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.

翻译：临近降水预报在多个行业至关重要，并在缓解和适应气候变化方面发挥着重要作用。我们提出了一种高效的深度学习模型用于临近降水预报，能够提前8小时预测降雨，其准确性优于现有的基于物理和基于外推的业务模型。该模型利用多源气象数据和基于物理的预报，在时间和空间上提供高分辨率预测。它通过时序注意力网络捕捉复杂的时空动态，并使用数据质量图和动态阈值进行优化。实验表明，该模型性能优于现有最先进方法，并突显了其对快速响应不断变化的天气条件的潜力。