TY - JOUR
AU - You, Sujeong
AB - 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.
TI - Self-Supervised Pre-Training for Precipitation Post-Processor
JO - Computing Research Repository
DO - 10.48550/arxiv.2310.20187
DA - 2023-10-31
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/self-supervised-pre-training-for-precipitation-post-processor-c3b0ZKt61E
VL - 2024
IS - 2310
DP - DeepDyve
ER -