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

文献类型：期刊论文

作者He, Xinlei1; Li, Yanping6; Liu, Shaomin1; Xu, Tongren1; Chen, Fei3; Li, Zhenhua6; Zhang, Zhe6; Liu, Rui5; Song, Lisheng2; Xu, Ziwei1
刊名HYDROLOGY AND EARTH SYSTEM SCIENCES
出版日期2023-04-17
卷号27期号:7页码:1583-1606
ISSN号1027-5606
DOI10.5194/hess-27-1583-2023
通讯作者Liu, Shaomin(smliu@bnu.edu.cn) ; Xu, Ziwei(xuzw@bnu.edu.cn)
英文摘要The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land-atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) 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.
WOS关键词LAND DATA ASSIMILATION ; HEIHE RIVER-BASIN ; LEAF-AREA INDEX ; ATMOSPHERE INTERACTIONS ; QILIAN MOUNTAINS ; SOIL-MOISTURE ; WATER-VAPOR ; MODEL ; SYSTEM ; SNOW
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20100101] ; National Natural Science Foundation of China[42171315]
WOS研究方向Geology ; Water Resources
语种英语
WOS记录号WOS:000973206000001
出版者COPERNICUS GESELLSCHAFT MBH
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/197006]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Shaomin; Xu, Ziwei
作者单位1.Beijing Normal Univ, Fac Geog Sci, Sch Nat Resources, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
2.Anhui Normal Univ, Sch Geog & Tourism, Wuhu, Peoples R China
3.Natl Ctr Atmospher Res, Boulder, CO USA
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
5.Shanghai Normal Univ, Inst Urban Study, Sch Environm & Geog Sci SEGS, Shanghai, Peoples R China
6.Univ Saskatchewan, Sch Environm & Sustainabil, Saskatoon, SK, Canada
推荐引用方式
GB/T 7714		 He, Xinlei,Li, Yanping,Liu, Shaomin,et al. Improving regional climate simulations based on a hybrid data assimilation and machine learning method[J]. HYDROLOGY AND EARTH SYSTEM SCIENCES,2023,27(7):1583-1606.
APA He, Xinlei.,Li, Yanping.,Liu, Shaomin.,Xu, Tongren.,Chen, Fei.,...&Zheng, Chen.(2023).Improving regional climate simulations based on a hybrid data assimilation and machine learning method.HYDROLOGY AND EARTH SYSTEM SCIENCES,27(7),1583-1606.
MLA He, Xinlei,et al."Improving regional climate simulations based on a hybrid data assimilation and machine learning method".HYDROLOGY AND EARTH SYSTEM SCIENCES 27.7(2023):1583-1606.

入库方式： OAI收割

来源：地理科学与资源研究所

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