RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery


  • Christian Schroeder de Witt University of Oxford
  • Catherine Tong University of Oxford
  • Valentina Zantedeschi INRIA, UCL
  • Daniele De Martini University of Oxford
  • Alfredo Kalaitzis University of Oxford
  • Matthew Chantry University of Oxford
  • Duncan Watson-Parris University of Oxford
  • Piotr Bilinski University of Warsaw / University of Oxford




Natural Sciences, Social Welfare, Justice, Fairness and Equality, Underserved communities, Other Social Impact


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.




How to Cite

Schroeder de Witt, C., Tong, C., Zantedeschi, V., De Martini, D., Kalaitzis, A., Chantry, M., Watson-Parris, D., & Bilinski, P. (2021). RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14902-14910. https://doi.org/10.1609/aaai.v35i17.17749



AAAI Special Track on AI for Social Impact