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

Serge Gratton; Selime Gürol; Ehouarn Simon; Philippe Toint

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

Serge Gratton, Selime Gürol, Ehouarn Simon, Philippe Toint

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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.

langue originale	Anglais
Éditeur	Arxiv
Volume	1709.06383
Etat de la publication	Soumis - 20 sept. 2017

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ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

SARTENAER, A. & TOINT, P.

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A primal-dual approach of weak-constrained variational data assimilation: (Iterate) History matters

Philippe Toint (Orateur)

13 oct. 2017

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Contient cette citation

Gratton, S., Gürol, S., Simon, E., & Toint, P. (2017). On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation. Arxiv.

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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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AU - Toint, Philippe

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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

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

PB - Arxiv

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