RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

TL;DR

RainBench is a new comprehensive benchmark dataset combining satellite, meteorological, and precipitation data to advance data-driven global precipitation forecasting, enabling better multi-day predictions and climate resilience.

Contribution

The paper introduces RainBench, the first dedicated benchmark dataset for global precipitation forecasting, along with PyRain library and baseline results for key forecasting tasks.

Findings

Established baseline results for medium-range precipitation forecasting

Provided extensive analysis of the RainBench dataset

Discussed future research directions in data-driven weather prediction

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.