Uncertainty Quantification and Assimilation for Efficient Coastal Ocean Forecasting
Name:
Adil Siripatana - Dissertation - Final Draft.pdf
Size:
25.27Mb
Format:
PDF
Description:
Adil Siripatana - Dissertation - Final Draft
Type
DissertationAuthors
Siripatana, Adil
Advisors
Hoteit, Ibrahim
Committee members
Knio, Omar
McCabe, Matthew

Dawson, Clint
Le Maitre, Olivier
Program
Earth Science and EngineeringKAUST Department
Physical Science and Engineering (PSE) DivisionDate
2019-04-21Permanent link to this record
http://hdl.handle.net/10754/652463
Metadata
Show full item recordAbstract
Bayesian inference is commonly used to quantify and reduce modeling uncertainties in coastal ocean models by computing the posterior probability distribution function (pdf) of some uncertain quantities to be estimated conditioned on available observations. The posterior can be computed either directly, using a Markov Chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation (DA) approach. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without a significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach often due to restricted Gaussian prior and noise assumptions. This thesis aims to develop, implement and test novel efficient Bayesian inference techniques to quantify and reduce modeling and parameter uncertainties of coastal ocean models. Both state and parameter estimations will be addressed within the framework of a state of-the-art coastal ocean model, the Advanced Circulation (ADCIRC) model. The first part of the thesis proposes efficient Bayesian inference techniques for uncertainty quantification (UQ) and state-parameters estimation. Based on a realistic framework of observation system simulation experiments (OSSEs), an ensemble Kalman filter (EnKF) is first evaluated against a Polynomial Chaos (PC)-surrogate MCMC method under identical scenarios. After demonstrating the relevance of the EnKF for parameters estimation, an iterative EnKF is introduced and validated for the estimation of a spatially varying Manning’s n coefficients field. Karhunen-Lo`eve (KL) expansion is also tested for dimensionality reduction and conditioning of the parameter search space. To further enhance the performance of PC-MCMC for estimating spatially varying parameters, a coordinate transformation of a Gaussian process with parameterized prior covariance function is next incorporated into the Bayesian inference framework to account for the uncertainty in covariance model hyperparameters. The second part of the thesis focuses on the use of UQ and DA on adaptive mesh models. We developed new approaches combining EnKF and multiresolution analysis, and demonstrated significant reduction in the cost of data assimilation compared to the traditional EnKF implemented on a non-adaptive mesh.Citation
Siripatana, A. (2019). Uncertainty Quantification and Assimilation for Efficient Coastal Ocean Forecasting. KAUST Research Repository. https://doi.org/10.25781/KAUST-IZ00Pae974a485f413a2113503eed53cd6c53
10.25781/KAUST-IZ00P