Statistical Rapid Intensity Prediction: Implications of Recent Model Results
Presented by: John Kaplan - NOAA/AOML/Hurricane Research Division
Hosted by: Galina Chirokova
Date: September 26, 2017 10:00 am
Location: CIRA Directors Conference Room
Despite recent improvements in tropical cyclone (TC) intensity forecasting skill, predicting changes in TC intensity remains problematic particularly the forecasting of episodes of rapid intensification (RI) which the National Hurricane Center (NHC) has declared as one of its highest operational forecasting priorities.
In recent years, a statistical rapid intensification index (SHIPS-RII) that employs environmental data from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) to estimate the probability of RI has been developed based upon linear discriminant analysis. Although the SHIPS-RII has been utilized as an operational forecasting tool by the NHC since 2004, its utility has been somewhat restricted since the original version only provided probabilistic forecasts for the single lead time of 24 h. Thus, additional versions of the SHIPS-RII as well as new logistic regression and bayesian RI models have been recently developed for the added lead times of 12-h, 36-h, 48-h, and 72-h. These new multi-lead time RI models became operational for the first time during the 2017 Hurricane Season.
In our upcoming presentation, a brief description of the new statistical RI models as well as an assessment of their overall level of skill will be provided. An evaluation of the ability of the current operational numerical intensity models to predict RI will also be presented and the implications of the current RI predictive skill of both the statistical and numerical models will be discussed.