Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Abstract H L E) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Improving regional climate simulations based on a hybrid data assimilation and machine learning method ; volume:27 ; number:7 ; year:2023 ; pages:1583-1606 ; extent:24 ; Hydrology and earth system sciences ; 27, Heft 7 (2023), 1583-1606 (gesamt 24)

Contributors, Places and Time

• Creator: He, Xinlei ; Li, Yanping ; Liu, Shaomin ; Xu, Tongren ; Chen, Fei ; Li, Zhenhua ; Zhang, Zhe ; Liu, Rui ; Song, Lisheng ; Xu, Ziwei ; Peng, Zhixing ; Zheng, Chen

Further information

• DOI: 10.5194/hess-27-1583-2023
• URN: urn:nbn:de:101:1-2023042005204776453264
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:45 AM CEST