PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting (Papers Track)

Yujin Tang (The Hong Kong University of Science and Technology (Guangzhou)); Jiaming Zhou (Hong Kong University of Science and Technology (Guangzhou)); Xiang Pan (Nanjing University); Zeying Gong (Hong Kong University of Science and Technology (Guangzhou)); Junwei Liang (The Hong Kong University of Science and Technology (Guangzhou))

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Extreme Weather Computer Vision & Remote Sensing


Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark, and CAMT, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3%, 4.7%, and 26.8% in rain CSI and improvements of 15.6%, 17.4%, and 31.8% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at