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.

翻译：我们提出了一种深度学习模型，用于在欧洲地区进行8小时时间尺度的高分辨率概率性降水预报，克服了仅基于雷达的深度学习模型预报时效短的局限性。我们的模型高效地融合了多种数据源——包括雷达、卫星和基于物理的数值天气预报（NWP）数据——同时捕捉长程相互作用，从而通过一致的降水概率图实现精准预报和稳健的不确定性量化。该模型架构紧凑，与现有模型相比，能够实现更高效的训练和更快的推理。大量实验表明，我们的模型超越了当前业务化的NWP系统、基于外推的方法以及深度学习临近预报模型，为欧洲高分辨率降水预报设立了新标准，确保了准确性、可解释性与计算效率之间的平衡。