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dc.contributor.authorLiu, Bo
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2016-02-23T14:20:36Z
dc.date.available2016-02-23T14:20:36Z
dc.date.issued2015-11-11
dc.identifier.citationEfficient Kernel-Based Ensemble Gaussian Mixture Filtering 2015:151111131815001 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/MWR-D-14-00292.1
dc.identifier.urihttp://hdl.handle.net/10754/597024
dc.description.abstractWe consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).
dc.language.isoen
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00292.1
dc.rightsArchived with thanks to Monthly Weather Review
dc.subjectForecasting
dc.subjectEnsembles
dc.subjectForecasting techniques
dc.subjectProbability forecasts/models/distribution
dc.subjectModels and modeling
dc.subjectData assimilation
dc.titleEfficient Kernel-Based Ensemble Gaussian Mixture Filtering
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.identifier.journalMonthly Weather Review
dc.eprint.versionPublisher's Version/PDF
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personLiu, Bo
kaust.personAit-El-Fquih, Boujemaa
kaust.personHoteit, Ibrahim
refterms.dateFOA2016-05-11T00:00:00Z
dc.date.published-online2015-11-11
dc.date.published-print2016-02


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