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dc.contributor.author Ruiz, Juan Jose
dc.contributor.author Pulido, Manuel Arturo
dc.date.available 2017-05-09T20:47:15Z
dc.date.issued 2015-05
dc.identifier.citation Ruiz, Juan Jose; Pulido, Manuel Arturo; Parameter estimation using ensemble based data assimilation in the presence of model error; American Meteorological Society; Monthly Energy Review; 143; 5-2015; 1568-1582
dc.identifier.issn 0027-0644
dc.identifier.uri http://hdl.handle.net/11336/16185
dc.description.abstract This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.
dc.format application/pdf
dc.language.iso eng
dc.publisher American Meteorological Society
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject PARAMETER ESTIMATION
dc.subject MODEL ERRORS
dc.subject BIAS
dc.subject KALMAN FILTER
dc.subject NUMERICAL WEATHER PREDICTION/FORECASTING
dc.subject DATA ASSIMILATION
dc.subject OPTIMIZATION
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 Parameter estimation using ensemble based data assimilation in the presence of model error
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-05-02T18:02:40Z
dc.identifier.eissn 1520-0493
dc.journal.volume 143
dc.journal.pagination 1568-1582
dc.journal.pais Estados Unidos
dc.journal.ciudad Boston
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. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina. Advanced Institute for Computational Science ; Japón
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 Tecnologica; Argentina. Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos; Argentina
dc.journal.title Monthly Energy Review
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-14-00017.1
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1175/MWR-D-14-00017.1


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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)