2025/05/15 by Rafael Pablos Sarabia, Joachim Nyborg, Sarabia, Rafael Pablos +9
Earth and Planetary Sciences · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Flood Risk Assessment and Management #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Precipitation Measurement and Analysis
paper · pdf · doi:10.48550/arxiv.2505.10271
openalex publication_date 2025/05/15 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
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.