Identifying spatio-temporal drivers of extreme events [data set]https://doi.org/10.60507/FK2/RD9E33Shams Eddin, Mohamad HakamGall, Juergenbonndata2024-10-212024-11-04T07:26:57ZThis data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.Agricultural SciencesComputer and Information ScienceEarth and Environmental SciencesPhysicsEnglisharXiv, 2410.24075, https://doi.org/10.48550/arXiv.2410.240752023-08-01Shams Eddin, Mohamad2024-10-09CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasetsNOAA/NESDIS Center for Satellite Applications and Research: https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/index.phpERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasetsCC BY 4.0