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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/2027
Ver los metadatos del registro completo
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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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score |
13.13397 |