Although operational tropical cyclone track forecasts have improved dramatically in the past 20 years, intensity forecasts have shown little improvement or even regressed. It could be helpful to both forecasters and end users to know whether certain synoptic environments are inherently more difficult for forecasting hurricane intensity. At the time of a forecast, certain initial conditions (“ predictors”) can provide useful estimates of the expected error in different models' intensity forecasts. These skill predictions can guide scientists on how to improve model performance and inform forecasters about which model forecasts will achieve higher accuracy on any given storm.

This study analyzes the 48 hour intensity error for three primary models of hurricane intensity forecasting: LGEM, DSHP, and GFDL, as well as the official forecast (OFCL). The data was obtained from NOAA's ATCF database. Several predictors were tested individually and in combinations to demonstrate different regimes that were more conducive to higher or lower skill forecasts. Examples of these predictors include initial shear, initial intensity, storm direction, and 48 hour average forecast shear. The results address conventional wisdom about which environmental conditions lead to better forecasts of hurricane intensity and highlight the different strengths of each model.