TY - JOUR

T1 - Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

AU - Siripatana, Adil

AU - Mayo, Talea

AU - Sraj, Ihab

AU - Knio, Omar

AU - Dawson, Clint

AU - Le Maitre, Olivier

AU - Hoteit, Ibrahim

N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): CRG3-2016
Acknowledgements: 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).

PY - 2017/6/8

Y1 - 2017/6/8

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/624968

UR - http://link.springer.com/article/10.1007/s10236-017-1074-z

UR - http://www.scopus.com/inward/record.url?scp=85020411682&partnerID=8YFLogxK

U2 - 10.1007/s10236-017-1074-z

DO - 10.1007/s10236-017-1074-z

M3 - Article

VL - 67

SP - 1067

EP - 1094

JO - Ocean Dynamics

JF - Ocean Dynamics

SN - 1616-7341

IS - 8

ER -