Using Data to Tune Nearshore Dynamics Models: A Bayesian Approach with Parametric Likelihood

TL;DR

This paper introduces a Bayesian parameter tuning method for nearshore models that improves computational efficiency and handles covariance data gaps, demonstrated through long shore current modeling.

Contribution

It presents a novel likelihood approach using polynomial approximations, enhancing parameter estimation in geophysical models with limited covariance information.

Findings

Better parameter estimates for bottom drag and surface forcing.

Enhanced computational efficiency over traditional methods.

Insights into model sensitivity and estimation procedures.

Abstract

We propose a modification of a maximum likelihood procedure for tuning parameter values in models, based upon the comparison of their output to field data. Our methodology, which uses polynomial approximations of the sample space to increase the computational efficiency, differs from similar Bayesian estimation frameworks in the use of an alternative likelihood distribution, is shown to better address problems in which covariance information is lacking, than its more conventional counterpart. Lack of covariance information is a frequent challenge in large-scale geophysical estimation. This is the case in the geophysical problem considered here. We use a nearshore model for long shore currents and observational data of the same to show the contrast between both maximum likelihood methodologies. Beyond a methodological comparison, this study gives estimates of parameter values for the bottom drag and surface forcing that make the particular model most consistent with data; furthermore, we also derive sensitivity estimates that provide useful insights regarding the estimation procedure as well as of the model itself.