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dc.contributor.author
Pulido, Manuel Arturo
dc.contributor.author
Scheffler, Guillermo Federico
dc.contributor.author
Ruiz, Juan Jose
dc.contributor.author
Lucini, Maria Magdalena
dc.contributor.author
Tandeo, Pierre
dc.date.available
2017-08-22T15:02:54Z
dc.date.issued
2016-10-02
dc.identifier.citation
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
dc.identifier.issn
0035-9009
dc.identifier.uri
http://hdl.handle.net/11336/22761
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Enkf
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Parameter Estimation
dc.subject
Subgrid-Scale Schemes
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Lorenz’96 System
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Parametrization
dc.subject.classification
Oceanografía, Hidrología, Recursos Hídricos
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-07-27T12:53:48Z
dc.identifier.eissn
1477-870X
dc.journal.volume
142
dc.journal.number
701
dc.journal.pagination
2974-2984
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.description.fil
Fil: Tandeo, Pierre. Lab-STICC- Pôle CID; Francia
dc.journal.title
Quarterly Journal of the Royal Meteorological Society
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/qj.2879/abstract
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/qj.2879
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