Projets par an
Résumé
We propose to use a decomposition of large-scale incremental four
dimensional (4D-Var) data assimilation problems in order to make their
numerical solution more efficient. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the solution of its corresponding optimization problem, observations are adaptively added based on a posteriori error estimates. The particular structure of the sequence of associated linear systems allows the use of a variant of the conjugate gradient algorithm which effectively exploits the fact that the number of observations is smaller than the size of the vector state in the 4D-Var model. The method proposed is justified by deriving the relevant error estimates at different levels of the hierarchy and a practical computational technique is then derived. The new algorithm is tested on a 1D-wave equation and on the Lorenz-96 system, the latter one being of special interest because of its similarity with Numerical Weather Prediction (NWP) systems.
dimensional (4D-Var) data assimilation problems in order to make their
numerical solution more efficient. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the solution of its corresponding optimization problem, observations are adaptively added based on a posteriori error estimates. The particular structure of the sequence of associated linear systems allows the use of a variant of the conjugate gradient algorithm which effectively exploits the fact that the number of observations is smaller than the size of the vector state in the 4D-Var model. The method proposed is justified by deriving the relevant error estimates at different levels of the hierarchy and a practical computational technique is then derived. The new algorithm is tested on a 1D-wave equation and on the Lorenz-96 system, the latter one being of special interest because of its similarity with Numerical Weather Prediction (NWP) systems.
langue originale | Anglais |
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Lieu de publication | Namur |
Éditeur | Namur center for complex systems |
Nombre de pages | 18 |
Volume | NTR-06-2013 |
Etat de la publication | Publié - 2013 |
Empreinte digitale Examiner les sujets de recherche de « Adaptive Observations And Multilevel Optimization In Data Assimilation ». Ensemble, ils forment une empreinte digitale unique.
Projets
- 2 Actif
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Développements de nouvelles méthodes d'optimisation pour l'assimilation de données en océanographie
SARTENAER, A., LALOYAUX, P., TOINT, P., Tshimanga Ilunga, J. & Gürol, S.
1/09/07 → …
Projet: Projet de thèse
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ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
1/01/87 → …
Projet: Axe de recherche
Activités
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Institut National Polytechnique de Toulouse
Philippe Toint (Chercheur visiteur)
2017 → 2019Activité: Types de Visite d'une organisation externe › Recherche/Enseignement dans une institution externe
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Data Assimilation for Weather Forecasting: Reducing the Curse of Dimensionality
Philippe Toint (Orateur invité)
1 déc. 2015Activité: Types de discours ou de présentation › Présentation orale
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Rapporteur de la thèse de E. Bergou
Philippe Toint (Examinateur)
11 déc. 2014Activité: Types d'examen › Thèse externe