TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions

<p>Numerical forecast products with high temporal resolution are crucial tools in atmospheric studies, allowing for accurate identification of rapid transitions and subtle changes that may be missed by lower-resolution data. However, the acquisition of high-resolution data is limited due to ex...

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Bibliographic Details
Main Authors: L. Wang, Q. Li, Q. Lv, X. Peng, W. You
Format: Article
Language:English
Published: Copernicus Publications 2025-04-01
Series:Geoscientific Model Development
Online Access:https://gmd.copernicus.org/articles/18/2427/2025/gmd-18-2427-2025.pdf
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