XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events

The automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change, which has changed their likelihood and intensity. One of the main objectives within the EXtreme Events: Artificial Intelligence for Detection and Attribution (XAIDA) project (https://xaida.eu/) is related to developing novel approaches for the detection and localization of extreme events, such as tropical cyclones and severe convective storms, heat waves and droughts, as well as persistent winter extremes, among others. Here we introduce the XAIDA4Detection toolbox that allows for tackling generic problems of detection and characterization. The open-source toolbox integrates a set of advanced ML models, ranging in complexity, assumptions, and sophistication, and yields spatio-temporal explicit detection maps with probabilistic heatmap estimates. We included supervised and unsupervised methods, deterministic and probabilistic, neural networks based on convolutional and recurrent nets, and density-based methods. The toolbox is intended for scientists, engineers, and students with basic knowledge of extreme events, outlier detection techniques, and Deep Learning (DL), as well as Python programming with basic packages (Numpy, Scikit-learn, Matplotlib) and DL packages (PyTorch, PyTorch Lightning). This presentation will summarize the available features and their potential to be adapted to multiple extreme event problems and use cases.