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dc.contributor.authorSiripatana, Adil
dc.contributor.authorMayo, Talea
dc.contributor.authorSraj, Ihab
dc.contributor.authorKnio, Omar
dc.contributor.authorDawson, Clint
dc.contributor.authorLe Maitre, Olivier
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2017-06-13T05:51:04Z
dc.date.available2017-06-13T05:51:04Z
dc.date.issued2017-06-08
dc.identifier.citationSiripatana A, Mayo T, Sraj I, Knio O, Dawson C, et al. (2017) Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1074-z.
dc.identifier.issn1616-7341
dc.identifier.issn1616-7228
dc.identifier.doi10.1007/s10236-017-1074-z
dc.identifier.urihttp://hdl.handle.net/10754/624968
dc.description.abstractBayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of the estimated quantities is obtained conditioned on available data. It can be computed either directly, using a Markov chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation approach, which is heavily exploited in large dimensional state estimation problems. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach due to the restricted Gaussian prior and noise assumptions that are generally imposed in these methods. This contribution aims at evaluating the effectiveness of utilizing an ensemble Kalman-based data assimilation method for parameter estimation of a coastal ocean model against an MCMC polynomial chaos (PC)-based scheme. We focus on quantifying the uncertainties of a coastal ocean ADvanced CIRCulation (ADCIRC) model with respect to the Manning’s n coefficients. Based on a realistic framework of observation system simulation experiments (OSSEs), we apply an ensemble Kalman filter and the MCMC method employing a surrogate of ADCIRC constructed by a non-intrusive PC expansion for evaluating the likelihood, and test both approaches under identical scenarios. We study the sensitivity of the estimated posteriors with respect to the parameters of the inference methods, including ensemble size, inflation factor, and PC order. A full analysis of both methods, in the context of coastal ocean model, suggests that an ensemble Kalman filter with appropriate ensemble size and well-tuned inflation provides reliable mean estimates and uncertainties of Manning’s n coefficients compared to the full posterior distributions inferred by MCMC.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia grant number CRG3-2016. C. Dawson also acknowledges support of the Gulf of Mexico Research Initiative Center for Advanced Research on Transport of Hydrocarbons in the Environment (CARTHE).
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s10236-017-1074-z
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1074-z
dc.subjectCoastal ocean model
dc.subjectManning’s n coefficients
dc.subjectParameter estimation
dc.subjectEnsemble Kalman filter
dc.subjectPolynomial chaos
dc.subjectMCMC
dc.titleAssessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Dynamics
dc.eprint.versionPost-print
dc.contributor.institutionPrinceton University, Princeton, USA
dc.contributor.institutionUniversity of Texas at Austin, Austin, USA
dc.contributor.institutionLaboratoire d’Informatique pour la Mecanique et les Sciences de l’Ingénieur, Paris, France
kaust.personSiripatana, Adil
kaust.personSraj, Ihab
kaust.personKnio, Omar
kaust.personHoteit, Ibrahim
kaust.grant.numberCRG3-2016
refterms.dateFOA2018-06-08T00:00:00Z
dc.date.published-online2017-06-08
dc.date.published-print2017-08


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