16C.4 Evaluating the Impact of Storm Structure on Statistical Rapid Intensification Model Skill

Friday, 20 April 2018: 11:45 AM
Champions ABC (Sawgrass Marriott)
John Kaplan, NOAA/AOML/HRD, Miami, FL; and G. Chirokova, J. Knaff, C. M. Rozoff, M. DeMaria, K. Sellwood, and K. D. Musgrave

Despite recent improvements in tropical cyclone (TC) intensity forecasting skill, predicting episodes of rapid intensification (RI) remains a very challenging problem and one of the highest operational forecasting priorities of the National Hurricane Center (NHC). 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 and is currently utilized as an operational forecasting tool by the NHC. The utility of the SHIPS-RII had been somewhat restricted since the original version provided probabilistic forecasts for only a single lead time of 24 h. Thus, additional versions of the SHIPS-RII as well as new logistic regression and Bayesian RI models were subsequently 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 in both the Atlantic and eastern North Pacific basins out to lead-times of 48-h and 72-h commencing with the 2016 and 2017 Hurricane Seasons, respectively.

While verification of both retrospective and operational RI model forecasts indicates that these new RI models are generally skillful, they still have a lot of room for improvement, especially for the Atlantic basin. Currently the RI models primarily utilize environmental data and lack predictors that provide detailed information related to the TC's inner core structure. Thus, efforts are currently underway to employ storm structural information to enhance the skill of the RI models as part of a new NOAA Joint Hurricane Testbed project. In our upcoming presentation, a brief description of the statistical RI models as well as an assessment of their overall level of skill during the 2017 season will be provided. In addition, preliminary results of recent efforts to employ storm structural information to enhance the skill of the RI models and the implication of those results on the capability of both current statistical and numerical models to predict RI will be addressed.

Disclaimer: The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. government position, policy, or decision.

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