融合雷达与卫星数据，提升城市强降雨短时预报精度。
Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
- 用雷达和卫星多模态数据联合建模预测降水
- 5分钟预报中强雨准确率提升4%，暴雨提升3%
- 适合需要精准防灾预警的城市气象部门
近年来强降雨事件频发，是城市内涝的主要成因，亟需高精度降水预报，尤其在地面传感器难以覆盖的局部区域。德国2001至2018年间仅有17.3%的小时级强降雨事件被雨量计记录，凸显传统监测系统局限性。雷达虽能追踪实时降水，但对短暂且突发的强降雨预报仍具挑战。本文评估融合卫星与雷达数据在短时预报中的效果，构建结合两者影像的多模态预报模型，预测5、15和30分钟后的降水。实验表明，该策略显著优于仅使用雷达的方法。融合卫星数据后，5分钟预报中强雨的临界成功指数（CSI）提升4%，暴雨提升3%。且在更长预报时效下，模型性能仍优于雷达单模态方案。对2021年德国北莱茵-威斯特法伦州特大洪灾的定性分析显示，相比仅依赖雷达的模型，多模态模型能更精确捕捉强降雨区域细节。更高的预报精度有助于及时发出可靠预警，挽救生命。代码已开源：https://github.com/RamaKassoumeh/Multimodal_heavy_rain
原文摘要 · Abstract (English)
The increasing frequency of heavy rainfall events, which are a major cause of urban flooding, underscores the urgent need for accurate precipitation forecasting - particularly in urban areas where localized events often go undetected by ground-based sensors. In Germany, only 17.3% of hourly heavy rain events between 2001 and 2018 were recorded by rain gauges, highlighting the limitations of traditional monitoring systems. Radar data are another source that effectively tracks ongoing precipitation; however, forecasting the development of heavy rain using radar alone remains challenging due to the brief and unpredictable nature of such events. Our focus is on evaluating the effectiveness of fusing satellite and radar data for nowcasting. We develop a multimodal nowcasting model that combines both radar and satellite imagery for predicting precipitation at lead times of 5, 15, and 30 minutes. We demonstrate that this multimodal strategy significantly outperforms radar-only approaches. Experimental results show that integrating satellite data improves prediction accuracy, particularly for intense precipitation. The proposed model increases the Critical Success Index for heavy rain by 4% and for violent rain by 3% at a 5-minute lead time. Moreover, it maintains higher predictive skill at longer lead times, where radar-only performance declines. A qualitative analysis of the severe flooding event in the state of North Rhine-Westphalia, Germany in 2021 further illustrates the superior performance of the multimodal model. Unlike the radar-only model, which captures general precipitation patterns, the multimodal model yields more detailed and accurate forecasts for regions affected by heavy rain. This improved precision enables timely, reliable, life-saving warnings. Implementation available at https://github.com/RamaKassoumeh/Multimodal_heavy_rain
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