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dc.contributor.author
Dreano, D.
dc.contributor.author
Tandeo, Pi.
dc.contributor.author
Pulido, Manuel Arturo
dc.contributor.author
Ait-El-Fquih, B.
dc.contributor.author
Chonavel, T.
dc.contributor.author
Hoteit, Ibrahim
dc.date.available
2017-09-13T19:29:27Z
dc.date.issued
2017-04
dc.identifier.citation
Dreano, D.; Tandeo, Pi.; Pulido, Manuel Arturo; Ait-El-Fquih, B.; Chonavel, T.; et al.; Estimating model-error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximization algorithm; Wiley; Quarterly Journal of the Royal Meteorological Society; 143; 705; 4-2017; 1877-1885
dc.identifier.issn
0035-9009
dc.identifier.uri
http://hdl.handle.net/11336/24172
dc.description.abstract
Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation-maximization (EM) algorithm to estimate the model-error covariances using classical extended and ensemble versions of the Kalman smoother. We show that, for additive model errors, the estimate of the error covariance converges. We also investigate other forms of model error, such as parametric or multiplicative errors. We show that additive Gaussian model error is able to compensate for non-additive sources of error in the algorithms we propose. We also demonstrate the limitations of the extended version of the algorithm and recommend the use of the more robust and flexible ensemble version. This article is a proof of concept of the methodology with the Lorenz-63 attractor. We developed an open-source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Enkf
dc.subject
Enks
dc.subject
Expectation-Maximization
dc.subject
Extended Kalman Filter
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Model Error
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State-Space Models
dc.subject.classification
Oceanografía, Hidrología, Recursos Hídricos
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Estimating model-error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximization algorithm
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-08-25T20:07:07Z
dc.identifier.eissn
1477-870X
dc.journal.volume
143
dc.journal.number
705
dc.journal.pagination
1877-1885
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Dreano, D.. King Abdullah University Of Science And Technology; Arabia Saudita
dc.description.fil
Fil: Tandeo, Pi.. Lab-STICC- Pôle CID, Telecom Bretagne; Francia
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: Ait-El-Fquih, B.. King Abdullah University Of Science And Technology; Arabia Saudita
dc.description.fil
Fil: Chonavel, T.. Lab-STICC – Pôle CID, Telecom Bretagne; Francia
dc.description.fil
Fil: Hoteit, Ibrahim. King Abdullah University Of Science And Technology; Arabia Saudita
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.3048/full
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/qj.3048
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