A Multimodal Geo Dataset for High-resolution Precipitation Forecasting

Accurate short-term precipitation prediction at a high spatial resolution is crucial for effective urban water management, flooding warning, and mitigation. However, conventional numerical weather models usually face the challenge of systematic errors and spatiotemporal biases due to an inadequate understanding of many processes and unrealistic parameterizations. In recent years, deep learning techniques have gained popularity as a tool in precipitation forecasting and risk pre-warning. To support deep learning for precipitation forecasting and flooding warning, this paper introduces a large-scale multimodal Geo dataset. This dataset incorporates spatially connected features and real-world climate data, enabling the prediction of extreme precipitations. The dataset comprises Multi-Radar/Multi-Sensor System (MRMS), High-Resolution Rapid Refresh (HRRR), Geostationary Satellite Server (GOES) data, and …