Feb. 13, 2024, 5:43 a.m. | Zhanxiang Hua Yutong He Chengqian Ma Alexandra Anderson-Frey

cs.LG updates on arXiv.org arxiv.org

Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions, leading to reliability concerns due to potential unphysical predictions. In response, we introduce a novel method that applies diffusion models (DM) for weather forecasting. In particular, our method can achieve both direct and iterative …

concerns cs.ai cs.lg deep learning diffusion domain face flexibility forecast forecasting limitations numerical numerical weather prediction nwp performance physics.ao-ph prediction predictions processes reliability training weather weather forecasting weather prediction

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