Accurate nowcasting is critical for preemptive action in response to heavy rainfall events (HREs). However, operational numerical weather prediction models have difficulty predicting HREs in the short term, especially for rapidly and sporadically developing cases. Here, we present multi-year evaluation statistics showing that deep-learning-based HRE nowcasting, trained with radar images and ground measurements, outperforms short-term numerical weather prediction at lead times of up to 6 h. The deep learning nowcasting shows an improved accuracy of 162%–31% over numerical prediction, at the 1-h to 6-h lead times, for predicting HREs in South Korea during the Asian summer monsoon. The spatial distribution and diurnal cycle of HREs are also well predicted. Isolated HRE predictions in the late afternoon to early evening which mostly result from convective processes associated with surface heating are particularly useful. This result suggests that the deep learning algorithm may be available for HRE nowcasting, potentially serving as an alternative to the operational numerical weather prediction model.

中文翻译：

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韩国暴雨临近预报的深度学习模型

准确的临近预报对于针对强降雨事件 (HRE) 采取先发制人的行动至关重要。然而，实用的数值天气预报模型很难在短期内预测 HRE，特别是对于快速且零星发展的情况。在这里，我们提供了多年的评估统计数据，显示基于深度学习的 HRE 临近预报，经过雷达图像和地面测量训练，在长达 6 小时的交付时间内优于短期数值天气预报。深度学习临近预报显示，在预测亚洲夏季风期间韩国的 HRE 时，在 1 小时到 6 小时的提前时间内，准确度比数值预测提高了 162%–31%。 HRE 的空间分布和昼夜周期也得到了很好的预测。下午晚些时候到傍晚早些时候的孤立 HRE 预测特别有用，这些预测主要是由与地表加热相关的对流过程引起的。这一结果表明深度学习算法可能可用于 HRE 临近预报，有可能作为操作数值天气预报模型的替代方案。