Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
oleh: Peter B. Gibson, William E. Chapman, Alphan Altinok, Luca Delle Monache, Michael J. DeFlorio, Duane E. Waliser
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2021-08-01 |
Deskripsi
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.