用多源气象数据和注意力机制，提前8小时更准预测降雨。
Multi-Source Temporal Attention Network for Precipitation Nowcasting
- 融合多源气象数据与物理预报，通过时序注意力捕捉复杂变化。
- 在8小时内预测精度超越现有物理模型和外推法。
- 适合需要快速响应天气变化的气象、防灾与交通领域。
降水临近预报在多个行业至关重要，在应对气候变化中发挥关键作用。本文提出一种高效的深度学习模型，可提前8小时进行降水预测，准确率高于现有基于物理或外推的业务模型。该模型利用多源气象数据与物理预报，实现时空高分辨率预测；通过时序注意力网络捕捉复杂的时空动态，并结合数据质量图与动态阈值进行优化。实验表明，该模型性能优于当前最先进的方法，展现出对动态天气条件快速可靠响应的潜力。
原文摘要 · Abstract (English)
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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