WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
<p>In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that machine learning (ML) parameterizations of subgrid phy...
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| Автори: | , , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Copernicus Publications
2023-01-01
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| Серія: | Geoscientific Model Development |
| Онлайн доступ: | https://gmd.copernicus.org/articles/16/199/2023/gmd-16-199-2023.pdf |
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