Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization

Autores
Tandeo, Pierre; Pulido, Manuel Arturo; Lott, Francois
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recent work has shown that the parameters controlling parametrizations of the physical processes in climate models can be estimated from observations using filtering techniques. In this article, we propose an offline parameter estimation approach, without estimating the state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and an iterative estimation of the error covariance matrices and of the background state using a maximum likelihood algorithm. The technique is implemented in a subgrid-scale orography (SSO) parametrization scheme which works in a single vertical column. First, the parameter estimation technique is evaluated using twin experiments. Then, the technique is used with synthetic observations to estimate how the parameters of the SSO scheme should change when the resolution of the input orography dataset of a general circulation model is increased. Our analysis reveals that, when the resolution of the orography dataset increases, the scheme should take into account the dynamical sheltering that can occur at low levels between mountain peaks located within the same gridbox area.
Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; Francia
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
Fil: Lott, Francois. Ecole Normale Superieure; Francia
Materia
Offline Parameter Estimation
Enkf
Em Algorithm
Subgrid-Scale Orography Parametrization
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:11336/16202
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/16202
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrizationTandeo, PierrePulido, Manuel ArturoLott, FrancoisOffline Parameter EstimationEnkfEm AlgorithmSubgrid-Scale Orography Parametrizationhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Recent work has shown that the parameters controlling parametrizations of the physical processes in climate models can be estimated from observations using filtering techniques. In this article, we propose an offline parameter estimation approach, without estimating the state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and an iterative estimation of the error covariance matrices and of the background state using a maximum likelihood algorithm. The technique is implemented in a subgrid-scale orography (SSO) parametrization scheme which works in a single vertical column. First, the parameter estimation technique is evaluated using twin experiments. Then, the technique is used with synthetic observations to estimate how the parameters of the SSO scheme should change when the resolution of the input orography dataset of a general circulation model is increased. Our analysis reveals that, when the resolution of the orography dataset increases, the scheme should take into account the dynamical sheltering that can occur at low levels between mountain peaks located within the same gridbox area.Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; FranciaFil: 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; ArgentinaFil: Lott, Francois. Ecole Normale Superieure; FranciaJohn Wiley & Sons Ltd2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/16202Tandeo, Pierre; Pulido, Manuel Arturo; Lott, Francois; Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization; John Wiley & Sons Ltd; Quarterly Journal Of The Royal Meteorological Society; 141; 687; 1-2015; 383-3950035-90091477-870Xenginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/qj.2357/abstractinfo:eu-repo/semantics/altIdentifier/doi/10.1002/qj.2357info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:58:42Zoai:ri.conicet.gov.ar:11336/16202instacron: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:34982025-10-15 14:58:42.605CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
title Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
spellingShingle Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
Tandeo, Pierre
Offline Parameter Estimation
Enkf
Em Algorithm
Subgrid-Scale Orography Parametrization
title_short Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
title_full Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
title_fullStr Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
title_full_unstemmed Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
title_sort Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization
dc.creator.none.fl_str_mv Tandeo, Pierre
Pulido, Manuel Arturo
Lott, Francois
author Tandeo, Pierre
author_facet Tandeo, Pierre
Pulido, Manuel Arturo
Lott, Francois
author_role author
author2 Pulido, Manuel Arturo
Lott, Francois
author2_role author
author
dc.subject.none.fl_str_mv Offline Parameter Estimation
Enkf
Em Algorithm
Subgrid-Scale Orography Parametrization
topic Offline Parameter Estimation
Enkf
Em Algorithm
Subgrid-Scale Orography Parametrization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recent work has shown that the parameters controlling parametrizations of the physical processes in climate models can be estimated from observations using filtering techniques. In this article, we propose an offline parameter estimation approach, without estimating the state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and an iterative estimation of the error covariance matrices and of the background state using a maximum likelihood algorithm. The technique is implemented in a subgrid-scale orography (SSO) parametrization scheme which works in a single vertical column. First, the parameter estimation technique is evaluated using twin experiments. Then, the technique is used with synthetic observations to estimate how the parameters of the SSO scheme should change when the resolution of the input orography dataset of a general circulation model is increased. Our analysis reveals that, when the resolution of the orography dataset increases, the scheme should take into account the dynamical sheltering that can occur at low levels between mountain peaks located within the same gridbox area.
Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; Francia
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
Fil: Lott, Francois. Ecole Normale Superieure; Francia
description Recent work has shown that the parameters controlling parametrizations of the physical processes in climate models can be estimated from observations using filtering techniques. In this article, we propose an offline parameter estimation approach, without estimating the state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and an iterative estimation of the error covariance matrices and of the background state using a maximum likelihood algorithm. The technique is implemented in a subgrid-scale orography (SSO) parametrization scheme which works in a single vertical column. First, the parameter estimation technique is evaluated using twin experiments. Then, the technique is used with synthetic observations to estimate how the parameters of the SSO scheme should change when the resolution of the input orography dataset of a general circulation model is increased. Our analysis reveals that, when the resolution of the orography dataset increases, the scheme should take into account the dynamical sheltering that can occur at low levels between mountain peaks located within the same gridbox area.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/16202
Tandeo, Pierre; Pulido, Manuel Arturo; Lott, Francois; Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization; John Wiley & Sons Ltd; Quarterly Journal Of The Royal Meteorological Society; 141; 687; 1-2015; 383-395
0035-9009
1477-870X
url http://hdl.handle.net/11336/16202
identifier_str_mv Tandeo, Pierre; Pulido, Manuel Arturo; Lott, Francois; Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: application to a subgrid-scale orography parametrization; John Wiley & Sons Ltd; Quarterly Journal Of The Royal Meteorological Society; 141; 687; 1-2015; 383-395
0035-9009
1477-870X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/qj.2357/abstract
info:eu-repo/semantics/altIdentifier/doi/10.1002/qj.2357
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 John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
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 13.22299

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