Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning
Abstract To make weather and climate models computationally affordable, small‐scale processes are usually represented in terms of the large‐scale, explicitly resolved processes using physics‐based/semi‐empirical parameterization schemes. Another approach, computationally more demanding but often mor...
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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
American Geophysical Union (AGU)
2020-11-01
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| Серія: | Journal of Advances in Modeling Earth Systems |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1029/2020MS002084 |
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