Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

Handle URI:
http://hdl.handle.net/10754/624968
Title:
Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC
Authors:
Siripatana, Adil; Mayo, Talea; Sraj, Ihab ( 0000-0002-6158-472X ) ; Knio, Omar; Dawson, Clint; Le Maitre, Olivier; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Bayesian 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.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Siripatana 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.
Publisher:
Springer Nature
Journal:
Ocean Dynamics
KAUST Grant Number:
CRG3-2016
Issue Date:
8-Jun-2017
DOI:
10.1007/s10236-017-1074-z
Type:
Article
ISSN:
1616-7341; 1616-7228
Sponsors:
This 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).
Additional Links:
http://link.springer.com/article/10.1007/s10236-017-1074-z
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Full metadata record

DC FieldValue Language
dc.contributor.authorSiripatana, Adilen
dc.contributor.authorMayo, Taleaen
dc.contributor.authorSraj, Ihaben
dc.contributor.authorKnio, Omaren
dc.contributor.authorDawson, Clinten
dc.contributor.authorLe Maitre, Olivieren
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2017-06-13T05:51:04Z-
dc.date.available2017-06-13T05:51:04Z-
dc.date.issued2017-06-08en
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.en
dc.identifier.issn1616-7341en
dc.identifier.issn1616-7228en
dc.identifier.doi10.1007/s10236-017-1074-zen
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.en
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).en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s10236-017-1074-zen
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1074-zen
dc.subjectCoastal ocean modelen
dc.subjectManning’s n coefficientsen
dc.subjectParameter estimationen
dc.subjectEnsemble Kalman filteren
dc.subjectPolynomial chaosen
dc.subjectMCMCen
dc.titleAssessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMCen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalOcean Dynamicsen
dc.eprint.versionPost-printen
dc.contributor.institutionPrinceton University, Princeton, USAen
dc.contributor.institutionUniversity of Texas at Austin, Austin, USAen
dc.contributor.institutionLaboratoire d’Informatique pour la Mecanique et les Sciences de l’Ingénieur, Paris, Franceen
kaust.authorSiripatana, Adilen
kaust.authorSraj, Ihaben
kaust.authorKnio, Omaren
kaust.authorHoteit, Ibrahimen
kaust.grant.numberCRG3-2016en
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