Surrogate Based Approaches to Parameter Inference in Ocean General Circulation Models

Wednesday, March 13, 2013 - 11:30am - 12:00pm
Keller 3-180
Omar Knio (Duke University)
This talk discusses the inference of physical parameters using model surrogates.
Attention is focused on the use of adaptive sampling schemes to build
suitable representations of the dependence of the model response on uncertain input
data. 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 infer dependence of the drag coefficient on wind-speed based on
AXBT temperature data obtained during Typhoon Fanapi.
MSC Code: