Three Multi_1 grids were chosen for comparison against two GFS-Wave grids.
|Multi_1 grid||GFS-Wave grid|
|Global 30 min Extended||Global 0p25 (15 min)|
|NW Atlantic 10 min||Global 0p16 (10 min)|
|US West Coast 10 min||Global 0p16 (10 min)|
The mean difference between the model and observations, measures the tendency of the model process to over- or under-estimate the value of a parameter. Smaller absolute bias values indicate better agreement between measured and calculated values. Positive bias means overprediction, negative means underprediction.
diff = model_data - observations bias = diff.mean()
Also called the root-mean-squared deviation, it's a measure of the differences between the observed and predicted values. Smaller RMSE values indicate better agreement between measured and calculated values.
Defined as the standard deviation of the difference between model and observations, normalised by the mean of the observations. Smaller values of SI indicate better agreement between the model and observations.
scatter_index=100.0*(((diff**2).mean())**0.5 - bias**2)/observations.mean()