Multi-Source Temporal Attention Network for Precipitation Nowcasting

• URL: http://arxiv.org/abs/2410.08641v1
• Date: Fri, 11 Oct 2024 09:09:07 GMT
• Title: Multi-Source Temporal Attention Network for Precipitation Nowcasting
• Authors: Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk, Jeppe Liborius Sjørup, Anders Lillevang Vesterholt, Ira Assent,
• Abstract summary: 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 models.
• Score: 4.726419619132143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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