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    Accounting for Model Error in Variational Data Assimilation: A deterministic formulation

    Authors
    Carrassi, A.
    Vannitsem, S.
    Discipline
    Earth and related Environmental sciences
    Subject
    Data Assimilation
    Variational analysis
    Model errors
    Audience
    General Public
    Scientific
    Date
    2010
    Publisher
    IRM
    KMI
    RMI
    Metadata
    Show full item record
    Description
    In data assimilation, observations are combined with the dynamics to get an estimate of the actual state of a natural system. The knowledge of the dynamics, under the form of a model, is unavoidably incomplete and model error affects the prediction accuracy together with the error in the initial condition. The variational assimilation theory provides a framework to deal with model error along with the uncertainties coming from other sources entering the state estimation. Nevertheless, even if the problem is formulated as Gaussian, accounting for model error requires the estimation of its covariances and correlations, which are difficult to estimate in practice, in particular because of the large system dimension and the lack of enough observations. Model error has been therefore either neglected or assumed to be an uncorrelated noise. In the present work, an approach to account for a deterministic model error in the variational assimilation is presented. Equations for its correlations are first derived along with an approximation suitable for practical applications. Based on these considerations, a new four-dimensional variational data assimilation (4DVar) weak-constraint algorithm is formulated and tested in the context of a linear unstable system and of the three-component Lorenz model, which has chaotic dynamics. The results demonstrate that this approach is superior in skill to both the strong-constraint and a weak-constraint variational assimilation that employs the uncorrelated noise model error assumption.
    Citation
    Carrassi, A.; Vannitsem, S. (2010). Accounting for Model Error in Variational Data Assimilation: A deterministic formulation. , Issue Monthly Weather Review - 138, 3369-3386, IRM,
    Identifiers
    uri: https://orfeo.belnet.be/handle/internal/8876
    Type
    Article
    Peer-Review
    Not pertinent
    Language
    eng
    Links
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