Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment

Autores
Pulido, Manuel Arturo; Scheffler, Guillermo Federico; Ruiz, Juan Jose; Lucini, Maria Magdalena; Tandeo, Pierre
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Oceanic and atmospheric global numerical models represent explicitly the large‐scale dynamics while the smaller‐scale processes are not resolved, so that their effects in the large‐scale dynamics are included through subgrid‐scale parametrizations. These parametrizations represent small‐scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid‐scale parametrizations but also to uncover the functional dependencies of subgrid‐scale processes as a function of large‐scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two‐scale Lorenz '96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large‐scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large‐scale variables and by a space‐dependent model error term. Then a polynomial regression is used to fit the estimated model error as a function of the large‐scale model variables in order to develop a parametrization of small‐scale dynamics. The online estimation shows a good performance when the parameter‐state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid‐scale processes. The nonlinear and non‐local dependence found in an experiment with shear‐generated small‐scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid‐scale parametrizations.
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: Scheffler, Guillermo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina
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. RIKEN Advanced Institute for Computational Science; Japón
Fil: Lucini, Maria Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; Francia
Materia
Enkf
Parameter Estimation
Subgrid-Scale Schemes
Lorenz’96 System
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:11336/22761
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experimentPulido, Manuel ArturoScheffler, Guillermo FedericoRuiz, Juan JoseLucini, Maria MagdalenaTandeo, PierreEnkfParameter EstimationSubgrid-Scale SchemesLorenz’96 SystemParametrizationhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Oceanic and atmospheric global numerical models represent explicitly the large‐scale dynamics while the smaller‐scale processes are not resolved, so that their effects in the large‐scale dynamics are included through subgrid‐scale parametrizations. These parametrizations represent small‐scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid‐scale parametrizations but also to uncover the functional dependencies of subgrid‐scale processes as a function of large‐scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two‐scale Lorenz '96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large‐scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large‐scale variables and by a space‐dependent model error term. Then a polynomial regression is used to fit the estimated model error as a function of the large‐scale model variables in order to develop a parametrization of small‐scale dynamics. The online estimation shows a good performance when the parameter‐state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid‐scale processes. The nonlinear and non‐local dependence found in an experiment with shear‐generated small‐scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid‐scale parametrizations.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; ArgentinaFil: Scheffler, Guillermo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; ArgentinaFil: 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. RIKEN Advanced Institute for Computational Science; JapónFil: Lucini, Maria Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Tandeo, Pierre. Lab-STICC- Pôle CID; FranciaWiley2016-10-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/22761Pulido, Manuel Arturo; Scheffler, Guillermo Federico; Ruiz, Juan Jose; Lucini, Maria Magdalena; Tandeo, Pierre; Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment; Wiley; Quarterly Journal of the Royal Meteorological Society; 142; 701; 2-10-2016; 2974-29840035-90091477-870XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/qj.2879/abstractinfo:eu-repo/semantics/altIdentifier/doi/10.1002/qj.2879info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:52Zoai:ri.conicet.gov.ar:11336/22761instacron: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 09:46:52.357CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
title Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
spellingShingle Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
Pulido, Manuel Arturo
Enkf
Parameter Estimation
Subgrid-Scale Schemes
Lorenz’96 System
Parametrization
title_short Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
title_full Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
title_fullStr Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
title_full_unstemmed Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
title_sort Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
dc.creator.none.fl_str_mv Pulido, Manuel Arturo
Scheffler, Guillermo Federico
Ruiz, Juan Jose
Lucini, Maria Magdalena
Tandeo, Pierre
author Pulido, Manuel Arturo
author_facet Pulido, Manuel Arturo
Scheffler, Guillermo Federico
Ruiz, Juan Jose
Lucini, Maria Magdalena
Tandeo, Pierre
author_role author
author2 Scheffler, Guillermo Federico
Ruiz, Juan Jose
Lucini, Maria Magdalena
Tandeo, Pierre
author2_role author
author
author
author
dc.subject.none.fl_str_mv Enkf
Parameter Estimation
Subgrid-Scale Schemes
Lorenz’96 System
Parametrization
topic Enkf
Parameter Estimation
Subgrid-Scale Schemes
Lorenz’96 System
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 Oceanic and atmospheric global numerical models represent explicitly the large‐scale dynamics while the smaller‐scale processes are not resolved, so that their effects in the large‐scale dynamics are included through subgrid‐scale parametrizations. These parametrizations represent small‐scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid‐scale parametrizations but also to uncover the functional dependencies of subgrid‐scale processes as a function of large‐scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two‐scale Lorenz '96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large‐scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large‐scale variables and by a space‐dependent model error term. Then a polynomial regression is used to fit the estimated model error as a function of the large‐scale model variables in order to develop a parametrization of small‐scale dynamics. The online estimation shows a good performance when the parameter‐state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid‐scale processes. The nonlinear and non‐local dependence found in an experiment with shear‐generated small‐scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid‐scale parametrizations.
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: Scheffler, Guillermo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina
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. RIKEN Advanced Institute for Computational Science; Japón
Fil: Lucini, Maria Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; Francia
description Oceanic and atmospheric global numerical models represent explicitly the large‐scale dynamics while the smaller‐scale processes are not resolved, so that their effects in the large‐scale dynamics are included through subgrid‐scale parametrizations. These parametrizations represent small‐scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid‐scale parametrizations but also to uncover the functional dependencies of subgrid‐scale processes as a function of large‐scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two‐scale Lorenz '96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large‐scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large‐scale variables and by a space‐dependent model error term. Then a polynomial regression is used to fit the estimated model error as a function of the large‐scale model variables in order to develop a parametrization of small‐scale dynamics. The online estimation shows a good performance when the parameter‐state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid‐scale processes. The nonlinear and non‐local dependence found in an experiment with shear‐generated small‐scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid‐scale parametrizations.
publishDate 2016
dc.date.none.fl_str_mv 2016-10-02
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/11336/22761
Pulido, Manuel Arturo; Scheffler, Guillermo Federico; Ruiz, Juan Jose; Lucini, Maria Magdalena; Tandeo, Pierre; Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment; Wiley; Quarterly Journal of the Royal Meteorological Society; 142; 701; 2-10-2016; 2974-2984
0035-9009
1477-870X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/22761
identifier_str_mv Pulido, Manuel Arturo; Scheffler, Guillermo Federico; Ruiz, Juan Jose; Lucini, Maria Magdalena; Tandeo, Pierre; Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment; Wiley; Quarterly Journal of the Royal Meteorological Society; 142; 701; 2-10-2016; 2974-2984
0035-9009
1477-870X
CONICET Digital
CONICET
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.2879/abstract
info:eu-repo/semantics/altIdentifier/doi/10.1002/qj.2879
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 Wiley
publisher.none.fl_str_mv Wiley
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 13.070432

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