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.