Session 6B.1 Seasonal prediction of Atlantic tropical cyclone activity

Tuesday, 29 April 2008: 10:15 AM
Palms E (Wyndham Orlando Resort)
Elinor Whitney Keith, North Carolina State University, Raleigh, NC; and L. Xie

Presentation PDF (143.1 kB)

One of the primary challenges in generating a seasonal hurricane forecasting model is the selection of the best set of predictors. This study uses a new methodology of cross-correlating potential predictors against Empirical Orthogonal Functions (EOFs) of the Hurricane Track Density Function (HTDF). Those predictors are then used in a Poisson regression model for forecasting seasonal counts of named storm, hurricane and major hurricane occurrence in the entire Atlantic, the Caribbean Sea, and the Gulf of Mexico. In addition, a scheme for predicting landfalling tropical systems along the U.S. Gulf of Mexico, Southeastern, and Northeastern coastlines is developed, but predicting landfalling storms adds an extra layer of uncertainty to an already complex problem, and on the whole these predictions do not perform well.

Testing of the final model shows that the model is quite promising for the Atlantic as a whole, but the subregions and landfalling predictions are not as strong, particularly for major (categories 3-5) hurricanes. Basinwide, the model's predictions of Caribbean hurricanes are found to be significant at a 95% confidence level. In the landfalling categories, only southeastern landfalling named storms and hurricanes and Gulf Coast named storms reach that significance.

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