A Posteriori Learning for Quasi‐Geostrophic Turbulence Parametrization
Abstract The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution mode...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2022-11-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022MS003124 |