Enhancements to the operational SHIPS rapid intensification index

Although some modest improvements have been made in tropical cyclone intensity guidance models over the past decade, predicting changes in tropical cyclone intensity remains a very challenging problem, especially for cases of rapid intensification (RI). In recent years, a statistically based rapid intensity index (RII) that employs large-scale predictors from the SHIPS model has been developed for operational use at the National Hurricane Center (NHC) for both the Atlantic and eastern North Pacific basins. Although the current operational RII uses only information from the large-scale environment, a validation of independent forecasts from the 2006-2007 Hurricane seasons showed that it was skillful when evaluated in terms of the probability of detection and false alarm rate for a lead-time of 24 h. Nevertheless, the low to moderate probability of detection and relatively high false alarm rate of the RII and the other operational intensity guidance underscore the difficulty of predicting RI particularly in the Atlantic basin. While the large-scale conditions help to set the stage for RI, the actual intensification process itself is related to the storm's inner core. Thus, this paper will explore the potential to improve the operational RII by including predictors derived from three new sources of inner-core information. The first of these three sources is the time evolution of inner-core structure as deduced from GOES infra-red (IR) imagery. Although some basic parameters from GOES IR imagery such as counts of cold cloud pixels are already included in the RII, the time evolution of the inner-core structure is not. In a recently completed study, complex principal component analysis was applied to tropical cyclones and consistent IR cloud-top patterns related to RI were found. Complimentary results have since been found using standard principle component analysis. Information from principal component analysis or GOES IR data will therefore be tested in the RII. The second source is microwave-derived total precipitable water. Previous research utilizing the SHIPS model has shown that total precipitable water is statistically correlated with intensity change. Thus, this study will seek to employ total precipitable water to improve the RII. Finally, inner-core surface fluxes of heat and moisture will be tested for their ability to improve the RII. Estimates of surface fluxes can be obtained by utilizing GFS surface temperature and relative humidity fields and the sea-surface temperature (SST) determined from the SHIPS inner-core SST cooling algorithm. In this paper, a new version of the RII that includes real-time predictors based upon the above three new sources of inner-core information will be developed and then evaluated against the current operational RII.