The analog data assimilation

Autores
Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel Arturo; Fablet, Ronan
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.
Fil: Lguensat, Redouane. Université Bretagne Loire; Francia
Fil: Tandeo, Pierre. Université Bretagne Loire; Francia
Fil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; Francia
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Fil: Fablet, Ronan. Université Bretagne Loire; Francia
Materia
DATA ASSIMILATION
ENSEMBLES
KALMAN FILTERS
STATISTICAL FORECASTING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/44461
Acceder
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network_name_str CONICET Digital (CONICET)
spelling The analog data assimilationLguensat, RedouaneTandeo, PierreAilliot, PierrePulido, Manuel ArturoFablet, RonanDATA ASSIMILATIONENSEMBLESKALMAN FILTERSSTATISTICAL FORECASTINGhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.Fil: Lguensat, Redouane. Université Bretagne Loire; FranciaFil: Tandeo, Pierre. Université Bretagne Loire; FranciaFil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; FranciaFil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: Fablet, Ronan. Université Bretagne Loire; FranciaAmerican Meteorological Society2017-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/mswordapplication/pdfhttp://hdl.handle.net/11336/44461Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel Arturo; Fablet, Ronan; The analog data assimilation; American Meteorological Society; Monthly Energy Review; 145; 10; 10-2017; 4093-41070027-06441520-0493CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-16-0441.1info:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/doi/10.1175/MWR-D-16-0441.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:38Zoai:ri.conicet.gov.ar:11336/44461instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:03:38.903CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The analog data assimilation
title The analog data assimilation
spellingShingle The analog data assimilation
Lguensat, Redouane
DATA ASSIMILATION
ENSEMBLES
KALMAN FILTERS
STATISTICAL FORECASTING
title_short The analog data assimilation
title_full The analog data assimilation
title_fullStr The analog data assimilation
title_full_unstemmed The analog data assimilation
title_sort The analog data assimilation
dc.creator.none.fl_str_mv Lguensat, Redouane
Tandeo, Pierre
Ailliot, Pierre
Pulido, Manuel Arturo
Fablet, Ronan
author Lguensat, Redouane
author_facet Lguensat, Redouane
Tandeo, Pierre
Ailliot, Pierre
Pulido, Manuel Arturo
Fablet, Ronan
author_role author
author2 Tandeo, Pierre
Ailliot, Pierre
Pulido, Manuel Arturo
Fablet, Ronan
author2_role author
author
author
author
dc.subject.none.fl_str_mv DATA ASSIMILATION
ENSEMBLES
KALMAN FILTERS
STATISTICAL FORECASTING
topic DATA ASSIMILATION
ENSEMBLES
KALMAN FILTERS
STATISTICAL FORECASTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.
Fil: Lguensat, Redouane. Université Bretagne Loire; Francia
Fil: Tandeo, Pierre. Université Bretagne Loire; Francia
Fil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; Francia
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Fil: Fablet, Ronan. Université Bretagne Loire; Francia
description In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.
publishDate 2017
dc.date.none.fl_str_mv 2017-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/44461
Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel Arturo; Fablet, Ronan; The analog data assimilation; American Meteorological Society; Monthly Energy Review; 145; 10; 10-2017; 4093-4107
0027-0644
1520-0493
CONICET Digital
CONICET
url http://hdl.handle.net/11336/44461
identifier_str_mv Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel Arturo; Fablet, Ronan; The analog data assimilation; American Meteorological Society; Monthly Energy Review; 145; 10; 10-2017; 4093-4107
0027-0644
1520-0493
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-16-0441.1
info:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/doi/10.1175/MWR-D-16-0441.1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/msword
application/pdf
dc.publisher.none.fl_str_mv American Meteorological Society
publisher.none.fl_str_mv American Meteorological Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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