Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

Autores
Ruiz, Juan Jose; Pulido, Manuel Arturo; Miyoshi, Takemasa
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques.
Fil: Ruiz, Juan Jose. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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: Miyoshi, Takemasa. University of Maryland; Estados Unidos de América;
Materia
DATA ASSIMILATION
ENSEMBLE KALMAN FILTER
ERROR COVARIANCE
PARAMETER ESTIMATION
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:11336/2027

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spelling Estimating Model Parameters with Ensemble-Based Data Assimilation: A ReviewRuiz, Juan JosePulido, Manuel ArturoMiyoshi, TakemasaDATA ASSIMILATIONENSEMBLE KALMAN FILTERERROR COVARIANCEPARAMETER ESTIMATIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques.Fil: Ruiz, Juan Jose. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: 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: Miyoshi, Takemasa. University of Maryland; Estados Unidos de América;Meteorological Soc Jpn2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/2027Ruiz, Juan Jose; Pulido, Manuel Arturo; Miyoshi, Takemasa; Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review; Meteorological Soc Jpn; Journal Of The Meteorological Society Of Japan; 91; 4; 6-2013; 453-4690026-1165enginfo:eu-repo/semantics/altIdentifier/url/https://www.jstage.jst.go.jp/article/jmsj/91/2/91_2013-201/_articleinfo:eu-repo/semantics/altIdentifier/doi/10.2151/jmsj.2013-403info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:12:03Zoai:ri.conicet.gov.ar:11336/2027instacron: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 10:12:04.208CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
title Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
spellingShingle Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
Ruiz, Juan Jose
DATA ASSIMILATION
ENSEMBLE KALMAN FILTER
ERROR COVARIANCE
PARAMETER ESTIMATION
title_short Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
title_full Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
title_fullStr Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
title_full_unstemmed Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
title_sort Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
dc.creator.none.fl_str_mv Ruiz, Juan Jose
Pulido, Manuel Arturo
Miyoshi, Takemasa
author Ruiz, Juan Jose
author_facet Ruiz, Juan Jose
Pulido, Manuel Arturo
Miyoshi, Takemasa
author_role author
author2 Pulido, Manuel Arturo
Miyoshi, Takemasa
author2_role author
author
dc.subject.none.fl_str_mv DATA ASSIMILATION
ENSEMBLE KALMAN FILTER
ERROR COVARIANCE
PARAMETER ESTIMATION
topic DATA ASSIMILATION
ENSEMBLE KALMAN FILTER
ERROR COVARIANCE
PARAMETER ESTIMATION
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 this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques.
Fil: Ruiz, Juan Jose. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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: Miyoshi, Takemasa. University of Maryland; Estados Unidos de América;
description In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques.
publishDate 2013
dc.date.none.fl_str_mv 2013-06
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/11336/2027
Ruiz, Juan Jose; Pulido, Manuel Arturo; Miyoshi, Takemasa; Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review; Meteorological Soc Jpn; Journal Of The Meteorological Society Of Japan; 91; 4; 6-2013; 453-469
0026-1165
url http://hdl.handle.net/11336/2027
identifier_str_mv Ruiz, Juan Jose; Pulido, Manuel Arturo; Miyoshi, Takemasa; Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review; Meteorological Soc Jpn; Journal Of The Meteorological Society Of Japan; 91; 4; 6-2013; 453-469
0026-1165
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.jstage.jst.go.jp/article/jmsj/91/2/91_2013-201/_article
info:eu-repo/semantics/altIdentifier/doi/10.2151/jmsj.2013-403
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
dc.publisher.none.fl_str_mv Meteorological Soc Jpn
publisher.none.fl_str_mv Meteorological Soc Jpn
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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