A review of methods to calculate extreme wind speeds

Abstract

High wind speeds pose a threat to the integrity of structures, particularly those at exposed sites such as bridges, wind turbines and radio masts. In any design project for large structures, safety considerations must be balanced against the additional cost of 'over-design'. Accurate estimation of the occurrence of extreme wind speeds is an important factor in achieving the correct balance. Such estimates are commonly expressed in terms of the quantile value X T , i.e., the maximum wind speed which is exceeded, on average, once every T years, the return period. Typically, for most users of wind data, estimates of the 50-year return period gust or wind speed are required, based on 10-20 (and often fewer) available years of observations. For this situation, the data are generally fitted to a theoretical distribution in order to calculate the quantiles. Fisher & Tippett (1928) showed that if a sample of n cases is chosen from a parent distribution, and the maximum (or minimum) of each sample is selected, then the distribution of the maxima (or minima) approaches one of three limiting forms as the size of the samples increases. Gumbel (1958) argued that, in the case of floods, each year of record constitutes a sample with 365 cases and that the annual extreme flood is the maximum value of the sample. Thus, the Fisher-Tippett distributions could be fitted to the set of annual maxima. This is the basis of all classical extreme value theory. The aim is to define the form of the limiting distribution and estimate the parameters, so that values of X T can be calculated.Here, we review and summarize the portion of the extensive literature on extreme value theory relevant to the analysis of wind and gust speed data. The review is carried out with reference to the requirements of a user seeking to select and apply a method appropriate to a particular real data set. We discuss first the available techniques and then the choices required for successful selection and implementation. The methods are organized broadly according to the length of the time series of observations available for analysis, beginning with those which require longer data sets, and moving towards those developed for use with short-duration series. The length of the observational data set is often a problem for those wishing to calculate extreme wind speed quantiles but is a common concern for users of geophysical data, and a substantial literature exists on estimation of extremes from short time series.Note that we do not consider here the matter of anemometer exposure. As with all analyses of wind data, the results of an extreme value analysis will be flawed if the data on which they are based are taken from an anemometer with a non-standard exposure (e.g., sheltered in one or more directions, or at a height above the ground different from the 10 m standard).Meteorol. Appl. 6, 119-132 (1999) A review of methods to calculate extreme wind speeds

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

“…This creates the possibility that a single 72-h footprint may have resulted from more than one extra-tropical cyclone crossing the domain. Choosing a shorter period raises the risk that a single extratropical cyclone could generate more than one storm event, thereby violating the independence requirement of the extreme value analyses undertaken in this study (Palutikof et al, 1999).…”

Assessment of extreme wind speeds from Regional Climate Models – Part 1: Estimation of return values and their evaluation

“…It comprises a family of three different probability distribution functions (Fisher and Tippett, 1928). The other approach is the peaks over threshold (POT) method, where all events above a chosen threshold are selected for the sample (Coles, 2001;Palutikof et al, 1999). This method increases the number of events included in the analysis and, correspondingly, reduces statistical uncertainty (Brabson and Palutikof, 2000).…”

A two-step framework for over-threshold modelling of environmental extremes

“…) values, the distribution of the data exceeding a given threshold converges towards a generalized Pareto distribution (GPD) (Pickands, 1975). Following this theoretical result, the over-threshold modelling (OTM) approach widely spread in extreme value analyses, together with the application of the GPD, (Davison and Smith, 1990;Simiu and Heckert, 1995;Embrechts et al, 1997;Palutikof et al, 1999;Coles, 2001;Mackay et al, 2001;Pandey et al, 2001;Rosbjerg and Madsen, 2004;Ribatet et al, 2007). It is widely recognized that the choice of the threshold is a critical point Published by Copernicus Publications on behalf of the European Geosciences Union.…”