Many geophysical models require parameters that are not tightly constrained by
observational data. Calibration represents methods by which these parameters
are estimated by minimizing the difference between observational data and
model simulated equivalents (the objective function). Additionally, uncertainty
in estimated parameters is determined.
In this clinic we will cover the basics of model calibration including:
(1) determining an appropriate objective function, (2) major classes of
calibration algorithms, (3) interpretation of results.
In the hands-on portion of the the clinic, we will apply multiple calibration
algorithms to a simple test case. For this, we will use Dakota, a package that
supports the application of many different calibration algorithms.