A Statistical Model of Atlantic Hurricane Intensity Forecast Certainty

Abstract. In recent decades, the skill of hurricane forecasts have improved dramatically, thanks to the continuing progress of NWP models. In the past 30 years, track forecast errors have decreased by about 67 %, but intensity forecasts have only recently begun to improve. The difficulty of forecasting rapid intensification events remains an especially difficult factor for forecasters. Data from the GFS archives and HURDAT2, a database maintained by the National Hurricane Center of best-estimate storm center locations and maximum wind speeds were collected, and all tropical systems in the North Atlantic basin that were present in both the GFS archives and HURDAT2 were analyzed. Corrections for GFS initialization error according to a transfer function found by statistical regression between GFS-reported and HURDAT2-reported max wind speeds were applied. After these corrections, the average max wind speed forecast error and its correlations with certain atmospheric and ocean conditions were analyzed. Then, using these correlations, a statistical model was constructed to predict the error range of hurricane intensity forecasts. This model demonstrated far more skill in stronger storms, and positive skill was only observed in storms Category 2 and stronger. Hurricanes Harvey and Dorian were used as test cases, with which the model was generally successful, despite problems during rapid intensification events.