Abstract
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.
Useful links
Paper: AAAI
Bibtex
@inproceedings{de2021rainbench,
title={RainBench: Towards data-driven global precipitation forecasting from satellite imagery},
author={de Witt, Christian Schroeder and Tong, Catherine and Zantedeschi, Valentina and De Martini, Daniele and Kalaitzis, Alfredo and Chantry, Matthew and Watson-Parris, Duncan and Bilinski, Piotr},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={35},
number={17},
pages={14902--14910},
year={2021}
}