Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific

Handle URI:
http://hdl.handle.net/10754/622821
Title:
Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific
Authors:
Sraj, Ihab ( 0000-0002-6158-472X ) ; Zedler, Sarah E.; Knio, Omar; Jackson, Charles S.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
The authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative. © 2016 American Meteorological Society.
KAUST Department:
King Abdullah University for Science and Technology, Thuwal, Saudi Arabia
Citation:
Sraj I, Zedler SE, Knio OM, Jackson CS, Hoteit I (2016) Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific. Monthly Weather Review 144: 4621–4640. Available: http://dx.doi.org/10.1175/MWR-D-15-0394.1.
Publisher:
American Meteorological Society
Journal:
Monthly Weather Review
KAUST Grant Number:
CRG-1-560 2012-HOT-007
Issue Date:
26-Aug-2016
DOI:
10.1175/MWR-D-15-0394.1
Type:
Article
ISSN:
0027-0644; 1520-0493
Sponsors:
This research made use of the resources of the Supercomputing Laboratory and computer clusters at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. IS, SZ, CJ, and IH are supported in part by KAUST Award CRG-1-560 2012-HOT-007; SZ and OK are supported in part by the Office of Advance Scientific Computing Research, U.S. Department of Energy, under Award DE-SC0008789.
Additional Links:
http://journals.ametsoc.org/doi/10.1175/MWR-D-15-0394.1
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorSraj, Ihaben
dc.contributor.authorZedler, Sarah E.en
dc.contributor.authorKnio, Omaren
dc.contributor.authorJackson, Charles S.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2017-01-30T13:02:47Z-
dc.date.available2017-01-30T13:02:47Z-
dc.date.issued2016-08-26en
dc.identifier.citationSraj I, Zedler SE, Knio OM, Jackson CS, Hoteit I (2016) Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific. Monthly Weather Review 144: 4621–4640. Available: http://dx.doi.org/10.1175/MWR-D-15-0394.1.en
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-15-0394.1en
dc.identifier.urihttp://hdl.handle.net/10754/622821-
dc.description.abstractThe authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative. © 2016 American Meteorological Society.en
dc.description.sponsorshipThis research made use of the resources of the Supercomputing Laboratory and computer clusters at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. IS, SZ, CJ, and IH are supported in part by KAUST Award CRG-1-560 2012-HOT-007; SZ and OK are supported in part by the Office of Advance Scientific Computing Research, U.S. Department of Energy, under Award DE-SC0008789.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/10.1175/MWR-D-15-0394.1en
dc.rights© Copyright [9 November 2016] American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (https://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.en
dc.subjectBayesian methodsen
dc.subjectOcean circulationen
dc.subjectSpectral analysis/models/distributionen
dc.titlePolynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacificen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University for Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDuke University, Durham, NC, United Statesen
dc.contributor.institutionThe University of Texas at Austin, Austin, TX, United Statesen
kaust.authorSraj, Ihaben
kaust.authorKnio, Omaren
kaust.authorHoteit, Ibrahimen
kaust.grant.numberCRG-1-560 2012-HOT-007en
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