Significant advancement in subseasonal-to-seasonal summer precipitation ensemble forecast skills in China mainland through an innovative hybrid CSG-UNET method

Significant advancement in subseasonal-to-seasonal summer precipitation ensemble forecast skills in China mainland through an innovative hybrid CSG-UNET method

Reliable Subseasonal-to-Seasonal (S2S) forecasts of precipitation are critical for disaster prevention and mitigation. In this study, an innovative hybrid method CSG-UNET combining the UNET with the censored and shifted gamma distribution based ensemble model output statistic (CSG-EMOS), is proposed...

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Detalles Bibliográficos
Autores principales: Yang Lyu, Shoupeng Zhu, Xiefei Zhi, Jingyu Wang, Yan Ji, Yi Fan, Fu Dong
Formato: Artículo
Lenguaje:English
Publicado: IOP Publishing 2024-01-01
Colección:Environmental Research Letters
Materias:
subseasonal precipitation forecast
deep learning method
extreme precipitation
probabilistic precipitation forecast
Acceso en línea:https://doi.org/10.1088/1748-9326/ad5577

Internet

https://doi.org/10.1088/1748-9326/ad5577

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