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    Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements

    Access Status
    Open access via publisher
    Authors
    Ren, Diandong
    Xue, M.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Ren, D. and Xue, M. 2016. Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements. Advances in Meteorology. 1905076.
    Source Title
    Advances in Meteorology
    DOI
    10.1155/2016/1905076
    ISSN
    1687-9309
    School
    Department of Physics and Astronomy
    URI
    http://hdl.handle.net/20.500.11937/9170
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 Diandong Ren and Ming Xue. This study demonstrates successful variational retrieval of land surface states by assimilating screen level atmospheric measurements of specific humidity and air temperature. To this end, the land surface scheme is first validated against the Oklahoma Atmospheric Surface Layer Instrumentation System measurements with necessary refinements to the forward model implemented. The retrieval scheme involves a 1D land surface-Atmosphere model, the corresponding adjoint codes, and a cost function that measures residuals between observed and modeled screen level atmospheric temperature and specific humidity. The retrieval scheme is robust when subjected to observational errors with magnitudes comparable to instrument accuracy and for initial guess errors larger than typical model forecast errors. Using varying assimilation window lengths centered on different periods of a day, the sampling strategy is assessed. The daytime observations are more informative compared to nocturnal observations. An assimilation window as narrow as four hours, if centered on local noon, contains comparable information to an expanded window covering the whole day. There exists an optimal assimilation window length resulting from the contest between degrading forecast accuracy and increasing information content. For an assimilation window less than two days, the "optimal" assimilation window length is inversely proportional to the data ingesting frequency.

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