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    Original Research Papers

    The maximum likelihood ensemble filter performances in chaotic systems

    Authors:

    Alberto Carrassi ,

    Institut Royal Météorologique de Belgique, Av. Circulaire 3, 1180 Brussels, BE
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    Stephane Vannitsem,

    Institut Royal Météorologique de Belgique, Av. Circulaire 3, 1180 Brussels, BE
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    Dusanka Zupanski,

    Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, US
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    Milija Zupanski

    Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, US
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    Abstract

    The performance of the maximum likelihood ensemble filter (MLEF), is investigated in the context of generic systems featuring the essential ingredients of unstable dynamics and on a spatially extended system displaying chaos. The main objective is to clarify the response of the filter to different regimes of motion and highlighting features which may help its optimization in more realistic applications. It is found that, in view of the minimization procedure involved in the filter analysis update, the algorithm provides accurate estimates even in the presence of prominent non-linearities. Most importantly, the filter ensemble size can be designed in connection to the properties of the system attractor (Kaplan—Yorke dimension), thus facilitating the filter setup and limiting the computational cost by using an optimal ensemble. As a corollary, this latter finding indicates that the ensemble perturbations in the MLEF reflect the intrinsic system error dynamics rather than a sampling of realizations of an unknown error covariance.

    How to Cite: Carrassi, A., Vannitsem, S., Zupanski, D. and Zupanski, M., 2009. The maximum likelihood ensemble filter performances in chaotic systems. Tellus A: Dynamic Meteorology and Oceanography, 61(5), pp.587–600. DOI: http://doi.org/10.1111/j.1600-0870.2009.00408.x
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      Published on 01 Jan 2009
    Peer Reviewed
     CC BY 4.0
     Accepted on 3 Jun 2008            Submitted on 9 Jan 2009

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