Extreme Value Analysis (EVA) in Python

Acclimate - an agent-based model for economic loss propagation

[NeurIPS'25] RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting

[NeurIPS'24] Identifying Spatio-Temporal Drivers of Extreme Events

Identification of compounding drivers of river floods

A repo for "Extreme Precipitation-Temperature Scaling in California: The Role of Atmospheric Rivers"

An RShiny web application for visualizing high frequency meteorological data and identifying climate anomalies in the McMurdo Dry Valleys of Antarctica.

🌊 MEANDRE présente de manière guidée un regard d'expert sur les résultats des projections hydrologiques réalisées sur la France. La mise à jour de ces projections a été réalisé entre 2022 et 2024 dans le cadre du projet national Explore2. Ces résultats sont un aperçu de quelques futurs possibles pour la ressource en eau.

[NeurIPS'24] Identifying Spatio-Temporal Drivers of Extreme Events

Implementation of classical and recurrence-free quantum reservoir computing for predicting chaotic dynamics

Physics-informed ensemble for 12-h city-region temperature forecasts. Advection–diffusion prior + ConvLSTM + RAFL + edRVFL-SC for extreme-event warnings.

R code and example data to determine temporal shifts in intervals between extreme total annual rainfall

Evaluating AI-Weather forecasts (Pangu AI) on predicting extreme events like tropical cyclones by comparing it to ERA5. // Master Course at the University of Bern: Seminar in Climatology (2024).

This repository provides a Python implementation of the Gaussian Mixture Model (GMM) algorithm for detecting extreme events in CMIP6 data.

Trends and variability of precipitation extremes in the Peruvian Altiplano (1971–2013)

This repo is the complete workflow for this publication: Tail associations in ecological variables and their impact on extinction risk, Ghosh et al., Ecosphere 11(5):e03132. For details and citation see here:

An explorative interface for spatial extreme events data

A Non-stationary Dependence Model for Extreme European Windstorms

A learn module on exposure of land and population to extreme events

(currently I plan on updating the repo stage by stage) Benchmarking ML and statistical models for non-stationary hydro-climatic time series and extremes.