Probabilistic Forecasting of Summer Wind Speed in China Using Multimodel Ensembles
Abstract Wind has a crucial impact on human socio‐economic activities as well as the safety of life and property. Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), are utilized to enhance the probabilistic forecasting skills for 10 m wind speed during the summer in China. A...
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| Главные авторы: | , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
American Geophysical Union (AGU)
2025-04-01
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| Серии: | Earth and Space Science |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1029/2024EA003850 |
| Метки: |
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