Prof. Omar M. Knio, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia

Parameter calibration in general circulation models using polynomial chaos surrogates

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to calibrate wind drag parametrizations, and to characterize the impact of initial conditions on the evolution of tropical cyclones.

 

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