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arXiv · 2310.20187

Self-Supervised Pre-Training for Precipitation Post-Processor

Sojung An·Junha Lee·Jiyeon Jang·Inchae Na·Wooyeon Park·Sujeong You
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Abstract

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

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Sojung An, Junha Lee, Jiyeon Jang, Inchae Na, Wooyeon Park, Sujeong You. 2023-10-31. Self-Supervised Pre-Training for Precipitation Post-Processor. https://arxiv.org/abs/2310.20187

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