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    Revising the Ensemble-Based Kalman Filter Covariance for the Retrieval of Deep-Layer Soil Moisture

    Source: Journal of Hydrometeorology:;2010:;Volume( 011 ):;issue: 001::page 219
    Author:
    Zhang, Shu-Wen
    ,
    Zeng, Xubin
    ,
    Zhang, Weidong
    ,
    Barlage, Michael
    DOI: 10.1175/2009JHM1146.1
    Publisher: American Meteorological Society
    Abstract: Previous studies have demonstrated that soil moisture in the top layers (e.g., within the top 1-m depth) can be retrieved by assimilating near-surface soil moisture observations into a land surface model using ensemble-based data assimilation algorithms. However, it remains a challenging issue to provide good estimates of soil moisture in the deep layers, because the error correlation between the surface and deep layers is low and hence is easily influenced by the physically limited range of soil moisture, probably resulting in a large noise-to-signal ratio. Furthermore, the temporally correlated errors between the surface and deep layers and the nonlinearity of the system make the retrieval even more difficult. To tackle these problems, a revised ensemble-based Kalman filter covariance method is proposed by constraining error covariance estimates in deep layers in two ways: 1) explicitly using the error covariance at the previous time step and 2) limiting the increase of the soil moisture error correlation with the increase of the vertical distance between the two layers. This method is then tested at three separate point locations representing different precipitation regimes. It is found that the proposed method can effectively control the abrupt changes of error covariance estimates between the surface layer and two deep layers. It significantly improves the estimates of soil moisture in the two deep layers with daily updating. For example, relative to the initial background error, after 150 daily updates, the error in the deepest layer reduces to 11.4%, 32.3%, and 27.1% at the wet, dry, and medium wetness locations, only reducing to 62.3%, 80.8%, and 47.5% with the original method, respectively. However, the improvement of deep-layer soil moisture retrieval is very slight when the updating frequency is reduced to once every three days.
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      Revising the Ensemble-Based Kalman Filter Covariance for the Retrieval of Deep-Layer Soil Moisture

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4210685
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    • Journal of Hydrometeorology

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    contributor authorZhang, Shu-Wen
    contributor authorZeng, Xubin
    contributor authorZhang, Weidong
    contributor authorBarlage, Michael
    date accessioned2017-06-09T16:30:16Z
    date available2017-06-09T16:30:16Z
    date copyright2010/02/01
    date issued2010
    identifier issn1525-755X
    identifier otherams-69058.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4210685
    description abstractPrevious studies have demonstrated that soil moisture in the top layers (e.g., within the top 1-m depth) can be retrieved by assimilating near-surface soil moisture observations into a land surface model using ensemble-based data assimilation algorithms. However, it remains a challenging issue to provide good estimates of soil moisture in the deep layers, because the error correlation between the surface and deep layers is low and hence is easily influenced by the physically limited range of soil moisture, probably resulting in a large noise-to-signal ratio. Furthermore, the temporally correlated errors between the surface and deep layers and the nonlinearity of the system make the retrieval even more difficult. To tackle these problems, a revised ensemble-based Kalman filter covariance method is proposed by constraining error covariance estimates in deep layers in two ways: 1) explicitly using the error covariance at the previous time step and 2) limiting the increase of the soil moisture error correlation with the increase of the vertical distance between the two layers. This method is then tested at three separate point locations representing different precipitation regimes. It is found that the proposed method can effectively control the abrupt changes of error covariance estimates between the surface layer and two deep layers. It significantly improves the estimates of soil moisture in the two deep layers with daily updating. For example, relative to the initial background error, after 150 daily updates, the error in the deepest layer reduces to 11.4%, 32.3%, and 27.1% at the wet, dry, and medium wetness locations, only reducing to 62.3%, 80.8%, and 47.5% with the original method, respectively. However, the improvement of deep-layer soil moisture retrieval is very slight when the updating frequency is reduced to once every three days.
    publisherAmerican Meteorological Society
    titleRevising the Ensemble-Based Kalman Filter Covariance for the Retrieval of Deep-Layer Soil Moisture
    typeJournal Paper
    journal volume11
    journal issue1
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/2009JHM1146.1
    journal fristpage219
    journal lastpage227
    treeJournal of Hydrometeorology:;2010:;Volume( 011 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian