Surrogate based approaches to parameter inference in ocean models
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 spectral
projections and 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 infer dependence of
wind drag, bottom drag, and internal mixing coefficients.