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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-04-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2427/2025/gmd-18-2427-2025.pdf |
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