Self-Supervised Pre-Training for Precipitation Post-Processor

arXiv:2310.20187v3 Announce Type: replace
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 …

abstract arxiv challenge change climate climate change cs.ai cs.lg deep learning events forecast global global warming numerical numerical weather prediction nwp paper precipitation prediction pre-training processor rainfall training type weather weather prediction