Self-supervised Pre-training for Precipitation Post-processor

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.