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

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.

Risk of Strong Hurricane Winds to Florida Cities

“…The boldface line and accompanying points show the 6-hourly observed wind speeds (maximum sustained) as taken directly from HURDAT. The smooth line shows the hourly spline interpolation used in this work and as derived in Jagger and Elsner (2006). distribution to obtain the mean recurrence intervals for extreme wind speeds at various locations along the U.S. coastline. Chu and Wang (1998) use extreme value distributions to model return periods for tropical cyclone wind speeds in the vicinity of Hawaii.…”

“…The hurricane data were interpolated hourly for the geographic position of the hurricane center (center fix) and wind speed using splines described in Jagger and Elsner (2006). Splines are parsimonious smooth representations of the data that preserve local properties.…”

Forecasting US insured hurricane losses

“…This is done in Jagger et al (2001), but the focus is on the probability of hurricanes of any intensity and not on the probability of the most extreme winds. Jagger and Elsner (2006) model the most extreme hurricane winds along the US coast and show how the probability of winds exceeding extreme thresholds changes with climate factors, including the North Atlantic Oscillation (NAO) and the El Ni˜no/Southern Oscillation (ENSO).…”

“…Studies focusing on climate factors that influence hurricane frequency regionally (Lehmiller et al, 1997;Bove et al, 1998;Maloney and Hartmann, 2000;Elsner et al, 2000a;Murnane et al, 2000;Saunders et al, 2000;Jagger et al, 2001;Larson et al, 2005) are more recent. Insights into climate conditions that affect regional hurricane activity are used to help predict landfall activity (Lehmiller et al, 1997;Elsner andJagger, 2004, 2006;Saunders and Lea, 2005).…”

Surface wind speed probability distribution in the Southeast Pacific of Marine Stratus and Stratocumulus regions

“…Knowledge of ocean surface wind speed (SWS) probability distribution function (PDF) has important applications in offshore wind energy development and management, extreme wind assessment, and surface flux parameterizations [1][2][3][4]. A number of studies have examined the parameterization of wind PDFs using surface wind speed [4-In offshore regions, extensive marine stratus and stratocumulus (MSC) covers a large fraction of the ocean surface.…”