@article{Schroeder de Witt_Tong_Zantedeschi_De Martini_Kalaitzis_Chantry_Watson-Parris_Bilinski_2021, title={RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17749}, DOI={10.1609/aaai.v35i17.17749}, abstractNote={Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce RainBench, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release PyRain, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.}, number={17}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Schroeder de Witt, Christian and Tong, Catherine and Zantedeschi, Valentina and De Martini, Daniele and Kalaitzis, Alfredo and Chantry, Matthew and Watson-Parris, Duncan and Bilinski, Piotr}, year={2021}, month={May}, pages={14902-14910} }