Methods commonly used to evaluate model performance, include:
- Mean absolute error (MAE)
where N is number of observations, y_i the actual expected output and hat{y}_{i} the model’s prediction (same notations below if not indicated otherwise).
- Mean bias error (MBE)
- Mean square error (MSE)
- Root mean square error (RMSE)
- Coefficient of determination (R^2)
where overline{y} is mean of observed y_i.
These presented with plots (e.g. scatter, time series) allow identification of periods where model perform well/poorly relative to observations. It should be remembered that both the model (e.g. parameters, forcing data) and the evaluation observations have errors.