Securing sufficient forecast lead time for local precipitation is essential
for preventing hazardous weather events. Nonetheless, global warming-induced
climate change is adding to the challenge of accurately predicting severe
precipitation events, such as heavy rainfall. In this work, we propose a deep
learning-based precipitation post-processor approach to numerical weather
prediction (NWP) models. The precipitation post-processor consists of (i)
self-supervised pre-training, where parameters of encoder are pre-trained on
the reconstruction of masked variables of the atmospheric physics domain, and
(ii) transfer learning on precipitation segmentation tasks (target domain) from
the pre-trained encoder. We also introduce a heuristic labeling approach for
effectively training class-imbalanced datasets. Our experiment results in
precipitation correction for regional NWP show that the proposed method
outperforms other approaches.

我们提出了一种基于深度学习的降水后处理方法来提高数值天气预报模型的降水预报能力，通过自监督预训练和迁移学习，在区域天气预报中得到了比其他方法更好的结果。