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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/16185
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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 Técnicas2025-09-03T09:53:39Zoai: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:34982025-09-03 09:53:39.532CONICET 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 |
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1842269238984704000 |
score |
13.13397 |