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dc.contributor.author
Scheffler, Guillermo Federico
dc.contributor.author
Ruiz Holgado, Juan Daniel
dc.contributor.author
Pulido, Manuel Arturo
dc.date.available
2020-12-22T13:03:09Z
dc.date.issued
2019-04
dc.identifier.citation
Scheffler, Guillermo Federico; Ruiz Holgado, Juan Daniel; Pulido, Manuel Arturo; Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 145; 722; 4-2019; 2028-2045
dc.identifier.issn
0035-9009
dc.identifier.uri
http://hdl.handle.net/11336/121017
dc.description.abstract
Stochastic parametrizations are increasingly used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challenges associated with the use of these parametrizations is the characterization of the statistical properties of the stochastic processes within their formulation. In this work, a hierarchical Bayesian approach based on two nested ensemble Kalman filters is proposed for inferring parameters associated with stochastic parametrizations. The proposed technique is based on the Rao-Blackwellization of the parameter estimation problem. It consists of an ensemble of ensemble Kalman filters, each of them using a different set of stochastic parameter values. We show the ability of the technique to infer parameters related to the covariance of stochastic representations of model error in the Lorenz-96 dynamical system. The evaluation is conducted with stochastic twin experiments and with imperfect model experiments with unresolved physics in the forecast model. The technique performs successfully under different model error covariance structures. The technique is conceived to be applied offline as part of an apriori optimization of the data assimilation system and could, in principle, be extended to the estimation of other hyperparameters of the data assimilation system.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
HIERARCHICAL KALMAN FILTERS
dc.subject
MODEL ERROR
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PARAMETER ESTIMATION
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STOCHASTIC PARAMETRIZATION
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
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Geociencias multidisciplinaria
dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-05-19T19:01:23Z
dc.journal.volume
145
dc.journal.number
722
dc.journal.pagination
2028-2045
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Scheffler, Guillermo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad Nacional del Nordeste; Argentina
dc.description.fil
Fil: Ruiz Holgado, Juan Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
dc.description.fil
Fil: Pulido, Manuel Arturo. Universidad Nacional del Nordeste; Argentina. University of Reading; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Quarterly Journal of the Royal Meteorological Society
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1002/qj.3542
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3542
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