Self-Supervised Pre-Training for Precipitation Post-Processor

• URL: http://arxiv.org/abs/2310.20187v3
• Date: Tue, 20 Feb 2024 01:45:22 GMT
• Title: Self-Supervised Pre-Training for Precipitation Post-Processor
• Authors: Sojung An, Junha Lee, Jiyeon Jang, Inchae Na, Wooyeon Park, Sujeong You
• Abstract summary: We propose a deep learning-based precipitation post-processor for numerical weather prediction (NWP) models. Our experiments on precipitation correction for regional NWP datasets show that the proposed method outperforms other approaches.
• Score: 1.5553847214012175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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