The calibration process typically involves comparing model outputs with observational data. If discrepancies are found, the model's parameters are adjusted accordingly. Calibration can be done using various statistical methods, such as regression analysis and Bayesian inference. These methods help to quantify the difference between predicted and observed values, allowing for systematic adjustments.
