RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917275021344768 |
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| author | Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Sjørup, Jeppe Liborius Vesterholt, Anders Lillevang Assent, Ira |
| author_facet | Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Sjørup, Jeppe Liborius Vesterholt, Anders Lillevang Assent, Ira |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10271 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Sjørup, Jeppe Liborius Vesterholt, Anders Lillevang Assent, Ira Machine Learning Computer Vision and Pattern Recognition 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. |
| title | RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.10271 |