Observation thinning in data assimilation computations

We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the sol...

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Autores principales:	Serge Gratton, Monserrat Rincon-Camacho, Ehouarn Simon, Philippe L. Toint
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2015
Materias:	90C06 90C90 65F08 93E24 93E11 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/62a298c003384283901627d22f1dae46
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spelling	oai:doaj.org-article:62a298c003384283901627d22f1dae462021-12-02T05:00:42ZObservation thinning in data assimilation computations2192-440610.1007/s13675-014-0025-4https://doaj.org/article/62a298c003384283901627d22f1dae462015-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621000381https://doaj.org/toc/2192-4406We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. 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 successively 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 new algorithm is tested on a one-dimensional-wave equation and on the Lorenz96 system, the latter one being of special interest because of its similarity with numerical weather prediction systems.Serge GrattonMonserrat Rincon-CamachoEhouarn SimonPhilippe L. TointElsevierarticle90C0690C9065F0893E2493E11Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 3, Iss 1, Pp 31-51 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	90C06 90C90 65F08 93E24 93E11 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90C06 90C90 65F08 93E24 93E11 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Serge Gratton Monserrat Rincon-Camacho Ehouarn Simon Philippe L. Toint Observation thinning in data assimilation computations
description	We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. 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 successively 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 new algorithm is tested on a one-dimensional-wave equation and on the Lorenz96 system, the latter one being of special interest because of its similarity with numerical weather prediction systems.
format	article
author	Serge Gratton Monserrat Rincon-Camacho Ehouarn Simon Philippe L. Toint
author_facet	Serge Gratton Monserrat Rincon-Camacho Ehouarn Simon Philippe L. Toint
author_sort	Serge Gratton
title	Observation thinning in data assimilation computations
title_short	Observation thinning in data assimilation computations
title_full	Observation thinning in data assimilation computations
title_fullStr	Observation thinning in data assimilation computations
title_full_unstemmed	Observation thinning in data assimilation computations
title_sort	observation thinning in data assimilation computations
publisher	Elsevier
publishDate	2015
url	https://doaj.org/article/62a298c003384283901627d22f1dae46
work_keys_str_mv	AT sergegratton observationthinningindataassimilationcomputations AT monserratrinconcamacho observationthinningindataassimilationcomputations AT ehouarnsimon observationthinningindataassimilationcomputations AT philippeltoint observationthinningindataassimilationcomputations
_version_	1718400830947721216

Observation thinning in data assimilation computations

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