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Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation

文献类型：期刊论文

作者Tian, Jiaxin5; Lu, Hui1,5; Yang, Kun4,5; Qin, Jun3; Zhao, Long2; Zhou, Jianhong5; Jiang, Yaozhi5; Ma, Xiaogang5
刊名SCIENCE CHINA-EARTH SCIENCES
出版日期2023-11-01
卷号66期号:11页码:2546-2562
ISSN号1674-7313
关键词Soil moisture Data assimilation Parameter optimization Bias correction Error estimation Automatic differentiation
DOI10.1007/s11430-022-1180-8
通讯作者Lu, Hui(luhui@tsinghua.edu.cn)
英文摘要Soil moisture plays a crucial role in drought monitoring, flood forecasting, and water resource management. Data assimilation methods can integrate the strengths of land surface models (LSM) and remote sensing data to generate high-precision and spatio-temporally continuous soil moisture products. However, one of the challenges of the land data assimilation system (LDAS) is how to accurately estimate model and observation errors. To address this, we had previously proposed a dual-cycle assimilation algorithm that can simultaneously estimate the model and observation errors, LSM parameters, and observation operator parameters. However, this algorithm requires a large ensemble size to guarantee stable parameter estimates, resulting in low efficiency and limiting its large-scale applications. To address this limitation, the authors employed the following approaches: (1) using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM; and (2) replacing the ensemble Kalman filter framework with the extended Kalman filter (EKF) framework to improve the efficiency of parameter optimization for the dual-cycle algorithm. The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h. To evaluate the dual-cycle LDAS at the regional-scale, it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau, and soil moisture estimates were validated using in situ observations from four different climatic areas. The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture, land surface temperature, and evapotranspiration than did the open loop with default parameters. Furthermore, the spatial distribution of soil parameters (sand content, clay content, and porosity) obtained from the LDAS was more reasonable than those of default values. The EKF-based dual-cycle algorithm developed in this study is expected to improve the assimilation skills of land surface, ecological, and hydrological studies.
WOS关键词SOIL-MOISTURE RETRIEVALS ; TIBETAN PLATEAU ; MODEL-ERROR ; SMAP ; IMPLEMENTATION ; CALIBRATION ; SATELLITE ; SMOS ; OBS
资助项目AcknowledgementsThis work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0206), the National Key Research and Development Program of China (Grant No. 2022YFC3002901), the Natio[2019QZKK0206] ; Second Tibetan Plateau Scientific Expedition and Research Program[2022YFC3002901] ; National Key Research and Development Program of China[42271491] ; National Natural Science Foundation of China[183311KYSB20200015] ; International Partnership Program of Chinese Academy of Sciences
WOS研究方向Geology
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:001088800900001
资助机构AcknowledgementsThis work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0206), the National Key Research and Development Program of China (Grant No. 2022YFC3002901), the Natio ; Second Tibetan Plateau Scientific Expedition and Research Program ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; International Partnership Program of Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/199151]  
专题中国科学院地理科学与资源研究所
通讯作者Lu, Hui
作者单位1.Tsinghua Univ, Xian Inst Surveying & Mapping Joint Res Ctr Next G, Dept Earth Syst Sci, Beijing 100084, Peoples R China
2.South west Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr, Key Lab Tibetan Environm Changes & Land Surfaces P, Beijing 100101, Peoples R China
5.Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ,Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Jiaxin,Lu, Hui,Yang, Kun,et al. Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation[J]. SCIENCE CHINA-EARTH SCIENCES,2023,66(11):2546-2562.
APA Tian, Jiaxin.,Lu, Hui.,Yang, Kun.,Qin, Jun.,Zhao, Long.,...&Ma, Xiaogang.(2023).Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation.SCIENCE CHINA-EARTH SCIENCES,66(11),2546-2562.
MLA Tian, Jiaxin,et al."Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation".SCIENCE CHINA-EARTH SCIENCES 66.11(2023):2546-2562.

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