On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation

Serge Gratton, Selime Gürol, Ehouarn Simon, Philippe Toint

Résultats de recherche: Papier de travailArticle de travail

Résumé

This paper discusses the practical use of the saddle variational formulation
for the weakly-constrained 4D-VAR method in data assimilation. It is shown
that the method, in its original form, may produce erratic results or diverge
because of the inherent lack of monotonicity of the produced objective
function values. Convergent, variationaly coherent variants of the algorithm
are then proposed whose practical performance is compared to that of other
formulations. This comparison is conducted on two data assimilation instances
(Burgers equation and the Quasi-Geostrophic model), using two different
assumptions on parallel computing environment. Because these
variants essentially retain the parallelization advantages of the original
proposal, they often --- but not always --- perform best, even for moderate
numbers of computing processes.
langueAnglais
EditeurArxiv
Volume1709.06383
étatSoumis - 20 sept. 2017

Empreinte digitale

Data Assimilation
Saddle
Parallel processing systems
data assimilation
parallel computing
Formulation
erratic
Parallel Computing
Burgers Equation
Parallelization
Monotonicity
Computing
method
Model
Form
comparison

mots-clés

    Citer ceci

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    abstract = "This paper discusses the practical use of the saddle variational formulationfor the weakly-constrained 4D-VAR method in data assimilation. It is shownthat the method, in its original form, may produce erratic results or divergebecause of the inherent lack of monotonicity of the produced objectivefunction values. Convergent, variationaly coherent variants of the algorithmare then proposed whose practical performance is compared to that of otherformulations. This comparison is conducted on two data assimilation instances(Burgers equation and the Quasi-Geostrophic model), using two differentassumptions on parallel computing environment. Because thesevariants essentially retain the parallelization advantages of the originalproposal, they often --- but not always --- perform best, even for moderatenumbers of computing processes.",
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    On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation. / Gratton, Serge; Gürol, Selime; Simon, Ehouarn; Toint, Philippe.

    Arxiv, 2017.

    Résultats de recherche: Papier de travailArticle de travail

    TY - UNPB

    T1 - On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation

    AU - Gratton, Serge

    AU - Gürol, Selime

    AU - Simon, Ehouarn

    AU - Toint, Philippe

    PY - 2017/9/20

    Y1 - 2017/9/20

    N2 - This paper discusses the practical use of the saddle variational formulationfor the weakly-constrained 4D-VAR method in data assimilation. It is shownthat the method, in its original form, may produce erratic results or divergebecause of the inherent lack of monotonicity of the produced objectivefunction values. Convergent, variationaly coherent variants of the algorithmare then proposed whose practical performance is compared to that of otherformulations. This comparison is conducted on two data assimilation instances(Burgers equation and the Quasi-Geostrophic model), using two differentassumptions on parallel computing environment. Because thesevariants essentially retain the parallelization advantages of the originalproposal, they often --- but not always --- perform best, even for moderatenumbers of computing processes.

    AB - This paper discusses the practical use of the saddle variational formulationfor the weakly-constrained 4D-VAR method in data assimilation. It is shownthat the method, in its original form, may produce erratic results or divergebecause of the inherent lack of monotonicity of the produced objectivefunction values. Convergent, variationaly coherent variants of the algorithmare then proposed whose practical performance is compared to that of otherformulations. This comparison is conducted on two data assimilation instances(Burgers equation and the Quasi-Geostrophic model), using two differentassumptions on parallel computing environment. Because thesevariants essentially retain the parallelization advantages of the originalproposal, they often --- but not always --- perform best, even for moderatenumbers of computing processes.

    KW - data assimilation

    KW - weather forecasting

    KW - Numerical optimization

    KW - Least-squares problems

    M3 - Working paper

    VL - 1709.06383

    BT - On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation

    PB - Arxiv

    ER -