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dc.contributor.author
Ruiz, Juan Jose
dc.contributor.author
Pulido, Manuel Arturo
dc.contributor.author
Miyoshi, Takemasa
dc.date.available
2015-09-22T17:53:16Z
dc.date.issued
2013-06
dc.identifier.citation
Ruiz, Juan Jose; Pulido, Manuel Arturo; Miyoshi, Takemasa; Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review; Meteorological Soc Jpn; Journal Of The Meteorological Society Of Japan; 91; 4; 6-2013; 453-469
dc.identifier.issn
0026-1165
dc.identifier.uri
http://hdl.handle.net/11336/2027
dc.description.abstract
In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Meteorological Soc Jpn
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DATA ASSIMILATION
dc.subject
ENSEMBLE KALMAN FILTER
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ERROR COVARIANCE
dc.subject
PARAMETER ESTIMATION
dc.subject.classification
Meteorología y Ciencias Atmosféricas
dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-03-30 10:35:44.97925-03
dc.journal.volume
91
dc.journal.number
4
dc.journal.pagination
453-469
dc.journal.pais
Japón
dc.journal.ciudad
Tokio
dc.conicet.avisoEditorial
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dc.description.fil
Fil: Ruiz, Juan Jose. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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: Miyoshi, Takemasa. University of Maryland; Estados Unidos de América;
dc.journal.title
Journal Of The Meteorological Society Of Japan
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.jstage.jst.go.jp/article/jmsj/91/2/91_2013-201/_article
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.2151/jmsj.2013-403
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