Predicting extreme hurricane winds in the United States
Thomas H. Jagger, Florida State University, Tallahassee, FL; and J. B. Elsner
We demonstrate the use of POT (Peaks over Threshold) models to evaluate distribution of extreme winds within near-coastal regions of the United States. Initially, we use maximum likelihood methods to estimate the return levels for various long-range return periods for each coastal region. Next, we show that the return levels depend on climate variables such as ENSO and NAO. Finally, we demonstrate the usefulness of the Bayesian approach to POT modeling using WinBUGS software to model the relationship between climate variables and hurricane intensity. Additionally, we show that the Bayesian approach is useful for extending the historical record, managing measurement error, imputing missing values, and simulating future hurricane intensities.
Joint Poster Session 4, Joint Poster: Climate & Extremes, Linking Weather and Climate (Joint with Second Symposium on Policy and Socio-economic Research, Symposium on Connections Between Mesoscale Processes and Climate Variability, 19th Conference on Climate Variability and Change, and Climate Change Manifested by Changes in Weather)
Wednesday, 17 January 2007, 2:30 PM-4:00 PM, Exhibit Hall C
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