On Estimating Hurricane Return Periods

Kerry Emanuel Program in Atmospheres, Oceans, and Climate, Massachusetts Institute of Technology, Cambridge, Massachusetts

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

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Abstract

Interest in hurricane risk usually focuses on landfalling events of the highest intensity, which cause a disproportionate amount of hurricane-related damage. Yet assessing the long-term risk of the most intense landfalling events is problematic because there are comparatively few of them in the historical record. For this reason, return periods of the most intense storms are usually estimated by first fitting standard probability distribution functions to records of lower-intensity events and then using such fits to estimate the high-intensity tails of the distributions. Here the authors attempt a modest improvement over this technique by making use of the much larger set of open-ocean hurricane records and postulating that hurricanes make landfall during a random stage of their open-ocean lifetime. After testing the validity of this assumption, an expression is derived for the probability density of maximum winds. The probability functions so derived are then used to estimate hurricane return periods for several highly populated regions, and these are compared with return periods calculated both from historical data and from a set of synthetic storms generated using a recently published downscaling technique. The resulting return-period distributions compare well to those estimated from extreme-value theory with parameter fitting using a peaks-over-threshold model, but they are valid over the whole range of hurricane wind speeds.

Corresponding author address: Kerry Emanuel, Rm. 54-1620, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139. Email: emanuel@mit.edu

Abstract

Interest in hurricane risk usually focuses on landfalling events of the highest intensity, which cause a disproportionate amount of hurricane-related damage. Yet assessing the long-term risk of the most intense landfalling events is problematic because there are comparatively few of them in the historical record. For this reason, return periods of the most intense storms are usually estimated by first fitting standard probability distribution functions to records of lower-intensity events and then using such fits to estimate the high-intensity tails of the distributions. Here the authors attempt a modest improvement over this technique by making use of the much larger set of open-ocean hurricane records and postulating that hurricanes make landfall during a random stage of their open-ocean lifetime. After testing the validity of this assumption, an expression is derived for the probability density of maximum winds. The probability functions so derived are then used to estimate hurricane return periods for several highly populated regions, and these are compared with return periods calculated both from historical data and from a set of synthetic storms generated using a recently published downscaling technique. The resulting return-period distributions compare well to those estimated from extreme-value theory with parameter fitting using a peaks-over-threshold model, but they are valid over the whole range of hurricane wind speeds.

Corresponding author address: Kerry Emanuel, Rm. 54-1620, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139. Email: emanuel@mit.edu

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