Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection
Abstract Using machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surface temperature and precipitation rate. However, ML corrections often introduce new biases in the upper atmosphere and causes inconsistent model...
| Autori principali: | , , , , , , |
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| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
American Geophysical Union (AGU)
2023-11-01
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| Serie: | Journal of Advances in Modeling Earth Systems |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1029/2023MS003809 |