RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

• URL: http://arxiv.org/abs/2012.09670v1
• Date: Thu, 17 Dec 2020 15:35:24 GMT
• Title: RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
• Authors: Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
• Abstract summary: Extreme precipitation events routinely ravage economies and livelihoods around the developing world. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts. There is currently no benchmark dataset dedicated to the study of global precipitation forecasts.
• Score: 6.462260770989231
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \textbf{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 \textbf{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.

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