Parameter estimation using ensemble based data assimilation in the presence of model error

Autores
Ruiz, Juan Jose; Pulido, Manuel Arturo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina. Advanced Institute for Computational Science ; Japón
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 Tecnologica; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina
Materia
PARAMETER ESTIMATION
MODEL ERRORS
BIAS
KALMAN FILTER
NUMERICAL WEATHER PREDICTION/FORECASTING
DATA ASSIMILATION
OPTIMIZATION
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:11336/16185
Acceder
id CONICETDig_0fe79adeac35ecd22ce7fc989bd3d33e
oai_identifier_str oai:ri.conicet.gov.ar:11336/16185
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Parameter estimation using ensemble based data assimilation in the presence of model errorRuiz, Juan JosePulido, Manuel ArturoPARAMETER ESTIMATIONMODEL ERRORSBIASKALMAN FILTERNUMERICAL WEATHER PREDICTION/FORECASTINGDATA ASSIMILATIONOPTIMIZATIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina. Advanced Institute for Computational Science ; JapónFil: 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 Tecnologica; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; ArgentinaAmerican Meteorological Society2015-05info: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/11336/16185Ruiz, Juan Jose; Pulido, Manuel Arturo; Parameter estimation using ensemble based data assimilation in the presence of model error; American Meteorological Society; Monthly Energy Review; 143; 5-2015; 1568-15820027-06441520-0493enginfo:eu-repo/semantics/altIdentifier/url/http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-14-00017.1info:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-14-00017.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:53:11Zoai:ri.conicet.gov.ar:11336/16185instacron: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:34982025-09-29 09:53:11.863CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Parameter estimation using ensemble based data assimilation in the presence of model error
title Parameter estimation using ensemble based data assimilation in the presence of model error
spellingShingle Parameter estimation using ensemble based data assimilation in the presence of model error
Ruiz, Juan Jose
PARAMETER ESTIMATION
MODEL ERRORS
BIAS
KALMAN FILTER
NUMERICAL WEATHER PREDICTION/FORECASTING
DATA ASSIMILATION
OPTIMIZATION
title_short Parameter estimation using ensemble based data assimilation in the presence of model error
title_full Parameter estimation using ensemble based data assimilation in the presence of model error
title_fullStr Parameter estimation using ensemble based data assimilation in the presence of model error
title_full_unstemmed Parameter estimation using ensemble based data assimilation in the presence of model error
title_sort Parameter estimation using ensemble based data assimilation in the presence of model error
dc.creator.none.fl_str_mv Ruiz, Juan Jose
Pulido, Manuel Arturo
author Ruiz, Juan Jose
author_facet Ruiz, Juan Jose
Pulido, Manuel Arturo
author_role author
author2 Pulido, Manuel Arturo
author2_role author
dc.subject.none.fl_str_mv PARAMETER ESTIMATION
MODEL ERRORS
BIAS
KALMAN FILTER
NUMERICAL WEATHER PREDICTION/FORECASTING
DATA ASSIMILATION
OPTIMIZATION
topic PARAMETER ESTIMATION
MODEL ERRORS
BIAS
KALMAN FILTER
NUMERICAL WEATHER PREDICTION/FORECASTING
DATA ASSIMILATION
OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina. Advanced Institute for Computational Science ; Japón
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 Tecnologica; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina
description This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.
publishDate 2015
dc.date.none.fl_str_mv 2015-05
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/11336/16185
Ruiz, Juan Jose; Pulido, Manuel Arturo; Parameter estimation using ensemble based data assimilation in the presence of model error; American Meteorological Society; Monthly Energy Review; 143; 5-2015; 1568-1582
0027-0644
1520-0493
url http://hdl.handle.net/11336/16185
identifier_str_mv Ruiz, Juan Jose; Pulido, Manuel Arturo; Parameter estimation using ensemble based data assimilation in the presence of model error; American Meteorological Society; Monthly Energy Review; 143; 5-2015; 1568-1582
0027-0644
1520-0493
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-14-00017.1
info:eu-repo/semantics/altIdentifier/doi/10.1175/MWR-D-14-00017.1
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 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
_version_ 1844613627216134144
score 13.069144

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