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Volume 22 Issue 6

Nov.  2005

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(6): 936-945

Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF)

  • 1.

    School of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,School of Physical Sciences and Technology, Lanzhou University, Lanzhou 730000,School of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000


doi: 10.1007/BF02918692

  • The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kalman filter (EnKF) assimilation scheme,including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The "true"soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.
  • [1] ZHANG Shuwen, LI Deqin, QIU Chongjian, 2011: A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 195-206.  doi: 10.1007/s00376-010-9200-6
    [2] ZHENG Xiaogu, WU Guocan, ZHANG Shupeng, LIANG Xiao, DAI Yongjiu, LI Yong, , 2013: Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1303-1312.  doi: 10.1007/s00376-012-2133-5
    [3] LIU Zhengyu, WU Shu, ZHANG Shaoqing, LIU Yun, RONG Xinyao, , 2013: Ensemble Data Assimilation in a Simple Coupled Climate Model: The Role of Ocean-Atmosphere Interaction, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1235-1248.  doi: 10.1007/s00376-013-2268-z
    [4] Fuqing ZHANG, Meng ZHANG, James A. HANSEN, 2009: Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1-8.  doi: 10.1007/s00376-009-0001-8
    [5] Youmin TANG, Jaison AMBANDAN, Dake CHEN, , , 2014: Nonlinear Measurement Function in the Ensemble Kalman Filter, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 551-558.  doi: 10.1007/s00376-013-3117-9
    [6] Jian YUE, Zhiyong MENG, Cheng-Ku YU, Lin-Wen CHENG, 2017: Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot (2009) Using WRF-based Ensemble Kalman Filter Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 66-78.  doi: 10.1007/s00376-016-6028-8
    [7] 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
    [8] 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
    [9] 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
    [10] Lili LEI, Yangjinxi GE, Zhe-Min TAN, Yi ZHANG, Kekuan CHU, Xin QIU, Qifeng QIAN, 2022: Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1816-1832.  doi: 10.1007/s00376-022-1444-4
    [11] Yong LI, Siming LI, Yao SHENG, Luheng WANG, 2018: Data Assimilation Method Based on the Constraints of Confidence Region, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 334-345.  doi: 10.1007/s00376-017-7045-y
    [12] Xinrong WU, Shaoqing ZHANG, Zhengyu LIU, 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 193-207.  doi: 10.1007/s00376-015-5099-2
    [13] Zhaoxia PU, Joshua HACKER, 2009: Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 373-380.  doi: 10.1007/s00376-009-0373-9
    [14] 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
    [15] Minwei Qian, N. Loglisci, C. Cassardo, A. Longhetto, C. Giraud, 2001: Energy and Water Balance at Soil-Air Interface in a Sahelian Region, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 897-909.
    [16] NIE Suping, LUO Yong, ZHU Jiang, 2008: Trends and Scales of Observed Soil Moisture Variations in China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 43-58.  doi: 10.1007/s00376-008-0043-3
    [17] Changyu ZHAO, Haishan CHEN, Shanlei SUN, 2018: Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 445-456.  doi: 10.1007/s00376-017-7006-5
    [18] 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
    [19] 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
    [20] WU Lingyun, ZHANG Jingyong, 2015: The Relationship between Spring Soil Moisture and Summer Hot Extremes over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1660-1668.  doi: 10.1007/s00376-015-5003-0
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    • Manuscript History

    Manuscript received: 10 November 2005
    Manuscript revised: 10 November 2005
    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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    Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF)

    • 1. School of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,School of Physical Sciences and Technology, Lanzhou University, Lanzhou 730000,School of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Lanzhou 730020,Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000
    • Manuscript received: 2005-11-10
    • Manuscript revised: 2005-11-10

    Abstract: The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kalman filter (EnKF) assimilation scheme,including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The "true"soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

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