Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz’96 Model
Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches requi...
| Hoofdauteurs: | , , , |
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| Formaat: | Journal article |
| Taal: | English |
| Gepubliceerd in: |
American Geophysical Union
2020
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