TOOLS

In the aftermath of an extreme weather event, the event is studied more and more often by an operational attribution service. Two examples of such operational attribution services are used within XAIDA. These operational services generally answer slightly different questions and are complementary.

ClimaMeter is an international consortium designed to attribute extreme weather events to climate change, providing scientifically robust and actionable insights to inform public discourse and policy.

In the aftermath of an extreme weather event, the event is studied more and more often by an operational attribution service. Two examples of such operational attribution services are used within XAIDA. These operational services generally answer slightly different questions and are complementary.

World Weather Attribution (WWA), an initiative founded in 2014, aims to answer the question whether and to what extent climate change altered the likelihood and intensity of an extreme weather event, focusing on events with large impacts and aiming for global coverage.

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.

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

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

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

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

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 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.