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Volume 28 Issue 1

Jan.  2011

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2011 >  28(1): 195-206

A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

  • 1.

    Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000


doi: 10.1007/s00376-010-9200-6

  • With the combination of three land surface models (LSMs) and the ensemble Kalman filter (EnKF), a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms: the Simple Model Average (SMA) and the Weighted Average Method (WAM). The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.
  • [1] ZHANG Shuwen, LI Haorui, ZHANG Weidong, QIU Chongjian, LI Xin, 2005: Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF), ADVANCES IN ATMOSPHERIC SCIENCES, 22, 936-945.  doi: 10.1007/BF02918692
    [2] Rong KONG, Ming XUE, Edward R. MANSELL, Chengsi LIU, Alexandre O. FIERRO, 2024: Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 263-277.  doi: 10.1007/s00376-023-2340-2
    [3] XIE Zhenghui, SU Fengge, LIANG Xu, ZENG Qingcun, HAO Zhenchun, GUO Yufu, 2003: Applications of a Surface Runoff Model with Horton and Dunne Runoff for VIC, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 165-172.  doi: 10.1007/s00376-003-0001-z
    [4] Binghao JIA, Longhuan WANG, Yan WANG, Ruichao LI, Xin LUO, Jinbo XIE, Zhenghui XIE, Si CHEN, Peihua QIN, Lijuan LI, Kangjun CHEN, 2021: CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 862-874.  doi: 10.1007/s00376-021-0293-x
    [5] DAN Li, JI Jinjun, LIU Huizhi, 2008: Use of a Land Surface Model to Evaluate the Observed Soil Moisture of Grassland at the Tongyu Reference Site, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 1073-1084.  doi: 10.1007/s00376-008-1073-6
    [6] Guo Weidong, Sun Shufen, Qian Yongfu, 2002: Case Analyses and Numerical Simulation of Soil Thermal Impacts on Land Surface Energy Budget Based on an Off-Line Land Surface Model, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 500-512.  doi: 10.1007/s00376-002-0082-0
    [7] Fuqiang YANG, Li DAN, Jing PENG, Xiujing YANG, Yueyue LI, Dongdong GAO, 2019: Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 79-92.  doi: 10.1007/s00376-018-8035-4
    [8] GUAN Xiaodan, HUANG Jianping, GUO Ni, BI Jianrong, WANG Guoyin, 2009: Variability of Soil Moisture and Its Relationship with Surface Albedo and Soil Thermal Parameters over the Loess Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 692-700.  doi: 10.1007/s00376-009-8198-0
    [9] Jianguo LIU, Zong-Liang YANG, Binghao JIA, Longhuan WANG, Ping WANG, Zhenghui XIE, Chunxiang SHI, 2023: Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 235-250.  doi: 10.1007/s00376-022-2091-5
    [10] LI Fang, ZENG Xiaodong, SONG Xiang, TIAN Dongxiao, SHAO Pu, ZHANG Dongling, 2011: Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupled with a Land Surface Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 775-788.  doi: 10.1007/s00376-010-0009-0
    [11] LI Hongqi, GUO Weidong, SUN Guodong, ZHANG Yaocun, FU Congbin, 2011: A New Approach for Parameter Optimization in Land Surface Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1056-1066.  doi: 10.1007/s00376-010-0050-z
    [12] LIANG Miaoling, XIE Zhenghui, 2008: Improving the Vegetation Dynamic Simulation in a Land Surface Model by Using a Statistical-dynamic Canopy Interception Scheme, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 610-618.  doi: 10.1007/s00376-008-0610-7
    [13] LI Tao, ZHENG Xiaogu, DAI Yongjiu, YANG Chi, CHEN Zhuoqi, ZHANG Shupeng, WU Guocan, WANG Zhonglei, HUANG Chengcheng, SHEN Yan, LIAO Rongwei, 2014: Mapping Near-surface Air Temperature, Pressure, Relative Humidity and Wind Speed over Mainland China with High Spatiotemporal Resolution, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1127-1135.  doi: 10.1007/s00376-014-3190-8
    [14] CHEN Feng, XIE Zhenghui, 2011: Effects of Crop Growth and Development on Land Surface Fluxes, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 927-944.  doi: 10.1007/s00376-010-0105-1
    [15] DAN Li, JI Jinjun, ZHANG Peiqun, 2005: The Soil Moisture of China in a High Resolution Climate-Vegetation Model, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 720-729.  doi: 10.1007/BF02918715
    [16] LIU Huizhi, WANG Baomin, FU Congbin, 2008: Relationships Between Surface Albedo, Soil Thermal Parameters and Soil Moisture in the Semi-arid Area of Tongyu, Northeastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 757-764.  doi: 10.1007/s00376-008-0757-2
    [17] Enda ZHU, Xing YUAN, 2021: Global Freshwater Storage Capability across Time Scales in the GRACE Satellite Era, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 905-917.  doi: 10.1007/s00376-021-0222-z
    [18] Dai Yongjiu, Zeng Qingcun, 1997: A Land Surface Model (IAP94) for Climate Studies Part I: Formulation and Validation in Off-line Experiments, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 433-460.  doi: 10.1007/s00376-997-0063-4
    [19] LI Mingxing, MA Zhuguo, 2010: Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1289-1302.  doi: 10.1007/s00376-010-9155-7
    [20] Dai Yongjiu, Xue Feng, Zeng Qingcun, 1998: A Land Surface Model (IAP94) for Climate Studies Part II: Implementation and Preliminary Results of Coupled Model with IAP GCM, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 47-62.  doi: 10.1007/s00376-998-0017-5
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    • Manuscript History

    Manuscript received: 10 January 2011
    Manuscript revised: 10 January 2011
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

    • 1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000
    • Manuscript received: 2011-01-10
    • Manuscript revised: 2011-01-10

    Abstract: With the combination of three land surface models (LSMs) and the ensemble Kalman filter (EnKF), a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms: the Simple Model Average (SMA) and the Weighted Average Method (WAM). The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.

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