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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/16202
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:02:35Zoai: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:34982025-09-29 10:02:35.599CONICET 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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13.070432 |