Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we...
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| Veröffentlicht in: | Journal of advances in modeling earth systems Jg. 14; H. 8; S. e2022MS003130 - n/a |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
John Wiley & Sons, Inc
01.08.2022
American Geophysical Union (AGU) |
| Schlagworte: | |
| ISSN: | 1942-2466, 1942-2466 |
| Online-Zugang: | Volltext |
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