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We propose a modification of a maximum likelihood procedure for tuningparameter values in models, based upon the comparison of their output to fielddata. Our methodology, which uses polynomial approximations of the sample spaceto increase the computational efficiency, differs from similar Bayesianestimation frameworks in the use of an alternative likelihood distribution, isshown 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-scalegeophysical estimation. This is the case in the geophysical problem consideredhere. We use a nearshore model for long shore currents and observational dataof the same to show the contrast between both maximum likelihood methodologies.  Beyond a methodological comparison, this study gives estimates of parametervalues for the bottom drag and surface forcing that make the particular modelmost consistent with data; furthermore, we also derive sensitivity estimatesthat provide useful insights regarding the estimation procedure as well as ofthe model itself.

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