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    Original Research Papers

    Initiation of ensemble data assimilation

    Authors:

    M. Zupanski ,

    Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, US
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    S. J. Fletcher,

    Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, US
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    I. M. Navon,

    Department of Mathematics and School of Computational Science and Information Technology, Florida State University, Tallahassee, FL, US
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    B. Uzunoglu,

    School of Computational Science and Information Technology, Florida State University, Tallahassee, FL, US
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    R. P. Heikes,

    Department of Atmospheric Science, Colorado State University, Fort Collins, CO, US
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    D. A. Randall,

    Department of Atmospheric Science, Colorado State University, Fort Collins, CO, US
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    T. D. Ringler,

    Department of Atmospheric Science, Colorado State University, Fort Collins, CO, US
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    D. Daescu

    Department of Mathematics and Statistics, Portland State University, Portland, OR, US
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    Abstract

    The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are considered: the first is based on the use of the Kardar-Parisi-Zhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, while the second is based on the spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallow-water model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in ensemble data assimilation is beneficial. The root-mean-square error rate of convergence of the data assimilation is improved, and the positive impact of initial correlations is notable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of ensemble data assimilation.

    How to Cite: Zupanski, M., Fletcher, S.J., Navon, I.M., Uzunoglu, B., Heikes, R.P., Randall, D.A., Ringler, T.D. and Daescu, D., 2006. Initiation of ensemble data assimilation. Tellus A: Dynamic Meteorology and Oceanography, 58(2), pp.159–170. DOI: http://doi.org/10.1111/j.1600-0870.2006.00173.x
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      Published on 01 Jan 2006
    Peer Reviewed
     CC BY 4.0
     Accepted on 31 Oct 2005            Submitted on 1 Feb 2005

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