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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