: 'extract_baseline'
 ```
+- download ibtracs file with following command...
+- extract storms from field data to enable fair comparison
 
 IBTrACS is used as ground truth for the reference tracks. However, due to various reasons simulation data is compared against extracting the tracks directly from ERA5...
 
@@ -71,3 +72,31 @@ Only works if exact same batch is reproduced, that means:
 - ensemble size required if final batch of ensemble shall be reproduced
 - random seed has to be provided for each ensemble member, but the global random seed won't have effect here, as it mainly impacts the generation of random seeds for members
 - works only on same machine using identical environment. No guarantees otherwise
+- works only with FCN3
+
+
+## visualise
+
+Have a stab at the notebook...
+
+
+## example workflow
+
+### extract baseline
+
+- download ibtraccs data
+- extract baseline
+
+### produce ensemble
+
+- run forecast loop
+
+### visualise members
+
+- visualise in notebook
+
+### re-run interesting members to extract fields
+
+- reproduce, this time store fields
+- visualise track and field
+
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 536d946ec..5961c5a7b 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -82,7 +82,7 @@ cases:
     #     year: 2017
     #     basin: southern_pacific
 
-ibtracs_source_data: "/fcn3_cyclones/ibtracs_data/ibtracs.ALL.list.v04r01.csv"
+ibtracs_source_data: "./test/aux_data/ibtracs.ALL.list.v04r01.csv"
 
 data_source:
     h5_era5_train:
@@ -117,7 +117,7 @@ data_source:
             _target_: earth2studio.data.GFS
 
 orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
-store_dir: "./outputs"
+store_dir: "./outputs_${project}"
 
 cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
@@ -126,3 +126,10 @@ cyclone_tracking:
     keep_raw_data: False
     use_ram: True
     print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index f8f4153e8..1f5d0e573 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -6,12 +6,12 @@ random_seed: 7777
 # ic_block_start: "2024-09-23 00:00:00" # first IC
 # ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
 # ic_block_step: 24            # number of hours between individual ICs
-ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 
-n_steps: 15
-ensemble_size: 4
+n_steps: 28
+ensemble_size: 16
 batch_size: 2
-orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+orography_path: './orography.nc'
 
 data_source:
     _target_: earth2studio.data.NCAR_ERA5
@@ -20,7 +20,7 @@ data_source:
     # _target_: earth2studio.data.CDS
 
 store_type: "netcdf"
-store_dir: "./outputs"
+store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
 cyclone_tracking:
@@ -32,3 +32,10 @@ cyclone_tracking:
     use_ram: True
     task_timeout_seconds: 120
     print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index 555b428e9..a7c3bd900 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -4,22 +4,20 @@ model: 'fcn3'
 random_seed: 7552
 
 reproduce_members: [
-                    # ['2024-09-24 00:00:00',  2, 4045540270],
                     ['2024-09-24 00:00:00',  3, 4045540270],
                     ['2024-09-24 00:00:00', 14, 3066645577],
-                    ]
+                   ]
 
 n_steps: 15
 ensemble_size: 15
 batch_size: 2
-orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+orography_path: './orography.nc'
 
 data_source:
     _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
 
 store_type: "netcdf"
-store_dir: "./outputs"
+store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
 cyclone_tracking:
@@ -31,3 +29,10 @@ cyclone_tracking:
     use_ram: True
     task_timeout_seconds: 120
     print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
index 5412f4b57..248ee6411 100644
--- a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
@@ -24,7 +24,7 @@ data_source:
 
 
 store_type: "none"
-store_dir:  "./outputs"
+store_dir:  "./outputs_${project}"
 out_vars: ['t1000', 'u250', 'v850']
 
 cyclone_tracking:
@@ -40,3 +40,10 @@ cyclone_tracking:
 stability_check:
     variables: ['t1000', 'u250', 'v850']
     thresholds: [10, 10, 10]
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index f8907fd0f..996be54e7 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -20,7 +20,7 @@ data_source:
     # _target_: earth2studio.data.CDS
 
 store_type: "netcdf"
-store_dir: "./outputs"
+store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
 cyclone_tracking:
@@ -35,4 +35,11 @@ cyclone_tracking:
 
 stability_check:
     variables: ['t1000', 'u250', 'v850']
-    thresholds: [10, 10, 10]
\ No newline at end of file
+    thresholds: [10, 10, 10]
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        # dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
diff --git a/recipes/tc_tracking/cfg/stability_test.yaml b/recipes/tc_tracking/cfg/stability_test.yaml
index 5df45e927..a2ce6b4ef 100644
--- a/recipes/tc_tracking/cfg/stability_test.yaml
+++ b/recipes/tc_tracking/cfg/stability_test.yaml
@@ -24,7 +24,7 @@ data_source:
 
 
 store_type: "netcdf"
-store_dir:  "./outputs"
+store_dir:  "./outputs_${project}"
 out_vars: ['t1000', 'u250', 'v850']
 
 cyclone_tracking:
@@ -36,3 +36,10 @@ cyclone_tracking:
     use_ram: True
     task_timeout_seconds: 120
     print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 139cade9c..3e2b6a06d 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -302,6 +302,8 @@ def generate_ensemble(cfg):
 def reproduce_members(cfg):
     if cfg.store_type == "zarr":
         raise ValueError("Zarr output not suported for reproducing ensemble members")
+    if cfg.model != 'fcn3':
+        raise ValueError('Currently, reproducibility works for FCN3 only')
 
     initialise(cfg)
 
@@ -309,7 +311,6 @@ def reproduce_members(cfg):
 
     model = load_model(cfg)
 
-    # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0)
     store, out_coords = (
         run_with_rank_ordered_execution(  # TODO: wrap only zarr store in that loop
             setup_output,
diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md
index 1ab5a7b85..8018696f2 100644
--- a/recipes/tc_tracking/test/README.md
+++ b/recipes/tc_tracking/test/README.md
@@ -8,8 +8,8 @@
 > If, at the end you see a message containing `all good, yay (:`, the test
 > finished successfully.
 
-This case will test the whether the pipeline can genreate an ensemble forecast
-using FCN3 and track tropical cylones using TempestExtremes.
+This case will test whether the pipeline can generate an ensemble forecast
+using FCN3 and track tropical cyclones using TempestExtremes.
 Furthermore, it is tested if those exact tracks can be reproduced.
 Note that reproducing with AIFS-ENS won't work as the model does not provide
 access to its internal random state.
@@ -33,7 +33,7 @@ in the yaml file with the seeds from the csv file names.
 Having made sure that the configurations for reproduicing the ensemble members
 is correct, let us now reproduce members three and four of the previous run:
 ```bash
-python ../tc_hunt.py --config-path=$(pwd) --config-name=reproduce_helene.yaml
+python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=reproduce_helene.yaml
 ```
 Note that the baseline run was produced with batch size two. For reproducibility,
 we need to apply the same batch size in the reproduction run and reproduce the full
@@ -44,7 +44,7 @@ that member two will also be reproduced, as it is in the same batch as member th
 
 There should now be three track files in `outputs_reproduce_helene/cyclone_tracks_te`,
 with file names identical to the associated files in `outputs_baseline_helene/cyclone_tracks_te`.
-If also the content of the files, ie the genereted tracks, is identical can be tested trhough
+Whether the content of the files (i.e., the generated tracks) is also identical can be tested through
 a simple `diff` call:
 
 ```bash
@@ -63,7 +63,7 @@ Repeat the diff call for all reproduced ensemble members, the return should alwa
 > the full test. If, at the end you see a message containing `all good, yay (:`,
 > the test finished successfully.
 
-This mode lets users extract stroms from historic data sets. The user can choose
+This mode lets users extract storms from historic data sets. The user can choose
 a storm, the script will look for that storm in the IBTrACS data base, obtain data
 around the storm's life time from a data source (on-prem or online) and then extract
 that storm using TempestExtremes.
@@ -71,13 +71,15 @@ that storm using TempestExtremes.
 For the current test, let us extract Typhoon Hato (2017) and Hurricane Helene (2024)
 from ERA5:
 ```bash
-python ../tc_hunt.py --config-path=$(pwd) --config-name=extract_era5.yaml
+cd earth2studio/recipes/tc_tracking/test
+python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=extract_era5.yaml
 ```
 
 ### Expected Result
 
 The run should produce two reference tracks in `outputs_reference_tracks/`.
-Now, let us compare the extracted tracks with the baseline:
+Now, let us compare the extracted tracks with the baseline, in both cases
+the files should be identical:
 ```bash
 diff outputs_reference_tracks/reference_track_hato_2017_west_pacific.csv \
      aux_data/reference_track_hato_2017_west_pacific.csv
diff --git a/recipes/tc_tracking/test/aux_data/ibtracs.ALL.list.v04r01.csv b/recipes/tc_tracking/test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv
similarity index 100%
rename from recipes/tc_tracking/test/aux_data/ibtracs.ALL.list.v04r01.csv
rename to recipes/tc_tracking/test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv
diff --git a/recipes/tc_tracking/test/cfg/extract_era5.yaml b/recipes/tc_tracking/test/cfg/extract_era5.yaml
index b7e1d3265..37b4138ea 100644
--- a/recipes/tc_tracking/test/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/test/cfg/extract_era5.yaml
@@ -10,7 +10,7 @@ cases:
         year: 2017
         basin: west_pacific
 
-ibtracs_source_data: "aux_data/ibtracs.ALL.list.v04r01.csv"
+ibtracs_source_data: "aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
     ncar_era5:
diff --git a/recipes/tc_tracking/test/test_historic_tc_extraction.sh b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
index c0a15e294..d159a112e 100644
--- a/recipes/tc_tracking/test/test_historic_tc_extraction.sh
+++ b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
@@ -31,6 +31,6 @@ done
 
 echo ""
 echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
-echo " >>> all good, yay (: track files have been reproduced successfully"
+echo " >>> all good, yay (: reference tracks have been reproduced successfully"
 echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
 echo ""
diff --git a/recipes/tc_tracking/test/test_tc_hunt.sh b/recipes/tc_tracking/test/test_tc_hunt.sh
index 6dd299683..c75b7aa49 100644
--- a/recipes/tc_tracking/test/test_tc_hunt.sh
+++ b/recipes/tc_tracking/test/test_tc_hunt.sh
@@ -20,13 +20,13 @@ seed_mem3=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0003' | grep -oP 'seed
 seed_mem4=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0004' | grep -oP 'seed_\K\d+')
 
 # Check if seeds in reproduce_helene.yaml match, update if not
-yaml_seed3=$(grep -oP "\['2024-09-24 00:00:00', 3, \K\d+" reproduce_helene.yaml)
-yaml_seed4=$(grep -oP "\['2024-09-24 00:00:00', 4, \K\d+" reproduce_helene.yaml)
+yaml_seed3=$(grep -oP "\['2024-09-24 00:00:00', 3, \K\d+" cfg/reproduce_helene.yaml)
+yaml_seed4=$(grep -oP "\['2024-09-24 00:00:00', 4, \K\d+" cfg/reproduce_helene.yaml)
 
 if [ "$seed_mem3" != "$yaml_seed3" ] || [ "$seed_mem4" != "$yaml_seed4" ]; then
-    echo "WARNING: Updating seeds in reproduce_helene.yaml (mem3: $yaml_seed3->$seed_mem3, mem4: $yaml_seed4->$seed_mem4)"
-    sed -i "s/\['2024-09-24 00:00:00', 3, [0-9]*\]/['2024-09-24 00:00:00', 3, $seed_mem3]/" reproduce_helene.yaml
-    sed -i "s/\['2024-09-24 00:00:00', 4, [0-9]*\]/['2024-09-24 00:00:00', 4, $seed_mem4]/" reproduce_helene.yaml
+    echo "WARNING: Updating seeds in cfg/reproduce_helene.yaml (mem3: $yaml_seed3->$seed_mem3, mem4: $yaml_seed4->$seed_mem4)"
+    sed -i "s/\['2024-09-24 00:00:00', 3, [0-9]*\]/['2024-09-24 00:00:00', 3, $seed_mem3]/" cfg/reproduce_helene.yaml
+    sed -i "s/\['2024-09-24 00:00:00', 4, [0-9]*\]/['2024-09-24 00:00:00', 4, $seed_mem4]/" cfg/reproduce_helene.yaml
 fi
 
 # Run reproduce

From 5b7ae87d52c24df761f6ff87099af3f509592baa Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 16 Jan 2026 01:45:47 -0800
Subject: [PATCH 50/69] track plotting notebook

---
 recipes/tc_tracking/cfg/helene.yaml           |   2 +-
 .../tc_tracking => plotting}/README.md        |   0
 .../analyse_n_plot.py                         |   2 +-
 .../tc_tracking => plotting}/data_handling.py |   4 +-
 .../plotting_helpers.py                       |   0
 .../plotting/tracks_slayground.ipynb          | 288 +++++++++
 .../post_proc/tc_tracking/.gitignore          |   2 -
 .../tc_tracking/ensemble_slayground.ipynb     | 573 ------------------
 .../tc_tracking/tracks_slayground.ipynb       | 223 -------
 9 files changed, 292 insertions(+), 802 deletions(-)
 rename recipes/tc_tracking/{post_proc/tc_tracking => plotting}/README.md (100%)
 rename recipes/tc_tracking/{post_proc/tc_tracking => plotting}/analyse_n_plot.py (99%)
 rename recipes/tc_tracking/{post_proc/tc_tracking => plotting}/data_handling.py (99%)
 rename recipes/tc_tracking/{post_proc/tc_tracking => plotting}/plotting_helpers.py (100%)
 create mode 100644 recipes/tc_tracking/plotting/tracks_slayground.ipynb
 delete mode 100644 recipes/tc_tracking/post_proc/tc_tracking/.gitignore
 delete mode 100644 recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
 delete mode 100644 recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb

diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index 1f5d0e573..5c2ec91ce 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -19,7 +19,7 @@ data_source:
     # _target_: earth2studio.data.IFS
     # _target_: earth2studio.data.CDS
 
-store_type: "netcdf"
+store_type: "none"
 store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/README.md b/recipes/tc_tracking/plotting/README.md
similarity index 100%
rename from recipes/tc_tracking/post_proc/tc_tracking/README.md
rename to recipes/tc_tracking/plotting/README.md
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py b/recipes/tc_tracking/plotting/analyse_n_plot.py
similarity index 99%
rename from recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
rename to recipes/tc_tracking/plotting/analyse_n_plot.py
index 6d758b4fa..236105ec2 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py
+++ b/recipes/tc_tracking/plotting/analyse_n_plot.py
@@ -27,7 +27,7 @@ def load_tracks(case, pred_track_dir, tru_track_dir, out_dir):
     )
     tru_track["dist"] = np.zeros(len(tru_track))
 
-    pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir, case))
+    pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir))
     n_members = len(pred_tracks)
 
     pred_tracks = match_tracks(pred_tracks, tru_track, case)
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py b/recipes/tc_tracking/plotting/data_handling.py
similarity index 99%
rename from recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
rename to recipes/tc_tracking/plotting/data_handling.py
index c6cf5fb7d..faa5c553b 100644
--- a/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py
+++ b/recipes/tc_tracking/plotting/data_handling.py
@@ -136,8 +136,8 @@ def extract_tracks(in_dir):
         # read csv, strip whitespace from column names
         _tracks = extract_tracks_from_file(csv_file)
 
-        mem = int(csv_file.split("_mems")[-1].split(".csv")[0])
-        ic = pd.to_datetime(csv_file.split("_mems")[0][-19:])
+        mem = int(csv_file.split("_mem_")[-1].split("_seed_")[0])
+        ic = pd.to_datetime(csv_file.split("_mem_")[0][-19:])
 
         tracks.append({"ic": ic, "member": mem, "tracks": _tracks})
 
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py b/recipes/tc_tracking/plotting/plotting_helpers.py
similarity index 100%
rename from recipes/tc_tracking/post_proc/tc_tracking/plotting_helpers.py
rename to recipes/tc_tracking/plotting/plotting_helpers.py
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
new file mode 100644
index 000000000..c9ccaf2ed
--- /dev/null
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## read csv file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  time  track_id     lon    lat        msl  wind_speed  \\\n",
+      "0  2024-09-23 12:00:00         0     NaN    NaN        NaN         NaN   \n",
+      "1  2024-09-23 18:00:00         0     NaN    NaN        NaN         NaN   \n",
+      "2  2024-09-24 00:00:00         0  278.75  18.75  100360.60   15.625810   \n",
+      "3  2024-09-24 06:00:00         0  276.75  19.50  100281.80   15.105420   \n",
+      "4  2024-09-24 12:00:00         0  276.00  19.25  100305.40   16.969630   \n",
+      "5  2024-09-24 18:00:00         0  275.75  19.50  100204.30   17.218350   \n",
+      "6  2024-09-25 00:00:00         0  275.00  19.75   99562.31   19.142070   \n",
+      "7  2024-09-25 06:00:00         0  274.25  20.50   99200.88   20.835070   \n",
+      "8  2024-09-25 12:00:00         0  274.00  21.50   98551.31   22.388660   \n",
+      "9  2024-09-25 18:00:00         0  273.50  22.00   98219.50   22.581220   \n",
+      "10 2024-09-26 00:00:00         0  273.25  22.75   97705.00   24.205950   \n",
+      "11 2024-09-26 06:00:00         0  273.50  23.50   97704.31   25.958410   \n",
+      "12 2024-09-26 12:00:00         0  274.25  24.75   96791.25   27.486420   \n",
+      "13 2024-09-26 18:00:00         0  275.25  26.25   96992.00   31.932790   \n",
+      "14 2024-09-27 00:00:00         0  275.75  28.50   96418.94   31.279350   \n",
+      "15 2024-09-27 06:00:00         0  276.75  31.25   97122.88   24.103720   \n",
+      "16 2024-09-27 12:00:00         0  276.75  34.50   97658.50   18.119590   \n",
+      "17 2024-09-27 18:00:00         0  275.50  37.50   98334.94   14.604080   \n",
+      "18 2024-09-28 00:00:00         0  273.00  38.00   98724.50   14.041340   \n",
+      "19 2024-09-28 06:00:00         0  271.75  37.25   99192.81   10.942640   \n",
+      "20 2024-09-28 12:00:00         0  272.00  36.50   99795.00    6.417272   \n",
+      "\n",
+      "    lon_ib  lat_ib  msl_ib  wind_speed_ib type  dist  \n",
+      "0    278.3    17.2  100400             30   DB   0.0  \n",
+      "1    278.1    17.8  100400             35   DB   0.0  \n",
+      "2    277.8    18.2  100200             35   DB   0.0  \n",
+      "3    277.2    18.6  100100             35   DB   0.0  \n",
+      "4    276.3    19.2   99900             40   TS   0.0  \n",
+      "5    275.4    19.4   99700             45   TS   0.0  \n",
+      "6    274.8    19.7   99000             50   TS   0.0  \n",
+      "7    274.1    20.3   98500             55   TS   0.0  \n",
+      "8    273.8    21.1   97900             65   HU   0.0  \n",
+      "9    273.5    22.0   97800             70   HU   0.0  \n",
+      "10   273.3    22.8   97300             70   HU   0.0  \n",
+      "11   273.5    23.6   97000             75   HU   0.0  \n",
+      "12   274.2    24.7   96300             85   HU   0.0  \n",
+      "13   275.0    26.6   95800            105   HU   0.0  \n",
+      "14   275.7    28.7   94100            120   HU   0.0  \n",
+      "15   276.7    31.3   95700             80   HU   0.0  \n",
+      "16   276.8    34.4   97300             45   TS   0.0  \n",
+      "17   275.1    36.8   98300             40   LO   0.0  \n",
+      "18   273.4    38.1   98500             40   LO   0.0  \n",
+      "19   271.9    37.4   99200             30   LO   0.0  \n",
+      "20   272.4    36.6   99700             25   LO   0.0  \n",
+      "====>>>>>>>>>>>>>>>>>>   []\n",
+      "HELENE 2024: matched 0 out of 0 tracks, with distances ranging from 25371.0 to -0.0 km\n",
+      "---->>>>>>>>>>>>>>>>>>   []\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 35\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'track_data/predictions/erin_lagged'\u001b[39;00m\n\u001b[1;32m     33\u001b[0m out_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./plots\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 35\u001b[0m tru_track, pred_tracks, ens_mean, n_members, out_dir \u001b[38;5;241m=\u001b[39m \u001b[43mload_tracks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcase\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     36\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mpred_track_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred_track_dir\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mtru_track_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtru_track_dir\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mout_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout_dir\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py:42\u001b[0m, in \u001b[0;36mload_tracks\u001b[0;34m(case, pred_track_dir, tru_track_dir, out_dir)\u001b[0m\n\u001b[1;32m     39\u001b[0m     out_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(out_dir, case)\n\u001b[1;32m     40\u001b[0m     os\u001b[38;5;241m.\u001b[39mmakedirs(out_dir, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 42\u001b[0m ens_mean \u001b[38;5;241m=\u001b[39m \u001b[43mget_ensemble_averages\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred_tracks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpred_tracks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtru_track\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtru_track\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tru_track, pred_tracks, ens_mean, n_members, out_dir\n",
+      "File \u001b[0;32m/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py:369\u001b[0m, in \u001b[0;36mget_ensemble_averages\u001b[0;34m(pred_tracks, tru_track, vars)\u001b[0m\n\u001b[1;32m    361\u001b[0m stats \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    363\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_members\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    364\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m: {var: [] \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m},\n\u001b[1;32m    365\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariance\u001b[39m\u001b[38;5;124m\"\u001b[39m: {var: [] \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m},\n\u001b[1;32m    366\u001b[0m }\n\u001b[1;32m    368\u001b[0m \u001b[38;5;66;03m# loop over all tracks, match with\u001b[39;00m\n\u001b[0;32m--> 369\u001b[0m this_is_the_last_time \u001b[38;5;241m=\u001b[39m \u001b[43mpred_tracks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mic\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    371\u001b[0m \u001b[38;5;66;03m# iterate over all tracks to get set of all times present\u001b[39;00m\n\u001b[1;32m    372\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m track \u001b[38;5;129;01min\u001b[39;00m pred_tracks:\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "from os.path import join\n",
+    "from analyse_n_plot import load_tracks\n",
+    "\n",
+    "# case = 'amphan_2020_north_indian'\n",
+    "# case = 'beryl_2024_north_atlantic'\n",
+    "# case = 'debbie_2017_southern_pacific'\n",
+    "# case = 'dorian_2019_north_atlantic'\n",
+    "# case = 'harvey_2017_north_atlantic'\n",
+    "# case = 'hato_2017_west_pacific'\n",
+    "case = 'helene_2024_north_atlantic'\n",
+    "# case = 'ian_2022_north_atlantic'\n",
+    "# case = 'iota_2020_north_atlantic'\n",
+    "# case = 'irma_2017_north_atlantic'\n",
+    "# case = 'lan_2017_west_pacific'\n",
+    "# case = 'lee_2023_north_atlantic'\n",
+    "# case = 'lorenzo_2019_north_atlantic'\n",
+    "# case = 'maria_2017_north_atlantic'\n",
+    "# case = 'mawar_2023_west_pacific'\n",
+    "# case = 'michael_2018_north_atlantic'\n",
+    "# case = 'milton_2024_north_atlantic'\n",
+    "# case = 'ophelia_2017_north_atlantic'\n",
+    "# case = 'yagi_2024_west_pacific'\n",
+    "\n",
+    "# case = 'erin_2025_north_atlantic'\n",
+    "\n",
+    "base_path = '/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking'\n",
+    "\n",
+    "pred_track_dir = f'plotting_data_helene/cyclone_tracks_te'\n",
+    "tru_track_dir = f'test/aux_data'\n",
+    "# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\n",
+    "# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\n",
+    "# pred_track_dir = 'track_data/predictions/erin_lagged'\n",
+    "out_dir = f'./plots'\n",
+    "\n",
+    "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",
+    "                                                                   pred_track_dir=join(base_path, pred_track_dir),\n",
+    "                                                                   tru_track_dir=join(base_path, tru_track_dir),\n",
+    "                                                                   out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot tracks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_spaghetti\n",
+    "\n",
+    "plot_spaghetti(true_track=tru_track,\n",
+    "               pred_tracks=pred_tracks,\n",
+    "               ensemble_mean=ens_mean['mean'],\n",
+    "               case=case,\n",
+    "               n_members=n_members,\n",
+    "               out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot intensities over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_over_time\n",
+    "\n",
+    "plot_over_time(pred_tracks=pred_tracks,\n",
+    "               tru_track=tru_track,\n",
+    "               ensemble_mean=ens_mean,\n",
+    "               case=case,\n",
+    "               n_members=n_members,\n",
+    "               out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot relative intensities over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_relative_over_time\n",
+    "\n",
+    "plot_relative_over_time(pred_tracks=pred_tracks,\n",
+    "                        tru_track=tru_track,\n",
+    "                        ensemble_mean=ens_mean,\n",
+    "                        case=case,\n",
+    "                        n_members=n_members,\n",
+    "                        out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot ERA5 over IB vars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_ib_era5\n",
+    "\n",
+    "plot_ib_era5(tru_track=tru_track,\n",
+    "             case=case,\n",
+    "             vars=['msl', 'wind_speed'],\n",
+    "             out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extreme values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotting_helpers import plot_extreme_extremes_histograms\n",
+    "\n",
+    "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n",
+    "                                 tru_track=tru_track,\n",
+    "                                 ensemble_mean=ens_mean,\n",
+    "                                 case=case,\n",
+    "                                 out_dir=out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## moments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from data_handling import compute_averages_of_errors_over_lead_time\n",
+    "from plotting_helpers import plot_errors_over_lead_time\n",
+    "\n",
+    "vars = ['wind_speed', 'msl', 'dist']\n",
+    "\n",
+    "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n",
+    "                                                        tru_track=tru_track,\n",
+    "                                                        vars=vars)\n",
+    "\n",
+    "plot_errors_over_lead_time(err_dict=err_dict,\n",
+    "                           case=case,\n",
+    "                           ic=pred_tracks[0]['ic'],\n",
+    "                           n_members=n_members,\n",
+    "                           n_tracks=len(pred_tracks),\n",
+    "                           out_dir=out_dir)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/.gitignore b/recipes/tc_tracking/post_proc/tc_tracking/.gitignore
deleted file mode 100644
index d2cbc61b5..000000000
--- a/recipes/tc_tracking/post_proc/tc_tracking/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-tracks_dat
-plots
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb b/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
deleted file mode 100644
index c192c783d..000000000
--- a/recipes/tc_tracking/post_proc/tc_tracking/ensemble_slayground.ipynb
+++ /dev/null
@@ -1,573 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## read csv file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:   0%|          | 0/19 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AMPHAN 2020: matched 256 out of 256 tracks, with distances ranging from 35.2 to 35.2 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:   5%|▌         | 1/19 [00:06<01:59,  6.63s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BERYL 2024: matched 242 out of 256 tracks, with distances ranging from 114.5 to 285.1 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  11%|█         | 2/19 [00:12<01:41,  6.00s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEBBIE 2017: matched 256 out of 256 tracks, with distances ranging from 7.7 to 7.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  16%|█▌        | 3/19 [00:20<01:54,  7.16s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DORIAN 2019: matched 70 out of 256 tracks, with distances ranging from 28.3 to 295.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  21%|██        | 4/19 [00:33<02:20,  9.38s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HARVEY 2017: matched 256 out of 256 tracks, with distances ranging from 42.2 to 42.2 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  26%|██▋       | 5/19 [00:47<02:33, 11.00s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HATO 2017: matched 253 out of 256 tracks, with distances ranging from 72.8 to 72.8 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:  32%|███▏      | 6/19 [01:01<02:36, 12.07s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HELENE 2024: matched 256 out of 256 tracks, with distances ranging from 21.7 to 21.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  37%|███▋      | 7/19 [01:15<02:32, 12.73s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IAN 2022: matched 256 out of 256 tracks, with distances ranging from 29.8 to 29.8 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:  42%|████▏     | 8/19 [01:29<02:22, 12.97s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IOTA 2020: matched 256 out of 256 tracks, with distances ranging from 73.7 to 73.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  47%|████▋     | 9/19 [01:37<01:53, 11.39s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IRMA 2017: matched 243 out of 256 tracks, with distances ranging from 7.7 to 7.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  53%|█████▎    | 10/19 [01:53<01:56, 13.00s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LAN 2017: matched 256 out of 256 tracks, with distances ranging from 15.6 to 15.6 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:  58%|█████▊    | 11/19 [02:02<01:33, 11.70s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LEE 2023: matched 256 out of 256 tracks, with distances ranging from 46.9 to 46.9 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  63%|██████▎   | 12/19 [02:20<01:35, 13.57s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LORENZO 2019: matched 256 out of 256 tracks, with distances ranging from 33.8 to 116.7 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:  68%|██████▊   | 13/19 [02:33<01:21, 13.60s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MARIA 2017: matched 256 out of 256 tracks, with distances ranging from 38.4 to 38.4 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  74%|███████▎  | 14/19 [02:46<01:06, 13.39s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAWAR 2023: matched 256 out of 256 tracks, with distances ranging from 85.0 to 85.0 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  79%|███████▉  | 15/19 [02:54<00:46, 11.55s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MICHAEL 2018: matched 256 out of 256 tracks, with distances ranging from 50.0 to 50.0 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  84%|████████▍ | 16/19 [03:08<00:37, 12.56s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MILTON 2024: matched 256 out of 256 tracks, with distances ranging from 44.8 to 44.8 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading storm data:  89%|████████▉ | 17/19 [03:22<00:25, 12.78s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OPHELIA 2017: matched 227 out of 256 tracks, with distances ranging from 11.1 to 101.6 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data:  95%|█████████▍| 18/19 [03:33<00:12, 12.20s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "YAGI 2024: matched 256 out of 256 tracks, with distances ranging from 17.5 to 17.5 km\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:356: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  stats['variance'][var] = np.nanvar(help, axis=0)\n",
-      "/home/mkoch/Documents/projects/fcn3_tcs/fcn3_tcs/post_proc/tc_tracking/data_handling.py:357: RuntimeWarning: Mean of empty slice\n",
-      "  stats['mean'][var] = np.nanmean(help, axis=0)\n",
-      "loading storm data: 100%|██████████| 19/19 [03:44<00:00, 11.80s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from analyse_n_plot import get_individual_storm_metrics, stack_cases, extract_weights, reduce_over_all_storms\n",
-    "\n",
-    "cases = [\n",
-    "    'amphan_2020_north_indian',      # 00\n",
-    "    'beryl_2024_north_atlantic',     # 01\n",
-    "    'debbie_2017_southern_pacific',  # 02\n",
-    "    'dorian_2019_north_atlantic',    # 03\n",
-    "    'harvey_2017_north_atlantic',    # 04\n",
-    "    'hato_2017_west_pacific',        # 05\n",
-    "    'helene_2024_north_atlantic',    # 06\n",
-    "    'ian_2022_north_atlantic',       # 07\n",
-    "    'iota_2020_north_atlantic',      # 08\n",
-    "    'irma_2017_north_atlantic',      # 09\n",
-    "    'lan_2017_west_pacific',         # 10\n",
-    "    'lee_2023_north_atlantic',       # 11\n",
-    "    'lorenzo_2019_north_atlantic',   # 12\n",
-    "    'maria_2017_north_atlantic',     # 13\n",
-    "    'mawar_2023_west_pacific',       # 14\n",
-    "    'michael_2018_north_atlantic',   # 15\n",
-    "    'milton_2024_north_atlantic',    # 16\n",
-    "    'ophelia_2017_north_atlantic',   # 17\n",
-    "    'yagi_2024_west_pacific',        # 18\n",
-    "]\n",
-    "\n",
-    "# case_selection = [7, 13]\n",
-    "case_selection = list(range(len(cases)))\n",
-    "\n",
-    "pred_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'\n",
-    "tru_track_dir = f'/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks'\n",
-    "out_dir = f'./plots'\n",
-    "\n",
-    "cases = [cases[ii] for ii in case_selection]\n",
-    "\n",
-    "storm_metrics, max_paired_len, ens_means, extremes = get_individual_storm_metrics(cases, pred_track_dir, tru_track_dir, out_dir)\n",
-    "\n",
-    "storm_metrics = stack_cases(storm_metrics, max_paired_len)\n",
-    "\n",
-    "storm_metrics = extract_weights(storm_metrics, max_paired_len)\n",
-    "\n",
-    "ensemble_metrics = reduce_over_all_storms(storm_metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot average error of mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def bring_to_common_length(arr, max_val):\n",
-    "    if max_paired_len - len(arr) <= 0:\n",
-    "        return arr[:max_paired_len]\n",
-    "    else:\n",
-    "        return np.pad(arr, (0, max_paired_len - len(arr)), 'constant' , constant_values=0)\n",
-    "\n",
-    "\n",
-    "def average_lead_time_errors_over_cases(ens_means):\n",
-    "    err_lt = {}\n",
-    "    vars = ['dist', 'msl_err_of_mean', 'wind_speed_err_of_mean']\n",
-    "    for var in vars:\n",
-    "        errs, weights = [], []\n",
-    "        for _, means in ens_means.items():\n",
-    "            errs.append(bring_to_common_length(means['mean'][var], max_paired_len))\n",
-    "            weights.append(bring_to_common_length( means['n_members'], max_paired_len))\n",
-    "\n",
-    "        errs = np.stack(errs)\n",
-    "        weights = np.stack(weights)\n",
-    "\n",
-    "        # set nans in err to 0 and their associated weights also to 0\n",
-    "        weights[np.isnan(errs)] = 0\n",
-    "        errs[np.isnan(errs)] = 0\n",
-    "\n",
-    "        # compute weighted average\n",
-    "        err_lt[var] = np.average(errs, axis=-2, weights=weights)\n",
-    "\n",
-    "    return err_lt\n",
-    "\n",
-    "\n",
-    "def plot_ensemble_mean_errors_over_lead_time(err_lt, lead_times, n_cases,\n",
-    "                               norm_dict = {'msl_err_of_mean': 100, 'wind_speed_err_of_mean': 1, 'dist': 1000},\n",
-    "                               unit_dict = {'msl_err_of_mean': 'hPa', 'wind_speed_err_of_mean': 'm/s', 'dist': 'km'},\n",
-    "                               out_path: str | None = None,\n",
-    "                               max_lt: np.timedelta64 | None = None):\n",
-    "\n",
-    "    \"\"\"\n",
-    "    plot mean absolute error of ensemble mean weighted by matched ensemble size\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # find last index in lead_times that is equal to or below max_lt (xlim doesn't work on timedelta)\n",
-    "    if max_lt:\n",
-    "        max_lt_idx = np.where(lead_times <= max_lt)[0][-1]\n",
-    "    else:\n",
-    "        max_lt_idx = len(lead_times)\n",
-    "\n",
-    "    fig, ax = plt.subplots(len(err_lt), figsize=((len(err_lt)+1)*2, 8), sharex=True)\n",
-    "\n",
-    "    # Convert lead_times to hours for plotting\n",
-    "    lead_times_hours = lead_times / np.timedelta64(1, 'h')\n",
-    "\n",
-    "    for ivar, var in enumerate(err_lt.keys()):\n",
-    "        ax[ivar].plot(lead_times_hours[:max_lt_idx], err_lt[var][:max_lt_idx]/norm_dict[var])\n",
-    "        ax[ivar].set_ylabel(f'{var} [{unit_dict[var]}]', fontsize=12, weight='bold')\n",
-    "\n",
-    "        if ivar == len(err_lt) - 1:\n",
-    "            ax[ivar].set_xlabel('lead time [h]', fontsize=12)\n",
-    "        ax[ivar].grid(True)\n",
-    "\n",
-    "    sup_title = f'average error of ensemble means over {n_cases} cases \\n'\n",
-    "    sup_title += '(weighted by number of matched tracks at lead time)'\n",
-    "    fig.suptitle(sup_title, fontsize=16)\n",
-    "\n",
-    "    fig.tight_layout()\n",
-    "    if out_path:\n",
-    "        plt.savefig(os.path.join(out_path, f'error_metrics_of_ens_mean_over_lead_time.png'))\n",
-    "\n",
-    "    return\n",
-    "\n",
-    "\n",
-    "def plot_ensemble_mean_metrics(ens_means, out_path=None):\n",
-    "\n",
-    "    err_lt = average_lead_time_errors_over_cases(ens_means)\n",
-    "\n",
-    "    plot_ensemble_mean_errors_over_lead_time(err_lt=err_lt,\n",
-    "                                    lead_times=np.arange(max_paired_len)*np.timedelta64(6, 'h'),\n",
-    "                                    n_cases=len(ens_means),\n",
-    "                                    out_path=out_path,\n",
-    "                                    max_lt=np.timedelta64(192, 'h'))\n",
-    "\n",
-    "    return\n",
-    "\n",
-    "plot_ensemble_mean_metrics(ens_means, out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## scatter plot of maximum intensities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "plt.close('all')\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(10, 8))\n",
-    "\n",
-    "for ii, (var, npfun, minmax) in enumerate(zip(['msl', 'wind_speed'], [np.nanmin, np.nanmax], ['min', 'max'])):\n",
-    "    tick_pos, tick_label = [], []\n",
-    "    for case, ext in extremes.items():\n",
-    "        yy = ext[var][minmax+'_pred']\n",
-    "        xx = ext[var][minmax+'_tru']*np.ones_like(yy)\n",
-    "        ax[ii].plot(xx, yy, marker='o', linestyle='None', color='black', alpha=.1)\n",
-    "\n",
-    "    legend = False\n",
-    "    for case, ext in extremes.items():\n",
-    "        yy = ext[var][minmax+'_pred']\n",
-    "        if not legend:\n",
-    "            ax[ii].plot(ext[var][minmax+'_tru'], npfun(ens_means[case]['mean'][var]), marker='o', linestyle='None', color='lime', alpha=1, label=f'{minmax} of mean')\n",
-    "            ax[ii].plot(ext[var][minmax+'_tru'], np.nanmean(yy), marker='o', linestyle='None', color='red', alpha=1, label=f'mean of {minmax}')\n",
-    "            legend = True\n",
-    "        else:\n",
-    "            ax[ii].plot(ext[var][minmax+'_tru'], npfun(ens_means[case]['mean'][var]), marker='o', linestyle='None', color='lime', alpha=1)\n",
-    "            ax[ii].plot(ext[var][minmax+'_tru'], np.nanmean(yy), marker='o', linestyle='None', color='red', alpha=1)\n",
-    "\n",
-    "        tick_pos.append(ext[var][minmax+'_tru'])\n",
-    "        tick_label.append(case)\n",
-    "\n",
-    "    # ax[ii].legend()\n",
-    "    ax[ii].set_aspect('equal')\n",
-    "    ax[ii].legend()\n",
-    "    tick_pos_dflt = ax[ii].get_xticks()\n",
-    "    labels_dflt = [f'{xx:.0f}' for xx in tick_pos_dflt]\n",
-    "\n",
-    "    tick_pos = np.append(tick_pos_dflt, tick_pos)\n",
-    "    tick_label = np.append(labels_dflt, tick_label)\n",
-    "\n",
-    "    ax[ii].set_xticks(tick_pos)\n",
-    "    ax[ii].set_xticklabels(tick_label, rotation=90, ha='right')\n",
-    "\n",
-    "    # 2. Get the limits of the axes\n",
-    "    low_x, high_x = ax[ii].get_xlim()\n",
-    "    low_y, high_y = ax[ii].get_ylim()\n",
-    "\n",
-    "    # 3. Calculate the range for the diagonal line\n",
-    "    # This ensures the line is drawn only within the plotted area\n",
-    "    low = max(low_x, low_y)\n",
-    "    high = min(high_x, high_y)\n",
-    "    ax[ii].set_xlim(low, high)\n",
-    "    ax[ii].set_ylim(low, high)\n",
-    "\n",
-    "    # 4. Plot the diagonal line\n",
-    "    ax[ii].plot([low, high], [low, high], ls='--', color='k', label='f(x) = x', zorder=0, alpha=.6)\n",
-    "\n",
-    "fig.tight_layout()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb b/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb
deleted file mode 100644
index 6e9eef56a..000000000
--- a/recipes/tc_tracking/post_proc/tc_tracking/tracks_slayground.ipynb
+++ /dev/null
@@ -1,223 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## read csv file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os.path import join\n",
-    "from analyse_n_plot import load_tracks\n",
-    "\n",
-    "# case = 'amphan_2020_north_indian'\n",
-    "# case = 'beryl_2024_north_atlantic'\n",
-    "# case = 'debbie_2017_southern_pacific'\n",
-    "# case = 'dorian_2019_north_atlantic'\n",
-    "# case = 'harvey_2017_north_atlantic'\n",
-    "# case = 'hato_2017_west_pacific'\n",
-    "case = 'helene_2024_north_atlantic'\n",
-    "# case = 'ian_2022_north_atlantic'\n",
-    "# case = 'iota_2020_north_atlantic'\n",
-    "# case = 'irma_2017_north_atlantic'\n",
-    "# case = 'lan_2017_west_pacific'\n",
-    "# case = 'lee_2023_north_atlantic'\n",
-    "# case = 'lorenzo_2019_north_atlantic'\n",
-    "# case = 'maria_2017_north_atlantic'\n",
-    "# case = 'mawar_2023_west_pacific'\n",
-    "# case = 'michael_2018_north_atlantic'\n",
-    "# case = 'milton_2024_north_atlantic'\n",
-    "# case = 'ophelia_2017_north_atlantic'\n",
-    "# case = 'yagi_2024_west_pacific'\n",
-    "\n",
-    "# case = 'erin_2025_north_atlantic'\n",
-    "\n",
-    "base_path_colibri = '/home/mkoch/Documents/projects/fcn3_tcs/track_data/'\n",
-    "base_path_eos = '/home/mkoch/coreai/fcn3_cyclones/results/'\n",
-    "\n",
-    "base_path = base_path_eos\n",
-    "\n",
-    "pred_track_dir = f'predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te'\n",
-    "tru_track_dir = f'reference_tracks'\n",
-    "# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\n",
-    "# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\n",
-    "# pred_track_dir = 'track_data/predictions/erin_lagged'\n",
-    "out_dir = f'./plots'\n",
-    "\n",
-    "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",
-    "                                                                   pred_track_dir=join(base_path, pred_track_dir),\n",
-    "                                                                   tru_track_dir=join(base_path, tru_track_dir),\n",
-    "                                                                   out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot tracks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_spaghetti\n",
-    "\n",
-    "plot_spaghetti(true_track=tru_track,\n",
-    "               pred_tracks=pred_tracks,\n",
-    "               ensemble_mean=ens_mean['mean'],\n",
-    "               case=case,\n",
-    "               n_members=n_members,\n",
-    "               out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot intensities over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_over_time\n",
-    "\n",
-    "plot_over_time(pred_tracks=pred_tracks,\n",
-    "               tru_track=tru_track,\n",
-    "               ensemble_mean=ens_mean,\n",
-    "               case=case,\n",
-    "               n_members=n_members,\n",
-    "               out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot relative intensities over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_relative_over_time\n",
-    "\n",
-    "plot_relative_over_time(pred_tracks=pred_tracks,\n",
-    "                        tru_track=tru_track,\n",
-    "                        ensemble_mean=ens_mean,\n",
-    "                        case=case,\n",
-    "                        n_members=n_members,\n",
-    "                        out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot ERA5 over IB vars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_ib_era5\n",
-    "\n",
-    "plot_ib_era5(tru_track=tru_track,\n",
-    "             case=case,\n",
-    "             vars=['msl', 'wind_speed'],\n",
-    "             out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## extreme values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_extreme_extremes_histograms\n",
-    "\n",
-    "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n",
-    "                                 tru_track=tru_track,\n",
-    "                                 ensemble_mean=ens_mean,\n",
-    "                                 case=case,\n",
-    "                                 out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## moments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from data_handling import compute_averages_of_errors_over_lead_time\n",
-    "from plotting_helpers import plot_errors_over_lead_time\n",
-    "\n",
-    "vars = ['wind_speed', 'msl', 'dist']\n",
-    "\n",
-    "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n",
-    "                                                        tru_track=tru_track,\n",
-    "                                                        vars=vars)\n",
-    "\n",
-    "plot_errors_over_lead_time(err_dict=err_dict,\n",
-    "                           case=case,\n",
-    "                           ic=pred_tracks[0]['ic'],\n",
-    "                           n_members=n_members,\n",
-    "                           n_tracks=len(pred_tracks),\n",
-    "                           out_dir=out_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 3002fd07a5adc2c5e3c168993ca270db6e19e5ca Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 16 Jan 2026 05:47:04 -0800
Subject: [PATCH 51/69] field and track notebook

---
 recipes/tc_tracking/plotting/.gitignore       |   1 +
 .../tc_tracking/plotting/analyse_n_plot.py    |   2 +-
 recipes/tc_tracking/plotting/data_handling.py |   5 +-
 .../plotting/plot_tracks_n_fields.ipynb       | 219 ++++++++++++++++++
 .../plotting/tracks_slayground.ipynb          |  73 +-----
 5 files changed, 224 insertions(+), 76 deletions(-)
 create mode 100644 recipes/tc_tracking/plotting/.gitignore
 create mode 100644 recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb

diff --git a/recipes/tc_tracking/plotting/.gitignore b/recipes/tc_tracking/plotting/.gitignore
new file mode 100644
index 000000000..e34b598e6
--- /dev/null
+++ b/recipes/tc_tracking/plotting/.gitignore
@@ -0,0 +1 @@
+plots/
diff --git a/recipes/tc_tracking/plotting/analyse_n_plot.py b/recipes/tc_tracking/plotting/analyse_n_plot.py
index 236105ec2..27bf7b126 100644
--- a/recipes/tc_tracking/plotting/analyse_n_plot.py
+++ b/recipes/tc_tracking/plotting/analyse_n_plot.py
@@ -30,7 +30,7 @@ def load_tracks(case, pred_track_dir, tru_track_dir, out_dir):
     pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir))
     n_members = len(pred_tracks)
 
-    pred_tracks = match_tracks(pred_tracks, tru_track, case)
+    pred_tracks = match_tracks(pred_tracks, tru_track)
 
     if out_dir:
         out_dir = os.path.join(out_dir, case)
diff --git a/recipes/tc_tracking/plotting/data_handling.py b/recipes/tc_tracking/plotting/data_handling.py
index faa5c553b..80cf08d79 100644
--- a/recipes/tc_tracking/plotting/data_handling.py
+++ b/recipes/tc_tracking/plotting/data_handling.py
@@ -51,7 +51,7 @@ def add_track_distance(track, tru_track):
     return track
 
 
-def match_tracks(pred_tracks, true_track, case):
+def match_tracks(pred_tracks, true_track):
     # iterate over tracks and see if track exists in simulation
     matched_tracks = []
     min_dist, max_dist = 25371000, -1.0
@@ -98,8 +98,7 @@ def match_tracks(pred_tracks, true_track, case):
                 )
                 break
 
-    line = case.split("_")[:2]
-    line = f"{line[0].upper()} {line[1]}: matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, "
+    line = f"matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, "
     line += f"with distances ranging from {min_dist/1000:.1f} to {max_dist/1000:.1f} km"
     print(line)
 
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
new file mode 100644
index 000000000..1af5d054d
--- /dev/null
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking'\n",
+    "field_data = 'plotting_data_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
+    "track_dir = f'plotting_data_helene/cyclone_tracks_te'\n",
+    "reference_track = 'test/aux_data/reference_track_helene_2024_north_atlantic.csv'\n",
+    "\n",
+    "variable = 'wind_speed'\n",
+    "ensemble_member = 12\n",
+    "\n",
+    "max_frames = 99 # maximum number of frames to plot\n",
+    "scale = 1\n",
+    "\n",
+    "region = 'gulf_of_mexico' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
+    "\n",
+    "if region == 'global':\n",
+    "    lat_min, lat_max = -90, 90\n",
+    "    lon_min, lon_max = 0, 360\n",
+    "elif region == 'north_atlantic':\n",
+    "    lat_min, lat_max = 9, 65\n",
+    "    lon_min, lon_max = 250, 360\n",
+    "elif region == 'gulf_of_mexico':\n",
+    "    lat_min, lat_max = 9, 45\n",
+    "    lon_min, lon_max = 250, 310\n",
+    "\n",
+    "save_frames = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.colors as mcolors\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib.colors import TwoSlopeNorm\n",
+    "import matplotlib.animation as animation\n",
+    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+    "import os\n",
+    "\n",
+    "import xarray as xr\n",
+    "\n",
+    "\n",
+    "field_data = os.path.join(base_dir, field_data)\n",
+    "base_name = field_data.split('/')[-1].split('.')[0]\n",
+    "out_dir = f'outputs_{base_name}'\n",
+    "if save_frames:\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "dx = scale*.25\n",
+    "ds = xr.open_dataset(field_data)\n",
+    "\n",
+    "countries = cfeature.NaturalEarthFeature(\n",
+    "    category=\"cultural\",\n",
+    "    name=\"admin_0_countries\",\n",
+    "    scale=\"110m\",\n",
+    "    facecolor=\"none\",\n",
+    "    edgecolor=\"black\",\n",
+    ")\n",
+    "\n",
+    "reg_ds = ds.sel(lat=list(np.arange(lat_min, lat_max, dx)),\n",
+    "                lon=list(np.arange(lon_min, lon_max, dx)))\n",
+    "\n",
+    "time_str = 'lead time:'\n",
+    "projection=ccrs.PlateCarree()\n",
+    "if variable == 'wind_speed':\n",
+    "    var_ds = np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
+    "else:\n",
+    "    var_ds = reg_ds[variable] # np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
+    "\n",
+    "min_val = float(np.min(var_ds[0,:,:,:]))\n",
+    "max_val = float(np.max(var_ds[0,:,:,:]))\n",
+    "\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from data_handling import extract_tracks_from_file, match_tracks\n",
+    "\n",
+    "# extract time steps of field data\n",
+    "time_stamps = ds.time.values + ds.lead_time.values\n",
+    "\n",
+    "# select track file of ensemble member\n",
+    "track_file = [f for f in os.listdir(os.path.join(base_dir, track_dir)) if f.endswith('.csv') and\n",
+    "              int(f.split('_mem_')[-1].split('_seed_')[0]) == ensemble_member][0]\n",
+    "track_file = os.path.join(base_dir, track_dir, track_file)\n",
+    "\n",
+    "# extract tracks from prediction and match with ground truth track\n",
+    "tru_track = extract_tracks_from_file(os.path.join(base_dir, reference_track))\n",
+    "track = extract_tracks_from_file(os.path.join(base_dir, track_file))\n",
+    "track = [{\"ic\": time_stamps[0], \"member\": ensemble_member, \"tracks\": track}]\n",
+    "track = match_tracks(track, tru_track)\n",
+    "\n",
+    "# extract the lines of tracks for wich track['time'] is in time_stamps\n",
+    "track = track[0]['tracks'][track[0]['tracks']['time'].isin(time_stamps)]\n",
+    "\n",
+    "# fill the 'time' column with the time_stamps values\n",
+    "track = track.set_index('time').reindex(time_stamps).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.colors as colors\n",
+    "\n",
+    "# min_val = -30\n",
+    "# max_val = 30\n",
+    "\n",
+    "# define plots\n",
+    "def make_figure():\n",
+    "    fig = plt.figure(figsize=(11,5))\n",
+    "    ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
+    "\n",
+    "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
+    "    lat_formatter = LatitudeFormatter()\n",
+    "    ax.xaxis.set_major_formatter(lon_formatter)\n",
+    "    ax.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "    return fig, ax\n",
+    "\n",
+    "\n",
+    "# make animation\n",
+    "%matplotlib inline\n",
+    "plt.rcParams[\"animation.html\"] = \"jshtml\"\n",
+    "fig, ax = make_figure()\n",
+    "\n",
+    "def make_frame(frame):\n",
+    "    # frame = frame + 700\n",
+    "\n",
+    "    print(f'\\rprocessing frame {frame+1} of {min(max_frames, var_ds.shape[2])}', end='')\n",
+    "\n",
+    "    # Clear previous plot objects\n",
+    "    for artist in ax.get_children():\n",
+    "        if hasattr(artist, 'get_array'):  # This targets pcolormesh objects\n",
+    "            artist.remove()\n",
+    "\n",
+    "\n",
+    "    plot_ds = var_ds[ensemble_member, 0, max(frame,0), :, :]\n",
+    "    pc = ax.pcolormesh(reg_ds.lon, reg_ds.lat, plot_ds, transform=projection,\n",
+    "                       cmap='plasma',\n",
+    "                       vmin=min_val, vmax=max_val,\n",
+    "                       )\n",
+    "\n",
+    "    ax.add_feature(cfeature.COASTLINE,lw=.5)\n",
+    "    ax.add_feature(cfeature.RIVERS,lw=.5)\n",
+    "\n",
+    "    ax.plot(track[\"lon\"][:frame+1], track[\"lat\"][:frame+1], transform=ccrs.PlateCarree(),\n",
+    "            color=\"black\", linewidth=1., alpha=1)\n",
+    "\n",
+    "    if frame==-1:\n",
+    "        cbar = fig.colorbar(pc, extend='both', shrink=0.8, ax=ax)\n",
+    "\n",
+    "    header =f'{base_name} {variable} {time_str} {frame*6}:00:00'\n",
+    "    ax.set_title(header, fontsize=14)\n",
+    "\n",
+    "    fig.savefig(f\"{out_dir}/{base_name}_{variable}_{frame*6:04d}.png\", dpi=450)\n",
+    "\n",
+    "    return pc\n",
+    "\n",
+    "def animate(frame):\n",
+    "    return make_frame(frame)\n",
+    "\n",
+    "def first_frame():\n",
+    "    return make_frame(-1)\n",
+    "\n",
+    "ani = animation.FuncAnimation(fig,\n",
+    "                              animate,\n",
+    "                              min(max_frames, var_ds.shape[2]),\n",
+    "                              init_func=first_frame,\n",
+    "                              blit=False,\n",
+    "                              repeat=False,\n",
+    "                              interval=.1)\n",
+    "plt.close('all')\n",
+    "ani"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
index c9ccaf2ed..5cafadb93 100644
--- a/recipes/tc_tracking/plotting/tracks_slayground.ipynb
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -11,75 +11,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                  time  track_id     lon    lat        msl  wind_speed  \\\n",
-      "0  2024-09-23 12:00:00         0     NaN    NaN        NaN         NaN   \n",
-      "1  2024-09-23 18:00:00         0     NaN    NaN        NaN         NaN   \n",
-      "2  2024-09-24 00:00:00         0  278.75  18.75  100360.60   15.625810   \n",
-      "3  2024-09-24 06:00:00         0  276.75  19.50  100281.80   15.105420   \n",
-      "4  2024-09-24 12:00:00         0  276.00  19.25  100305.40   16.969630   \n",
-      "5  2024-09-24 18:00:00         0  275.75  19.50  100204.30   17.218350   \n",
-      "6  2024-09-25 00:00:00         0  275.00  19.75   99562.31   19.142070   \n",
-      "7  2024-09-25 06:00:00         0  274.25  20.50   99200.88   20.835070   \n",
-      "8  2024-09-25 12:00:00         0  274.00  21.50   98551.31   22.388660   \n",
-      "9  2024-09-25 18:00:00         0  273.50  22.00   98219.50   22.581220   \n",
-      "10 2024-09-26 00:00:00         0  273.25  22.75   97705.00   24.205950   \n",
-      "11 2024-09-26 06:00:00         0  273.50  23.50   97704.31   25.958410   \n",
-      "12 2024-09-26 12:00:00         0  274.25  24.75   96791.25   27.486420   \n",
-      "13 2024-09-26 18:00:00         0  275.25  26.25   96992.00   31.932790   \n",
-      "14 2024-09-27 00:00:00         0  275.75  28.50   96418.94   31.279350   \n",
-      "15 2024-09-27 06:00:00         0  276.75  31.25   97122.88   24.103720   \n",
-      "16 2024-09-27 12:00:00         0  276.75  34.50   97658.50   18.119590   \n",
-      "17 2024-09-27 18:00:00         0  275.50  37.50   98334.94   14.604080   \n",
-      "18 2024-09-28 00:00:00         0  273.00  38.00   98724.50   14.041340   \n",
-      "19 2024-09-28 06:00:00         0  271.75  37.25   99192.81   10.942640   \n",
-      "20 2024-09-28 12:00:00         0  272.00  36.50   99795.00    6.417272   \n",
-      "\n",
-      "    lon_ib  lat_ib  msl_ib  wind_speed_ib type  dist  \n",
-      "0    278.3    17.2  100400             30   DB   0.0  \n",
-      "1    278.1    17.8  100400             35   DB   0.0  \n",
-      "2    277.8    18.2  100200             35   DB   0.0  \n",
-      "3    277.2    18.6  100100             35   DB   0.0  \n",
-      "4    276.3    19.2   99900             40   TS   0.0  \n",
-      "5    275.4    19.4   99700             45   TS   0.0  \n",
-      "6    274.8    19.7   99000             50   TS   0.0  \n",
-      "7    274.1    20.3   98500             55   TS   0.0  \n",
-      "8    273.8    21.1   97900             65   HU   0.0  \n",
-      "9    273.5    22.0   97800             70   HU   0.0  \n",
-      "10   273.3    22.8   97300             70   HU   0.0  \n",
-      "11   273.5    23.6   97000             75   HU   0.0  \n",
-      "12   274.2    24.7   96300             85   HU   0.0  \n",
-      "13   275.0    26.6   95800            105   HU   0.0  \n",
-      "14   275.7    28.7   94100            120   HU   0.0  \n",
-      "15   276.7    31.3   95700             80   HU   0.0  \n",
-      "16   276.8    34.4   97300             45   TS   0.0  \n",
-      "17   275.1    36.8   98300             40   LO   0.0  \n",
-      "18   273.4    38.1   98500             40   LO   0.0  \n",
-      "19   271.9    37.4   99200             30   LO   0.0  \n",
-      "20   272.4    36.6   99700             25   LO   0.0  \n",
-      "====>>>>>>>>>>>>>>>>>>   []\n",
-      "HELENE 2024: matched 0 out of 0 tracks, with distances ranging from 25371.0 to -0.0 km\n",
-      "---->>>>>>>>>>>>>>>>>>   []\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 35\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# pred_track_dir = 'track_data/predictions/erin_lagged'\u001b[39;00m\n\u001b[1;32m     33\u001b[0m out_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./plots\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 35\u001b[0m tru_track, pred_tracks, ens_mean, n_members, out_dir \u001b[38;5;241m=\u001b[39m \u001b[43mload_tracks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcase\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     36\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mpred_track_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred_track_dir\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mtru_track_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtru_track_dir\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m                                                                   \u001b[49m\u001b[43mout_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout_dir\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking/post_proc/tc_tracking/analyse_n_plot.py:42\u001b[0m, in \u001b[0;36mload_tracks\u001b[0;34m(case, pred_track_dir, tru_track_dir, out_dir)\u001b[0m\n\u001b[1;32m     39\u001b[0m     out_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(out_dir, case)\n\u001b[1;32m     40\u001b[0m     os\u001b[38;5;241m.\u001b[39mmakedirs(out_dir, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 42\u001b[0m ens_mean \u001b[38;5;241m=\u001b[39m \u001b[43mget_ensemble_averages\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred_tracks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpred_tracks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtru_track\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtru_track\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tru_track, pred_tracks, ens_mean, n_members, out_dir\n",
-      "File \u001b[0;32m/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking/post_proc/tc_tracking/data_handling.py:369\u001b[0m, in \u001b[0;36mget_ensemble_averages\u001b[0;34m(pred_tracks, tru_track, vars)\u001b[0m\n\u001b[1;32m    361\u001b[0m stats \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    363\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_members\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    364\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m: {var: [] \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m},\n\u001b[1;32m    365\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariance\u001b[39m\u001b[38;5;124m\"\u001b[39m: {var: [] \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m},\n\u001b[1;32m    366\u001b[0m }\n\u001b[1;32m    368\u001b[0m \u001b[38;5;66;03m# loop over all tracks, match with\u001b[39;00m\n\u001b[0;32m--> 369\u001b[0m this_is_the_last_time \u001b[38;5;241m=\u001b[39m \u001b[43mpred_tracks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mic\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    371\u001b[0m \u001b[38;5;66;03m# iterate over all tracks to get set of all times present\u001b[39;00m\n\u001b[1;32m    372\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m track \u001b[38;5;129;01min\u001b[39;00m pred_tracks:\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from os.path import join\n",
     "from analyse_n_plot import load_tracks\n",
@@ -110,9 +42,6 @@
     "\n",
     "pred_track_dir = f'plotting_data_helene/cyclone_tracks_te'\n",
     "tru_track_dir = f'test/aux_data'\n",
-    "# pred_track_dir = 'predictions/helene_diag_test/cyclone_tracks_te'\n",
-    "# pred_track_dir = 'track_data/predictions/erin_live_forecasting/cyclone_tracks_te'\n",
-    "# pred_track_dir = 'track_data/predictions/erin_lagged'\n",
     "out_dir = f'./plots'\n",
     "\n",
     "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",

From 0b968b35a67d13e368a7a8d123067e3065d157b6 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Mon, 19 Jan 2026 05:35:55 -0800
Subject: [PATCH 52/69] plotting tracks and fields notebook

---
 recipes/tc_tracking/.gitignore                |   1 +
 recipes/tc_tracking/cfg/reproduce_helene.yaml |  13 +-
 .../plotting/plot_tracks_n_fields.ipynb       | 537 +++++++++++-------
 3 files changed, 329 insertions(+), 222 deletions(-)

diff --git a/recipes/tc_tracking/.gitignore b/recipes/tc_tracking/.gitignore
index 45e978e37..0eb96be1b 100644
--- a/recipes/tc_tracking/.gitignore
+++ b/recipes/tc_tracking/.gitignore
@@ -8,6 +8,7 @@ wheels/
 outputs*/
 *.zarr
 *.nc
+*.gif
 .python-version
 
 # Virtual environments
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index a7c3bd900..7f637236d 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -1,15 +1,16 @@
-project: 'helene'
+project: 'reproduce_helene'
 mode: 'reproduce_members'
 model: 'fcn3'
-random_seed: 7552
 
 reproduce_members: [
-                    ['2024-09-24 00:00:00',  3, 4045540270],
-                    ['2024-09-24 00:00:00', 14, 3066645577],
+                    ['2024-09-24 00:00:00',  0, 2148531792],
+                    ['2024-09-24 00:00:00',  8, 1342799488],
+                    ['2024-09-24 00:00:00',  9, 1342799488],
+                    ['2024-09-24 00:00:00', 12, 1968734508],
                    ]
 
-n_steps: 15
-ensemble_size: 15
+n_steps: 28
+ensemble_size: 16
 batch_size: 2
 orography_path: './orography.nc'
 
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index 1af5d054d..c4d2e4403 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -1,219 +1,324 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking'\n",
-    "field_data = 'plotting_data_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
-    "track_dir = f'plotting_data_helene/cyclone_tracks_te'\n",
-    "reference_track = 'test/aux_data/reference_track_helene_2024_north_atlantic.csv'\n",
-    "\n",
-    "variable = 'wind_speed'\n",
-    "ensemble_member = 12\n",
-    "\n",
-    "max_frames = 99 # maximum number of frames to plot\n",
-    "scale = 1\n",
-    "\n",
-    "region = 'gulf_of_mexico' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
-    "\n",
-    "if region == 'global':\n",
-    "    lat_min, lat_max = -90, 90\n",
-    "    lon_min, lon_max = 0, 360\n",
-    "elif region == 'north_atlantic':\n",
-    "    lat_min, lat_max = 9, 65\n",
-    "    lon_min, lon_max = 250, 360\n",
-    "elif region == 'gulf_of_mexico':\n",
-    "    lat_min, lat_max = 9, 45\n",
-    "    lon_min, lon_max = 250, 310\n",
-    "\n",
-    "save_frames = False"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Plot Evolution of Storm Tracks and Atmospheric Fields\n",
+        "\n",
+        "This notebook demonstrates how to visualize tropical cyclone (TC) tracks overlaid on atmospheric field data. Use this to validate tracking algorithm outputs or create publication-ready animations.\n",
+        "\n",
+        "### Workflow Overview\n",
+        "1. Configure paths and plotting parameters\n",
+        "2. Load and subset field data from NetCDF\n",
+        "3. Load and align track data from CSV files\n",
+        "4. Generate animated visualization\n",
+        "5. (Optional) Save animation to file\n",
+        "\n",
+        "**Data Requirements:**\n",
+        "- NetCDF file containing atmospheric field data\n",
+        "- Track CSV files generated by the TC tracking algorithm\n",
+        "Both files can be produced by configuring a tc_hunt run with `store_type: \"netcdf\"` and cyclone tracking switched on, as e.g. done in `cfg/reproduce_helene.yaml`.\n",
+        "\n",
+        "### Configuration\n",
+        "\n",
+        "**Path Settings:**\n",
+        "- `base_dir` - root directory containing track and field data\n",
+        "- `field_data` - NetCDF filename (relative to `base_dir`)\n",
+        "- `track_dir` - folder containing track CSVs (relative to `base_dir`)\n",
+        "\n",
+        "**Plotting Settings:**\n",
+        "- `variable` - field variable to plot (e.g., `'u10m'`, `'mslp'`). Use `'wind_speed'` to compute 10m wind magnitude from `u10m` and `v10m`\n",
+        "- `ensemble_member` - which ensemble member to visualize\n",
+        "- `region` - geographic region (`'global'`, `'north_atlantic'`, `'gulf_of_mexico'`)\n",
+        "\n",
+        "**Animation Settings:**\n",
+        "- `max_frames` - limit number of frames (set high to include full forecast)\n",
+        "- `scale` - spatial downsampling factor (1 = full resolution, 2 = half, etc.)\n",
+        "- `fps` - frames per second for animation playback\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "base_dir = '/path/to/outputs_reproduce_helene'\n",
+        "field_data = 'helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
+        "track_dir = f'cyclone_tracks_te'\n",
+        "\n",
+        "variable = 'wind_speed'\n",
+        "ensemble_member = 1\n",
+        "region = 'gulf_of_mexico' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
+        "\n",
+        "max_frames = 99 # maximum number of frames to plot\n",
+        "scale = 1\n",
+        "fps = 4\n",
+        "\n",
+        "if region == 'global':\n",
+        "    lat_min, lat_max = -90, 90\n",
+        "    lon_min, lon_max = 0, 360\n",
+        "elif region == 'north_atlantic':\n",
+        "    lat_min, lat_max = 9, 65\n",
+        "    lon_min, lon_max = 250, 360\n",
+        "elif region == 'gulf_of_mexico':\n",
+        "    lat_min, lat_max = 9, 45\n",
+        "    lon_min, lon_max = 250, 310\n",
+        "else:\n",
+        "    raise ValueError(f'region {region} not yet implemented. Feel free to add it by providing lat/lon coords of the bounding box')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Step 1: Load Field Data\n",
+        "\n",
+        "- Read field data from NetCDF file\n",
+        "- Sub-select region and variable; coarsen data if `scale > 1` for faster iteration\n",
+        "- Compute min/max values for consistent colormap across all timesteps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import cartopy.crs as ccrs\n",
+        "import cartopy.feature as cfeature\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import matplotlib.animation as animation\n",
+        "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+        "import xarray as xr\n",
+        "\n",
+        "\n",
+        "field_data = os.path.join(base_dir, field_data)\n",
+        "base_name = field_data.split('/')[-1].split('.')[0]\n",
+        "out_dir = f'outputs_{base_name}'\n",
+        "\n",
+        "ds = xr.open_dataset(field_data)\n",
+        "\n",
+        "countries = cfeature.NaturalEarthFeature(\n",
+        "    category=\"cultural\",\n",
+        "    name=\"admin_0_countries\",\n",
+        "    scale=\"110m\",\n",
+        "    facecolor=\"none\",\n",
+        "    edgecolor=\"black\",\n",
+        ")\n",
+        "\n",
+        "# subselect lat/lon box, coarsen data if scale factor > 1\n",
+        "sub_ds = ds.sel(lat=list(np.arange(lat_min, lat_max, scale*.25)),\n",
+        "                lon=list(np.arange(lon_min, lon_max, scale*.25)))\n",
+        "\n",
+        "# extract variable and obtain global min/max values\n",
+        "if variable == 'wind_speed':\n",
+        "    sub_ds = np.sqrt(np.square(sub_ds.u10m) + np.square(sub_ds.v10m))\n",
+        "else:\n",
+        "    sub_ds = sub_ds[variable] # np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
+        "\n",
+        "ds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Step 2: Load Track Data\n",
+        "\n",
+        "This step loads and processes the cyclone track data to align with field timestamps.\n",
+        "\n",
+        "**Processing steps:**\n",
+        "1. **Select file** - filter by `ensemble_member` from filenames ignoring the random seed\n",
+        "2. **Separate tracks** - split into individual DataFrames for each track\n",
+        "3. **Align timestamps** - reindex each track to match field data timesteps\n",
+        "\n",
+        "**Why reindexing matters:**\n",
+        "Tracks may not have positions at every timestep (e.g., storm genesis/dissipation). Reindexing fills gaps with NaN, enabling frame-by-frame plotting without index errors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from data_handling import extract_tracks_from_file\n",
+        "\n",
+        "# extract time steps of field data\n",
+        "time_stamps = ds.time.values + ds.lead_time.values\n",
+        "\n",
+        "# select track file of ensemble member\n",
+        "track_file = [f for f in os.listdir(os.path.join(base_dir, track_dir)) if f.endswith('.csv') and\n",
+        "              int(f.split('_mem_')[-1].split('_seed_')[0]) == ensemble_member][0]\n",
+        "track_file = os.path.join(base_dir, track_dir, track_file)\n",
+        "\n",
+        "# extract tracks from prediction\n",
+        "tracks = extract_tracks_from_file(os.path.join(base_dir, track_file))\n",
+        "\n",
+        "# separate individual tracks in prediction\n",
+        "n_tracks = tracks[\"track_id\"].iloc[-1] + 1\n",
+        "tracks = [tracks.loc[tracks[\"track_id\"] == ii].copy() for ii in range(n_tracks)]\n",
+        "\n",
+        "# align track data with simulation time steps\n",
+        "for ii in range(n_tracks):\n",
+        "    # extract the lines of tracks for which track['time'] is in time_stamps\n",
+        "    tracks[ii] = tracks[ii][tracks[ii]['time'].isin(time_stamps)]\n",
+        "\n",
+        "    # fill the 'time' column with the time_stamps values\n",
+        "    tracks[ii] = tracks[ii].set_index('time').reindex(time_stamps).reset_index()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Step 3: Create Animation\n",
+        "\n",
+        "Creates an interactive animation where you can click through timesteps. Each frame shows the field data with tracks drawn progressively up to that point."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "colour_map = 'plasma'\n",
+        "projection=ccrs.PlateCarree()\n",
+        "\n",
+        "min_val = float(np.min(sub_ds[ensemble_member, ...]))\n",
+        "max_val = float(np.max(sub_ds[ensemble_member, ...]))\n",
+        "\n",
+        "# suppress line warnings stemming from potential NANs in track data\n",
+        "warnings.filterwarnings(\"ignore\", message=\"invalid value encountered in linestrings\")\n",
+        "\n",
+        "# get index of ensemble member\n",
+        "ensemble_idx = np.argwhere(ds.ensemble.values == ensemble_member)[0,0]\n",
+        "\n",
+        "# define plots\n",
+        "def make_figure():\n",
+        "    fig = plt.figure(figsize=(11,5))\n",
+        "    ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
+        "\n",
+        "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
+        "    lat_formatter = LatitudeFormatter()\n",
+        "    ax.xaxis.set_major_formatter(lon_formatter)\n",
+        "    ax.yaxis.set_major_formatter(lat_formatter)\n",
+        "\n",
+        "    return fig, ax\n",
+        "\n",
+        "\n",
+        "# make animation\n",
+        "%matplotlib inline\n",
+        "plt.rcParams[\"animation.html\"] = \"jshtml\"\n",
+        "fig, ax = make_figure()\n",
+        "\n",
+        "def make_frame(frame):\n",
+        "    print(f'\\rprocessing frame {frame+1} of {min(max_frames, sub_ds.shape[2])}', end='')\n",
+        "\n",
+        "    # Clear previous plot objects\n",
+        "    for artist in ax.get_children():\n",
+        "        if hasattr(artist, 'get_array'):  # This targets pcolormesh objects\n",
+        "            artist.remove()\n",
+        "\n",
+        "\n",
+        "    plot_ds = sub_ds[ensemble_idx, 0, max(frame,0), :, :]\n",
+        "    pc = ax.pcolormesh(sub_ds.lon, sub_ds.lat, plot_ds, transform=projection,\n",
+        "                       cmap=colour_map,\n",
+        "                       vmin=min_val, vmax=max_val,\n",
+        "                       )\n",
+        "\n",
+        "    ax.add_feature(cfeature.COASTLINE,lw=.5)\n",
+        "    ax.add_feature(cfeature.RIVERS,lw=.5)\n",
+        "    for track in tracks:\n",
+        "        ax.plot(track[\"lon\"][:frame+1], track[\"lat\"][:frame+1],\n",
+        "                transform=ccrs.PlateCarree(),\n",
+        "                color=\"lime\", linewidth=1., alpha=1)\n",
+        "\n",
+        "    # Enforce the plotting region extent\n",
+        "    ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())\n",
+        "\n",
+        "    if frame==-1:\n",
+        "        cbar = fig.colorbar(pc, extend='both', shrink=0.8, ax=ax)\n",
+        "\n",
+        "    header =f'{base_name} {variable} {'lead time'} {frame*6}:00:00'\n",
+        "    ax.set_title(header, fontsize=14)\n",
+        "\n",
+        "    return pc\n",
+        "\n",
+        "def animate(frame):\n",
+        "    return make_frame(frame)\n",
+        "\n",
+        "def first_frame():\n",
+        "    return make_frame(-1)\n",
+        "\n",
+        "ani = animation.FuncAnimation(fig,\n",
+        "                              animate,\n",
+        "                              min(max_frames, sub_ds.shape[2]),\n",
+        "                              init_func=first_frame,\n",
+        "                              blit=False,\n",
+        "                              repeat=False,\n",
+        "                              interval=1000/fps)\n",
+        "plt.close('all')\n",
+        "ani"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Step 4 (Optional): Save Animation\n",
+        "\n",
+        "Uncomment and run the cell below to save the animation as GIF."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "plt.close('all')\n",
+        "\n",
+        "# Recreate figure and animation\n",
+        "cbar = None\n",
+        "fig = plt.figure(figsize=(12, 8))\n",
+        "ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
+        "\n",
+        "ani = animation.FuncAnimation(fig, animate, min(max_frames, sub_ds.shape[2]),\n",
+        "                              init_func=first_frame, blit=False,\n",
+        "                              repeat=False, interval=1000/fps)\n",
+        "\n",
+        "ani.save('tracks_n_fields_ani.gif', fps=fps)\n",
+        "plt.close(fig)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from matplotlib.colors import TwoSlopeNorm\n",
-    "import matplotlib.animation as animation\n",
-    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
-    "import os\n",
-    "\n",
-    "import xarray as xr\n",
-    "\n",
-    "\n",
-    "field_data = os.path.join(base_dir, field_data)\n",
-    "base_name = field_data.split('/')[-1].split('.')[0]\n",
-    "out_dir = f'outputs_{base_name}'\n",
-    "if save_frames:\n",
-    "    os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "\n",
-    "dx = scale*.25\n",
-    "ds = xr.open_dataset(field_data)\n",
-    "\n",
-    "countries = cfeature.NaturalEarthFeature(\n",
-    "    category=\"cultural\",\n",
-    "    name=\"admin_0_countries\",\n",
-    "    scale=\"110m\",\n",
-    "    facecolor=\"none\",\n",
-    "    edgecolor=\"black\",\n",
-    ")\n",
-    "\n",
-    "reg_ds = ds.sel(lat=list(np.arange(lat_min, lat_max, dx)),\n",
-    "                lon=list(np.arange(lon_min, lon_max, dx)))\n",
-    "\n",
-    "time_str = 'lead time:'\n",
-    "projection=ccrs.PlateCarree()\n",
-    "if variable == 'wind_speed':\n",
-    "    var_ds = np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
-    "else:\n",
-    "    var_ds = reg_ds[variable] # np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
-    "\n",
-    "min_val = float(np.min(var_ds[0,:,:,:]))\n",
-    "max_val = float(np.max(var_ds[0,:,:,:]))\n",
-    "\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from data_handling import extract_tracks_from_file, match_tracks\n",
-    "\n",
-    "# extract time steps of field data\n",
-    "time_stamps = ds.time.values + ds.lead_time.values\n",
-    "\n",
-    "# select track file of ensemble member\n",
-    "track_file = [f for f in os.listdir(os.path.join(base_dir, track_dir)) if f.endswith('.csv') and\n",
-    "              int(f.split('_mem_')[-1].split('_seed_')[0]) == ensemble_member][0]\n",
-    "track_file = os.path.join(base_dir, track_dir, track_file)\n",
-    "\n",
-    "# extract tracks from prediction and match with ground truth track\n",
-    "tru_track = extract_tracks_from_file(os.path.join(base_dir, reference_track))\n",
-    "track = extract_tracks_from_file(os.path.join(base_dir, track_file))\n",
-    "track = [{\"ic\": time_stamps[0], \"member\": ensemble_member, \"tracks\": track}]\n",
-    "track = match_tracks(track, tru_track)\n",
-    "\n",
-    "# extract the lines of tracks for wich track['time'] is in time_stamps\n",
-    "track = track[0]['tracks'][track[0]['tracks']['time'].isin(time_stamps)]\n",
-    "\n",
-    "# fill the 'time' column with the time_stamps values\n",
-    "track = track.set_index('time').reindex(time_stamps).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.colors as colors\n",
-    "\n",
-    "# min_val = -30\n",
-    "# max_val = 30\n",
-    "\n",
-    "# define plots\n",
-    "def make_figure():\n",
-    "    fig = plt.figure(figsize=(11,5))\n",
-    "    ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
-    "\n",
-    "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
-    "    lat_formatter = LatitudeFormatter()\n",
-    "    ax.xaxis.set_major_formatter(lon_formatter)\n",
-    "    ax.yaxis.set_major_formatter(lat_formatter)\n",
-    "\n",
-    "    return fig, ax\n",
-    "\n",
-    "\n",
-    "# make animation\n",
-    "%matplotlib inline\n",
-    "plt.rcParams[\"animation.html\"] = \"jshtml\"\n",
-    "fig, ax = make_figure()\n",
-    "\n",
-    "def make_frame(frame):\n",
-    "    # frame = frame + 700\n",
-    "\n",
-    "    print(f'\\rprocessing frame {frame+1} of {min(max_frames, var_ds.shape[2])}', end='')\n",
-    "\n",
-    "    # Clear previous plot objects\n",
-    "    for artist in ax.get_children():\n",
-    "        if hasattr(artist, 'get_array'):  # This targets pcolormesh objects\n",
-    "            artist.remove()\n",
-    "\n",
-    "\n",
-    "    plot_ds = var_ds[ensemble_member, 0, max(frame,0), :, :]\n",
-    "    pc = ax.pcolormesh(reg_ds.lon, reg_ds.lat, plot_ds, transform=projection,\n",
-    "                       cmap='plasma',\n",
-    "                       vmin=min_val, vmax=max_val,\n",
-    "                       )\n",
-    "\n",
-    "    ax.add_feature(cfeature.COASTLINE,lw=.5)\n",
-    "    ax.add_feature(cfeature.RIVERS,lw=.5)\n",
-    "\n",
-    "    ax.plot(track[\"lon\"][:frame+1], track[\"lat\"][:frame+1], transform=ccrs.PlateCarree(),\n",
-    "            color=\"black\", linewidth=1., alpha=1)\n",
-    "\n",
-    "    if frame==-1:\n",
-    "        cbar = fig.colorbar(pc, extend='both', shrink=0.8, ax=ax)\n",
-    "\n",
-    "    header =f'{base_name} {variable} {time_str} {frame*6}:00:00'\n",
-    "    ax.set_title(header, fontsize=14)\n",
-    "\n",
-    "    fig.savefig(f\"{out_dir}/{base_name}_{variable}_{frame*6:04d}.png\", dpi=450)\n",
-    "\n",
-    "    return pc\n",
-    "\n",
-    "def animate(frame):\n",
-    "    return make_frame(frame)\n",
-    "\n",
-    "def first_frame():\n",
-    "    return make_frame(-1)\n",
-    "\n",
-    "ani = animation.FuncAnimation(fig,\n",
-    "                              animate,\n",
-    "                              min(max_frames, var_ds.shape[2]),\n",
-    "                              init_func=first_frame,\n",
-    "                              blit=False,\n",
-    "                              repeat=False,\n",
-    "                              interval=.1)\n",
-    "plt.close('all')\n",
-    "ani"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

From 67f7fc6e18f3556defc5da1f3c3388a0b962f8b2 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 20 Jan 2026 04:34:13 -0800
Subject: [PATCH 53/69] case study notebook

---
 recipes/tc_tracking/cfg/extract_era5.yaml     |  60 +--
 recipes/tc_tracking/cfg/hato.yaml             |  40 ++
 .../plotting/plot_tracks_n_fields.ipynb       |  14 +-
 .../plotting/tracks_slayground.ipynb          | 466 ++++++++++--------
 .../baseline_extraction.py                    |   7 +-
 5 files changed, 331 insertions(+), 256 deletions(-)
 create mode 100644 recipes/tc_tracking/cfg/hato.yaml

diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 5961c5a7b..2333a4e87 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -62,9 +62,9 @@ cases:
     #     year: 2017
     #     basin: west_pacific
 
-    # hato:
-    #     year: 2017
-    #     basin: west_pacific
+    hato:
+        year: 2017
+        basin: west_pacific
 
     # mawar:
     #     year: 2023
@@ -82,41 +82,41 @@ cases:
     #     year: 2017
     #     basin: southern_pacific
 
-ibtracs_source_data: "./test/aux_data/ibtracs.ALL.list.v04r01.csv"
+ibtracs_source_data: "./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
-    h5_era5_train:
-        years: "1980-2016"
-        source:
-            _target_: src.data.localh5.LocalArchiveHDF5
-            dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/train"
-            metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
-
-    h5_era5_test:
-        years: 2017
-        source:
-            _target_: src.data.localh5.LocalArchiveHDF5
-            dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/test"
-            metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
-
-    h5_era5_oos:
-        years: "2018-2022"
-        source:
-            _target_: src.data.localh5.LocalArchiveHDF5
-            dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-            metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
+    # h5_era5_train:
+    #     years: "1980-2016"
+    #     source:
+    #         _target_: src.data.localh5.LocalArchiveHDF5
+    #         dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/train"
+    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
+
+    # h5_era5_test:
+    #     years: 2017
+    #     source:
+    #         _target_: src.data.localh5.LocalArchiveHDF5
+    #         dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/test"
+    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
+
+    # h5_era5_oos:
+    #     years: "2018-2022"
+    #     source:
+    #         _target_: src.data.localh5.LocalArchiveHDF5
+    #         dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
+    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
 
     ncar_era5:
-        years: "2023-2024"
+        years: "2017-2024"
         source:
             _target_: earth2studio.data.NCAR_ERA5
 
-    gfs:
-        years: "2025"
-        source:
-            _target_: earth2studio.data.GFS
+    # gfs:
+    #     years: "2025"
+    #     source:
+    #         _target_: earth2studio.data.GFS
 
-orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
+orography_path: './orography.nc'
 store_dir: "./outputs_${project}"
 
 cyclone_tracking:
diff --git a/recipes/tc_tracking/cfg/hato.yaml b/recipes/tc_tracking/cfg/hato.yaml
new file mode 100644
index 000000000..1e82d3afa
--- /dev/null
+++ b/recipes/tc_tracking/cfg/hato.yaml
@@ -0,0 +1,40 @@
+project: 'hato'
+mode: 'generate_ensemble'
+model: 'fcn3' # 'fcn3' 'aifs'
+
+# ic_block_start: "2024-09-23 00:00:00" # first IC
+# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
+# ic_block_step: 24            # number of hours between individual ICs
+ics: ["2017-08-21 12:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+
+n_steps: 20
+ensemble_size: 16
+batch_size: 2
+orography_path: './orography.nc'
+
+data_source:
+    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.GFS
+    # _target_: earth2studio.data.IFS
+    # _target_: earth2studio.data.CDS
+
+store_type: "none"
+store_dir: "./outputs_${project}"
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
+
+cyclone_tracking:
+    asynchronous: True
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    keep_raw_data: False
+    use_ram: True
+    task_timeout_seconds: 120
+    print_te_output: False
+
+# hydra logs
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index c4d2e4403..2e828d2c5 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -15,24 +15,24 @@
         "4. Generate animated visualization\n",
         "5. (Optional) Save animation to file\n",
         "\n",
-        "**Data Requirements:**\n",
+        "### Data Prerequesits\n",
         "- NetCDF file containing atmospheric field data\n",
         "- Track CSV files generated by the TC tracking algorithm\n",
         "Both files can be produced by configuring a tc_hunt run with `store_type: \"netcdf\"` and cyclone tracking switched on, as e.g. done in `cfg/reproduce_helene.yaml`.\n",
         "\n",
         "### Configuration\n",
         "\n",
-        "**Path Settings:**\n",
-        "- `base_dir` - root directory containing track and field data\n",
-        "- `field_data` - NetCDF filename (relative to `base_dir`)\n",
-        "- `track_dir` - folder containing track CSVs (relative to `base_dir`)\n",
+        "**Path Settings**\n",
+        "- `base_dir` - root directory containing track and field data (e.g. `/path/to/outputs_reproduce_helene`)\n",
+        "- `field_data` - NetCDF filename (relative to `base_dir`, e.g. `helene_2024-09-24T00.00.00_mems0000-0015.nc`)\n",
+        "- `track_dir` - folder containing track CSVs (relative to `base_dir`, most likely `cyclone_tracks_te`)\n",
         "\n",
-        "**Plotting Settings:**\n",
+        "**Plotting Settings**\n",
         "- `variable` - field variable to plot (e.g., `'u10m'`, `'mslp'`). Use `'wind_speed'` to compute 10m wind magnitude from `u10m` and `v10m`\n",
         "- `ensemble_member` - which ensemble member to visualize\n",
         "- `region` - geographic region (`'global'`, `'north_atlantic'`, `'gulf_of_mexico'`)\n",
         "\n",
-        "**Animation Settings:**\n",
+        "**Animation Settings**\n",
         "- `max_frames` - limit number of frames (set high to include full forecast)\n",
         "- `scale` - spatial downsampling factor (1 = full resolution, 2 = half, etc.)\n",
         "- `fps` - frames per second for animation playback\n"
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
index 5cafadb93..decc31c4d 100644
--- a/recipes/tc_tracking/plotting/tracks_slayground.ipynb
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -1,217 +1,255 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## read csv file"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Analyse Tropical Cyclone Track Ensembles\n",
+        "\n",
+        "This notebook demonstrates how to analyse and validate ensemble tropical cyclone (TC) track predictions. Use this to compare forecast tracks against observations ([IBTrACS](https://www.ncei.noaa.gov/products/international-best-track-archive)) and reanalysis-based reference tracks (ERA5). We explain the workflow using the example of [Hurricane Helene](https://en.wikipedia.org/wiki/Hurricane_Helene), but it can be easily configured to investigate other storms.\n",
+        "\n",
+        "### Workflow Overview\n",
+        "1. Configure case and paths for predicted ensemble and reference track\n",
+        "2. Plot track trajectories (spaghetti plot)\n",
+        "3. Plot absolute intensities over time (wind speed, MSLP)\n",
+        "4. Plot relative intensities over time (normalised by reference)\n",
+        "5. Compare ERA5 reference against IBTrACS observations\n",
+        "6. Analyse extreme value statistics (histograms)\n",
+        "7. Compute error moments over lead time\n",
+        "\n",
+        "### Data Prerequisites\n",
+        "- Ensemble of predicted TC tracks (CSV files from tracking algorithm)\n",
+        "- Reference track from ERA5 or IBTrACS observations (CSV file)\n",
+        "\n",
+        "Both can be produced by configuring a `tc_hunt.py` run with cyclone tracking enabled. The ensemble of predicted tracks can, for example, be produced with the config `cfg/helene.yaml`. Reference tracks can be extracted using `cfg/extract_era5.yaml`.\n",
+        "\n",
+        "### Analysis Notes\n",
+        "- Track positions are typically well-represented by the model\n",
+        "- Intensity metrics (wind speed, pressure) show larger biases due to the coarse (~0.25°) resolution, which cannot fully resolve TC structure\n",
+        "- IBTrACS provides observed best track data; ERA5 represents the best achievable reference for reanalysis-driven forecasts\n",
+        "\n",
+        "### Configuration\n",
+        "- `case` - named storm to analyse. Can be expanded with additional named storms by following the pattern `{name}_{YYYY}_{basin}`\n",
+        "- `pred_track_dir` - folder containing predicted track CSVs (if using data produced with `cfg/helene.yaml`, this would be `/path/to/outputs_helene/cyclone_tracks_te`)\n",
+        "- `tru_track_dir` - folder containing reference track CSV file (if using data produced with `cfg/extract_era5.yaml`, this would be `/path/to/outputs_reference_tracks/`)\n",
+        "- `out_dir` - path for storing plots"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from analyse_n_plot import load_tracks\n",
+        "\n",
+        "# case = 'amphan_2020_north_indian'\n",
+        "# case = 'beryl_2024_north_atlantic'\n",
+        "# case = 'debbie_2017_southern_pacific'\n",
+        "# case = 'dorian_2019_north_atlantic'\n",
+        "# case = 'harvey_2017_north_atlantic'\n",
+        "# case = 'hato_2017_west_pacific'\n",
+        "case = 'helene_2024_north_atlantic'\n",
+        "# case = 'ian_2022_north_atlantic'\n",
+        "# case = 'iota_2020_north_atlantic'\n",
+        "# case = 'irma_2017_north_atlantic'\n",
+        "# case = 'lan_2017_west_pacific'\n",
+        "# case = 'lee_2023_north_atlantic'\n",
+        "# case = 'lorenzo_2019_north_atlantic'\n",
+        "# case = 'maria_2017_north_atlantic'\n",
+        "# case = 'mawar_2023_west_pacific'\n",
+        "# case = 'michael_2018_north_atlantic'\n",
+        "# case = 'milton_2024_north_atlantic'\n",
+        "# case = 'ophelia_2017_north_atlantic'\n",
+        "# case = 'yagi_2024_west_pacific'\n",
+        "# case = 'erin_2025_north_atlantic'\n",
+        "\n",
+        "pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'\n",
+        "tru_track_dir = '/path/to/outputs_reference_tracks'\n",
+        "out_dir = './plots'\n",
+        "\n",
+        "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",
+        "                                                                   pred_track_dir=pred_track_dir,\n",
+        "                                                                   tru_track_dir=tru_track_dir,\n",
+        "                                                                   out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Spaghetti Plot\n",
+        "\n",
+        "- **Ensemble members** are shown in grey\n",
+        "- **Ensemble mean** is displayed in green\n",
+        "- **ERA5 reference** is shown in red"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotting_helpers import plot_spaghetti\n",
+        "\n",
+        "plot_spaghetti(true_track=tru_track,\n",
+        "               pred_tracks=pred_tracks,\n",
+        "               ensemble_mean=ens_mean['mean'],\n",
+        "               case=case,\n",
+        "               n_members=n_members,\n",
+        "               out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot Absolute Intensities and Track Distance Over Time\n",
+        "\n",
+        "This section examines the temporal evolution of cyclone intensities and the distance from the reference track. Intensities are represented by minimum sea level pressure and maximum wind speed. The reference track should mostly fall within the ensemble spread for intensity predictions. However, some storms exhibit phenomena such as rapid intensification that cannot be adequately captured by models on quarter-degree resolution. In such cases, the reference intensity may lie outside the ensemble spread, indicating model limitations in resolving fine-scale processes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotting_helpers import plot_over_time\n",
+        "\n",
+        "plot_over_time(pred_tracks=pred_tracks,\n",
+        "               tru_track=tru_track,\n",
+        "               ensemble_mean=ens_mean,\n",
+        "               case=case,\n",
+        "               n_members=n_members,\n",
+        "               out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot Relative Intensities Over Time\n",
+        "\n",
+        "This cell shows the same intensity metrics as in the previous cell, this time normalised by the reference. Note that for the pressure field we normalise the deviation from normal pressure. This normalisation helps to identify systematic biases in the ensemble predictions and highlights periods where the model over- or underestimates cyclone intensity relative to observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotting_helpers import plot_relative_over_time\n",
+        "\n",
+        "plot_relative_over_time(pred_tracks=pred_tracks,\n",
+        "                        tru_track=tru_track,\n",
+        "                        ensemble_mean=ens_mean,\n",
+        "                        case=case,\n",
+        "                        n_members=n_members,\n",
+        "                        out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot ERA5 Against IBTrACS Variables\n",
+        "\n",
+        "This cell compares the intensities reached in the ERA5 reanalysis data against those obtained from IBTrACS observations. The deviation between both datasets is usually larger the more intense the storm becomes. Note that there are two separate y-axes for the different intensity metrics (pressure and wind speed)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotting_helpers import plot_ib_era5\n",
+        "\n",
+        "plot_ib_era5(tru_track=tru_track,\n",
+        "             case=case,\n",
+        "             vars=['msl', 'wind_speed'],\n",
+        "             out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extreme Values Over Lifetime of Storm\n",
+        "\n",
+        "This cell computes the maximum intensity reached along each track throughout the storm's lifetime and displays the distribution across ensemble members as histograms. For comparison, the extreme values from the reference track are shown as vertical lines."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotting_helpers import plot_extreme_extremes_histograms\n",
+        "\n",
+        "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n",
+        "                                 tru_track=tru_track,\n",
+        "                                 ensemble_mean=ens_mean,\n",
+        "                                 case=case,\n",
+        "                                 out_dir=out_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Statistics\n",
+        "\n",
+        "This cell computes error metrics as a function of lead time across all ensemble members. The following statistics are calculated: mean absolute error, root mean square error, and standard deviation for wind speed, pressure intensity, and track distance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from data_handling import compute_averages_of_errors_over_lead_time\n",
+        "from plotting_helpers import plot_errors_over_lead_time\n",
+        "\n",
+        "vars = ['wind_speed', 'msl', 'dist']\n",
+        "\n",
+        "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n",
+        "                                                        tru_track=tru_track,\n",
+        "                                                        vars=vars)\n",
+        "\n",
+        "plot_errors_over_lead_time(err_dict=err_dict,\n",
+        "                           case=case,\n",
+        "                           ic=pred_tracks[0]['ic'],\n",
+        "                           n_members=n_members,\n",
+        "                           n_tracks=len(pred_tracks),\n",
+        "                           out_dir=out_dir)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os.path import join\n",
-    "from analyse_n_plot import load_tracks\n",
-    "\n",
-    "# case = 'amphan_2020_north_indian'\n",
-    "# case = 'beryl_2024_north_atlantic'\n",
-    "# case = 'debbie_2017_southern_pacific'\n",
-    "# case = 'dorian_2019_north_atlantic'\n",
-    "# case = 'harvey_2017_north_atlantic'\n",
-    "# case = 'hato_2017_west_pacific'\n",
-    "case = 'helene_2024_north_atlantic'\n",
-    "# case = 'ian_2022_north_atlantic'\n",
-    "# case = 'iota_2020_north_atlantic'\n",
-    "# case = 'irma_2017_north_atlantic'\n",
-    "# case = 'lan_2017_west_pacific'\n",
-    "# case = 'lee_2023_north_atlantic'\n",
-    "# case = 'lorenzo_2019_north_atlantic'\n",
-    "# case = 'maria_2017_north_atlantic'\n",
-    "# case = 'mawar_2023_west_pacific'\n",
-    "# case = 'michael_2018_north_atlantic'\n",
-    "# case = 'milton_2024_north_atlantic'\n",
-    "# case = 'ophelia_2017_north_atlantic'\n",
-    "# case = 'yagi_2024_west_pacific'\n",
-    "\n",
-    "# case = 'erin_2025_north_atlantic'\n",
-    "\n",
-    "base_path = '/lustre/fs11/portfolios/coreai/projects/coreai_climate_earth2/users/mkoch/tc_tracking_e2s/earth2studio/recipes/tc_tracking'\n",
-    "\n",
-    "pred_track_dir = f'plotting_data_helene/cyclone_tracks_te'\n",
-    "tru_track_dir = f'test/aux_data'\n",
-    "out_dir = f'./plots'\n",
-    "\n",
-    "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",
-    "                                                                   pred_track_dir=join(base_path, pred_track_dir),\n",
-    "                                                                   tru_track_dir=join(base_path, tru_track_dir),\n",
-    "                                                                   out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot tracks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_spaghetti\n",
-    "\n",
-    "plot_spaghetti(true_track=tru_track,\n",
-    "               pred_tracks=pred_tracks,\n",
-    "               ensemble_mean=ens_mean['mean'],\n",
-    "               case=case,\n",
-    "               n_members=n_members,\n",
-    "               out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot intensities over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_over_time\n",
-    "\n",
-    "plot_over_time(pred_tracks=pred_tracks,\n",
-    "               tru_track=tru_track,\n",
-    "               ensemble_mean=ens_mean,\n",
-    "               case=case,\n",
-    "               n_members=n_members,\n",
-    "               out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot relative intensities over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_relative_over_time\n",
-    "\n",
-    "plot_relative_over_time(pred_tracks=pred_tracks,\n",
-    "                        tru_track=tru_track,\n",
-    "                        ensemble_mean=ens_mean,\n",
-    "                        case=case,\n",
-    "                        n_members=n_members,\n",
-    "                        out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot ERA5 over IB vars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_ib_era5\n",
-    "\n",
-    "plot_ib_era5(tru_track=tru_track,\n",
-    "             case=case,\n",
-    "             vars=['msl', 'wind_speed'],\n",
-    "             out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## extreme values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from plotting_helpers import plot_extreme_extremes_histograms\n",
-    "\n",
-    "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n",
-    "                                 tru_track=tru_track,\n",
-    "                                 ensemble_mean=ens_mean,\n",
-    "                                 case=case,\n",
-    "                                 out_dir=out_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## moments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from data_handling import compute_averages_of_errors_over_lead_time\n",
-    "from plotting_helpers import plot_errors_over_lead_time\n",
-    "\n",
-    "vars = ['wind_speed', 'msl', 'dist']\n",
-    "\n",
-    "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n",
-    "                                                        tru_track=tru_track,\n",
-    "                                                        vars=vars)\n",
-    "\n",
-    "plot_errors_over_lead_time(err_dict=err_dict,\n",
-    "                           case=case,\n",
-    "                           ic=pred_tracks[0]['ic'],\n",
-    "                           n_members=n_members,\n",
-    "                           n_tracks=len(pred_tracks),\n",
-    "                           out_dir=out_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index e2e8056b9..bcb601d41 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -1,4 +1,3 @@
-import copy
 import os
 
 import numpy as np
@@ -7,9 +6,7 @@
 from physicsnemo.distributed import DistributedManager
 
 from earth2studio.data import fetch_data
-from earth2studio.io import KVBackend
-from earth2studio.utils.coords import split_coords
-from src.tempest_extremes import AsyncTempestExtremes, TempestExtremes
+from src.tempest_extremes import TempestExtremes
 from src.data.utils import DataSourceManager, load_heights
 from src.utils import great_circle_distance
 
@@ -188,7 +185,7 @@ def extract_baseline(cfg,
     ibtracs = tropytracks.TrackDataset(basin='all',
                                        source='ibtracs',
                                        ibtracs_mode='jtwc_neumann',
-                                       ibtracs_url=cfg.ibtracs_source_data)
+                                       ibtracs_url=os.path.abspath(cfg.ibtracs_source_data))
 
     data_source_mngr = DataSourceManager(cfg)
 

From 0f046a1c79e4260143cc3d37b8e2d7ff04782820 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Tue, 20 Jan 2026 07:16:01 -0800
Subject: [PATCH 54/69] REAMEs and markdowns in notebooks

---
 recipes/tc_tracking/README.md                 | 104 +++++++++++++++---
 recipes/tc_tracking/cfg/extract_era5.yaml     |   2 +-
 recipes/tc_tracking/cfg/hato.yaml             |  10 +-
 recipes/tc_tracking/cfg/helene.yaml           |  10 +-
 recipes/tc_tracking/cfg/reproduce_helene.yaml |   2 +-
 .../cfg/reproduce_stability_test.yaml         |   3 +-
 recipes/tc_tracking/cfg/slay_helena.yaml      |   2 +-
 recipes/tc_tracking/cfg/stability_test.yaml   |   3 +-
 recipes/tc_tracking/plotting/README.md        |  24 ++--
 .../tc_tracking/plotting/analyse_n_plot.py    |  27 +----
 .../plotting/plot_tracks_n_fields.ipynb       |   6 +-
 .../plotting/tracks_slayground.ipynb          |   4 +-
 .../baseline_extraction.py                    |   6 +-
 recipes/tc_tracking/src/generate_ensembles.py |   4 +-
 14 files changed, 126 insertions(+), 81 deletions(-)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 07fa81f1c..6bbe68d20 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -1,18 +1,29 @@
 # cyclone tracking
 
-Baseline to acheive this is an ensembling setup. Due to the inherently stochastic nature of the model, initial condition perturbation is not required.
+- Pipeline to produce an ensemble forecast and track tropical cyclones (TCs).
+- pipeline shows on example of [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) how to implement downstream models in a prediction pipeline and avoid storing atmospheric fields to disc, as such data can grow quite large and just the process of writing to disk can significantly impact inference speed.
+- TempestExtremes is not GPU-accelerated. Yet, the run time is usually shorter than what the CPU takes to produce forecast. We therefore implemented it such that the CPU looks for tracks while the GPU produces the next prediction, resulting in virually no overhead of tracking.
+
 
 ## Setting up the environment
 
-There are two ways to set up the environment, either directly using an uv environment or using uv inside a container.
-Since torch-harmonics has to be compiled and TempestExtreme is a commandline tool, the container is the recommended way.
+There are two ways to set up the environment, either directly using an uv environment or using uv to install inside a container.
+Since flash-attention, torch-harmonics and TmepestExtremes have to be compiled, the container is the recommended way.
 
-1. **container**
+1. **container** (recommended)
 
     There is a Dockerfile provided in the top level directory of the repository.
+    Highly recommend using the Dockerfile to build an image, comes with pre-nstalled flash-attention which otherwise takes a sweet age to compile. Also, TempestExtremes will be installed into the image.
 
 2. **uv**
 
+    > [!Note]
+    > When installing through uv note that compiling `flash-attn` (for AIFS-ENS) might take
+    > up to hours to compile, more info in the [troubleshooting notes](https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html#flash-attention-has-long-build-time-for-aifs-models). Consider using the container instead, where `flash-attn` is pre-installed
+    > in the base image.
+    > Additionally, you need to install TempestExtremes yourself and point the config files to the
+    > executibles.
+
     The project contains a pyproject.toml and a uv.lock file for setting up the envirnment. All you need to do is run:
 
     ```bash
@@ -34,38 +45,99 @@ Since torch-harmonics has to be compiled and TempestExtreme is a commandline too
     ```
 
 
-## run
+## Configure and Execute runs
 
-The project contains a script for running the code.
+Runs are configured through yaml files. to run the pipeline, do:
 
 ```bash
-python tc_hunt.py --config-name=tc_selection_ens.yaml
+python tc_hunt.py --config-name=config.yaml
 ```
 
-## modes
+The pipleine has three different flavours:
+- `generate_ensemble`: generate an ensemble forecast to extract storms from
+- `reproduce_members`: reproduce individual members, e.g. to store atmospheric fields of interesting tracks (NOTE: currently only works with FCN3, as AIFS-ENS is missing a method to set the random state of the model)
+- `extract_baseline`: extract storms from historic data
 
-### extract Reference tracks from ERA5 using IBTrACS as ground truth
+
+### generate ensemble
+
+Produce an ensemble forecast with either FCN3 or AIFS-ENS and track TCs.
+
+**basics**
 
 ```yaml
-mode: 'extract_baseline'
+project: 'helene'          # project name used for file names
+mode: 'generate_ensemble'  # choose mode
+model: 'aifs'              # 'fcn3' 'aifs'
+random_seed: 7777          # optional: random seed (will not directly impact model-internal random state)
 ```
-- download ibtracs file with following command...
-- extract storms from field data to enable fair comparison
 
-IBTrACS is used as ground truth for the reference tracks. However, due to various reasons simulation data is compared against extracting the tracks directly from ERA5...
+**ensemble**
 
-### generate ensemble
+```yaml
+ics: ["2024-09-24 00:00:00, 2024-09-25 18:00:00"] # list of initial conditions
+n_steps: 28                                       # number of forecast steps
+ensemble_size: 16                                 # number of ensemble members for each initial condition
+batch_size: 2                                     # batch size of inference
+```
+
+Note that for a batch size of 1, AIFS-ENS requires around 50GB of GPU memory but scales almost linearly with batch size.
+FCN3 in contrast requires over 60GB for batch size of 1, but hardly increases when increasing batch size.
+
+**IO**
+
+```yaml
+data_source:                                      # data source for initial conditions
+    _target_: earth2studio.data.NCAR_ERA5
+
+store_type: "netcdf" # 'none' 'netcdf', 'zarr'    # file type of field data output. no field output if none
+store_dir: "./outputs_${project}"                 # directory to which all outputs are written to
+out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] # field variables to write to file
+
+```
+
+**Cyclone Tracking with TmepestExtremes**
 
 ```yaml
-mode: 'generate_ensemble'
+
+cyclone_tracking:
+    asynchronous: True                             # use asyncronos mode
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']  # variables which are required by TE
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+           # detect node and stitch node commands from TE
+    orography_path: './orography.nc'               # path to orograohy file (required only if height relevant to TE)
+    keep_raw_data: False                           # keep raw field data for tracking
+    use_ram: True                                  # write in-memory files to avoid writing to and reading from disk
+    task_timeout_seconds: 120                      # timeout for tracking
+    print_te_output: False                         # write TE output to terminal
 ```
 
-### reproduce individual members
+
+#### configuration
+
+**now you got me messed up**
+
+**please believe me**
+
+
+### reproduce individual ensemble members
 
 ```yaml
 mode: 'reproduce_members'
 ```
 
+### extract Reference tracks from ERA5 using IBTrACS as ground truth
+
+```yaml
+mode: 'extract_baseline'
+```
+- download ibtracs file with following command...
+- point the config to the loacation
+- extract storms from field data to enable fair comparison
+
+IBTrACS is used as ground truth for the reference tracks. However, due to various reasons simulation data is compared against extracting the tracks directly from ERA5...
+
 Only works if exact same batch is reproduced, that means:
 - produces full batches
 - important to set batch size
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 2333a4e87..9cf84771e 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -116,13 +116,13 @@ data_source:
     #     source:
     #         _target_: earth2studio.data.GFS
 
-orography_path: './orography.nc'
 store_dir: "./outputs_${project}"
 
 cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     print_te_output: False
diff --git a/recipes/tc_tracking/cfg/hato.yaml b/recipes/tc_tracking/cfg/hato.yaml
index 1e82d3afa..d5f1a889f 100644
--- a/recipes/tc_tracking/cfg/hato.yaml
+++ b/recipes/tc_tracking/cfg/hato.yaml
@@ -1,6 +1,6 @@
 project: 'hato'
 mode: 'generate_ensemble'
-model: 'fcn3' # 'fcn3' 'aifs'
+model: 'aifs' # 'fcn3' 'aifs'
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
 # ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
@@ -10,15 +10,14 @@ ics: ["2017-08-21 12:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 n_steps: 20
 ensemble_size: 16
 batch_size: 2
-orography_path: './orography.nc'
 
 data_source:
-    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.NCAR_ERA5
     # _target_: earth2studio.data.GFS
     # _target_: earth2studio.data.IFS
-    # _target_: earth2studio.data.CDS
+    _target_: earth2studio.data.CDS
 
-store_type: "none"
+store_type: "netcdf"
 store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
@@ -27,6 +26,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index 5c2ec91ce..805c3c56f 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -1,6 +1,6 @@
 project: 'helene'
 mode: 'generate_ensemble'
-model: 'fcn3' # 'fcn3' 'sfno'
+model: 'aifs' # 'fcn3' 'aifs'
 random_seed: 7777
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
@@ -11,15 +11,14 @@ ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 n_steps: 28
 ensemble_size: 16
 batch_size: 2
-orography_path: './orography.nc'
 
 data_source:
-    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.NCAR_ERA5
     # _target_: earth2studio.data.GFS
     # _target_: earth2studio.data.IFS
-    # _target_: earth2studio.data.CDS
+    _target_: earth2studio.data.CDS
 
-store_type: "none"
+store_type: "netcdf"
 store_dir: "./outputs_${project}"
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
 
@@ -28,6 +27,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index 7f637236d..1982843c8 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -12,7 +12,6 @@ reproduce_members: [
 n_steps: 28
 ensemble_size: 16
 batch_size: 2
-orography_path: './orography.nc'
 
 data_source:
     _target_: earth2studio.data.NCAR_ERA5
@@ -26,6 +25,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
index 248ee6411..5fbf8dc8c 100644
--- a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
@@ -11,8 +11,6 @@ reproduce_members: [
 n_steps: 300
 ensemble_size: 96
 batch_size: 2
-# model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
-orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
     # _target_: src.data.localh5.LocalArchiveHDF5
@@ -32,6 +30,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index 996be54e7..33af7e51f 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -11,7 +11,6 @@ ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 n_steps: 15
 ensemble_size: 6
 batch_size: 2
-orography_path: './orography.nc'
 
 data_source:
     _target_: earth2studio.data.NCAR_ERA5
@@ -28,6 +27,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/stability_test.yaml b/recipes/tc_tracking/cfg/stability_test.yaml
index a2ce6b4ef..b2d085311 100644
--- a/recipes/tc_tracking/cfg/stability_test.yaml
+++ b/recipes/tc_tracking/cfg/stability_test.yaml
@@ -11,8 +11,6 @@ ics: ["2023-05-01 00:00:00"]
 n_steps: 300 #280
 ensemble_size: 96 # 1024 # 43
 batch_size: 2
-# model_package: "/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs"
-orography_path: '/fcn3_cyclones/checkpoints/fcn2_sc2_edim45_layers10_finetune_2013-2016_8step_centered_4member/flexible_5epochs/orography.nc'
 
 data_source:
     # _target_: src.data.localh5.LocalArchiveHDF5
@@ -32,6 +30,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: './orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/plotting/README.md b/recipes/tc_tracking/plotting/README.md
index 7e420da9e..86550a357 100644
--- a/recipes/tc_tracking/plotting/README.md
+++ b/recipes/tc_tracking/plotting/README.md
@@ -1,21 +1,11 @@
-# cyclone tracking
+# Analysing and Plotting TC Tracks
 
-This script is used to analyse cyclone tracks extracted using this framework from either model output or from ERA5.
+## Notebooks
 
-## Usage
+- **`plot_tracks_n_fields.ipynb`** – Plotting tracks and fields for individual ensemble members
+- **`tracks_slayground.ipynb`** – Analysing and plotting complete tracks from a full ensemble run for a case study on a given storm
 
-```bash
-python analyse_n_plot_tracks.py
-```
+## Additional Information
 
-### individual storms
-
-set the `individual_storms` flag to `True` in `analyse_n_plot_tracks.py`.
-
-### ensemble of storms
-
-set the `ensemble_of_storms` flag to `True` in `analyse_n_plot_tracks.py`.
-
-
-## Output
-...
+- This directory includes Python scripts with methods for plotting and analysing results, which are called from the notebooks
+- Each notebook specifies at the beginning what data is required and how to produce it using the TC tracking pipeline
diff --git a/recipes/tc_tracking/plotting/analyse_n_plot.py b/recipes/tc_tracking/plotting/analyse_n_plot.py
index 27bf7b126..d8e06daed 100644
--- a/recipes/tc_tracking/plotting/analyse_n_plot.py
+++ b/recipes/tc_tracking/plotting/analyse_n_plot.py
@@ -259,27 +259,6 @@ def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path):
     ensemble_metrics = reduce_over_all_storms(storm_metrics)
 
     plot_ensemble_mean_metrics(ens_means, out_path)
-    # plot average dist in space and intensity over lead time (reduced over all storms)
-
-    # print()
-    # print(f'{storm_metrics.keys()=}')
-    # print(f'{storm_metrics['case']=}')
-    # print(f'{storm_metrics['var']=}')
-    # print(f'{storm_metrics['metric']=}')
-    # print(f'{storm_metrics['data'].shape=}')
-    # print(f'{storm_metrics['weights'].shape=}')
-
-    # print()
-    # print(f'{ensemble_metrics.keys()=}')
-    # print(f'{ensemble_metrics['msl']['mae'].shape=}')
-    # print(f'{ensemble_metrics['n_members'].shape=}')
-
-    # print()
-    # print(f'{ens_means['helene_2024_north_atlantic'].keys()=}')
-    # print(f'{ens_means['helene_2024_north_atlantic']['mean'].keys()=}')
-    # print(f'{ens_means['helene_2024_north_atlantic']['time']=}')
-    # print(f'{ens_means['helene_2024_north_atlantic']['n_members'].shape=}')
-    # print(f'{ens_means['helene_2024_north_atlantic']['mean']['dist'].shape=}')
 
     return ensemble_metrics
 
@@ -312,11 +291,11 @@ def analyse_n_plot_tracks():
     individual_storms = False
     ensemble_of_storms = True
 
-    pred_track_dir = f"/home/mkoch/Documents/projects/fcn3_tcs/track_data/predictions/case_studies_256_mem_random_seed_issue/cyclone_tracks_te"
+    pred_track_dir = f"/path/to/predictions/cyclone_tracks_te"
     tru_track_dir = (
-        f"/home/mkoch/Documents/projects/fcn3_tcs/track_data/reference_tracks"
+        f"/path/to/reference_tracks"
     )
-    out_dir = f"/home/mkoch/Documents/projects/fcn3_tcs/plots/case_studies_256_random_seed_issue"
+    out_dir = f"./plots"
 
     if individual_storms:
         analyse_individual_storms(
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index 2e828d2c5..327781d3c 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -44,9 +44,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "base_dir = '/path/to/outputs_reproduce_helene'\n",
+        "# base_dir = '/path/to/outputs_reproduce_helene'\n",
+        "# base_dir = '/path/to/outputs_helene'\n",
         "field_data = 'helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
         "track_dir = f'cyclone_tracks_te'\n",
+        "base_dir = '~/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato'\n",
         "\n",
         "variable = 'wind_speed'\n",
         "ensemble_member = 1\n",
@@ -120,7 +122,7 @@
         "else:\n",
         "    sub_ds = sub_ds[variable] # np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n",
         "\n",
-        "ds"
+        "display(ds)"
       ]
     },
     {
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
index decc31c4d..77af85e15 100644
--- a/recipes/tc_tracking/plotting/tracks_slayground.ipynb
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -48,8 +48,8 @@
         "# case = 'debbie_2017_southern_pacific'\n",
         "# case = 'dorian_2019_north_atlantic'\n",
         "# case = 'harvey_2017_north_atlantic'\n",
-        "# case = 'hato_2017_west_pacific'\n",
-        "case = 'helene_2024_north_atlantic'\n",
+        "case = 'hato_2017_west_pacific'\n",
+        "# case = 'helene_2024_north_atlantic'\n",
         "# case = 'ian_2022_north_atlantic'\n",
         "# case = 'iota_2020_north_atlantic'\n",
         "# case = 'irma_2017_north_atlantic'\n",
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index bcb601d41..6577eb444 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -31,7 +31,11 @@ def extract_from_historic_data(cfg,
     coords["ensemble"] = np.array([0])
     coords.move_to_end("ensemble", last=False)
 
-    heights, height_coords = load_heights(cfg.orography_path)
+    heights, height_coords = (
+        load_heights(cfg.cyclone_tracking.orography_path)
+        if "orography_path" in cfg.cyclone_tracking
+        else (None, None)
+    )
 
     # set up TC tracking, add downloaded data and execute tracking algorithm
     cyclone_tracking = TempestExtremes(detect_cmd=cfg.cyclone_tracking.detect_cmd,
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 3e2b6a06d..850813d8a 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -88,8 +88,8 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
         oco = model.output_coords(model.input_coords())
 
         heights, height_coords = (
-            load_heights(cfg.orography_path)
-            if "orography_path" in cfg
+            load_heights(cfg.cyclone_tracking.orography_path)
+            if "orography_path" in cfg.cyclone_tracking
             else (None, None)
         )
 

From fbdbc9f1838863d328ce4bbf0bd176674897c52f Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 21 Jan 2026 08:11:52 -0800
Subject: [PATCH 55/69] drafted readme

---
 recipes/tc_tracking/README.md                 | 221 +++++++++++++++---
 recipes/tc_tracking/cfg/extract_era5.yaml     |  40 +---
 recipes/tc_tracking/cfg/hato.yaml             |  13 +-
 recipes/tc_tracking/cfg/helene.yaml           |  18 +-
 recipes/tc_tracking/cfg/slay_helena.yaml      |  20 +-
 recipes/tc_tracking/src/data/localh5.py       |  89 -------
 .../baseline_extraction.py                    |   3 +-
 recipes/tc_tracking/src/generate_ensembles.py |   2 +-
 8 files changed, 226 insertions(+), 180 deletions(-)
 delete mode 100644 recipes/tc_tracking/src/data/localh5.py

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 6bbe68d20..0c47b6964 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -47,12 +47,18 @@ Since flash-attention, torch-harmonics and TmepestExtremes have to be compiled,
 
 ## Configure and Execute runs
 
-Runs are configured through yaml files. to run the pipeline, do:
+Runs are configured through yaml files which are put in `tc_tracking/cfg`. to run the pipeline, do:
 
 ```bash
 python tc_hunt.py --config-name=config.yaml
 ```
 
+The script can also be exectued in distributed settings using slurm, mpi, or torchrun. eg:
+
+```bash
+torchrun --nproc-per-node=2 tc_hunt.py --config-name=config.yaml
+```
+
 The pipleine has three different flavours:
 - `generate_ensemble`: generate an ensemble forecast to extract storms from
 - `reproduce_members`: reproduce individual members, e.g. to store atmospheric fields of interesting tracks (NOTE: currently only works with FCN3, as AIFS-ENS is missing a method to set the random state of the model)
@@ -72,7 +78,7 @@ model: 'aifs'              # 'fcn3' 'aifs'
 random_seed: 7777          # optional: random seed (will not directly impact model-internal random state)
 ```
 
-**ensemble**
+**<a name="config_ens"></a>ensemble**
 
 ```yaml
 ics: ["2024-09-24 00:00:00, 2024-09-25 18:00:00"] # list of initial conditions
@@ -88,87 +94,244 @@ FCN3 in contrast requires over 60GB for batch size of 1, but hardly increases wh
 
 ```yaml
 data_source:                                      # data source for initial conditions
-    _target_: earth2studio.data.NCAR_ERA5
+    _target_: earth2studio.data.NCAR_ERA5         # choose any of the online or on-prem sources
 
 store_type: "netcdf" # 'none' 'netcdf', 'zarr'    # file type of field data output. no field output if none
 store_dir: "./outputs_${project}"                 # directory to which all outputs are written to
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] # field variables to write to file
 
 ```
+- data source can provide any of the e2studio data sources or a custom data source
 
-**Cyclone Tracking with TmepestExtremes**
+- be careful with storing field varibales, as the storage requirements grow quickly and also writing the data to disc can signifcantly slow down the inference
+- Note that with FCN3, runs can be reproduced (see section YY), opening the option to store and analyse tracks only, and then reproduce those ensemble members with interesting tracks where you might want to see the fields
 
-```yaml
+**Cyclone Tracking with TmepestExtremes** (optional)
 
+```yaml
 cyclone_tracking:
-    asynchronous: True                             # use asyncronos mode
+    asynchronous: True                             # Let GPU produce next prediction while CPU executes TempestExtremes
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']  # variables which are required by TE
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-           # detect node and stitch node commands from TE
+                                                   # detect node and stitch node commands from TE
     orography_path: './orography.nc'               # path to orograohy file (required only if height relevant to TE)
     keep_raw_data: False                           # keep raw field data for tracking
     use_ram: True                                  # write in-memory files to avoid writing to and reading from disk
     task_timeout_seconds: 120                      # timeout for tracking
     print_te_output: False                         # write TE output to terminal
 ```
+- for more infos on [asynchronous mode](#async) have a look at the section below
+- tip: For debugging `detect_cmd` and `stitch_cmd` you can set `print_te_output` to `True` to see where `TempestExtremes` fails. However, if more iterations are required to get TmepestExtremes to run, debugging through inference runs can be a rather cumbersome. Consider setting `keep_raw_data` to `True` and `use_ram` to `False` to write a field snapshot to a netcdf file to `${store_dir}/cyclone_tracks_te/raw_data`. Then you can debug directly pasting the commands in the command line. Once you have found the correct commands, just copy the working commands to the config file. When operating in the command line, you need to point TempestExtremes to the files (see [documentation](https://climate.ucdavis.edu/tempestextremes.php) for more details): for `detect_cmd`, this will be `--in_data_list` and `--out_file_list`, for `stitch_cmd` this will be `--in` and `--out`.
+
+**stability check** (optional)
+
+- FCN3 can do stable rollouts on subseasonal time scales, stability check is only relevant for (sub)seasonal time scales
+- THe long term behaviour of AIFS-ENS is still something we need to investigate, so the current method is for FCN3
+- however blows up some time
+- simple stability check was implemented
+    - instability can be seen in global averages of field variables
+    - build up over like 10 days, the escape towards infinity
+    - so it is easy to chatch one of them if yo ucan allow letting the simulation run for 10 extra days in (sub)seasonal
+    - if cautch, the ensemble member can be re-run with differrent random seed
+    - to implement stability check, provide list of variables to monitor and a threshold for each var. from these varables, global averages will be taken at initial conditions. At each time step global average is computed again and if it surpasses the threshold, the run will be interrupted and re-started with new random seed.
+    - Note that this all currently is rather basic implementation, more advanced methods for detection that might not require 10 extra days of simulation and method for better load-balancing in case a lot of runs get unstable would definitely be beneficial.
 
+```yaml
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
+```
 
-#### configuration
-
-**now you got me messed up**
 
-**please believe me**
+**hydra** (optional)
+```yaml
+hydra:
+    job:
+        name: ${project}
+    run:
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
+```
+- propagating project name and output directory for logs to hydra.
+- most likely never need to change that section
 
 
 ### reproduce individual ensemble members
 
+- run without storing fields, as tracks contain roughly 5 orders of magnitude less data that the associated field data required to extract the tracks
+- send results of hundreds of hypothetical cyclone seasons as an email attachment
+- however, for interesting tracks it can be that looking at field could be interesting
+
+- FCN3 allows accessing its random internal state, enabling reproducing individual ensemble members
+- AIFS should have this capability as well in theory, however, the internal random state cannot is not exposed through anemoi models, which is why we cannot reproduce AIFS-ENS runs
+- not that reproducibility [is hard](https://arxiv.org/abs/1605.04339) only works when the same environment on the same system is used to produce the prediction.
+
+- to reproduce a run you need the following
+    - config file of the original run (ideally)
+    - ensemble member ID and random seed of all members which shall be reproduced (both are provided in file name of track csv file)
+    - batch size (also provided in name of track csv file)
+
+here we describe what to change in the file to reproduce, you can also compare `cfg/helene.yaml` and `cfg/reproduce_helene.yaml`.
+in the original config, consider switching the project name to avoid ovewriting files and change the mode to:
+
 ```yaml
 mode: 'reproduce_members'
 ```
 
+in the [**ensembles section**](#config_ens), remove the ic varaible and replace it with a list of ensemble members you want to reproduce.
+For each ensemble member, provide intitial condition, member ID and random seed:
+
+```yaml
+reproduce_members: [
+                    ['2024-09-24 00:00:00',  5, 2148531792],
+                    ['2024-09-24 00:00:00',  8, 1342799488],
+                    ['2024-09-24 12:00:00',  4, 2528887706],
+                    ['2024-09-24 12:00:00',  5, 2528887706],
+                    ['2024-09-24 12:00:00', 13, 1968734508],
+                   ]
+```
+
+The above example would reproduce members 4 and 8 from the ensemble starting at midnight and members 4, 5 and 13 from the ensemble starting midday.
+Not that the internal random state can only be set for the full batch. While the individual members of a batch will provide independend predictions, they have the same random seed. This also means that always a full batch has to be reporduced:
+Consider a batch size of 2 for the example above. In that case, the run would reproduce members 4, 5, 8 and 9 of the ensemble starting at midnight, and members 4, 5, 12 and 13 of the ensemble starting midday.
+
+
 ### extract Reference tracks from ERA5 using IBTrACS as ground truth
 
+- IBTrACS is considered gold standard for TC data
+- However, it is observation data which a global model at quartely degree is not able to reproduce
+- Hence for comparison it can make sense to extract storms directly from analysis data like ERA5 and score prediction results against these
+- This pipeline shows how to extract reference tracks for named storms using IBtracs as reference to identify tracks
+
+**basics**
+
 ```yaml
+project: 'reference_tracks'
 mode: 'extract_baseline'
 ```
-- download ibtracs file with following command...
-- point the config to the loacation
-- extract storms from field data to enable fair comparison
 
-IBTrACS is used as ground truth for the reference tracks. However, due to various reasons simulation data is compared against extracting the tracks directly from ERA5...
+**cases**
+
+- choose storms. for each storm which shall be extracted, provide name, year and basin as per below
+- the script will look in IBTRACS data during which time the storm was active, pull the relevant data and extracts TC tracks using TempestExtremes
+- at any given time there might be multiple storms on the globe, hence in a second step all extracted tracks are compared if they are close to the IBTRACS groundtruth to find the right track.
+
+```yaml
+cases:
+    hato:
+        year: 2017
+        basin: west_pacific
+    helene:
+        year: 2024
+        basin: north_atlantic
+```
 
-Only works if exact same batch is reproduced, that means:
-- produces full batches
-- important to set batch size
-- ensemble size required if final batch of ensemble shall be reproduced
-- random seed has to be provided for each ensemble member, but the global random seed won't have effect here, as it mainly impacts the generation of random seeds for members
-- works only on same machine using identical environment. No guarantees otherwise
-- works only with FCN3
+**IO**
+ibtracs data:
+- download ibtracs data in csv format with following command: `wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv` Note that file is over 300MB and might take a couple of minutes to download
+- note that `./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv` can be used if just testing with Helene and Hato.
+
+analysis data:
+- for extracting esp if more storms shall be extracted we recommend using an on-prem data set, as otherwise download times will be limiting here
+- either, data source can just be implemented the standard way as above
+- or different data sets can be prescribed for differnet years, esp helpfull when available data set is split up in train/test/outofsample
+- as an example here we show with different versions of ERA5
+
+```yaml
+store_dir: "./outputs_${project}"
+ibtracs_source_data: "/path/to/ibtracs.ALL.list.v04r01.csv"
+
+data_source:
+    era5_train:
+        years: "1980-2016"
+        source:
+            _target_: earth2studio.data.NCAR_ERA5
+
+    era5_oos:
+        years: "2017-2022"
+        source:
+            _target_: earth2studio.data.CDS
+```
 
 
 ## visualise
 
 Have a stab at the notebook...
 
+## TempestExtremes integration
+
+- using TempestExtremes as example to demonstrate how to implement custom downstream models in pipeline
+- increased complexity, as TE does not run on GPU and cannot be installed as python library.
+- implementing that way to avoid writing atmospheric field data to and reading it again from disk, as this slows down inference and can be prohibitive with available storage
+
+**general integration**
+
+- implemented like a diagnostic model, but without return type
+- that diagnostic model class then spawns a subprocess to call TE
+- result will be tracks file directly written to disk
+- data access for TE has to be provided though file
+- to avoid reading/writing large amounts of data to/from disk, we write to `/dev/shm` effectively producing in-memory file
+
+
+**<a name="async"></a>asynchronos mode**
+
+- on many platforms, running TmepestExtremes on the CPU is still faster than producing the forecast on the GPU. Hence, we let a CPU core execute TempestExtremes while the GPU already starts producing the next prediction
+- note that in asynchronos mode each member of a batch gets its own process assigned. Hence, if TE is slower than the prediction (could e.g. happen when using AIFS on B200), consider increading the batch size. The time the GPU takes to produce a batched predcition scales more or less linearly with batch size, while the time that a single CPU core takes to porcess one member is constant.
+- if you see issues, first see if they also appear when asyncronos mode is switched off.
+
 
 ## example workflow
 
-### extract baseline
+### extract baseline (optional)
 
+- note that this requires downloading the field data for Hato and Helene. you can skip this step and use the refernce tracks under `./test/aux_data` instead
 - download ibtraccs data
-- extract baseline
+- extract baseline by running
+```bash
+python tc_hunt.py --config-name=extract_era5.yaml
+```
+
+- That should produce a folder called `outputs_reference_tracks/` that includes the two reference tracks:
+    - `reference_track_hato_2017_west_pacific.csv`
+    - `reference_track_helene_2024_north_atlantic.csv`
 
 ### produce ensemble
 
-- run forecast loop
+- run forecast loop for helen with FCN3 and for hato using AIFS-ENS:
+    - `python tc_hunt.py --config-name=helene.yaml`
+    - `python tc_hunt.py --config-name=hato.yaml`
+- this should produce two folders, `outputs_helene` and `outputs_hato`
 
-### visualise members
+### analyse tracks
 
-- visualise in notebook
+- visualise in notebook `plotting/tracks_slayground.ipynb`
+- in first cell of notebook, set for helene
+    - `case = 'helene_2024_north_atlantic'`
+    - `pred_track_dir = '/path/to/outputs_helene/cyclone_tracks_te'`
+    - `tru_track_dir = '/path/to/outputs_reference_tracks'`
+    - in case you did not run the extraction yourself you can use `tru_track_dir = '/path/to/test/aux_data'`
+- for hato
+    - `case = 'hato_2017_west_pacific'`
+    - `pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'`
+    - `tru_track_dir = '/path/to/outputs_reference_tracks'`
 
-### re-run interesting members to extract fields
+### re-produce interesting members to extract fields
 
+- let's assume the above analysis has shown that we want to have a closer look at members 0, 8, 9 and 12 of the Helene prediction
+- luckily, that was run with FCN3 so we can reproduce and store the fields
 - reproduce, this time store fields
-- visualise track and field
+    - in `cfg/reproduce_helene.yaml` make sure random seeds provided to the `reproduce_members` variable match the seeds from the track files in `outputs_helene/cyclone_tracks_te`
+    - double-check that batch size and ensemble size and data source are identical to the original run
+    - execute the pipeline: `python tc_hunt.py --config-name=reproduce_helene.yaml`
+    - should produce folder `outputs_reprduce_helene` that now also includes a netcdf file called `reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc`
+- visualise track and field in `plotting/plot_track_n_fields.ipynb`
+    - TODO: remove base dir there
+    - set `field_data = '/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'`
+    - set `track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'`
+
+    - select a variable you want to visualise for the field, eg `variable = 'wind_speed'` or `variable = 'msl'`
+    - select the ensemble member you want to look at (note that you only reproduced a subset)
+    - select the region for plotting: `region = 'gulf_of_mexico'`
+    - at the end of the notebook you will have be rewarded with an animation where you can click through time
+    - as test if reproducibility worked, point `track_dir` to the original Helene run
 
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 9cf84771e..45a96baa1 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -54,9 +54,9 @@ cases:
     #     year: 2024
     #     basin: north_atlantic
 
-    helene:
-        year: 2024
-        basin: north_atlantic
+    # helene:
+    #     year: 2024
+    #     basin: north_atlantic
 
     # lan:
     #     year: 2017
@@ -82,39 +82,19 @@ cases:
     #     year: 2017
     #     basin: southern_pacific
 
+# ibtracs file for hato and helene only. obtain full data at https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv
 ibtracs_source_data: "./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
-    # h5_era5_train:
-    #     years: "1980-2016"
-    #     source:
-    #         _target_: src.data.localh5.LocalArchiveHDF5
-    #         dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/train"
-    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
-
-    # h5_era5_test:
-    #     years: 2017
-    #     source:
-    #         _target_: src.data.localh5.LocalArchiveHDF5
-    #         dirs: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/test"
-    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/datasets/era5_75var/metadata/data.json"
-
-    # h5_era5_oos:
-    #     years: "2018-2022"
-    #     source:
-    #         _target_: src.data.localh5.LocalArchiveHDF5
-    #         dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-    #         metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
-
-    ncar_era5:
-        years: "2017-2024"
+    era5_train:
+        years: "1980-2017"
         source:
             _target_: earth2studio.data.NCAR_ERA5
 
-    # gfs:
-    #     years: "2025"
-    #     source:
-    #         _target_: earth2studio.data.GFS
+    era5_oos:
+        years: "2018-2022"
+        source:
+            _target_: earth2studio.data.CDS
 
 store_dir: "./outputs_${project}"
 
diff --git a/recipes/tc_tracking/cfg/hato.yaml b/recipes/tc_tracking/cfg/hato.yaml
index d5f1a889f..acd8b4615 100644
--- a/recipes/tc_tracking/cfg/hato.yaml
+++ b/recipes/tc_tracking/cfg/hato.yaml
@@ -2,19 +2,12 @@ project: 'hato'
 mode: 'generate_ensemble'
 model: 'aifs' # 'fcn3' 'aifs'
 
-# ic_block_start: "2024-09-23 00:00:00" # first IC
-# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
-# ic_block_step: 24            # number of hours between individual ICs
-ics: ["2017-08-21 12:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
-
+ics: ["2017-08-21 12:00:00"]
 n_steps: 20
 ensemble_size: 16
 batch_size: 2
 
 data_source:
-    # _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
     _target_: earth2studio.data.CDS
 
 store_type: "netcdf"
@@ -32,6 +25,10 @@ cyclone_tracking:
     task_timeout_seconds: 120
     print_te_output: False
 
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
+
 # hydra logs
 hydra:
     job:
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index 805c3c56f..b35cf3ff8 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -1,22 +1,14 @@
 project: 'helene'
 mode: 'generate_ensemble'
-model: 'aifs' # 'fcn3' 'aifs'
-random_seed: 7777
-
-# ic_block_start: "2024-09-23 00:00:00" # first IC
-# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
-# ic_block_step: 24            # number of hours between individual ICs
-ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+model: 'fcn3' # 'fcn3' 'aifs'
 
+ics: ["2024-09-24 00:00:00"]
 n_steps: 28
 ensemble_size: 16
 batch_size: 2
 
 data_source:
-    # _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
-    _target_: earth2studio.data.CDS
+    _target_: earth2studio.data.NCAR_ERA5
 
 store_type: "netcdf"
 store_dir: "./outputs_${project}"
@@ -33,6 +25,10 @@ cyclone_tracking:
     task_timeout_seconds: 120
     print_te_output: False
 
+stability_check:
+    variables: ['t1000', 'u250', 'v850']
+    thresholds: [10, 10, 10]
+
 # hydra logs
 hydra:
     job:
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index 33af7e51f..3e48c61c5 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -1,22 +1,22 @@
-project: 'helene'
+project: 'slay_helena'
 mode: 'generate_ensemble'
-model: 'fcn3' # 'fcn3' 'sfno' 'aifs'
+model: 'aifs' # 'fcn3' 'sfno' 'aifs'
 random_seed: 7777
 
 # ic_block_start: "2024-09-23 00:00:00" # first IC
 # ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
 # ic_block_step: 24            # number of hours between individual ICs
-ics: ["2024-09-29 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
+ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 
 n_steps: 15
-ensemble_size: 6
-batch_size: 2
+ensemble_size: 1
+batch_size: 1
 
 data_source:
-    _target_: earth2studio.data.NCAR_ERA5
+    # _target_: earth2studio.data.NCAR_ERA5
     # _target_: earth2studio.data.GFS
     # _target_: earth2studio.data.IFS
-    # _target_: earth2studio.data.CDS
+    _target_: earth2studio.data.CDS
 
 store_type: "netcdf"
 store_dir: "./outputs_${project}"
@@ -28,8 +28,8 @@ cyclone_tracking:
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
     orography_path: './orography.nc'
-    keep_raw_data: False
-    use_ram: True
+    keep_raw_data: True
+    use_ram: False
     task_timeout_seconds: 120
     print_te_output: False
 
@@ -42,4 +42,4 @@ hydra:
     job:
         name: ${project}
     run:
-        # dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
+        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
diff --git a/recipes/tc_tracking/src/data/localh5.py b/recipes/tc_tracking/src/data/localh5.py
deleted file mode 100644
index 76a371e41..000000000
--- a/recipes/tc_tracking/src/data/localh5.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import glob
-import json
-import os
-from collections.abc import Iterable
-from datetime import datetime, timedelta
-from typing import Tuple
-
-import h5py
-import numpy as np
-import xarray as xr
-
-from earth2studio.utils.type import TimeArray, VariableArray
-
-DIMS = ["time", "variable", "lat", "lon"]
-
-
-class LocalArchiveHDF5:
-    def __init__(
-        self,
-        dirs: str | Iterable,
-        metadata_file: str,
-        latlon_box: tuple[tuple[float, float], tuple[float, int]] | None = None,
-    ):
-        if isinstance(dirs, str):
-            dirs = [dirs]
-
-        self.files_by_year = {}
-        for dir in dirs:
-            for path in glob.glob(os.path.join(dir, "????.h5")):
-                year = int(os.path.basename(path).split(".")[0])
-                self.files_by_year[year] = path
-
-        with open(metadata_file) as f:
-            metadata = json.load(f)
-        self.lat = np.array(metadata["coords"]["lat"])
-        self.lon = np.array(metadata["coords"]["lon"])
-        if latlon_box is not None:
-            (self.lat, self.lat_slice) = _find_index_range(self.lat, *latlon_box[0])
-            (self.lon, self.lon_slice) = _find_index_range(self.lon, *latlon_box[1])
-        else:
-            self.lat_slice = self.lon_slice = slice(None)
-        self.dt = timedelta(hours=metadata["dhours"])
-        self.variables = metadata["coords"]["channel"]
-        self.field_shape = (len(self.lat), len(self.lon))
-
-    def __call__(
-        self,
-        time: datetime | list[datetime] | TimeArray,
-        variable: str | list[str] | VariableArray,
-    ) -> xr.DataArray:
-        if isinstance(time, datetime):
-            time = [time]
-        time = [
-            datetime.fromisoformat(str(t)[:19]) if isinstance(t, np.datetime64) else t
-            for t in time
-        ]
-        if isinstance(variable, str):
-            variable = [variable]
-
-        var_indices = [self.variables.index(v) for v in variable]
-
-        x = np.zeros((len(time), len(variable)) + self.field_shape, dtype=np.float32)
-
-        for k, t in enumerate(time):
-            time_idx = int((t - datetime(t.year, 1, 1)) / self.dt)
-            with h5py.File(self.files_by_year[t.year], "r") as ds:
-                for i, var_idx in enumerate(var_indices):
-                    x[k, i, :, :] = ds["fields"][
-                        time_idx, var_idx, self.lat_slice, self.lon_slice
-                    ]
-
-        coords = {
-            "time": np.array(time, copy=False),
-            "variable": np.array(variable, copy=False),
-            "lat": self.lat,
-            "lon": self.lon,
-        }
-
-        return xr.DataArray(data=x, coords=coords, dims=DIMS)
-
-
-def _find_index_range(seq, x0, x1):
-    in_range = (x0 <= seq) & (seq <= x1)
-    subseq = np.array(seq[in_range])
-    ind = np.arange(len(seq))[in_range]
-    i0 = ind[0]
-    i1 = ind[-1] + 1
-
-    return (subseq, slice(i0, i1))
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
index 6577eb444..444a6153b 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
@@ -107,8 +107,7 @@ def extract_from_ibtracs(cfg, ibtracs, case, time_step):
 
 def match_tracks(ib_storm, hist_tracks, case):
     # add time as np.datetime64
-    times = np.array([pd.to_datetime(f'{hist_tracks["year"].iloc[jj]}-{int(hist_tracks.iloc[jj]["month"]):02d}-{int(hist_tracks.iloc[jj]["day"]):02d} {int(hist_tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(hist_tracks))])#.astype('datetime64[h]')
-    # hist_tracks.drop(columns=['year', 'month', 'day', 'hour', 'i', 'j', 'track_id'], inplace=True)
+    times = np.array([pd.to_datetime(f'{hist_tracks["year"].iloc[jj]}-{int(hist_tracks.iloc[jj]["month"]):02d}-{int(hist_tracks.iloc[jj]["day"]):02d} {int(hist_tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(hist_tracks))])
     hist_tracks.insert(0, 'time', times)
 
     n_tracks = hist_tracks['track_id'].nunique()
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/generate_ensembles.py
index 850813d8a..3701207fa 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/generate_ensembles.py
@@ -180,7 +180,7 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
 
         if cyclone_tracking and (not stability_check or stab.all()):
             cyclone_tracking(
-                out_file_names=[
+                out_file_names=[  # TODO add seed only for FCN3 members, as the seed in AIFS is not exposed. also check for netcdf output
                     f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv"
                     for mem in mems
                 ]

From b495b31f277f7520cf18fcac018a0e97151a1e09 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 22 Jan 2026 07:07:30 -0800
Subject: [PATCH 56/69] first iteration over readme

---
 recipes/tc_tracking/README.md | 429 +++++++++++++++++++---------------
 1 file changed, 241 insertions(+), 188 deletions(-)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 0c47b6964..a36977697 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -1,141 +1,159 @@
-# cyclone tracking
+# Cyclone Tracking
 
-- Pipeline to produce an ensemble forecast and track tropical cyclones (TCs).
-- pipeline shows on example of [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) how to implement downstream models in a prediction pipeline and avoid storing atmospheric fields to disc, as such data can grow quite large and just the process of writing to disk can significantly impact inference speed.
-- TempestExtremes is not GPU-accelerated. Yet, the run time is usually shorter than what the CPU takes to produce forecast. We therefore implemented it such that the CPU looks for tracks while the GPU produces the next prediction, resulting in virually no overhead of tracking.
+> [!Note]
+> This recipe serves as an example of how to integrate custom downstream models into a forecasting pipeline. Whilst TempestExtremes is used here for tropical cyclone tracking, the patterns demonstrated—such as asynchronous CPU/GPU execution, in-memory file handling, and diagnostic model integration—can be adapted for other downstream analysis tools.
 
+This pipeline produces ensemble prediction and tracks tropical cyclones (TCs) within the prediction. Here, we demonstrate how to integrate downstream analysis tools like [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) into a prediction workflow whilst avoiding the need to store large atmospheric field data to disk, which can grow prohibitively large and significantly impact inference speed.
 
-## Setting up the environment
+Although TempestExtremes is not GPU-accelerated, its execution time on CPU is typically shorter than the time required for the GPU to produce the next prediction. We therefore implement an asynchronous mode where the CPU executes TempestExtremes on completed time steps whilst the GPU continues producing subsequent predictions, resulting in virtually no computational overhead from the tracking process.
 
-There are two ways to set up the environment, either directly using an uv environment or using uv to install inside a container.
-Since flash-attention, torch-harmonics and TmepestExtremes have to be compiled, the container is the recommended way.
+## Table of Contents
 
-1. **container** (recommended)
+- [Setting up the Environment](#setting-up-the-environment)
+  - [Container (Recommended)](#1-container-recommended)
+  - [UV Environment](#2-uv-environment)
+- [Configure and Execute Runs](#configure-and-execute-runs)
+  - [Generate Ensemble](#generate-ensemble)
+  - [Reproduce Individual Ensemble Members](#reproduce-individual-ensemble-members)
+  - [Extract Reference Tracks from ERA5](#extract-reference-tracks-from-era5-using-ibtracs-as-ground-truth)
+- [Visualisation](#visualisation)
+- [TempestExtremes Integration](#tempestextremes-integration)
+- [Example Workflow](#example-workflow)
+  - [Extract Baseline](#extract-baseline-optional)
+  - [Produce Ensemble Forecasts](#produce-ensemble-forecasts)
+  - [Analyse Tracks](#analyse-tracks)
+  - [Reproduce Interesting Members](#reproduce-interesting-members-to-extract-fields)
 
-    There is a Dockerfile provided in the top level directory of the repository.
-    Highly recommend using the Dockerfile to build an image, comes with pre-nstalled flash-attention which otherwise takes a sweet age to compile. Also, TempestExtremes will be installed into the image.
 
-2. **uv**
+## Setting up the Environment
 
-    > [!Note]
-    > When installing through uv note that compiling `flash-attn` (for AIFS-ENS) might take
-    > up to hours to compile, more info in the [troubleshooting notes](https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html#flash-attention-has-long-build-time-for-aifs-models). Consider using the container instead, where `flash-attn` is pre-installed
-    > in the base image.
-    > Additionally, you need to install TempestExtremes yourself and point the config files to the
-    > executibles.
+There are two ways to set up the environment: either directly using a uv environment, or using uv to install dependencies inside a container. Since flash-attention, torch-harmonics and TempestExtremes all require compilation, **the container approach is strongly recommended**.
 
-    The project contains a pyproject.toml and a uv.lock file for setting up the envirnment. All you need to do is run:
+### 1. Container (Recommended)
 
-    ```bash
-    uv sync --frozen
-    ```
+A Dockerfile is provided in the top-level directory of the repository. [Building an image from this Dockerfile](https://docs.docker.com/get-started/docker-concepts/building-images/build-tag-and-publish-an-image/#build-an-image) is highly recommended as it avoids the need to compile flash-attention (which otherwise can take hours) and installs TempestExtremes.
 
-    This will create a uv environment in the .venv directory. The fronzen flag will make sure the exact version specified in the lock file is used.
-    Optionally, trigger the virtual environment with:
+### 2. UV Environment
 
-    ```bash
-    source .venv/bin/activate
-    ```
+> [!Note]
+> When installing through uv, note that compiling `flash-attn` (required for AIFS-ENS) can take several hours. See the [troubleshooting notes](https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html#flash-attention-has-long-build-time-for-aifs-models) for more information. Consider using the container instead, where `flash-attn` is pre-installed in the base image.
+>
+> Additionally, you will need to install TempestExtremes yourself and point the configuration files to the TempestExtremes executables.
 
-    **issues with torch-harmoics**
-    if `--frozen` flag in `uv sync` cannot be used eg for updating the dependencies to the latest versions, it can happen that torch harmonics fails during dependency resolution phase. In that phase, uv has to build th to resolve dependencies, but does not yet install it. In these cases it can happen that th build fails as other dependencies are not installed yet, Installation of packages happens in a second phase, the install phase. Additionally, there is a git_lfs error that appears every now and then. To adress both or either, split the installation up in a two-step process and skip GIT_LFS:
-    ```bash
-    GIT_LFS_SKIP_SMUDGE=1 uv sync --no-install-package torch-harmonics
-    GIT_LFS_SKIP_SMUDGE=1 uv sync
-    ```
+The project contains a `pyproject.toml` and a `uv.lock` file for setting up the environment. To install with locked dependencies:
 
+```bash
+uv sync --frozen
+```
+
+This creates a uv environment in the `.venv` directory. The `--frozen` flag ensures the exact versions specified in the lock file are used. Optionally, activate the virtual environment:
+
+```bash
+source .venv/bin/activate
+```
+
+Additionally, you need to [install TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes?tab=readme-ov-file#installation-via-cmake-recommended) and either add the executables for `DetectNodes` and `StitchNodes` to the `PATH` or point the associated commands in the configs to these executables.
 
-## Configure and Execute runs
+#### Potential Issues with torch-harmonics
 
-Runs are configured through yaml files which are put in `tc_tracking/cfg`. to run the pipeline, do:
+If the `--frozen` flag cannot be used (e.g. when updating dependencies to their latest versions), torch-harmonics may fail during the dependency resolution phase. During this phase, uv must build torch-harmonics to resolve dependencies before installation, but the build can fail if other dependencies are not yet installed. Additionally, a Git LFS error occasionally appears. To address these issues, split the installation into a two-step process and skip Git LFS:
+
+```bash
+GIT_LFS_SKIP_SMUDGE=1 uv sync --no-install-package torch-harmonics
+GIT_LFS_SKIP_SMUDGE=1 uv sync
+```
+
+
+## Configure and Execute Runs
+
+Runs are configured through YAML files located in `tc_tracking/cfg`. To execute the pipeline:
 
 ```bash
 python tc_hunt.py --config-name=config.yaml
 ```
 
-The script can also be exectued in distributed settings using slurm, mpi, or torchrun. eg:
+The script can also be executed in distributed settings using Slurm, MPI, or torchrun. For example:
 
 ```bash
 torchrun --nproc-per-node=2 tc_hunt.py --config-name=config.yaml
 ```
 
-The pipleine has three different flavours:
-- `generate_ensemble`: generate an ensemble forecast to extract storms from
-- `reproduce_members`: reproduce individual members, e.g. to store atmospheric fields of interesting tracks (NOTE: currently only works with FCN3, as AIFS-ENS is missing a method to set the random state of the model)
-- `extract_baseline`: extract storms from historic data
+The pipeline has three operational modes:
+
+- **`generate_ensemble`**: Generate an ensemble prediction and extract tropical cyclones.
+- **`reproduce_members`**: Reproduce individual ensemble members to store atmospheric fields of interesting tracks. Note: Currently only works with FCN3, as AIFS-ENS does not expose a method to set the model's internal random state.
+- **`extract_baseline`**: Extract tropical cyclone tracks from historical reanalysis data (e.g. ERA5) for validation purposes.
 
+In the following we will explain how to configure the yaml files for those three modes. You can find example configs in `./cfg`.
 
-### generate ensemble
+### Generate Ensemble
 
-Produce an ensemble forecast with either FCN3 or AIFS-ENS and track TCs.
+Here we explain how to configure a yaml file to produce an ensemble prediction with either FCN3 or AIFS-ENS and track tropical cyclones within the predictions. A full example can be found at `cfg/helene.yaml`.
 
-**basics**
+**Basics**
 
 ```yaml
-project: 'helene'          # project name used for file names
+project: 'helene'          # project name used for output names
 mode: 'generate_ensemble'  # choose mode
-model: 'aifs'              # 'fcn3' 'aifs'
-random_seed: 7777          # optional: random seed (will not directly impact model-internal random state)
+model: 'aifs'              # 'fcn3' or 'aifs'
+random_seed: 7777          # optional: random seed (DOES NOT directly control model-internal random state)
 ```
 
-**<a name="config_ens"></a>ensemble**
+**<a name="config_ens"></a>Ensemble Configuration**
 
 ```yaml
-ics: ["2024-09-24 00:00:00, 2024-09-25 18:00:00"] # list of initial conditions
-n_steps: 28                                       # number of forecast steps
-ensemble_size: 16                                 # number of ensemble members for each initial condition
-batch_size: 2                                     # batch size of inference
+ics: ["2024-09-24 00:00:00", "2024-09-25 18:00:00"]  # list of initial conditions
+n_steps: 28                                          # number of forecast steps
+ensemble_size: 16                                    # number of ensemble members FOR EACH initial condition
+batch_size: 2                                        # batch size for inference
 ```
 
-Note that for a batch size of 1, AIFS-ENS requires around 50GB of GPU memory but scales almost linearly with batch size.
-FCN3 in contrast requires over 60GB for batch size of 1, but hardly increases when increasing batch size.
+**GPU Memory Requirements**: For a batch size of 1, AIFS-ENS requires approximately 50GB of GPU memory and scales nearly linearly with increasing batch size. FCN3 requires over 60GB for batch size 1, but memory usage increases only marginally with larger batch sizes, making it more memory-efficient for batched inference.
 
-**IO**
+**IO Configuration**
 
 ```yaml
 data_source:                                      # data source for initial conditions
     _target_: earth2studio.data.NCAR_ERA5         # choose any of the online or on-prem sources
 
-store_type: "netcdf" # 'none' 'netcdf', 'zarr'    # file type of field data output. no field output if none
-store_dir: "./outputs_${project}"                 # directory to which all outputs are written to
+store_type: "netcdf" # 'none', 'netcdf', 'zarr'   # file type for field data output; 'none' produces no field output
+store_dir: "./outputs_${project}"                 # directory to which all outputs are written
 out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] # field variables to write to file
-
 ```
-- data source can provide any of the e2studio data sources or a custom data source
 
-- be careful with storing field varibales, as the storage requirements grow quickly and also writing the data to disc can signifcantly slow down the inference
-- Note that with FCN3, runs can be reproduced (see section YY), opening the option to store and analyse tracks only, and then reproduce those ensemble members with interesting tracks where you might want to see the fields
+The data source can be any of the Earth-2 Studio data sources or a custom data source implementation.
+
+**Storage Considerations**: Exercise caution when storing field variables, as storage requirements grow rapidly (proportional to number of ICs × ensemble size × forecast length). Additionally, writing large volumes of data to disk can significantly slow down inference. For FCN3 workflows, consider storing only track data initially and using the [reproducibility feature](#reproduce-individual-ensemble-members) to regenerate fields for ensemble members with interesting tracks.
 
-**Cyclone Tracking with TmepestExtremes** (optional)
+**Cyclone Tracking with TempestExtremes** (optional)
 
 ```yaml
 cyclone_tracking:
-    asynchronous: True                             # Let GPU produce next prediction while CPU executes TempestExtremes
-    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']  # variables which are required by TE
-    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
+    asynchronous: True                             # let GPU produce next prediction whilst CPU executes TempestExtremes
+    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']  # variables required by TempestExtremes
+    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,1.0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-                                                   # detect node and stitch node commands from TE
-    orography_path: './orography.nc'               # path to orograohy file (required only if height relevant to TE)
-    keep_raw_data: False                           # keep raw field data for tracking
-    use_ram: True                                  # write in-memory files to avoid writing to and reading from disk
-    task_timeout_seconds: 120                      # timeout for tracking
-    print_te_output: False                         # write TE output to terminal
+                                                   # detect node and stitch node commands from TempestExtremes
+    orography_path: './orography.nc'               # path to orography file (required only if height is relevant to TempestExtremes)
+    keep_raw_data: False                           # whether to keep raw field data used for tracking
+    use_ram: True                                  # write in-memory files to avoid disk I/O
+    task_timeout_seconds: 120                      # timeout for tracking tasks
+    print_te_output: False                         # print TempestExtremes output to terminal
 ```
-- for more infos on [asynchronous mode](#async) have a look at the section below
-- tip: For debugging `detect_cmd` and `stitch_cmd` you can set `print_te_output` to `True` to see where `TempestExtremes` fails. However, if more iterations are required to get TmepestExtremes to run, debugging through inference runs can be a rather cumbersome. Consider setting `keep_raw_data` to `True` and `use_ram` to `False` to write a field snapshot to a netcdf file to `${store_dir}/cyclone_tracks_te/raw_data`. Then you can debug directly pasting the commands in the command line. Once you have found the correct commands, just copy the working commands to the config file. When operating in the command line, you need to point TempestExtremes to the files (see [documentation](https://climate.ucdavis.edu/tempestextremes.php) for more details): for `detect_cmd`, this will be `--in_data_list` and `--out_file_list`, for `stitch_cmd` this will be `--in` and `--out`.
-
-**stability check** (optional)
-
-- FCN3 can do stable rollouts on subseasonal time scales, stability check is only relevant for (sub)seasonal time scales
-- THe long term behaviour of AIFS-ENS is still something we need to investigate, so the current method is for FCN3
-- however blows up some time
-- simple stability check was implemented
-    - instability can be seen in global averages of field variables
-    - build up over like 10 days, the escape towards infinity
-    - so it is easy to chatch one of them if yo ucan allow letting the simulation run for 10 extra days in (sub)seasonal
-    - if cautch, the ensemble member can be re-run with differrent random seed
-    - to implement stability check, provide list of variables to monitor and a threshold for each var. from these varables, global averages will be taken at initial conditions. At each time step global average is computed again and if it surpasses the threshold, the run will be interrupted and re-started with new random seed.
-    - Note that this all currently is rather basic implementation, more advanced methods for detection that might not require 10 extra days of simulation and method for better load-balancing in case a lot of runs get unstable would definitely be beneficial.
+
+For more information on [asynchronous mode](#async), see the dedicated section below.
+
+**Debugging TempestExtremes Commands**: To debug `detect_cmd` and `stitch_cmd`, set `print_te_output` to `True` to see TempestExtremes logs. However, iterating through full inference runs for debugging can be cumbersome. For more efficient debugging, set `keep_raw_data` to `True` and `use_ram` to `False` to write field snapshots as NetCDF files to `${store_dir}/cyclone_tracks_te/raw_data`. You can then debug the TempestExtremes commands directly from the command line using these files. Once the commands work correctly, copy them back to the configuration file. When running TempestExtremes from the command line, you need to specify file paths explicitly (see [TempestExtremes documentation](https://climate.ucdavis.edu/tempestextremes.php)): use `--in_data_list` and `--out_file_list` for `DetectNodes`, and `--in` and `--out` for `StitchNodes`.
+
+**Stability Check** (optional)
+
+FCN3 can perform stable rollouts on subseasonal timescales (weeks to months), but occasionally experiences numerical instabilities on seasonal timescales. A simple stability check has been implemented to detect and handle these cases:
+
+**Key Points**:
+- **Applicable to**: FCN3 for (sub)seasonal forecasts. The long-term behaviour of AIFS-ENS is still under investigation.
+- **Detection mechanism**: Instabilities manifest in global averages of field variables, which typically build up over ~10 days before diverging rapidly.
+- **Implementation**: Provide a list of variables to monitor and a threshold for each. Global averages are computed at initialisation and at each time step. If any variable's global average exceeds its threshold, the simulation is interrupted and restarted with a new random seed.
+- **Limitation**: This is a basic implementation. More advanced detection methods that don't require 10 extra simulation days, and better load-balancing when many ensemble members become unstable, would be beneficial improvements.
 
 ```yaml
 stability_check:
@@ -143,8 +161,8 @@ stability_check:
     thresholds: [10, 10, 10]
 ```
 
+**Hydra Configuration** (optional)
 
-**hydra** (optional)
 ```yaml
 hydra:
     job:
@@ -152,34 +170,34 @@ hydra:
     run:
         dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
 ```
-- propagating project name and output directory for logs to hydra.
-- most likely never need to change that section
 
+[expand below]
+This propagates the project name and output directory to Hydra's logging system. You will most likely never need to modify this section.
 
-### reproduce individual ensemble members
 
-- run without storing fields, as tracks contain roughly 5 orders of magnitude less data that the associated field data required to extract the tracks
-- send results of hundreds of hypothetical cyclone seasons as an email attachment
-- however, for interesting tracks it can be that looking at field could be interesting
+### Reproduce Individual Ensemble Members
 
-- FCN3 allows accessing its random internal state, enabling reproducing individual ensemble members
-- AIFS should have this capability as well in theory, however, the internal random state cannot is not exposed through anemoi models, which is why we cannot reproduce AIFS-ENS runs
-- not that reproducibility [is hard](https://arxiv.org/abs/1605.04339) only works when the same environment on the same system is used to produce the prediction.
+[expand below]
+A common workflow is to run ensemble forecasts without storing atmospheric fields, since track data is approximately five orders of magnitude smaller than the associated field data. This enables sending results of hundreds of hypothetical cyclone seasons as an email attachment. However, for ensemble members with particularly interesting tracks, examining the atmospheric fields can provide valuable insights into the storm's evolution.
 
-- to reproduce a run you need the following
-    - config file of the original run (ideally)
-    - ensemble member ID and random seed of all members which shall be reproduced (both are provided in file name of track csv file)
-    - batch size (also provided in name of track csv file)
+FCN3 provides access to its internal random state, enabling exact reproduction of individual ensemble members. In theory, AIFS-ENS should have this capability as well, but the internal random state is not exposed through the Anemoi models interface, preventing reproducibility of AIFS-ENS runs.
 
-here we describe what to change in the file to reproduce, you can also compare `cfg/helene.yaml` and `cfg/reproduce_helene.yaml`.
-in the original config, consider switching the project name to avoid ovewriting files and change the mode to:
+Note that reproducibility [is challenging](https://arxiv.org/abs/1605.04339) and only works reliably when using the same environment on the same system to produce the predictions.
+
+To reproduce a run, you need:
+- Configuration file from the original run (ideally)
+- Ensemble member ID and random seed for all members to be reproduced (both provided in the track CSV filename)
+- Batch size (also provided in the track CSV filename)
+
+**Configuration Changes**
+
+The following describes what to change in the configuration file to reproduce ensemble members. You can compare `cfg/helene.yaml` and `cfg/reproduce_helene.yaml` as reference examples. In the original configuration, consider changing the project name to avoid overwriting files, and set the mode to:
 
 ```yaml
 mode: 'reproduce_members'
 ```
 
-in the [**ensembles section**](#config_ens), remove the ic varaible and replace it with a list of ensemble members you want to reproduce.
-For each ensemble member, provide intitial condition, member ID and random seed:
+In the [**ensemble configuration section**](#config_ens), remove the `ics` variable and replace it with a list of ensemble members to reproduce. For each ensemble member, provide the initial condition, member ID, and random seed:
 
 ```yaml
 reproduce_members: [
@@ -191,30 +209,25 @@ reproduce_members: [
                    ]
 ```
 
-The above example would reproduce members 4 and 8 from the ensemble starting at midnight and members 4, 5 and 13 from the ensemble starting midday.
-Not that the internal random state can only be set for the full batch. While the individual members of a batch will provide independend predictions, they have the same random seed. This also means that always a full batch has to be reporduced:
-Consider a batch size of 2 for the example above. In that case, the run would reproduce members 4, 5, 8 and 9 of the ensemble starting at midnight, and members 4, 5, 12 and 13 of the ensemble starting midday.
+The example above would reproduce members 5 and 8 from the ensemble starting at midnight, and members 4, 5, and 13 from the ensemble starting at midday.
+
+**Batch Reproduction Behaviour**: Note that the internal random state can only be set for a complete batch. Whilst individual members within a batch will produce independent predictions, they share the same random seed. This means a full batch must always be reproduced. For example, with a batch size of 2 in the example above, the run would reproduce members 4, 5, 8, and 9 from the midnight ensemble, and members 4, 5, 12, and 13 from the midday ensemble.
 
 
-### extract Reference tracks from ERA5 using IBTrACS as ground truth
+### Extract Reference Tracks from ERA5 Using IBTrACS as Ground Truth
 
-- IBTrACS is considered gold standard for TC data
-- However, it is observation data which a global model at quartely degree is not able to reproduce
-- Hence for comparison it can make sense to extract storms directly from analysis data like ERA5 and score prediction results against these
-- This pipeline shows how to extract reference tracks for named storms using IBtracs as reference to identify tracks
+[IBTrACS](https://www.ncei.noaa.gov/products/international-best-track-archive) (International Best Track Archive for Climate Stewardship) is considered the gold standard for tropical cyclone observation data. However, it represents point observations that a global model at quarter-degree resolution cannot reproduce accurately. Therefore, a meaningful validation of model predictions must be performed against ERA5. To do so, we first have to extract tropical cyclone tracks directly from reanalysis data (such as ERA5) and compare the predictions against these reference tracks. This pipeline demonstrates how to extract reference tracks for named storms, using IBTrACS to identify the temporal windows and approximate locations of storms of interest.
 
-**basics**
+**Basics**
 
 ```yaml
 project: 'reference_tracks'
 mode: 'extract_baseline'
 ```
 
-**cases**
+**Cases**
 
-- choose storms. for each storm which shall be extracted, provide name, year and basin as per below
-- the script will look in IBTRACS data during which time the storm was active, pull the relevant data and extracts TC tracks using TempestExtremes
-- at any given time there might be multiple storms on the globe, hence in a second step all extracted tracks are compared if they are close to the IBTRACS groundtruth to find the right track.
+Specify which storms to extract by providing their name, year, and basin for each case:
 
 ```yaml
 cases:
@@ -226,16 +239,23 @@ cases:
         basin: north_atlantic
 ```
 
-**IO**
-ibtracs data:
-- download ibtracs data in csv format with following command: `wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv` Note that file is over 300MB and might take a couple of minutes to download
-- note that `./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv` can be used if just testing with Helene and Hato.
+The script queries the IBTrACS database to determine when each storm was active, retrieves the corresponding atmospheric data from the specified data source, and applies TempestExtremes to extract tropical cyclone tracks. Since multiple storms may be active globally at any given time, a second matching step compares all extracted tracks against the IBTrACS ground truth to identify the correct track for each named storm.
 
-analysis data:
-- for extracting esp if more storms shall be extracted we recommend using an on-prem data set, as otherwise download times will be limiting here
-- either, data source can just be implemented the standard way as above
-- or different data sets can be prescribed for differnet years, esp helpfull when available data set is split up in train/test/outofsample
-- as an example here we show with different versions of ERA5
+**IO Configuration**
+
+**IBTrACS Data**:
+- Download the IBTrACS data in CSV format using:
+  ```bash
+  wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv
+  ```
+  Note: This file is over 300MB and may take several minutes to download.
+- For testing with only Helene and Hato, you can use the subset provided at `./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv`.
+
+**Reanalysis Data**:
+- For extracting multiple storms, we recommend using an on-premise dataset to avoid lengthy download times.
+- The data source can be configured using the standard approach (as shown in the ensemble generation section).
+- Alternatively, different data sources can be specified for different year ranges, which is particularly useful when datasets are partitioned into training/testing/out-of-sample periods.
+- The example below shows how to configure different ERA5 sources for different time periods:
 
 ```yaml
 store_dir: "./outputs_${project}"
@@ -254,84 +274,117 @@ data_source:
 ```
 
 
-## visualise
+## Visualisation
+
+Two Jupyter notebooks are provided in `./plotting` for analysing and visualising tropical cyclone tracking results:
 
-Have a stab at the notebook...
+- **`tracks_slayground.ipynb`**: Comprehensive ensemble track analysis including spaghetti plots (trajectory visualisation), absolute and relative intensity metrics (wind speed, MSLP), comparisons against ERA5 reference tracks and IBTrACS observations, extreme value statistics, and error moment analysis over lead time.
 
-## TempestExtremes integration
+- **`plot_tracks_n_fields.ipynb`**: Create animated visualisations of storm tracks overlaid on atmospheric field data.
 
-- using TempestExtremes as example to demonstrate how to implement custom downstream models in pipeline
-- increased complexity, as TE does not run on GPU and cannot be installed as python library.
-- implementing that way to avoid writing atmospheric field data to and reading it again from disk, as this slows down inference and can be prohibitive with available storage
+## TempestExtremes Integration
 
-**general integration**
+This pipeline uses TempestExtremes to demonstrate how to integrate custom downstream analysis tools into a prediction workflow. TempestExtremes requires file-based input, does not run on GPU, and cannot be installed as a Python library—making it a useful reference case, as tools with fewer constraints should be simpler to integrate. The current implementation avoids writing atmospheric field data to disk and reading it back, which would otherwise slow down inference significantly and become prohibitive given the large data volumes involved.
 
-- implemented like a diagnostic model, but without return type
-- that diagnostic model class then spawns a subprocess to call TE
-- result will be tracks file directly written to disk
-- data access for TE has to be provided though file
-- to avoid reading/writing large amounts of data to/from disk, we write to `/dev/shm` effectively producing in-memory file
+**General Integration Approach**
 
+TempestExtremes is implemented as a diagnostic model class but without returning any data, since results are written directly to disk. The diagnostic model class spawns a subprocess to execute TempestExtremes command-line tools. Track files are produced directly by TempestExtremes and written to the specified output directory.
 
-**<a name="async"></a>asynchronos mode**
+Since TempestExtremes requires file-based input, we must provide atmospheric data through files. To avoid the overhead of writing and reading large volumes of data to/from disk, we write temporary files to `/dev/shm` (shared memory filesystem), effectively creating in-memory files that TempestExtremes can access while avoiding physical disk I/O.
 
-- on many platforms, running TmepestExtremes on the CPU is still faster than producing the forecast on the GPU. Hence, we let a CPU core execute TempestExtremes while the GPU already starts producing the next prediction
-- note that in asynchronos mode each member of a batch gets its own process assigned. Hence, if TE is slower than the prediction (could e.g. happen when using AIFS on B200), consider increading the batch size. The time the GPU takes to produce a batched predcition scales more or less linearly with batch size, while the time that a single CPU core takes to porcess one member is constant.
-- if you see issues, first see if they also appear when asyncronos mode is switched off.
+**<a name="async"></a>Asynchronous Mode**
 
+On many platforms, executing TempestExtremes on the CPU is faster than producing the prediction on the GPU. We therefore implemented an asynchronous mode where a CPU thread executes TempestExtremes on completed time steps while the GPU continues producing the next prediction, enabling tracking with virtually no overhead.
 
-## example workflow
+**Implementation Details**:
+- In asynchronous mode, each ensemble member in a batch is assigned its own processing thread, allowing parallel tracking across the batch.
+- If TempestExtremes execution becomes slower than GPU prediction (which might occur with AIFS-ENS on newer GPUs like B200), consider increasing the batch size. GPU prediction time scales approximately linearly with batch size, while the time for a single CPU thread to process one member remains constant.
+- If you encounter issues, first verify whether they also occur with asynchronous mode disabled before debugging further.
 
-### extract baseline (optional)
 
-- note that this requires downloading the field data for Hato and Helene. you can skip this step and use the refernce tracks under `./test/aux_data` instead
-- download ibtraccs data
-- extract baseline by running
+## Example Workflow
+
+This section walks through an example workflow demonstrating the pipeline's core functionality: generating an ensemble prediction, reproducing individual members, visualising results, and comparing predicted tracks against reference tracks extracted from ERA5 data.
+
+### Extract Baseline (Optional)
+
+This step extracts reference tracks from ERA5 reanalysis for comparison with forecast predictions. Note that downloading the ERA5 field data for Hato and Helene is required and can take some time. If it takes too long, you can skip this step and use the pre-computed reference tracks provided in `./test/aux_data` instead.
+
+First, download the IBTrACS data, then extract the baseline tracks:
+
 ```bash
 python tc_hunt.py --config-name=extract_era5.yaml
 ```
 
-- That should produce a folder called `outputs_reference_tracks/` that includes the two reference tracks:
-    - `reference_track_hato_2017_west_pacific.csv`
-    - `reference_track_helene_2024_north_atlantic.csv`
-
-### produce ensemble
-
-- run forecast loop for helen with FCN3 and for hato using AIFS-ENS:
-    - `python tc_hunt.py --config-name=helene.yaml`
-    - `python tc_hunt.py --config-name=hato.yaml`
-- this should produce two folders, `outputs_helene` and `outputs_hato`
-
-### analyse tracks
-
-- visualise in notebook `plotting/tracks_slayground.ipynb`
-- in first cell of notebook, set for helene
-    - `case = 'helene_2024_north_atlantic'`
-    - `pred_track_dir = '/path/to/outputs_helene/cyclone_tracks_te'`
-    - `tru_track_dir = '/path/to/outputs_reference_tracks'`
-    - in case you did not run the extraction yourself you can use `tru_track_dir = '/path/to/test/aux_data'`
-- for hato
-    - `case = 'hato_2017_west_pacific'`
-    - `pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'`
-    - `tru_track_dir = '/path/to/outputs_reference_tracks'`
-
-### re-produce interesting members to extract fields
-
-- let's assume the above analysis has shown that we want to have a closer look at members 0, 8, 9 and 12 of the Helene prediction
-- luckily, that was run with FCN3 so we can reproduce and store the fields
-- reproduce, this time store fields
-    - in `cfg/reproduce_helene.yaml` make sure random seeds provided to the `reproduce_members` variable match the seeds from the track files in `outputs_helene/cyclone_tracks_te`
-    - double-check that batch size and ensemble size and data source are identical to the original run
-    - execute the pipeline: `python tc_hunt.py --config-name=reproduce_helene.yaml`
-    - should produce folder `outputs_reprduce_helene` that now also includes a netcdf file called `reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc`
-- visualise track and field in `plotting/plot_track_n_fields.ipynb`
-    - TODO: remove base dir there
-    - set `field_data = '/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'`
-    - set `track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'`
-
-    - select a variable you want to visualise for the field, eg `variable = 'wind_speed'` or `variable = 'msl'`
-    - select the ensemble member you want to look at (note that you only reproduced a subset)
-    - select the region for plotting: `region = 'gulf_of_mexico'`
-    - at the end of the notebook you will have be rewarded with an animation where you can click through time
-    - as test if reproducibility worked, point `track_dir` to the original Helene run
+This should produce a folder called `outputs_reference_tracks/` containing two reference track files:
+- `reference_track_hato_2017_west_pacific.csv`
+- `reference_track_helene_2024_north_atlantic.csv`
+
+### Produce Ensemble Forecasts
+
+Run the forecast loop for Helene using FCN3 and for Hato using AIFS-ENS:
+
+```bash
+python tc_hunt.py --config-name=helene.yaml
+python tc_hunt.py --config-name=hato.yaml
+```
+
+This should produce two output folders: `outputs_helene` and `outputs_hato`, each containing tracked tropical cyclone trajectories.
+
+### Analyse Tracks
+
+Visualise the results using the notebook `plotting/tracks_slayground.ipynb`.
+
+**For Helene**, configure the first cell of the notebook with:
+```python
+case = 'helene_2024_north_atlantic'
+pred_track_dir = '/path/to/outputs_helene/cyclone_tracks_te'
+tru_track_dir = '/path/to/outputs_reference_tracks'
+# If you skipped baseline extraction, use:
+# tru_track_dir = '/path/to/test/aux_data'
+```
+
+**For Hato**, use:
+```python
+case = 'hato_2017_west_pacific'
+pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'
+tru_track_dir = '/path/to/outputs_reference_tracks'
+# If you skipped baseline extraction, use:
+# tru_track_dir = '/path/to/test/aux_data'
+```
+
+### Reproduce Interesting Members to Extract Fields
+
+Suppose that after conducting the above anysis you want to take a closer look at ensemble members 0, 8, 9, and 12 of the Helene prediction. Since the forecast was produced with FCN3, we can reproduce these members and store the atmospheric fields for detailed analysis.
+
+**Reproduce with Field Storage**:
+
+1. In `cfg/reproduce_helene.yaml`, verify that the random seeds in the `reproduce_members` variable match the seeds from the track filenames in `outputs_helene/cyclone_tracks_te`. If they do not, update the seeds in the config accordingly.
+2. Double-check that the batch size, ensemble size, and data source are identical to the original run.
+3. Execute the pipeline:
+   ```bash
+   python tc_hunt.py --config-name=reproduce_helene.yaml
+   ```
+4. This should produce a folder `outputs_reproduce_helene` containing both track files and a NetCDF file named `reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc` with the atmospheric fields.
+
+**Visualise Tracks and Fields**:
+
+Use the notebook `plotting/plot_tracks_n_fields.ipynb` to create animated visualisations:
+
+1. Configure the file paths:
+   ```python
+   field_data = '/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'
+   track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'
+   ```
+
+2. Select visualisation parameters:
+   ```python
+   variable = 'wind_speed'   # or 'msl', etc.
+   ensemble_member = 8       # choose from the reproduced subset
+   region = 'gulf_of_mexico'
+   ```
+
+3. Run the notebook to generate an interactive animation where you can step through time.
+
+**Verification**: To verify that reproducibility worked correctly, point `track_dir` to the original Helene run (`outputs_helene/cyclone_tracks_te`) and confirm that the tracks are identical.
 

From ad4717c7c74e5d6c71fce2701226b9e24864c14d Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 22 Jan 2026 08:19:46 -0800
Subject: [PATCH 57/69] final touches README

---
 recipes/tc_tracking/README.md                 | 76 +++++++++----------
 .../plotting/plot_tracks_n_fields.ipynb       | 27 +++----
 2 files changed, 50 insertions(+), 53 deletions(-)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index a36977697..89c5bb5f5 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -1,39 +1,41 @@
 # Cyclone Tracking
 
+![Helene Prediction](helene_pred.gif)
+
 > [!Note]
-> This recipe serves as an example of how to integrate custom downstream models into a forecasting pipeline. Whilst TempestExtremes is used here for tropical cyclone tracking, the patterns demonstrated—such as asynchronous CPU/GPU execution, in-memory file handling, and diagnostic model integration—can be adapted for other downstream analysis tools.
+> This recipe serves as an example of how to integrate custom downstream models into a forecasting pipeline. While TempestExtremes is used here for tropical cyclone tracking, the patterns demonstrated—such as asynchronous CPU/GPU execution, in-memory file handling, and diagnostic model integration—can be adapted for other downstream analysis tools.
 
-This pipeline produces ensemble prediction and tracks tropical cyclones (TCs) within the prediction. Here, we demonstrate how to integrate downstream analysis tools like [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) into a prediction workflow whilst avoiding the need to store large atmospheric field data to disk, which can grow prohibitively large and significantly impact inference speed.
+This pipeline produces ensemble prediction and tracks tropical cyclones (TCs) within the prediction. Here, we demonstrate how to integrate downstream analysis tools like [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) into a prediction workflow while avoiding the need to store large atmospheric field data to disk, which can grow prohibitively large and significantly impact inference speed.
 
-Although TempestExtremes is not GPU-accelerated, its execution time on CPU is typically shorter than the time required for the GPU to produce the next prediction. We therefore implement an asynchronous mode where the CPU executes TempestExtremes on completed time steps whilst the GPU continues producing subsequent predictions, resulting in virtually no computational overhead from the tracking process.
+Although TempestExtremes is not GPU-accelerated, its execution time on CPU is typically shorter than the time required for the GPU to produce the next prediction. We therefore implement an asynchronous mode where the CPU executes TempestExtremes on completed time steps while the GPU continues producing subsequent predictions, resulting in virtually no computational overhead from the tracking process.
 
 ## Table of Contents
 
-- [Setting up the Environment](#setting-up-the-environment)
-  - [Container (Recommended)](#1-container-recommended)
-  - [UV Environment](#2-uv-environment)
-- [Configure and Execute Runs](#configure-and-execute-runs)
-  - [Generate Ensemble](#generate-ensemble)
-  - [Reproduce Individual Ensemble Members](#reproduce-individual-ensemble-members)
-  - [Extract Reference Tracks from ERA5](#extract-reference-tracks-from-era5-using-ibtracs-as-ground-truth)
-- [Visualisation](#visualisation)
-- [TempestExtremes Integration](#tempestextremes-integration)
-- [Example Workflow](#example-workflow)
-  - [Extract Baseline](#extract-baseline-optional)
-  - [Produce Ensemble Forecasts](#produce-ensemble-forecasts)
-  - [Analyse Tracks](#analyse-tracks)
-  - [Reproduce Interesting Members](#reproduce-interesting-members-to-extract-fields)
+1. [Setting up the Environment](#1-setting-up-the-environment)
+   - [1.1 Container (Recommended)](#11-container-recommended)
+   - [1.2 UV Environment](#12-uv-environment)
+2. [Configure and Execute Runs](#2-configure-and-execute-runs)
+   - [2.1 Generate Ensemble](#21-generate-ensemble)
+   - [2.2 Reproduce Individual Ensemble Members](#22-reproduce-individual-ensemble-members)
+   - [2.3 Extract Reference Tracks from ERA5](#23-extract-reference-tracks-from-era5-using-ibtracs-as-ground-truth)
+3. [Visualisation](#3-visualisation)
+4. [TempestExtremes Integration](#4-tempestextremes-integration)
+5. [Example Workflow](#5-example-workflow)
+   - [5.1 Extract Baseline](#51-extract-baseline-optional)
+   - [5.2 Produce Ensemble Forecasts](#52-produce-ensemble-forecasts)
+   - [5.3 Analyse Tracks](#53-analyse-tracks)
+   - [5.4 Reproduce Interesting Members](#54-reproduce-interesting-members-to-extract-fields)
 
 
-## Setting up the Environment
+## 1. Setting up the Environment
 
 There are two ways to set up the environment: either directly using a uv environment, or using uv to install dependencies inside a container. Since flash-attention, torch-harmonics and TempestExtremes all require compilation, **the container approach is strongly recommended**.
 
-### 1. Container (Recommended)
+### 1.1 Container (Recommended)
 
 A Dockerfile is provided in the top-level directory of the repository. [Building an image from this Dockerfile](https://docs.docker.com/get-started/docker-concepts/building-images/build-tag-and-publish-an-image/#build-an-image) is highly recommended as it avoids the need to compile flash-attention (which otherwise can take hours) and installs TempestExtremes.
 
-### 2. UV Environment
+### 1.2 UV Environment
 
 > [!Note]
 > When installing through uv, note that compiling `flash-attn` (required for AIFS-ENS) can take several hours. See the [troubleshooting notes](https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html#flash-attention-has-long-build-time-for-aifs-models) for more information. Consider using the container instead, where `flash-attn` is pre-installed in the base image.
@@ -64,7 +66,7 @@ GIT_LFS_SKIP_SMUDGE=1 uv sync
 ```
 
 
-## Configure and Execute Runs
+## 2. Configure and Execute Runs
 
 Runs are configured through YAML files located in `tc_tracking/cfg`. To execute the pipeline:
 
@@ -86,7 +88,7 @@ The pipeline has three operational modes:
 
 In the following we will explain how to configure the yaml files for those three modes. You can find example configs in `./cfg`.
 
-### Generate Ensemble
+### 2.1 Generate Ensemble
 
 Here we explain how to configure a yaml file to produce an ensemble prediction with either FCN3 or AIFS-ENS and track tropical cyclones within the predictions. A full example can be found at `cfg/helene.yaml`.
 
@@ -123,13 +125,13 @@ out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] # field variables to write to
 
 The data source can be any of the Earth-2 Studio data sources or a custom data source implementation.
 
-**Storage Considerations**: Exercise caution when storing field variables, as storage requirements grow rapidly (proportional to number of ICs × ensemble size × forecast length). Additionally, writing large volumes of data to disk can significantly slow down inference. For FCN3 workflows, consider storing only track data initially and using the [reproducibility feature](#reproduce-individual-ensemble-members) to regenerate fields for ensemble members with interesting tracks.
+**Storage Considerations**: Exercise caution when storing field variables, as storage requirements grow rapidly (proportional to number of ICs × ensemble size × forecast length). Additionally, writing large volumes of data to disk can significantly slow down inference. For FCN3 workflows, consider storing only track data initially and using the [reproducibility feature](#22-reproduce-individual-ensemble-members) to regenerate fields for ensemble members with interesting tracks.
 
 **Cyclone Tracking with TempestExtremes** (optional)
 
 ```yaml
 cyclone_tracking:
-    asynchronous: True                             # let GPU produce next prediction whilst CPU executes TempestExtremes
+    asynchronous: True                             # let GPU produce next prediction while CPU executes TempestExtremes
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']  # variables required by TempestExtremes
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,1.0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
@@ -171,14 +173,12 @@ hydra:
         dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
 ```
 
-[expand below]
 This propagates the project name and output directory to Hydra's logging system. You will most likely never need to modify this section.
 
 
-### Reproduce Individual Ensemble Members
+### 2.2 Reproduce Individual Ensemble Members
 
-[expand below]
-A common workflow is to run ensemble forecasts without storing atmospheric fields, since track data is approximately five orders of magnitude smaller than the associated field data. This enables sending results of hundreds of hypothetical cyclone seasons as an email attachment. However, for ensemble members with particularly interesting tracks, examining the atmospheric fields can provide valuable insights into the storm's evolution.
+A common workflow is to run ensemble forecasts without storing atmospheric fields, since track data is approximately five orders of magnitude smaller than the associated field data. This enables sending results of hundreds of hypothetical cyclone seasons as an email attachment. However, for ensemble members with particularly interesting tracks, examining the atmospheric fields can help understand the storm's evolution.
 
 FCN3 provides access to its internal random state, enabling exact reproduction of individual ensemble members. In theory, AIFS-ENS should have this capability as well, but the internal random state is not exposed through the Anemoi models interface, preventing reproducibility of AIFS-ENS runs.
 
@@ -211,10 +211,10 @@ reproduce_members: [
 
 The example above would reproduce members 5 and 8 from the ensemble starting at midnight, and members 4, 5, and 13 from the ensemble starting at midday.
 
-**Batch Reproduction Behaviour**: Note that the internal random state can only be set for a complete batch. Whilst individual members within a batch will produce independent predictions, they share the same random seed. This means a full batch must always be reproduced. For example, with a batch size of 2 in the example above, the run would reproduce members 4, 5, 8, and 9 from the midnight ensemble, and members 4, 5, 12, and 13 from the midday ensemble.
+**Batch Reproduction Behaviour**: Note that the internal random state can only be set for a complete batch. While individual members within a batch will produce independent predictions, they share the same random seed. This means a full batch must always be reproduced. For example, with a batch size of 2 in the example above, the run would reproduce members 4, 5, 8, and 9 from the midnight ensemble, and members 4, 5, 12, and 13 from the midday ensemble.
 
 
-### Extract Reference Tracks from ERA5 Using IBTrACS as Ground Truth
+### 2.3 Extract Reference Tracks from ERA5 Using IBTrACS as Ground Truth
 
 [IBTrACS](https://www.ncei.noaa.gov/products/international-best-track-archive) (International Best Track Archive for Climate Stewardship) is considered the gold standard for tropical cyclone observation data. However, it represents point observations that a global model at quarter-degree resolution cannot reproduce accurately. Therefore, a meaningful validation of model predictions must be performed against ERA5. To do so, we first have to extract tropical cyclone tracks directly from reanalysis data (such as ERA5) and compare the predictions against these reference tracks. This pipeline demonstrates how to extract reference tracks for named storms, using IBTrACS to identify the temporal windows and approximate locations of storms of interest.
 
@@ -274,15 +274,15 @@ data_source:
 ```
 
 
-## Visualisation
+## 3. Visualisation
 
 Two Jupyter notebooks are provided in `./plotting` for analysing and visualising tropical cyclone tracking results:
 
-- **`tracks_slayground.ipynb`**: Comprehensive ensemble track analysis including spaghetti plots (trajectory visualisation), absolute and relative intensity metrics (wind speed, MSLP), comparisons against ERA5 reference tracks and IBTrACS observations, extreme value statistics, and error moment analysis over lead time.
+- **`tracks_slayground.ipynb`**: Ensemble track analysis including spaghetti plots (trajectory visualisation), absolute and relative intensity metrics (wind speed, MSLP), comparisons against ERA5 reference tracks and IBTrACS observations, extreme value statistics, and error moment analysis over lead time.
 
 - **`plot_tracks_n_fields.ipynb`**: Create animated visualisations of storm tracks overlaid on atmospheric field data.
 
-## TempestExtremes Integration
+## 4. TempestExtremes Integration
 
 This pipeline uses TempestExtremes to demonstrate how to integrate custom downstream analysis tools into a prediction workflow. TempestExtremes requires file-based input, does not run on GPU, and cannot be installed as a Python library—making it a useful reference case, as tools with fewer constraints should be simpler to integrate. The current implementation avoids writing atmospheric field data to disk and reading it back, which would otherwise slow down inference significantly and become prohibitive given the large data volumes involved.
 
@@ -302,11 +302,11 @@ On many platforms, executing TempestExtremes on the CPU is faster than producing
 - If you encounter issues, first verify whether they also occur with asynchronous mode disabled before debugging further.
 
 
-## Example Workflow
+## 5. Example Workflow
 
 This section walks through an example workflow demonstrating the pipeline's core functionality: generating an ensemble prediction, reproducing individual members, visualising results, and comparing predicted tracks against reference tracks extracted from ERA5 data.
 
-### Extract Baseline (Optional)
+### 5.1 Extract Baseline (Optional)
 
 This step extracts reference tracks from ERA5 reanalysis for comparison with forecast predictions. Note that downloading the ERA5 field data for Hato and Helene is required and can take some time. If it takes too long, you can skip this step and use the pre-computed reference tracks provided in `./test/aux_data` instead.
 
@@ -320,7 +320,7 @@ This should produce a folder called `outputs_reference_tracks/` containing two r
 - `reference_track_hato_2017_west_pacific.csv`
 - `reference_track_helene_2024_north_atlantic.csv`
 
-### Produce Ensemble Forecasts
+### 5.2 Produce Ensemble Forecasts
 
 Run the forecast loop for Helene using FCN3 and for Hato using AIFS-ENS:
 
@@ -331,7 +331,7 @@ python tc_hunt.py --config-name=hato.yaml
 
 This should produce two output folders: `outputs_helene` and `outputs_hato`, each containing tracked tropical cyclone trajectories.
 
-### Analyse Tracks
+### 5.3 Analyse Tracks
 
 Visualise the results using the notebook `plotting/tracks_slayground.ipynb`.
 
@@ -353,7 +353,7 @@ tru_track_dir = '/path/to/outputs_reference_tracks'
 # tru_track_dir = '/path/to/test/aux_data'
 ```
 
-### Reproduce Interesting Members to Extract Fields
+### 5.4 Reproduce Interesting Members to Extract Fields
 
 Suppose that after conducting the above anysis you want to take a closer look at ensemble members 0, 8, 9, and 12 of the Helene prediction. Since the forecast was produced with FCN3, we can reproduce these members and store the atmospheric fields for detailed analysis.
 
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index 327781d3c..19cfaa3a0 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -23,9 +23,8 @@
         "### Configuration\n",
         "\n",
         "**Path Settings**\n",
-        "- `base_dir` - root directory containing track and field data (e.g. `/path/to/outputs_reproduce_helene`)\n",
-        "- `field_data` - NetCDF filename (relative to `base_dir`, e.g. `helene_2024-09-24T00.00.00_mems0000-0015.nc`)\n",
-        "- `track_dir` - folder containing track CSVs (relative to `base_dir`, most likely `cyclone_tracks_te`)\n",
+        "- `field_data` - path to NetCDF file (e.g. `/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0013.nc`)\n",
+        "- `track_dir` - folder containing track CSVs (e.g. `/path/to/outputs_reproduce_helene/cyclone_tracks_te`)\n",
         "\n",
         "**Plotting Settings**\n",
         "- `variable` - field variable to plot (e.g., `'u10m'`, `'mslp'`). Use `'wind_speed'` to compute 10m wind magnitude from `u10m` and `v10m`\n",
@@ -34,7 +33,7 @@
         "\n",
         "**Animation Settings**\n",
         "- `max_frames` - limit number of frames (set high to include full forecast)\n",
-        "- `scale` - spatial downsampling factor (1 = full resolution, 2 = half, etc.)\n",
+        "- `scale` - spatial coarsening factor (1 = full resolution, 2 = half, etc.)\n",
         "- `fps` - frames per second for animation playback\n"
       ]
     },
@@ -44,14 +43,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# base_dir = '/path/to/outputs_reproduce_helene'\n",
-        "# base_dir = '/path/to/outputs_helene'\n",
-        "field_data = 'helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
-        "track_dir = f'cyclone_tracks_te'\n",
-        "base_dir = '~/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato'\n",
+        "field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n",
+        "track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n",
         "\n",
         "variable = 'wind_speed'\n",
-        "ensemble_member = 1\n",
+        "ensemble_member = 0\n",
         "region = 'gulf_of_mexico' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
         "\n",
         "max_frames = 99 # maximum number of frames to plot\n",
@@ -98,7 +94,6 @@
         "import xarray as xr\n",
         "\n",
         "\n",
-        "field_data = os.path.join(base_dir, field_data)\n",
         "base_name = field_data.split('/')[-1].split('.')[0]\n",
         "out_dir = f'outputs_{base_name}'\n",
         "\n",
@@ -150,16 +145,18 @@
       "source": [
         "from data_handling import extract_tracks_from_file\n",
         "\n",
+        "track_dir = os.path.abspath(track_dir)\n",
+        "\n",
         "# extract time steps of field data\n",
         "time_stamps = ds.time.values + ds.lead_time.values\n",
         "\n",
         "# select track file of ensemble member\n",
-        "track_file = [f for f in os.listdir(os.path.join(base_dir, track_dir)) if f.endswith('.csv') and\n",
+        "track_file = [f for f in os.listdir(track_dir) if f.endswith('.csv') and\n",
         "              int(f.split('_mem_')[-1].split('_seed_')[0]) == ensemble_member][0]\n",
-        "track_file = os.path.join(base_dir, track_dir, track_file)\n",
+        "track_file = os.path.join(track_dir, track_file)\n",
         "\n",
         "# extract tracks from prediction\n",
-        "tracks = extract_tracks_from_file(os.path.join(base_dir, track_file))\n",
+        "tracks = extract_tracks_from_file(track_file)\n",
         "\n",
         "# separate individual tracks in prediction\n",
         "n_tracks = tracks[\"track_id\"].iloc[-1] + 1\n",
@@ -249,7 +246,7 @@
         "    if frame==-1:\n",
         "        cbar = fig.colorbar(pc, extend='both', shrink=0.8, ax=ax)\n",
         "\n",
-        "    header =f'{base_name} {variable} {'lead time'} {frame*6}:00:00'\n",
+        "    header =f'{base_name} {variable} {'lead time'} {frame*6}h'\n",
         "    ax.set_title(header, fontsize=14)\n",
         "\n",
         "    return pc\n",

From 34fda29619f4d38d07ed65a768f754846da6fe60 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Thu, 22 Jan 2026 08:20:32 -0800
Subject: [PATCH 58/69] ...gif

---
 recipes/tc_tracking/helene_pred.gif | Bin 0 -> 4548128 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 recipes/tc_tracking/helene_pred.gif

diff --git a/recipes/tc_tracking/helene_pred.gif b/recipes/tc_tracking/helene_pred.gif
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literal 0
HcmV?d00001


From 78dc346f819cc633626cf58767016e376b96a419 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 00:43:04 -0800
Subject: [PATCH 59/69] wip

---
 .../plotting/plot_tracks_n_fields.ipynb       | 20 ++++++++++++++-----
 .../plotting/tracks_slayground.ipynb          |  7 +++++++
 2 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index 19cfaa3a0..73fa8a489 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -43,12 +43,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n",
-        "track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n",
+        "# field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n",
+        "# track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n",
         "\n",
-        "variable = 'wind_speed'\n",
-        "ensemble_member = 0\n",
-        "region = 'gulf_of_mexico' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
+        "field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/hato_2017-08-21T12.00.00_mems0000-0015.nc'\n",
+        "track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
+        "\n",
+        "\n",
+        "# field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/plotting_data_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
+        "# track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/plotting_data_helene/cyclone_tracks_te'\n",
+        "\n",
+        "variable = 'msl'\n",
+        "ensemble_member = 9\n",
+        "region = 'west_pacific' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
         "\n",
         "max_frames = 99 # maximum number of frames to plot\n",
         "scale = 1\n",
@@ -63,6 +70,9 @@
         "elif region == 'gulf_of_mexico':\n",
         "    lat_min, lat_max = 9, 45\n",
         "    lon_min, lon_max = 250, 310\n",
+        "elif region == 'west_pacific':\n",
+        "    lat_min, lat_max = 9, 60\n",
+        "    lon_min, lon_max = 100, 180\n",
         "else:\n",
         "    raise ValueError(f'region {region} not yet implemented. Feel free to add it by providing lat/lon coords of the bounding box')"
       ]
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
index 77af85e15..2868705eb 100644
--- a/recipes/tc_tracking/plotting/tracks_slayground.ipynb
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -66,6 +66,13 @@
         "\n",
         "pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'\n",
         "tru_track_dir = '/path/to/outputs_reference_tracks'\n",
+        "\n",
+        "\n",
+        "pred_track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
+        "tru_track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_reference_tracks'\n",
+        "# field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/hato_2017-08-21T12.00.00_mems0000-0015.nc'\n",
+        "# track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
+        "\n",
         "out_dir = './plots'\n",
         "\n",
         "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",

From fb7f207dbbf9d6f8705e5e2067e768e6420a4c21 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 01:40:15 -0800
Subject: [PATCH 60/69] verified plotting for west-pacific

---
 recipes/tc_tracking/cfg/extract_era5.yaml     |  8 ++---
 .../plotting/plot_tracks_n_fields.ipynb       | 35 +++++++++----------
 .../plotting/tracks_slayground.ipynb          |  7 ----
 3 files changed, 21 insertions(+), 29 deletions(-)

diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index 45a96baa1..c91ec28b8 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -54,9 +54,9 @@ cases:
     #     year: 2024
     #     basin: north_atlantic
 
-    # helene:
-    #     year: 2024
-    #     basin: north_atlantic
+    helene:
+        year: 2024
+        basin: north_atlantic
 
     # lan:
     #     year: 2017
@@ -82,7 +82,7 @@ cases:
     #     year: 2017
     #     basin: southern_pacific
 
-# ibtracs file for hato and helene only. obtain full data at https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv
+# CAUTION: ibtracs file for hato and helene only. obtain full data at https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv
 ibtracs_source_data: "./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
index 73fa8a489..885531ed7 100644
--- a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
+++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb
@@ -43,19 +43,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n",
-        "# track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n",
+        "field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n",
+        "track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n",
         "\n",
-        "field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/hato_2017-08-21T12.00.00_mems0000-0015.nc'\n",
-        "track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
-        "\n",
-        "\n",
-        "# field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/plotting_data_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc'\n",
-        "# track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/plotting_data_helene/cyclone_tracks_te'\n",
-        "\n",
-        "variable = 'msl'\n",
-        "ensemble_member = 9\n",
-        "region = 'west_pacific' # 'global' 'north_atlantic' 'gulf_of_mexico'\n",
+        "variable = 'wind_speed'\n",
+        "ensemble_member = 3\n",
+        "region = 'gulf_of_mexico'\n",
         "\n",
         "max_frames = 99 # maximum number of frames to plot\n",
         "scale = 1\n",
@@ -63,16 +56,22 @@
         "\n",
         "if region == 'global':\n",
         "    lat_min, lat_max = -90, 90\n",
-        "    lon_min, lon_max = 0, 360\n",
+        "    lon_min, lon_max = 0, 359.75\n",
+        "elif region == 'west_pacific':\n",
+        "    lat_min, lat_max = 9, 60\n",
+        "    lon_min, lon_max = 100, 180\n",
         "elif region == 'north_atlantic':\n",
         "    lat_min, lat_max = 9, 65\n",
         "    lon_min, lon_max = 250, 360\n",
         "elif region == 'gulf_of_mexico':\n",
         "    lat_min, lat_max = 9, 45\n",
         "    lon_min, lon_max = 250, 310\n",
-        "elif region == 'west_pacific':\n",
-        "    lat_min, lat_max = 9, 60\n",
-        "    lon_min, lon_max = 100, 180\n",
+        "elif region == 'north_indian':\n",
+        "    lat_min, lat_max = 9, 40\n",
+        "    lon_min, lon_max = 50, 100\n",
+        "elif region == 'southern_pacific':\n",
+        "    lat_min, lat_max = -40, 5\n",
+        "    lon_min, lon_max = 140, 240\n",
         "else:\n",
         "    raise ValueError(f'region {region} not yet implemented. Feel free to add it by providing lat/lon coords of the bounding box')"
       ]
@@ -212,7 +211,7 @@
         "\n",
         "# define plots\n",
         "def make_figure():\n",
-        "    fig = plt.figure(figsize=(11,5))\n",
+        "    fig = plt.figure(figsize=(11,7))\n",
         "    ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
         "\n",
         "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
@@ -304,7 +303,7 @@
         "                              init_func=first_frame, blit=False,\n",
         "                              repeat=False, interval=1000/fps)\n",
         "\n",
-        "ani.save('tracks_n_fields_ani.gif', fps=fps)\n",
+        "ani.save('tracks_n_fields_ani.gif', fps=fps, savefig_kwargs={'bbox_inches': 'tight', 'pad_inches': 0.1})\n",
         "plt.close(fig)"
       ]
     }
diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
index 2868705eb..77af85e15 100644
--- a/recipes/tc_tracking/plotting/tracks_slayground.ipynb
+++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb
@@ -66,13 +66,6 @@
         "\n",
         "pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'\n",
         "tru_track_dir = '/path/to/outputs_reference_tracks'\n",
-        "\n",
-        "\n",
-        "pred_track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
-        "tru_track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_reference_tracks'\n",
-        "# field_data = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/hato_2017-08-21T12.00.00_mems0000-0015.nc'\n",
-        "# track_dir = '/home/mkoch/coreai/tc_tracking_e2s/earth2studio/recipes/tc_tracking/outputs_hato/cyclone_tracks_te'\n",
-        "\n",
         "out_dir = './plots'\n",
         "\n",
         "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n",

From d4926cf14d403c7575184d5a9ccbb97c14098d58 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 03:26:26 -0800
Subject: [PATCH 61/69] final touches

---
 recipes/tc_tracking/README.md                 |   2 +-
 .../ibtracs.HATO_HELENE.list.v04r01.csv       |   0
 .../tc_tracking/{ => aux_data}/orography.nc   | Bin
 ...reference_track_hato_2017_west_pacific.csv |   0
 ...rence_track_helene_2024_north_atlantic.csv |   0
 recipes/tc_tracking/cfg/extract_era5.yaml     |   6 +-
 recipes/tc_tracking/cfg/hato.yaml             |   2 +-
 recipes/tc_tracking/cfg/helene.yaml           |   2 +-
 recipes/tc_tracking/cfg/reproduce_helene.yaml |   2 +-
 .../cfg/reproduce_stability_test.yaml         |   2 +-
 recipes/tc_tracking/cfg/slay_helena.yaml      |   2 +-
 recipes/tc_tracking/cfg/stability_test.yaml   |   2 +-
 .../tc_tracking/plotting/plotting_helpers.py  |   2 -
 recipes/tc_tracking/pyproject.toml            |   2 +-
 recipes/tc_tracking/src/data/utils.py         |   4 +-
 .../__init__.py                               |   0
 .../baseline_extraction.py                    |   4 +-
 .../src/{ => modes}/generate_ensembles.py     |   4 +-
 recipes/tc_tracking/src/tempest_extremes.py   |   3 +-
 recipes/tc_tracking/src/utils.py              |   2 +-
 recipes/tc_tracking/tc_hunt.py                |   4 +-
 recipes/tc_tracking/test/README.md            |   6 +-
 .../tc_tracking/test/cfg/baseline_helene.yaml |   4 +-
 .../tc_tracking/test/cfg/extract_era5.yaml    |   3 +-
 .../test/cfg/reproduce_helene.yaml            |   3 +-
 .../test/test_historic_tc_extraction.sh       |   4 +-
 recipes/tc_tracking/uv.lock                   | 727 +++++++++++++-----
 27 files changed, 551 insertions(+), 241 deletions(-)
 rename recipes/tc_tracking/{test => }/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv (100%)
 rename recipes/tc_tracking/{ => aux_data}/orography.nc (100%)
 rename recipes/tc_tracking/{test => }/aux_data/reference_track_hato_2017_west_pacific.csv (100%)
 rename recipes/tc_tracking/{test => }/aux_data/reference_track_helene_2024_north_atlantic.csv (100%)
 rename recipes/tc_tracking/src/{extract_from_data_source => modes}/__init__.py (100%)
 rename recipes/tc_tracking/src/{extract_from_data_source => modes}/baseline_extraction.py (98%)
 rename recipes/tc_tracking/src/{ => modes}/generate_ensembles.py (98%)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 89c5bb5f5..5f740719f 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -355,7 +355,7 @@ tru_track_dir = '/path/to/outputs_reference_tracks'
 
 ### 5.4 Reproduce Interesting Members to Extract Fields
 
-Suppose that after conducting the above anysis you want to take a closer look at ensemble members 0, 8, 9, and 12 of the Helene prediction. Since the forecast was produced with FCN3, we can reproduce these members and store the atmospheric fields for detailed analysis.
+Suppose that after conducting the above analysis you want to take a closer look at ensemble members 0, 8, 9, and 12 of the Helene prediction. Since the forecast was produced with FCN3, we can reproduce these members and store the atmospheric fields for detailed analysis.
 
 **Reproduce with Field Storage**:
 
diff --git a/recipes/tc_tracking/test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv b/recipes/tc_tracking/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv
similarity index 100%
rename from recipes/tc_tracking/test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv
rename to recipes/tc_tracking/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv
diff --git a/recipes/tc_tracking/orography.nc b/recipes/tc_tracking/aux_data/orography.nc
similarity index 100%
rename from recipes/tc_tracking/orography.nc
rename to recipes/tc_tracking/aux_data/orography.nc
diff --git a/recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv b/recipes/tc_tracking/aux_data/reference_track_hato_2017_west_pacific.csv
similarity index 100%
rename from recipes/tc_tracking/test/aux_data/reference_track_hato_2017_west_pacific.csv
rename to recipes/tc_tracking/aux_data/reference_track_hato_2017_west_pacific.csv
diff --git a/recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv b/recipes/tc_tracking/aux_data/reference_track_helene_2024_north_atlantic.csv
similarity index 100%
rename from recipes/tc_tracking/test/aux_data/reference_track_helene_2024_north_atlantic.csv
rename to recipes/tc_tracking/aux_data/reference_track_helene_2024_north_atlantic.csv
diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml
index c91ec28b8..352564991 100644
--- a/recipes/tc_tracking/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/cfg/extract_era5.yaml
@@ -83,7 +83,7 @@ cases:
     #     basin: southern_pacific
 
 # CAUTION: ibtracs file for hato and helene only. obtain full data at https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv
-ibtracs_source_data: "./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
+ibtracs_source_data: "./aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
     era5_train:
@@ -92,7 +92,7 @@ data_source:
             _target_: earth2studio.data.NCAR_ERA5
 
     era5_oos:
-        years: "2018-2022"
+        years: "2018-2026"
         source:
             _target_: earth2studio.data.CDS
 
@@ -102,7 +102,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     print_te_output: False
diff --git a/recipes/tc_tracking/cfg/hato.yaml b/recipes/tc_tracking/cfg/hato.yaml
index acd8b4615..9167a7544 100644
--- a/recipes/tc_tracking/cfg/hato.yaml
+++ b/recipes/tc_tracking/cfg/hato.yaml
@@ -19,7 +19,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml
index b35cf3ff8..5143f8f4e 100644
--- a/recipes/tc_tracking/cfg/helene.yaml
+++ b/recipes/tc_tracking/cfg/helene.yaml
@@ -19,7 +19,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml
index 1982843c8..9fc750022 100644
--- a/recipes/tc_tracking/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml
@@ -25,7 +25,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
index 5fbf8dc8c..1cb0e4480 100644
--- a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
+++ b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
@@ -30,7 +30,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
index 3e48c61c5..1212866fd 100644
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ b/recipes/tc_tracking/cfg/slay_helena.yaml
@@ -27,7 +27,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: True
     use_ram: False
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/cfg/stability_test.yaml b/recipes/tc_tracking/cfg/stability_test.yaml
index b2d085311..4661a333e 100644
--- a/recipes/tc_tracking/cfg/stability_test.yaml
+++ b/recipes/tc_tracking/cfg/stability_test.yaml
@@ -30,7 +30,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './orography.nc'
+    orography_path: './aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/plotting/plotting_helpers.py b/recipes/tc_tracking/plotting/plotting_helpers.py
index 19dda8b1e..5d698785f 100644
--- a/recipes/tc_tracking/plotting/plotting_helpers.py
+++ b/recipes/tc_tracking/plotting/plotting_helpers.py
@@ -534,7 +534,5 @@ def plot_ensemble_mean_metrics(ens_means, out_path):
         errs.append(means)
 
     errs = np.stack(errs)
-    print(errs.shape)
-    exit()
 
     return
diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index ae90a0822..fd207c2a5 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -26,7 +26,7 @@ dependencies = [
 
 [tool.uv.sources]
 torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" }
-earth2studio = { git = "https://github.com/mariusaurus/earth2studio.git", branch = "mkoch/tc_tracking" } # CAUTION: replace with NV main branch
+earth2studio = { git = "https://github.com/NVIDIA/earth2studio.git" }
 
 [tool.uv]
 no-build-isolation-package = ["torch-harmonics"]
diff --git a/recipes/tc_tracking/src/data/utils.py b/recipes/tc_tracking/src/data/utils.py
index c60905c5e..65612387c 100644
--- a/recipes/tc_tracking/src/data/utils.py
+++ b/recipes/tc_tracking/src/data/utils.py
@@ -12,13 +12,13 @@ def load_heights(oro_path):
     coords = OrderedDict(
         {"variable": np.array(["height"]), "lat": oro.latitude, "lon": oro.longitude}
     )
-    geop = torch.Tensor(oro["Z"].to_numpy()) / 9.80665  # devide by gravi to get height
+    geop = torch.Tensor(oro["Z"].to_numpy()) / 9.80665  # divide by gravity to get height
 
     return geop, coords
 
 
 class DataSourceManager:
-    # TODO: catch case in which ther's only single data source
+    # TODO: catch case in which there's only single data source
 
     def __init__(self, cfg):
         self.cfg = cfg
diff --git a/recipes/tc_tracking/src/extract_from_data_source/__init__.py b/recipes/tc_tracking/src/modes/__init__.py
similarity index 100%
rename from recipes/tc_tracking/src/extract_from_data_source/__init__.py
rename to recipes/tc_tracking/src/modes/__init__.py
diff --git a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py b/recipes/tc_tracking/src/modes/baseline_extraction.py
similarity index 98%
rename from recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
rename to recipes/tc_tracking/src/modes/baseline_extraction.py
index 444a6153b..363fb58b9 100644
--- a/recipes/tc_tracking/src/extract_from_data_source/baseline_extraction.py
+++ b/recipes/tc_tracking/src/modes/baseline_extraction.py
@@ -112,7 +112,7 @@ def match_tracks(ib_storm, hist_tracks, case):
 
     n_tracks = hist_tracks['track_id'].nunique()
 
-    # go over tracks and see if they match ibtracks
+    # go over tracks and see if they match ibtracs
     matched_track = None
     for time in ib_storm['time']:
         lat_ib = ib_storm.loc[ib_storm['time'] == time]['lat'].item()
@@ -128,7 +128,7 @@ def match_tracks(ib_storm, hist_tracks, case):
                 dist = great_circle_distance(lat_ib, lon_ib, lat_track, lon_track)
                 if dist < 300000:
                     matched_track = track_id
-                    print(f'matched track {matched_track} for strom {case} with distance {dist/1000:.2f}km')
+                    print(f'matched track {matched_track} for storm {case} with distance {dist/1000:.2f}km')
                     break
 
         if matched_track is not None:
diff --git a/recipes/tc_tracking/src/generate_ensembles.py b/recipes/tc_tracking/src/modes/generate_ensembles.py
similarity index 98%
rename from recipes/tc_tracking/src/generate_ensembles.py
rename to recipes/tc_tracking/src/modes/generate_ensembles.py
index 3701207fa..9d22161be 100644
--- a/recipes/tc_tracking/src/generate_ensembles.py
+++ b/recipes/tc_tracking/src/modes/generate_ensembles.py
@@ -192,7 +192,7 @@ def run_inference(model, cfg, store, out_coords, ic_mems):
 
     # Consolidate metadata in zarr files
     if dist.rank == 0 and cfg.store_type == "zarr":
-        # TODO add barrier such that rank 0 finsihes last
+        # TODO add barrier such that rank 0 finishes last
         consolidate_metadata(store.store)
 
     return store
@@ -301,7 +301,7 @@ def generate_ensemble(cfg):
 
 def reproduce_members(cfg):
     if cfg.store_type == "zarr":
-        raise ValueError("Zarr output not suported for reproducing ensemble members")
+        raise ValueError("Zarr output not supported for reproducing ensemble members")
     if cfg.model != 'fcn3':
         raise ValueError('Currently, reproducibility works for FCN3 only')
 
diff --git a/recipes/tc_tracking/src/tempest_extremes.py b/recipes/tc_tracking/src/tempest_extremes.py
index cc18cc39d..a96da4bcb 100644
--- a/recipes/tc_tracking/src/tempest_extremes.py
+++ b/recipes/tc_tracking/src/tempest_extremes.py
@@ -111,7 +111,7 @@ def __init__(
         print_te_output: bool = False,
         use_ram: bool = True,
         **kwargs: Any,
-    ):  # leave kwars to make call robust to switching between this and async version
+    ):  # leave kwargs to make call robust to switching between this and async version
 
         self.rank = DistributedManager().rank
         self.print_te_output = print_te_output
@@ -185,7 +185,6 @@ def check_tempest_extremes_availability(self) -> None:
             try:
                 run(cmd, capture_output=True)  # noqa: S603
             except Exception as e:
-                # TODO discuss implementation of below before merging
                 self.dependency_failure(e.__traceback__, e)
 
         return
diff --git a/recipes/tc_tracking/src/utils.py b/recipes/tc_tracking/src/utils.py
index bdf50acac..fbab30d1b 100644
--- a/recipes/tc_tracking/src/utils.py
+++ b/recipes/tc_tracking/src/utils.py
@@ -22,7 +22,7 @@ def set_initial_times(cfg: DictConfig) -> list[np.datetime64]:
     Returns
     -------
     list[np.datetime64]
-        Dictionary containing model, model class and model package.
+        List of initial condition times.
     """
     # list of ICs
     if "ics" in cfg:
diff --git a/recipes/tc_tracking/tc_hunt.py b/recipes/tc_tracking/tc_hunt.py
index cb042d11a..257ed4893 100644
--- a/recipes/tc_tracking/tc_hunt.py
+++ b/recipes/tc_tracking/tc_hunt.py
@@ -9,8 +9,8 @@
 
 import hydra
 from omegaconf import DictConfig
-from src.extract_from_data_source.baseline_extraction import extract_baseline
-from src.generate_ensembles import generate_ensemble, reproduce_members
+from src.modes.baseline_extraction import extract_baseline
+from src.modes.generate_ensembles import generate_ensemble, reproduce_members
 
 
 @hydra.main(version_base=None, config_path="cfg", config_name="none")
diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md
index 8018696f2..136100324 100644
--- a/recipes/tc_tracking/test/README.md
+++ b/recipes/tc_tracking/test/README.md
@@ -21,7 +21,7 @@ python ../tc_hunt.py --config-path=$(pwd) --config-name=baseline_helene.yaml
 ```
 
 You can find the track files in `outputs_baseline_helene/cyclone_tracks_te`.
-The directory should contain five csv files with naming pattern accrding to
+The directory should contain five csv files with naming pattern according to
 `tracks_2024-09-29T00:00:00_mem_000X_seed_YYYYYYYYY_bs_2.csv`
 This pattern provides information about initial condition, ensemble member ID
 and batch size.
@@ -30,7 +30,7 @@ Next, check if the random seeds for members three and four provided in `reproduc
 are identical to the seeds in the csv file names. If not, replace the seeds for both members
 in the yaml file with the seeds from the csv file names.
 
-Having made sure that the configurations for reproduicing the ensemble members
+Having made sure that the configurations for reproducing the ensemble members
 is correct, let us now reproduce members three and four of the previous run:
 ```bash
 python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=reproduce_helene.yaml
@@ -55,7 +55,7 @@ diff outputs_baseline_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_00
 Repeat the diff call for all reproduced ensemble members, the return should always be empty.
 
 
-## Test 2: Extracting indivdual storms from historic data
+## Test 2: Extracting individual storms from historic data
 
 > [!Note]
 > The test can be done manually following the steps below. Alternatively,
diff --git a/recipes/tc_tracking/test/cfg/baseline_helene.yaml b/recipes/tc_tracking/test/cfg/baseline_helene.yaml
index 8574640c4..247c55286 100644
--- a/recipes/tc_tracking/test/cfg/baseline_helene.yaml
+++ b/recipes/tc_tracking/test/cfg/baseline_helene.yaml
@@ -8,10 +8,9 @@ ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
 n_steps: 15
 ensemble_size: 5
 batch_size: 2
-orography_path: '../orography.nc'
 
 data_source:
-    _target_: earth2studio.data.NCAR_ERA5
+    _target_: earth2studio.data.CDS
 
 store_type: "none"
 store_dir: ./outputs_${project}
@@ -21,6 +20,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: '../aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/test/cfg/extract_era5.yaml b/recipes/tc_tracking/test/cfg/extract_era5.yaml
index 37b4138ea..b9dc83c31 100644
--- a/recipes/tc_tracking/test/cfg/extract_era5.yaml
+++ b/recipes/tc_tracking/test/cfg/extract_era5.yaml
@@ -10,7 +10,7 @@ cases:
         year: 2017
         basin: west_pacific
 
-ibtracs_source_data: "aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
+ibtracs_source_data: "../aux_data/ibtracs.HATO_HELENE.list.v04r01.csv"
 
 data_source:
     ncar_era5:
@@ -25,6 +25,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: '../aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     print_te_output: False
diff --git a/recipes/tc_tracking/test/cfg/reproduce_helene.yaml b/recipes/tc_tracking/test/cfg/reproduce_helene.yaml
index d420e8cf5..5f6d4a0cf 100644
--- a/recipes/tc_tracking/test/cfg/reproduce_helene.yaml
+++ b/recipes/tc_tracking/test/cfg/reproduce_helene.yaml
@@ -13,7 +13,7 @@ batch_size: 2
 orography_path: '../orography.nc'
 
 data_source:
-    _target_: earth2studio.data.NCAR_ERA5
+    _target_: earth2studio.data.CDS
 
 store_type: "none"
 store_dir: "./outputs_${project}"
@@ -23,6 +23,7 @@ cyclone_tracking:
     vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
     detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
     stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
+    orography_path: '../aux_data/orography.nc'
     keep_raw_data: False
     use_ram: True
     task_timeout_seconds: 120
diff --git a/recipes/tc_tracking/test/test_historic_tc_extraction.sh b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
index d159a112e..a8265f408 100644
--- a/recipes/tc_tracking/test/test_historic_tc_extraction.sh
+++ b/recipes/tc_tracking/test/test_historic_tc_extraction.sh
@@ -17,13 +17,13 @@ reference_tracks=(
     "reference_track_helene_2024_north_atlantic.csv"
 )
 for f in "${reference_tracks[@]}"; do
-    if ! diff -q "aux_data/$f" "outputs_reference_tracks/$f" > /dev/null 2>&1; then
+    if ! diff -q "../aux_data/$f" "outputs_reference_tracks/$f" > /dev/null 2>&1; then
         echo ""
         echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
         echo " >>> oh no!!! ERROR: $f differs between baseline and reproduce"
         echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
         echo ""
-        diff "aux_data/$f" "outputs_reference_tracks/$f"
+        diff "../aux_data/$f" "outputs_reference_tracks/$f"
         exit 1
     fi
 done
diff --git a/recipes/tc_tracking/uv.lock b/recipes/tc_tracking/uv.lock
index 8f7ead6c7..4df23ad68 100644
--- a/recipes/tc_tracking/uv.lock
+++ b/recipes/tc_tracking/uv.lock
@@ -16,6 +16,15 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'darwin'",
 ]
 
+[[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" }
+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" },
+]
+
 [[package]]
 name = "aiobotocore"
 version = "2.23.0"
@@ -116,30 +125,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
-[[package]]
-name = "anemoi-graphs"
-version = "0.6.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "anemoi-utils" },
-    { name = "hydra-core" },
-    { name = "matplotlib" },
-    { name = "netcdf4" },
-    { name = "networkx" },
-    { name = "numpy" },
-    { name = "plotly" },
-    { name = "scikit-learn" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
-    { name = "torch-geometric" },
-    { name = "typeguard" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/71/33/68a6ed99616b0fded780786d95f981d3ae95df8a3490259122fd8f09d006/anemoi_graphs-0.6.2.tar.gz", hash = "sha256:a2a8b7ce9839fe304e2616798b4fc9c45c417c2f355426de86e6fea32f38a79f", size = 1637662, upload-time = "2025-06-20T13:29:42.704Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/08/ef/48949e0a95e3c7b0443b00c67284e610bec925ee6357e7e9bca49543473e/anemoi_graphs-0.6.2-py3-none-any.whl", hash = "sha256:543b065b06c1ae077efdefef9770fcfb369ee0bbd7f3ac928c472f6b07cf4772", size = 92561, upload-time = "2025-06-20T13:29:41.085Z" },
-]
-
 [[package]]
 name = "anemoi-models"
 version = "0.5.1"
@@ -148,9 +133,7 @@ dependencies = [
     { name = "anemoi-utils" },
     { name = "einops" },
     { name = "hydra-core" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "torch" },
     { name = "torch-geometric" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d0/a7/1e17a2e99a965362dce49b921d660377b2922f9a5914f33e2498996509b6/anemoi_models-0.5.1.tar.gz", hash = "sha256:6986a8bf4c2f7341a14d25ee8426e0dbc586d1d28720334dc6e2032585273850", size = 432243, upload-time = "2025-04-30T16:50:53.669Z" }
@@ -203,6 +186,19 @@ version = "4.9.3"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
 
+[[package]]
+name = "anyio"
+version = "4.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -251,6 +247,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/91/48/08b2382e739236aa3360b7976360ba3e0c043b6234e25951c18c1eb6fa06/bokeh-3.7.3-py3-none-any.whl", hash = "sha256:b0e79dd737f088865212e4fdcb0f3b95d087f0f088bf8ca186a300ab1641e2c7", size = 7031447, upload-time = "2025-05-12T12:13:27.47Z" },
 ]
 
+[[package]]
+name = "boto3"
+version = "1.38.27"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "botocore" },
+    { name = "jmespath" },
+    { name = "s3transfer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e7/96/fc74d8521d2369dd8c412438401ff12e1350a1cd3eab5c758ed3dd5e5f82/boto3-1.38.27.tar.gz", hash = "sha256:94bd7fdd92d5701b362d4df100d21e28f8307a67ff56b6a8b0398119cf22f859", size = 111875, upload-time = "2025-05-30T19:32:41.352Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/8b/b2361188bd1e293eede1bc165e2461d390394f71ec0c8c21211c8dabf62c/boto3-1.38.27-py3-none-any.whl", hash = "sha256:95f5fe688795303a8a15e8b7e7f255cadab35eae459d00cc281a4fd77252ea80", size = 139938, upload-time = "2025-05-30T19:32:38.006Z" },
+]
+
 [[package]]
 name = "botocore"
 version = "1.38.27"
@@ -265,6 +275,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/83/a753562020b69fa90cebc39e8af2c753b24dcdc74bee8355ee3f6cefdf34/botocore-1.38.27-py3-none-any.whl", hash = "sha256:a785d5e9a5eda88ad6ab9ed8b87d1f2ac409d0226bba6ff801c55359e94d91a8", size = 13580545, upload-time = "2025-05-30T19:32:26.712Z" },
 ]
 
+[[package]]
+name = "bracex"
+version = "2.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/63/9a/fec38644694abfaaeca2798b58e276a8e61de49e2e37494ace423395febc/bracex-2.6.tar.gz", hash = "sha256:98f1347cd77e22ee8d967a30ad4e310b233f7754dbf31ff3fceb76145ba47dc7", size = 26642, upload-time = "2025-06-22T19:12:31.254Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9d/2a/9186535ce58db529927f6cf5990a849aa9e052eea3e2cfefe20b9e1802da/bracex-2.6-py3-none-any.whl", hash = "sha256:0b0049264e7340b3ec782b5cb99beb325f36c3782a32e36e876452fd49a09952", size = 11508, upload-time = "2025-06-22T19:12:29.781Z" },
+]
+
 [[package]]
 name = "cachetools"
 version = "5.5.2"
@@ -369,18 +388,14 @@ wheels = [
 
 [[package]]
 name = "cfgrib"
-version = "0.9.15.0"
-source = { registry = "https://pypi.org/simple" }
+version = "0.9.15.1"
+source = { git = "https://github.com/NickGeneva/cfgrib.git?rev=6a9af3173d7050b5fa5d221755f97e6ecb8c3433#6a9af3173d7050b5fa5d221755f97e6ecb8c3433" }
 dependencies = [
     { name = "attrs" },
     { name = "click" },
     { name = "eccodes" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3e/3b/0ccbbc67866a4a2df570d6bf0f53d6d22220c44e1f3684455b5eae298936/cfgrib-0.9.15.0.tar.gz", hash = "sha256:d455034e19b9560a75d008ba9d09b2d4e65762adfb2e911f28b841f4b9c6b47f", size = 6511752, upload-time = "2024-12-18T11:10:17.728Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7d/d7/96b4209c99f1fd6c19f502cebe8c91983c23331c380f3f521250f268ae8c/cfgrib-0.9.15.0-py3-none-any.whl", hash = "sha256:469cfd25dc173863795e596263b3b6b5ea1402b1715f2b7b1d4b995b40b32c18", size = 48908, upload-time = "2024-12-18T11:10:13.381Z" },
-]
 
 [[package]]
 name = "cftime"
@@ -442,14 +457,26 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.2.1"
+version = "8.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+]
+
+[[package]]
+name = "cligj"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/0d/837dbd5d8430fd0f01ed72c4cfb2f548180f4c68c635df84ce87956cff32/cligj-0.7.2.tar.gz", hash = "sha256:a4bc13d623356b373c2c27c53dbd9c68cae5d526270bfa71f6c6fa69669c6b27", size = 9803, upload-time = "2021-05-28T21:23:27.935Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/73/86/43fa9f15c5b9fb6e82620428827cd3c284aa933431405d1bcf5231ae3d3e/cligj-0.7.2-py3-none-any.whl", hash = "sha256:c1ca117dbce1fe20a5809dc96f01e1c2840f6dcc939b3ddbb1111bf330ba82df", size = 7069, upload-time = "2021-05-28T21:23:26.877Z" },
 ]
 
 [[package]]
@@ -784,10 +811,9 @@ wheels = [
 
 [[package]]
 name = "earth2studio"
-version = "0.10.0a0"
-source = { git = "https://github.com/mariusaurus/earth2studio.git?branch=mkoch%2Ftc_tracking#a4d59708fba9884b79e08a76819d5e9fef84396d" }
+version = "0.12.0a0"
+source = { git = "https://github.com/NVIDIA/earth2studio.git#ed655b0f33cc7e977750a1ce26fcf576e5ed622d" }
 dependencies = [
-    { name = "cfgrib" },
     { name = "cftime" },
     { name = "fsspec" },
     { name = "gcsfs" },
@@ -797,27 +823,47 @@ dependencies = [
     { name = "loguru" },
     { name = "nest-asyncio" },
     { name = "netcdf4" },
-    { name = "numcodecs" },
+    { name = "pandas" },
+    { name = "pygrib" },
     { name = "python-dotenv" },
     { name = "rich" },
     { name = "s3fs" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin')" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "torch" },
     { name = "tqdm" },
     { name = "xarray", extra = ["parallel"] },
     { name = "zarr" },
 ]
 
 [package.optional-dependencies]
+aifsens = [
+    { name = "anemoi-models" },
+    { name = "earthkit-regrid" },
+    { name = "ecmwf-opendata" },
+    { name = "flash-attn" },
+]
 data = [
     { name = "cdsapi" },
+    { name = "cfgrib" },
     { name = "eccodes" },
     { name = "ecmwf-opendata" },
+    { name = "globus-sdk" },
+    { name = "httpx" },
     { name = "intake-esgf" },
+    { name = "multi-storage-client", extra = ["boto3", "fsspec", "google-cloud-storage"] },
+    { name = "planetary-computer" },
     { name = "pyproj" },
+    { name = "pystac-client" },
+    { name = "rasterio" },
+    { name = "rioxarray" },
     { name = "scipy" },
 ]
+fcn3 = [
+    { name = "makani" },
+    { name = "moviepy" },
+    { name = "ruamel-yaml" },
+    { name = "scipy" },
+    { name = "torch-harmonics" },
+]
 
 [[package]]
 name = "earthkit-regrid"
@@ -924,24 +970,20 @@ name = "fcn3-tcs"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "anemoi-graphs" },
-    { name = "anemoi-models" },
     { name = "cartopy" },
-    { name = "earth2studio", extra = ["data"] },
-    { name = "earthkit-regrid" },
+    { name = "earth2studio", extra = ["aifsens", "data", "fcn3"] },
     { name = "hydra-core" },
     { name = "importlib-metadata" },
     { name = "ipykernel" },
-    { name = "makani" },
     { name = "matplotlib" },
     { name = "more-itertools" },
     { name = "moviepy" },
     { name = "ninja" },
     { name = "omegaconf" },
+    { name = "pytest" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
     { name = "setuptools" },
-    { name = "torch-harmonics" },
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 [[package]]
 name = "xyzservices"
 version = "2025.4.0"

From f0fcff0e6d419bf365d37f3044e73581b43cb05c Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 03:31:37 -0800
Subject: [PATCH 62/69] removed some configs

---
 .../cfg/reproduce_stability_test.yaml         | 48 -------------------
 recipes/tc_tracking/cfg/slay_helena.yaml      | 45 -----------------
 recipes/tc_tracking/cfg/stability_test.yaml   | 44 -----------------
 3 files changed, 137 deletions(-)
 delete mode 100644 recipes/tc_tracking/cfg/reproduce_stability_test.yaml
 delete mode 100644 recipes/tc_tracking/cfg/slay_helena.yaml
 delete mode 100644 recipes/tc_tracking/cfg/stability_test.yaml

diff --git a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml b/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
deleted file mode 100644
index 1cb0e4480..000000000
--- a/recipes/tc_tracking/cfg/reproduce_stability_test.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-project: 'stability_test'
-mode: 'reproduce_members'
-model: 'fcn3' # 'aifs-ens' 'fcn3' 'sfno'
-random_seed: 7778
-
-reproduce_members: [
-                    ['2023-05-01 00:00:00',  84, 481776871],
-                    ['2023-05-01 00:00:00',  85, 481776871],
-                    ]
-
-n_steps: 300
-ensemble_size: 96
-batch_size: 2
-
-data_source:
-    # _target_: src.data.localh5.LocalArchiveHDF5
-    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
-    _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
-
-
-store_type: "none"
-store_dir:  "./outputs_${project}"
-out_vars: ['t1000', 'u250', 'v850']
-
-cyclone_tracking:
-    asynchronous: True
-    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
-    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
-    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './aux_data/orography.nc'
-    keep_raw_data: False
-    use_ram: True
-    task_timeout_seconds: 120
-    print_te_output: False
-
-stability_check:
-    variables: ['t1000', 'u250', 'v850']
-    thresholds: [10, 10, 10]
-
-# hydra logs
-hydra:
-    job:
-        name: ${project}
-    run:
-        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/recipes/tc_tracking/cfg/slay_helena.yaml b/recipes/tc_tracking/cfg/slay_helena.yaml
deleted file mode 100644
index 1212866fd..000000000
--- a/recipes/tc_tracking/cfg/slay_helena.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-project: 'slay_helena'
-mode: 'generate_ensemble'
-model: 'aifs' # 'fcn3' 'sfno' 'aifs'
-random_seed: 7777
-
-# ic_block_start: "2024-09-23 00:00:00" # first IC
-# ic_block_end: "2024-09-27 00:00:00" # upper boundary for ICs
-# ic_block_step: 24            # number of hours between individual ICs
-ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"]
-
-n_steps: 15
-ensemble_size: 1
-batch_size: 1
-
-data_source:
-    # _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
-    _target_: earth2studio.data.CDS
-
-store_type: "netcdf"
-store_dir: "./outputs_${project}"
-out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m']
-
-cyclone_tracking:
-    asynchronous: True
-    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
-    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
-    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './aux_data/orography.nc'
-    keep_raw_data: True
-    use_ram: False
-    task_timeout_seconds: 120
-    print_te_output: False
-
-stability_check:
-    variables: ['t1000', 'u250', 'v850']
-    thresholds: [10, 10, 10]
-
-# hydra logs
-hydra:
-    job:
-        name: ${project}
-    run:
-        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}
\ No newline at end of file
diff --git a/recipes/tc_tracking/cfg/stability_test.yaml b/recipes/tc_tracking/cfg/stability_test.yaml
deleted file mode 100644
index 4661a333e..000000000
--- a/recipes/tc_tracking/cfg/stability_test.yaml
+++ /dev/null
@@ -1,44 +0,0 @@
-project: 'stability_test'
-mode: 'generate_ensemble'
-model: 'fcn3' # 'aifs-ens' 'fcn3' 'sfno'
-random_seed: 7778
-
-# ic_block_start: "2025-08-29 06:00:00" # first IC
-# ic_block_end: "2025-10-01 00:00:00" # upper boundary for ICs
-# ic_block_step: 6            # number of hours between individual ICs
-ics: ["2023-05-01 00:00:00"]
-
-n_steps: 300 #280
-ensemble_size: 96 # 1024 # 43
-batch_size: 2
-
-data_source:
-    # _target_: src.data.localh5.LocalArchiveHDF5
-    # dirs: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/out_of_sample"
-    # metadata_file: "/lustre/fsw/coreai_climate_earth2/tkurth/73varQ-hourly/metadata/data.json"
-    _target_: earth2studio.data.NCAR_ERA5
-    # _target_: earth2studio.data.GFS
-    # _target_: earth2studio.data.IFS
-
-
-store_type: "netcdf"
-store_dir:  "./outputs_${project}"
-out_vars: ['t1000', 'u250', 'v850']
-
-cyclone_tracking:
-    asynchronous: True
-    vars: ['msl', 'u10m', 'v10m', 'z300', 'z500']
-    detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0'
-    stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10'
-    orography_path: './aux_data/orography.nc'
-    keep_raw_data: False
-    use_ram: True
-    task_timeout_seconds: 120
-    print_te_output: False
-
-# hydra logs
-hydra:
-    job:
-        name: ${project}
-    run:
-        dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S}

From 2c8eaf3e88c31f5aedcb30ff2147b09a0d929810 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 03:34:21 -0800
Subject: [PATCH 63/69] pyproject project name

---
 recipes/tc_tracking/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml
index fd207c2a5..8e9af4441 100644
--- a/recipes/tc_tracking/pyproject.toml
+++ b/recipes/tc_tracking/pyproject.toml
@@ -1,5 +1,5 @@
 [project]
-name = "fcn3-tcs"
+name = "tc_hunt"
 version = "0.1.0"
 description = "no, i won't"
 readme = "README.md"

From b4af03e5a7fbaf33dea5453c479255d1f25652f4 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 03:40:44 -0800
Subject: [PATCH 64/69] README comment about conainer build time

---
 recipes/tc_tracking/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 5f740719f..66317bec6 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -33,7 +33,7 @@ There are two ways to set up the environment: either directly using a uv environ
 
 ### 1.1 Container (Recommended)
 
-A Dockerfile is provided in the top-level directory of the repository. [Building an image from this Dockerfile](https://docs.docker.com/get-started/docker-concepts/building-images/build-tag-and-publish-an-image/#build-an-image) is highly recommended as it avoids the need to compile flash-attention (which otherwise can take hours) and installs TempestExtremes.
+A Dockerfile is provided in the top-level directory of the repository. [Building an image from this Dockerfile](https://docs.docker.com/get-started/docker-concepts/building-images/build-tag-and-publish-an-image/#build-an-image) is highly recommended as it avoids the need to compile flash-attention (which otherwise can take hours) and installs TempestExtremes. Note that compiling torch-harmonics and therefore building the container might still take around 20min.
 
 ### 1.2 UV Environment
 

From 1bf9850542744c23065517ddfc3f8c89723b3bc9 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 05:24:34 -0800
Subject: [PATCH 65/69] removed TE from models/dx/__init__

---
 earth2studio/models/dx/__init__.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/earth2studio/models/dx/__init__.py b/earth2studio/models/dx/__init__.py
index 7505d669f..50bb71c6e 100644
--- a/earth2studio/models/dx/__init__.py
+++ b/earth2studio/models/dx/__init__.py
@@ -46,7 +46,6 @@
 from earth2studio.models.dx.wind_gust import WindgustAFNO  # noqa
 
 __all__ = [
-    "AsyncTempestExtremes",
     "ClimateNet",
     "CorrDiff",
     "CorrDiffCMIP6",
@@ -55,6 +54,5 @@
     "PrecipitationAFNOv2",
     "SolarRadiationAFNO1H",
     "SolarRadiationAFNO6H",
-    "TempestExtremes",
     "WindgustAFNO",
 ]

From 99ae8bacd2120084131442f19f1a0c331df2c592 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Fri, 23 Jan 2026 07:09:22 -0800
Subject: [PATCH 66/69] moving a bracket around

---
 recipes/tc_tracking/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md
index 66317bec6..f057ff33c 100644
--- a/recipes/tc_tracking/README.md
+++ b/recipes/tc_tracking/README.md
@@ -182,7 +182,7 @@ A common workflow is to run ensemble forecasts without storing atmospheric field
 
 FCN3 provides access to its internal random state, enabling exact reproduction of individual ensemble members. In theory, AIFS-ENS should have this capability as well, but the internal random state is not exposed through the Anemoi models interface, preventing reproducibility of AIFS-ENS runs.
 
-Note that reproducibility [is challenging](https://arxiv.org/abs/1605.04339) and only works reliably when using the same environment on the same system to produce the predictions.
+Note that [reproducibility is challenging](https://arxiv.org/abs/1605.04339) and only works reliably when using the same environment on the same system to produce the predictions.
 
 To reproduce a run, you need:
 - Configuration file from the original run (ideally)

From 5da785461bf26043adb00483d9136154eb6203ef Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 18 Feb 2026 10:36:16 +0100
Subject: [PATCH 67/69] updated base container

---
 recipes/tc_tracking/Dockerfile | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile
index 4d5dbf31d..e99e9dd53 100644
--- a/recipes/tc_tracking/Dockerfile
+++ b/recipes/tc_tracking/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvcr.io/nvidia/pytorch:25.11-py3
+FROM nvcr.io/nvidia/physicsnemo/physicsnemo:25.11
 
 # update repo info
 RUN apt update -y && \
@@ -25,12 +25,11 @@ RUN cmake .. && \
     cp ./bin/DetectNodes /usr/local/bin && \
     cp ./bin/StitchNodes /usr/local/bin
 
-
 # copy source into the container.
-RUN mkdir -p /tc_tracking
+RUN mkdir -p /tc_tracking_src
 COPY . /tc_tracking_src
 WORKDIR /tc_tracking_src
 
 ENV FORCE_CUDA_EXTENSION=1
-ENV TORCH_CUDA_ARCH_LIST="8.0 8.6 9.0 10.0 12.0+PTX"
+ENV TORCH_CUDA_ARCH_LIST="8.0 8.6 9.0 10.0 12.0, 13.0+PTX"
 RUN uv pip install --system --break-system-packages --no-cache-dir .

From 2f4b4953620cfbfb6b04685d6ddc0f1c723c8802 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 11 Mar 2026 03:24:16 -0700
Subject: [PATCH 68/69] fixed some bugs to be in line with new main

---
 recipes/tc_tracking/src/data/utils.py       | 2 +-
 recipes/tc_tracking/src/tempest_extremes.py | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/recipes/tc_tracking/src/data/utils.py b/recipes/tc_tracking/src/data/utils.py
index 65612387c..197982e0d 100644
--- a/recipes/tc_tracking/src/data/utils.py
+++ b/recipes/tc_tracking/src/data/utils.py
@@ -10,7 +10,7 @@ def load_heights(oro_path):
     oro = xr.load_dataset(oro_path)
 
     coords = OrderedDict(
-        {"variable": np.array(["height"]), "lat": oro.latitude, "lon": oro.longitude}
+        {"variable": np.array(["height"]), "lat": oro.latitude.values, "lon": oro.longitude.values}
     )
     geop = torch.Tensor(oro["Z"].to_numpy()) / 9.80665  # divide by gravity to get height
 
diff --git a/recipes/tc_tracking/src/tempest_extremes.py b/recipes/tc_tracking/src/tempest_extremes.py
index a96da4bcb..e2aabd551 100644
--- a/recipes/tc_tracking/src/tempest_extremes.py
+++ b/recipes/tc_tracking/src/tempest_extremes.py
@@ -23,7 +23,7 @@
     cat_coords,
     map_coords,
     split_coords,
-    tile_xx_to_yy,
+    tile_coords,
 )
 
 
@@ -499,12 +499,12 @@ def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None:
         if self.static_vars is not None:
             self.static_vars = self.static_vars.to(xx.device)
             if len(xx.shape) > len(self.static_vars.shape):
-                _static_vars, _static_coords = tile_xx_to_yy(
-                    self.static_vars, self.static_coords, xx, coords
+                _static_vars, _static_coords = tile_coords(
+                    self.static_vars, self.static_coords, coords
                 )
             else:
                 _static_vars, _static_coords = self.static_vars, self.static_coords
-            xx, coords = cat_coords(xx, coords, _static_vars, _static_coords)
+            xx, coords = cat_coords((xx, _static_vars), (coords, _static_coords))
 
         # select output data and write to store
         xx_sub, coords_sub = map_coords(xx, coords, self._store_coords)

From d240ae2da154e034f0347071ff9034f464c36828 Mon Sep 17 00:00:00 2001
From: mariusaurus <22983240+mariusaurus@users.noreply.github.com>
Date: Wed, 11 Mar 2026 03:37:40 -0700
Subject: [PATCH 69/69] split plan

---
 recipes/tc_tracking/PR_SPLIT_PLAN.md | 116 +++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 recipes/tc_tracking/PR_SPLIT_PLAN.md

diff --git a/recipes/tc_tracking/PR_SPLIT_PLAN.md b/recipes/tc_tracking/PR_SPLIT_PLAN.md
new file mode 100644
index 000000000..aeac33bc6
--- /dev/null
+++ b/recipes/tc_tracking/PR_SPLIT_PLAN.md
@@ -0,0 +1,116 @@
+# Splitting the TC Tracking Recipe into Incremental PRs
+
+Breakdown into 4 PRs, ordered from foundational to supplementary. Each PR is self-contained and reviewable on its own.
+
+---
+
+## PR 1 -- Core infrastructure + `generate_ensemble` mode
+
+The bulk of the code. Introduces the full pipeline for running AI weather model ensembles and tracking tropical cyclones with TempestExtremes.
+
+**Files to include:**
+
+- `tc_hunt.py` -- entry point, but only dispatching `generate_ensemble` (no import of `baseline_extraction`, no `reproduce_members` dispatch)
+- `src/__init__.py`
+- `src/tempest_extremes.py` -- the full TempestExtremes + async wrapper (~1250 lines, core of the recipe)
+- `src/utils.py` -- shared helpers
+- `src/data/utils.py` -- `DataSourceManager`, `load_heights`
+- `src/data/file_output.py` -- output setup for Zarr/NetCDF
+- `src/modes/generate_ensembles.py` -- **full file including `reproduce_members`**. Since `reproduce_members` and `generate_ensemble` share `initialise`, `load_model`, `run_inference`, `distribute_runs`, and `configure_runs`, splitting the file would be artificial. The function simply sits unused until PR 2 wires it up.
+- `cfg/helene.yaml`, `cfg/hato.yaml` -- example configs for tracking
+- `pyproject.toml` -- **without `tropycal`** (only needed by baseline extraction in PR 3)
+- `Dockerfile`, `set_envs.sh`, `.gitignore`
+- `README.md` -- documenting only the `generate_ensemble` mode
+- `test/test_tc_hunt.sh`, `test/cfg/baseline_helene.yaml`, `test/README.md`, `test/.gitignore` -- basic test for the generate mode
+
+**Notes:**
+
+- This is the largest PR but it is all one coherent feature: "run ensemble forecasts and track cyclones".
+- `tropycal`, `moviepy`, and plotting-only dependencies can be dropped from `pyproject.toml` for this PR to keep the dependency surface small.
+- The `testsource.py` debug script is not part of the recipe proper; leave it out (it is an untracked file anyway).
+
+---
+
+## PR 2 -- Reproduction mode
+
+A very small, easy-to-review PR. Wires up the `reproduce_members` function that already exists in `generate_ensembles.py`.
+
+**Changes:**
+
+- `tc_hunt.py` -- add `reproduce_members` import and dispatch case (~3 lines changed)
+- `cfg/reproduce_helene.yaml` -- example config for reproducing specific ensemble members
+- `test/cfg/reproduce_helene.yaml` -- test config
+- `README.md` -- add documentation for the `reproduce_members` mode
+
+This PR is intentionally tiny. The only new logic is the dispatch wiring and configs; the implementation already landed in PR 1 as part of `generate_ensembles.py`.
+
+---
+
+## PR 3 -- Baseline extraction from reanalysis
+
+Adds the `extract_baseline` mode, which fetches ERA5 reanalysis data, runs TempestExtremes on it, and matches the detected tracks against IBTrACS ground truth.
+
+**Files to include:**
+
+- `src/modes/baseline_extraction.py` -- the full extraction pipeline (~208 lines)
+- `tc_hunt.py` -- add `extract_baseline` import and dispatch case
+- `cfg/extract_era5.yaml` -- config for Helene + Hato extraction
+- `aux_data/ibtracs.HATO_HELENE.list.v04r01.csv` -- IBTrACS subset
+- `aux_data/reference_track_hato_2017_west_pacific.csv` -- reference track
+- `aux_data/reference_track_helene_2024_north_atlantic.csv` -- reference track
+- `test/test_historic_tc_extraction.sh`, `test/cfg/extract_era5.yaml` -- extraction test
+- `pyproject.toml` -- add `tropycal>=1.4` dependency
+- `README.md` -- add documentation for `extract_baseline`
+
+**Notes:**
+
+- This is the only PR that adds `tropycal` as a dependency (used for IBTrACS access).
+- The `aux_data/` CSV files are small reference datasets, fine to commit.
+
+---
+
+## PR 4 -- Plotting and analysis tools
+
+Adds the visualisation and analysis tooling. Entirely optional for the core pipeline to work; can be merged last or even deferred.
+
+**Files to include:**
+
+- `plotting/analyse_n_plot.py`
+- `plotting/data_handling.py`
+- `plotting/plotting_helpers.py`
+- `plotting/plot_tracks_n_fields.ipynb`
+- `plotting/tracks_slayground.ipynb`
+- `plotting/README.md`
+- `plotting/.gitignore`
+- `pyproject.toml` -- ensure `cartopy`, `matplotlib`, `moviepy` are present (likely already there from PR 1, but verify)
+
+---
+
+## Dependency flow between PRs
+
+```mermaid
+graph LR
+    PR1["PR 1: Core + generate_ensemble"]
+    PR2["PR 2: reproduce_members"]
+    PR3["PR 3: extract_baseline"]
+    PR4["PR 4: Plotting"]
+    PR1 --> PR2
+    PR1 --> PR3
+    PR1 --> PR4
+    PR3 --> PR4
+```
+
+PR 2, PR 3, and PR 4 all depend on PR 1. PR 3 and PR 4 are independent of PR 2. PR 4 may reference outputs from PR 3 (reference tracks), so ordering PR 3 before PR 4 is ideal but not strictly required.
+
+---
+
+## Implementation approach
+
+For each PR, we create a branch off main and stage only the relevant files. Since all files are new (no modifications to existing e2s files), this is straightforward -- each PR is a subset of the current `recipes/tc_tracking/` directory. The main work is:
+
+1. For PR 1: temporarily strip `tc_hunt.py` to only handle `generate_ensemble`, and trim `pyproject.toml` dependencies.
+2. For PR 2: minimal diff -- add 3 lines to `tc_hunt.py` + config files.
+3. For PR 3: add `baseline_extraction.py` + wiring + configs + aux data + tropycal dep.
+4. For PR 4: add `plotting/` directory.
+
+Each subsequent PR is a clean additive diff on top of the previous one.