A Dynamic Probability Model of Hurricane Winds in Coastal Counties of the United States

Thomas Jagger Department of Statistics, The Florida State University, Tallahassee, Florida

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James B. Elsner Department of Geography, The Florida State University, Tallahassee, Florida

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Xufeng Niu Department of Statistics, The Florida State University, Tallahassee, Florida

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Abstract

The authors develop and apply a model that uses hurricane-experience data in counties along the U.S. hurricane coast to give annual exceedence probabilities to maximum tropical cyclone wind events. The model uses a maximum likelihood estimator to determine a linear regression for the scale and shape parameters of the Weibull distribution for maximum wind speed. Model simulations provide quantiles for the probabilities at prescribed hurricane intensities. When the model is run in the raw climatological mode, median probabilities compare favorably with probabilities from the National Hurricane Center’s risk analysis program “HURISK” model. When the model is run in the conditional climatological mode, covariate information in the form of regression equations for the distributional parameters allows probabilities to be estimated that are conditioned on climate factors. Changes to annual hurricane probabilities with respect to a combined effect of a La Niña event and a negative phase of the North Atlantic oscillation mapped from Texas to North Carolina indicate an increased likelihood of hurricanes along much of the coastline. Largest increases are noted along the central Gulf coast.

* Current affiliation: MathSoft Company, Seattle, Washington.

Corresponding author address: James B. Elsner, Dept. of Geography, The Florida State University, Tallahassee, FL 32306.

jbelsner@elsner.coss.fsu.edu

Abstract

The authors develop and apply a model that uses hurricane-experience data in counties along the U.S. hurricane coast to give annual exceedence probabilities to maximum tropical cyclone wind events. The model uses a maximum likelihood estimator to determine a linear regression for the scale and shape parameters of the Weibull distribution for maximum wind speed. Model simulations provide quantiles for the probabilities at prescribed hurricane intensities. When the model is run in the raw climatological mode, median probabilities compare favorably with probabilities from the National Hurricane Center’s risk analysis program “HURISK” model. When the model is run in the conditional climatological mode, covariate information in the form of regression equations for the distributional parameters allows probabilities to be estimated that are conditioned on climate factors. Changes to annual hurricane probabilities with respect to a combined effect of a La Niña event and a negative phase of the North Atlantic oscillation mapped from Texas to North Carolina indicate an increased likelihood of hurricanes along much of the coastline. Largest increases are noted along the central Gulf coast.

* Current affiliation: MathSoft Company, Seattle, Washington.

Corresponding author address: James B. Elsner, Dept. of Geography, The Florida State University, Tallahassee, FL 32306.

jbelsner@elsner.coss.fsu.edu

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