1998Journal of Engineering MechanicsRequires access

Estimation of Long Return Period Design Values for Wind Speeds

Arvid Næss

Open publisher page 57 citations

Abstract

The paper describes a method for extrapolation of extreme value data for estimating long return period values. The specific application here is the estimation of wind speeds relevant for the design of civil engineering structures. It is assumed that the observed data represent statistically independent samples from an extreme value probability distribution, and that the underlying phenomenon giving rise to the extreme wind speeds can be modeled as a stochastic process. A specific feature of the proposed method is a transformation of the observed extreme value data to obtain better fit to a Gumbel-type extreme value distribution.

About this research paper

What this paper is about

The paper describes a method for extrapolation of extreme value data for estimating long return period values. The specific application here is the estimation of wind speeds relevant for the design of civil engineering structures. It is assumed that the observed data represent statistically independent samples from an extreme value probability distribution, and that the underlying phenomenon giving rise to the extreme wind speeds can be modeled as a stochastic process. A specific feature of the proposed method is a transformation of the observed extreme value data to obtain better fit to a Gumbel-type extreme value distribution.

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OpenAlex reports 57 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The paper describes a method for extrapolation of extreme value data for estimating long return period values. The specific application here is the estimation of wind speeds relevant for the design of civil engineering structures. It is assumed that the observed data represent statistically independent samples from an extreme value probability distribution, and that the underlying phenomenon giving rise to the extreme wind speeds can be modeled as a stochastic process. A specific feature of the proposed method is a transformation of the observed extreme value data to obtain better fit to a Gumbel-type extreme value distribution.

Key concepts: Gumbel distribution, Extreme value theory, Return period, Extrapolation, Generalized extreme value distribution, Wind speed, Statistics, Mathematics

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