Climatology Models for Extreme Hurricane Winds near the United States

Abstract

Abstract The rarity of severe coastal hurricanes implies that empirical estimates of extreme wind speed return levels will be unreliable. Here climatology models derived from extreme value theory are estimated using data from the best-track [Hurricane Database (HURDAT)] record. The occurrence of a hurricane above a specified threshold intensity level is assumed to follow a Poisson distribution, and the distribution of the maximum wind is assumed to follow a generalized Pareto distribution. The likelihood function is the product of the generalized Pareto probabilities for each wind speed estimate. A geographic region encompassing the entire U.S. coast vulnerable to Atlantic hurricanes is of primary interest, but the Gulf Coast, Florida, and the East Coast regions are also considered. Model parameters are first estimated using a maximum likelihood (ML) procedure. Results estimate the 100-yr return level for the entire coast at 157 kt (±10 kt), but at 117 kt (±4 kt) for the East Coast region (1 kt = 0.514 m s−1). Highest wind speed return levels are noted along the Gulf Coast from Texas to Alabama. The study also examines how the extreme wind return levels change depending on climate conditions including El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature. The mean 5-yr return level during La Niña (El Niño) conditions is 125 (116) kt, but is 140 (164) kt for the 100-yr return level. This indicates that La Niña years are the most active for the occurrence of strong hurricanes, but that extreme hurricanes are more likely during El Niño years. Although El Niño inhibits hurricane formation in part through wind shear, the accompanying cooler lower stratosphere appears to increase the potential intensity of hurricanes that do form. To take advantage of older, less reliable data, the models are reformulated using Bayesian methods. Gibbs sampling is used to integrate the prior over the likelihood to obtain the posterior distributions for the model parameters conditional on global temperature. Higher temperatures are conditionally associated with more strong hurricanes and higher return levels for the strongest hurricane winds. Results compare favorably with an ML approach as well as with recent modeling and observational studies. The maximum possible near-coastal wind speed is estimated to be 208 kt (183 kt) using the Bayesian (ML) approach.