用多源数据融合提升8小时降雨概率预测精度
RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
- 融合雷达、卫星与数值预报数据，捕捉长程影响
- 在欧洲区域8小时预报中优于现有系统
- 模型轻量高效，适合实时业务部署
我们提出一种深度学习模型，用于欧洲地区8小时高分辨率概率性降水预报，克服了仅依赖雷达的深度学习模型预报时效短的局限。该模型高效融合雷达、卫星和基于物理的数值天气预报（NWP）多源数据，同时捕捉长距离相互作用，通过一致的概率图实现精准预报与可靠的不确定性量化。模型采用紧凑架构，训练更高效，推理速度更快。大量实验表明，该模型超越当前运行中的NWP系统、外推方法及现有深度学习短临预报模型，在准确率、可解释性与计算效率间取得良好平衡，为欧洲高分辨率降水预报树立新标准。
原文摘要 · Abstract (English)
We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radar-only deep learning models with short forecast lead times. Our model efficiently integrates multiple data sources - including radar, satellite, and physics-based numerical weather prediction (NWP) - while capturing long-range interactions, resulting in accurate forecasts with robust uncertainty quantification through consistent probabilistic maps. Featuring a compact architecture, it enables more efficient training and faster inference than existing models. Extensive experiments demonstrate that our model surpasses current operational NWP systems, extrapolation-based methods, and deep-learning nowcasting models, setting a new standard for high-resolution precipitation forecasting in Europe, ensuring a balance between accuracy, interpretability, and computational efficiency.
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