GreenEarthNet

GreenEarthNet is a machine learning-powered toolkit for predicting ecosystem responses to climate changes by leveraging Earth observation data and climate models. It supports effective environmental monitoring and forecasting for improved climate action.

AIDE Toolbox

The Artificial Intelligence for Disentangling Extremes (AIDE) toolbox allows for tackling generic problems of detection and impact assessment of events such as tropical cyclones and severe convective storms, heat waves, and droughts, as well as persistent winter extremes, among others

Weather Regimes

This tool explores potential energy risk scenarios using weather regimes. This allows users to tailor the tool to their relevant impact and weather regimes.

Causal Inference: CAUSEME web-platform

CAUSEME web-platform is a platform to benchmark causal discovery methods based on ground truth benchmark datasets featuring different real data challenges.

Causal Inference: Tigramite

We contributed to the continuous development of the tigramite (https://github.com/jakobrunge/tigramite/) Python package for causal inference methods.
This package provides a wide range of constraint-based causal discovery and causal effect estimation methods.

Stochastic Weather Generation

Stochastic Weather Generation is a computationally light tool to simulate temperatures for worse case heat extremes, at city up to country level.

Importance Sampling

Importance sampling is a way to preferentially select out of a range of model simulations the ones that will lead to extremes of the metric of interest in an early stage of simulation, thus increasing computing efficiency for the cases of interest.

Storyline; 50°C in Paris

Storyline is a methodology to determine when in the future extreme heat events above a chosen threshold become likely in cities, and to present the meteorological conditions that lead up to it.

Ensemble Boosting

Ensemble boosting uses climate models to efficiently generate very intense and rare weather and climate extremes that can be analyzed for planning and stress testing of critical infrastructure.

XAIDA Webinar #16

The 16th session of our webinar will occur on March 18th, 2025 at 2PM (14:00) CET. Oana-Iuliana Popescu (TU Dresden, TU Berlin) will talk about ‘Causal discovery with endogenous context variables’
Check out the details to attend, discuss and follow the discussion!