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A statistical model for predicting intensity changes of Atlantic tropical cyclones at 12, 24, 36, 48, and 72 h is described. The model was developed using a standard multiple regression technique with climatological, persistence, and synoptic predictors. The model developmental sample includes all of the named Atlantic tropical cyclones from 1989 to 1992, with a few additional cases from 1982 to 1988. The sample includes only the times when the storms were over the ocean. The four primary predictors are 1) the difference between the current storm intensity and an estimate of the maximum possible intensity determined from the sea surface temperature, 2) the vertical shear of the horizontal wind, 3) persistence, and 4) the flux convergence of eddy angular momentum evaluated at 200 mb. The sea surface temperature and vertical shear variables are averaged along the track of the storm during the forecast period. The sea surface temperatures along the storm track are determined from monthly climatological analyses linearly interpolated to the position and date of the storm. The vertical shear values along the track of the storm are estimated using the synoptic analysis at the beginning of the forecast period. All other predictors are evaluated at the beginning of the forecast period. The model is tested using a jackknife procedure where the regression coefficients for a particular tropical cyclone are determined with all of the forecasts for that storm removed from the sample. Operational estimates of the storm track and initial storm intensity are used in place of best track information in the jackknife procedure. Results show that the average intensity errors are 10%–15% smaller than the errors from a model that uses only climatology and persistence (SHIFOR), and the error differences at 24, 36, and 48 h are statistically significant at the 99% level.
DeMaria et al. (Wed,) studied this question.