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dc.contributor.author
Lucini, María Magdalena
dc.contributor.author
Leeuwen, Peter Jan van
dc.contributor.author
Pulido, Manuel Arturo
dc.date.available
2021-12-23T14:56:51Z
dc.date.issued
2021-03
dc.identifier.citation
Lucini, María Magdalena; Leeuwen, Peter Jan van; Pulido, Manuel Arturo; Model error estimation using the expectation maximization algorithm and a particle flow filter; Society of Industrial and Applied Mathematics; Journal on Uncertainty Quantification; 9; 2; 3-2021; 681-707
dc.identifier.issn
2166-2525
dc.identifier.uri
http://hdl.handle.net/11336/149232
dc.description.abstract
Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown in most of the applications. A misspecification of the model error covariance has a strong impact on the computation of the posterior probability density function, leading to unreliable estimations and even to a total failure of the assimilation procedure. In this work, we propose the combination of the expectation maximization (EM) algorithm with an efficient particle filter to estimate the model error covariance using a batch of observations. Based on the EM algorithm principles, the proposed method encompasses two stages: the expectation stage, in which a particle filter is used with the present updated value of the model error covariance as given to find the probability density function that maximizes the likelihood, followed by a maximization stage, in which the expectation under the probability density function found in the expectation step is maximized as a function of the elements of the model error covariance. This novel algorithm here presented combines the EM algorithm with a fixed point algorithm and does not require a particle smoother to approximate the posterior densities. We demonstrate that the new method accurately and efficiently solves the linear model problem. Furthermore, for the chaotic nonlinear Lorenz-96 model the method is stable even for observation error covariance 10 times larger than the estimated model error covariance matrix and also is successful in moderately large dimensional situations where the dimension of the estimated matrix is 40 x 40.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Society of Industrial and Applied Mathematics
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
EM ALGORITHM
dc.subject
MODEL ERROR COVARIANCE
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PARTICLE FILTERS
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STATE-SPACE MODELS
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Model error estimation using the expectation maximization algorithm and a particle flow filter
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-12-03T20:36:52Z
dc.journal.volume
9
dc.journal.number
2
dc.journal.pagination
681-707
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Philadelfia
dc.description.fil
Fil: Lucini, María Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
dc.description.fil
Fil: Leeuwen, Peter Jan van. State University of Colorado - Fort Collins; Estados Unidos
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.journal.title
Journal on Uncertainty Quantification
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1137/19M1297300
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