Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.
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| Principais autores: | , , , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
Nature Portfolio
2021-08-01
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| Serier: | Communications Earth & Environment |
| Online adgang: | https://doi.org/10.1038/s43247-021-00225-4 |
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