Estimation of Hurricane Wind Speed Probablities: Application to New York City and Other Coastal Locations

Published

October 11, 2012

Author(s)

Dong Hun Yeo, Emil Simiu, Ning Lin

Abstract

This paper presents a procedure for estimating parametric probabilistic models of hurricane wind speeds from existing information on estimated wind speeds with various mean recurrence intervals (MRIs). Such models may be needed, for example, for the estimation of hurricane wind speeds with long MRIs required for the performance-based design of structures susceptible of experiencing nonlinear behavior. The paper first describes the procedure as applied to the case where that information is obtained from ASCE 7-10 wind maps, and provides examples of its application to a number of coastal mileposts on the Gulf and Atlantic coasts. Next, the procedure is applied by using, in addition to the ASCE 7-10 information, hurricane wind speeds with 1,000,000- and 10,000,000-year MRIs estimated in a 2011 Nuclear Regulatory Commission report. It is then argued that ASCE 7-10 Standard basic wind speeds for New York City are unconservative with respect to their counterparts specified in the Standard for other U.S. hurricane-prone locations. Finally, it is shown that best fitting extreme value distributions of hurricane wind speeds typically have finite upper tails of the reverse Weibull type, rather than infinite upper tails of the Gumbel type. This result may help to change the still widely held belief that extreme wind speeds are appropriately modeled only by the Gumbel distribution.

Citation

Technical Note (NIST TN) - 1773

Report Number

1773

NIST Pub Series

Technical Note (NIST TN)

Pub Type

NIST Pubs

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Keywords

Extreme values, hurricanes, New York City wind climate, risk consistency, wind engineering, wind speeds.

Citation

, D. , Simiu, E. and Lin, N. (2012), Estimation of Hurricane Wind Speed Probablities: Application to New York City and Other Coastal Locations, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=912130 (Accessed April 19, 2024)