论文标题
Rainbench：从卫星图像预测全球降水
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
论文作者
论文摘要
极端的降水事件，例如暴力降雨和冰雹风暴，经常破坏了发展中国家的经济体和生计。气候变化进一步加剧了这个问题。数据驱动的深度学习方法可以扩大对准确的多天预测的访问，以减轻此类事件。但是，目前尚无专门用于研究全球降水预测的基准数据集。在本文中，我们介绍了\ textbf {Rainbench}，这是一种用于数据驱动的沉淀预测的新的多模式基准数据集。它包括模拟的卫星数据，来自ERA5重新分析产品的相关气象数据的选择以及imerg降水数据。我们还发布\ textbf {pyrain}，一个库有效地处理大型沉淀数据集。我们对新型数据集进行了广泛的分析，并为两个基准中等降水预测任务建立了基线结果。最后，我们讨论了现有的数据驱动天气预报方法，并提出了未来的研究途径。
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.
