Multi-Source Temporal Attention Network for Precipitation Nowcasting
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916497323982848 |
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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 | Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational physics-based and extrapolation-based models. Our model leverages multi-source meteorological data and physics-based forecasts to deliver high-resolution predictions in both time and space. It captures complex spatio-temporal dynamics through temporal attention networks and is optimized using data quality maps and dynamic thresholds. Experiments demonstrate that our model outperforms state-of-the-art, and highlight its potential for fast reliable responses to evolving weather conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08641 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Source Temporal Attention Network for Precipitation Nowcasting Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Sjørup, Jeppe Liborius Vesterholt, Anders Lillevang Assent, Ira Machine Learning Computer Vision and Pattern Recognition Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational physics-based and extrapolation-based models. Our model leverages multi-source meteorological data and physics-based forecasts to deliver high-resolution predictions in both time and space. It captures complex spatio-temporal dynamics through temporal attention networks and is optimized using data quality maps and dynamic thresholds. Experiments demonstrate that our model outperforms state-of-the-art, and highlight its potential for fast reliable responses to evolving weather conditions. |
| title | Multi-Source Temporal Attention Network for Precipitation Nowcasting |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.08641 |