The probabilities [p.sub.persis] of the persistence forecast are 60% for the forecasted tercile and 20% for the other two terciles.
The inset table shows various measures of forecast quality: i) linear correlation index (Corr), ii) Kendall ranked correlation (Rank), and the mean absolute SS with respect to iii) a climatological forecast (Clim) and iv) a 10-yr persistence forecast (Pers).
Using both deterministic and probabilistic approaches, we show that these forecast systems have a significant level of skill and can improve on simple alternatives, such as climatological and persistence forecasts.
While the current skill is still low compared to seasonal hurricane forecasts, they are better than climatological forecasts and at least as good as, but probably better than, 10-yr persistence forecasts. The constant improvement in climate models, combined with the ever-growing network of observations available to initialize them, offers hope that these forecasts will follow a path similar to that of seasonal forecasts and start providing reliable, skillful information in the not-so-distant future.
In addition, the values for RMSE and MAE resulting from using a persistence forecast (a persistence forecast uses the current wind speed to predict the value of the future wind speed) are also included in the table.
For reference, the values of RMSE and MAE for a simple persistence forecast are also included in the table.
Finally, Table 6 provides the values of RMSE and MAE for the various ANN predictions which can be compared with the simple persistence forecast (reference).