State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters

Autores
Guillot, Jules; Ailliot, Pierre; Frénod, Emmanuel; Ruiz, Juan Jose; Tandeo, Pierre
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantifying uncertainties is a key aspect of data assimilation systems since it has alarge impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms,such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additiveGaussian noises and use both inflation and localization to avoid filter degeneracy and compensatefor misspecifications. This introduces different stochastic parameters which need to be carefullyestimated in order to get a reliable estimate of the latent state of the system. A classical approachto estimate unknown parameters in data assimilation consists in using state-augmentation, wherethe unknown parameters are included in the latent space and are updated at each iteration of theEnKF. However, it is well-known that this approach is not efficient to estimate stochastic parametersbecause of the complex (non-Gaussian and non-linear) relationship between the observations andthe stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm failsto estimate high-dimensional systems due to the curse of dimensionality. The strengths of thesetwo methods are gathered in the proposed algorithm, where the PF first generates the particles thatestimate the stochastic parameters, then using the mean particle the EnKF generates the membersthat estimate the geophysical variables. This generic method is first detailed for the estimation ofparameters related to the model or observation error and then for the joint estimation of inflationand localization parameters. Numerical experiments are performed using the Lorenz-96 model tocompare our approach with state-of-the-art methods. The results show the ability of the new methodto retrieve the geophysical state and to estimate online time-dependent stochastic parameters. Thealgorithm can be easily built from an existing EnKF with low additional cost and without furtherrunning the dynamical model.
Fil: Guillot, Jules. Centre National de la Recherche Scientifique; Francia
Fil: Ailliot, Pierre. Universidad de Brest; Francia
Fil: Frénod, Emmanuel. Centre National de la Recherche Scientifique; Francia
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia
Materia
data assimilation
parameter estimation
uncertainty quantification
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:11336/283018
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/283018
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle FiltersGuillot, JulesAilliot, PierreFrénod, EmmanuelRuiz, Juan JoseTandeo, Pierredata assimilationparameter estimationuncertainty quantificationhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Quantifying uncertainties is a key aspect of data assimilation systems since it has alarge impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms,such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additiveGaussian noises and use both inflation and localization to avoid filter degeneracy and compensatefor misspecifications. This introduces different stochastic parameters which need to be carefullyestimated in order to get a reliable estimate of the latent state of the system. A classical approachto estimate unknown parameters in data assimilation consists in using state-augmentation, wherethe unknown parameters are included in the latent space and are updated at each iteration of theEnKF. However, it is well-known that this approach is not efficient to estimate stochastic parametersbecause of the complex (non-Gaussian and non-linear) relationship between the observations andthe stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm failsto estimate high-dimensional systems due to the curse of dimensionality. The strengths of thesetwo methods are gathered in the proposed algorithm, where the PF first generates the particles thatestimate the stochastic parameters, then using the mean particle the EnKF generates the membersthat estimate the geophysical variables. This generic method is first detailed for the estimation ofparameters related to the model or observation error and then for the joint estimation of inflationand localization parameters. Numerical experiments are performed using the Lorenz-96 model tocompare our approach with state-of-the-art methods. The results show the ability of the new methodto retrieve the geophysical state and to estimate online time-dependent stochastic parameters. Thealgorithm can be easily built from an existing EnKF with low additional cost and without furtherrunning the dynamical model.Fil: Guillot, Jules. Centre National de la Recherche Scientifique; FranciaFil: Ailliot, Pierre. Universidad de Brest; FranciaFil: Frénod, Emmanuel. Centre National de la Recherche Scientifique; FranciaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; FranciaAmer Meteorological Soc2025-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/283018Guillot, Jules; Ailliot, Pierre; Frénod, Emmanuel; Ruiz, Juan Jose; Tandeo, Pierre; State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters; Amer Meteorological Soc; Monthly Weather Review; 153; 7; 5-2025; 1141-11540027-0644CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/view/journals/mwre/aop/MWR-D-24-0123.1/MWR-D-24-0123.1.xmlinfo:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-24-0123.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:36:11Zoai:ri.conicet.gov.ar:11336/283018instacron: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:34982026-08-25 14:36:11.921CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
title State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
spellingShingle State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
Guillot, Jules
data assimilation
parameter estimation
uncertainty quantification
title_short State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
title_full State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
title_fullStr State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
title_full_unstemmed State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
title_sort State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters
dc.creator.none.fl_str_mv Guillot, Jules
Ailliot, Pierre
Frénod, Emmanuel
Ruiz, Juan Jose
Tandeo, Pierre
author Guillot, Jules
author_facet Guillot, Jules
Ailliot, Pierre
Frénod, Emmanuel
Ruiz, Juan Jose
Tandeo, Pierre
author_role author
author2 Ailliot, Pierre
Frénod, Emmanuel
Ruiz, Juan Jose
Tandeo, Pierre
author2_role author
author
author
author
dc.subject.none.fl_str_mv data assimilation
parameter estimation
uncertainty quantification
topic data assimilation
parameter estimation
uncertainty quantification
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Quantifying uncertainties is a key aspect of data assimilation systems since it has alarge impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms,such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additiveGaussian noises and use both inflation and localization to avoid filter degeneracy and compensatefor misspecifications. This introduces different stochastic parameters which need to be carefullyestimated in order to get a reliable estimate of the latent state of the system. A classical approachto estimate unknown parameters in data assimilation consists in using state-augmentation, wherethe unknown parameters are included in the latent space and are updated at each iteration of theEnKF. However, it is well-known that this approach is not efficient to estimate stochastic parametersbecause of the complex (non-Gaussian and non-linear) relationship between the observations andthe stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm failsto estimate high-dimensional systems due to the curse of dimensionality. The strengths of thesetwo methods are gathered in the proposed algorithm, where the PF first generates the particles thatestimate the stochastic parameters, then using the mean particle the EnKF generates the membersthat estimate the geophysical variables. This generic method is first detailed for the estimation ofparameters related to the model or observation error and then for the joint estimation of inflationand localization parameters. Numerical experiments are performed using the Lorenz-96 model tocompare our approach with state-of-the-art methods. The results show the ability of the new methodto retrieve the geophysical state and to estimate online time-dependent stochastic parameters. Thealgorithm can be easily built from an existing EnKF with low additional cost and without furtherrunning the dynamical model.
Fil: Guillot, Jules. Centre National de la Recherche Scientifique; Francia
Fil: Ailliot, Pierre. Universidad de Brest; Francia
Fil: Frénod, Emmanuel. Centre National de la Recherche Scientifique; Francia
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia
description Quantifying uncertainties is a key aspect of data assimilation systems since it has alarge impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms,such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additiveGaussian noises and use both inflation and localization to avoid filter degeneracy and compensatefor misspecifications. This introduces different stochastic parameters which need to be carefullyestimated in order to get a reliable estimate of the latent state of the system. A classical approachto estimate unknown parameters in data assimilation consists in using state-augmentation, wherethe unknown parameters are included in the latent space and are updated at each iteration of theEnKF. However, it is well-known that this approach is not efficient to estimate stochastic parametersbecause of the complex (non-Gaussian and non-linear) relationship between the observations andthe stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm failsto estimate high-dimensional systems due to the curse of dimensionality. The strengths of thesetwo methods are gathered in the proposed algorithm, where the PF first generates the particles thatestimate the stochastic parameters, then using the mean particle the EnKF generates the membersthat estimate the geophysical variables. This generic method is first detailed for the estimation ofparameters related to the model or observation error and then for the joint estimation of inflationand localization parameters. Numerical experiments are performed using the Lorenz-96 model tocompare our approach with state-of-the-art methods. The results show the ability of the new methodto retrieve the geophysical state and to estimate online time-dependent stochastic parameters. Thealgorithm can be easily built from an existing EnKF with low additional cost and without furtherrunning the dynamical model.
publishDate 2025
dc.date.none.fl_str_mv 2025-05
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/11336/283018
Guillot, Jules; Ailliot, Pierre; Frénod, Emmanuel; Ruiz, Juan Jose; Tandeo, Pierre; State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters; Amer Meteorological Soc; Monthly Weather Review; 153; 7; 5-2025; 1141-1154
0027-0644
CONICET Digital
CONICET
url http://hdl.handle.net/11336/283018
identifier_str_mv Guillot, Jules; Ailliot, Pierre; Frénod, Emmanuel; Ruiz, Juan Jose; Tandeo, Pierre; State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters; Amer Meteorological Soc; Monthly Weather Review; 153; 7; 5-2025; 1141-1154
0027-0644
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/view/journals/mwre/aop/MWR-D-24-0123.1/MWR-D-24-0123.1.xml
info:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-24-0123.1
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/pdf
application/pdf
dc.publisher.none.fl_str_mv Amer Meteorological Soc
publisher.none.fl_str_mv Amer Meteorological Soc
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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