The skill of the operational forecasts is measured by using two metrics, a Root-Mean-Square Error (RMSE) and Pattern Correlation Coeffcient (PCC). These numbers are computed model level by model level (1 to 16), and as a volume average. The perfect values of the PCC and RMSE are, respectively, one and zero. Note, however, that in practice, these values are never realized.
A classic measure of skill is to compare the RMS and PCC of the forecast with that of the initial conditions (IC) (persistence forecast, in short persistence). If the RMSE of the forecast is smaller than that of the IC, the forecast has RMS-skill or beats persistence. Similarly, if the PCC of the forecast is larger than that of the IC, the forecast has PCC-skill or has better patterns than persistence.
These skill metrics compare model results with those data not utilized for the forecast due to collection after the initalization of the model run.
Statistical Summary
The statistical summary below has been broken into "Upper Ocean" and "Deep Ocean" evaluations. As the deep ocean changes very little, the useful metrics compare the upper ocean and deep ocean separately. The separation was chosen at the level where the change of the metrics from level to level is small. The numbers entered in the "Ave" row are hand-calculated averages over the set of levels. A modification is being made to the computation of the skill metrics to provide a level-by-level analysis.