An extreme-value theory model for dependent observations

Tawn, Jonathan A.

doi:10.1016/0022-1694(88)90037-6

Cited by 162 publications

(125 citation statements)

References 29 publications

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“…In applying this method, it is necessary to decide, first, how to maintain the independence of extremes and, second, the size of r (Tawn, 1988). The extremes are more likely to be independent if r is kept small (Smith, 1986).…”

Section: Extending the Classical Methods To The R-largest Valuesmentioning

confidence: 99%

“…He prefers the latter. Tawn (1988) emphasises that parameter and quantile estimates based on the r-largest annual events should be more accurate that those derived from the classical method. He argues that, since α and β are independent of r, they are precisely the same parameters as would be estimated by the classical method.…”

Section: Extending the Classical Methods To The R-largest Valuesmentioning

confidence: 99%

“…A good example is sea level, which at many locations exhibits an increase with time in response to some combination of subsiding land due to isostatic adjustment or human activity and rising absolute sea level due to climate change. For this case, Tawn (1988) presents an extreme value model which allows for a linear trend.…”

Section: Non-stationaritymentioning

confidence: 99%

“…A simple time series plot of the data may be sufficient to reveal the presence of trend or step jumps, and is a necessary precursor to further analysis. It may, for example, prove necessary to reject part of the time series in order to create a homogeneous data set for analysis (e.g., Tawn, 1988).…”

Section: Non-stationaritymentioning

confidence: 99%

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A review of methods to calculate extreme wind speeds

Palutikof

Brabson

Lister

et al. 1999

Meteorological Applications

326

209

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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

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Section: Extending the Classical Methods To The R-largest Valuesmentioning

confidence: 99%

Section: Extending the Classical Methods To The R-largest Valuesmentioning

confidence: 99%

Section: Non-stationaritymentioning

confidence: 99%

Section: Non-stationaritymentioning

confidence: 99%

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A review of methods to calculate extreme wind speeds

Palutikof

Brabson

Lister

et al. 1999

Meteorological Applications

326

209

View full text Add to dashboard Cite

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“…The technique used is a version of the generalized extreme value (GEV) method (Pugh, 1987), in which the statistical distributions of the '1-largest' events per year are investigated. The formulation of the GEV method in this case is that of Tawn (1988), which has the advantage of providing formal error estimates. The method requires typically 30 years' of data in order to provide standard errors of adequate accuracy.…”

Section: Extreme Level Distributionsmentioning

confidence: 99%

Changes in extreme high waters at Liverpool since 1768

Woodworth

Blackman

2002

Intl Journal of Climatology

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Changes in values of annual maximum high water (AMxHW), annual maximum surge-at-high-water (AMxSHW) and surge at annual maximum high water (SAMxHW) have been investigated using tide gauge data from Liverpool for the period 1768-1999. AMxHW and SAMxHW (measured with respect to mean high water) were found to vary considerably from year to year, but to exhibit no long-term change over the 232 years. On the other hand, values of AMxSHW were found to be larger in the late-18th, late-19th and late-20th centuries than for most of the 20th century, qualitatively consistent with knowledge of temporal variations in storminess in the region based on meteorological data and anecdotal information. The generalized extreme value method was used to present the available data on AMxHW and other annual extreme parameters in the 'return period' form most often employed by coastal engineers, with conclusions on the differences between each set of parameters in each epoch consistent with those obtained from the original time series. Finally, changes in the statistical distribution of surge-at-high-water (SHW), demonstrated by investigation of variations of percentile levels of SHW values, provided additional information on the temporal variations in extreme surges to that provided by AMxSHW values, pointing in particular to increased storminess during the late-18th and late-20th centuries, with a suggested secular trend in distribution shape from the late-18th century until recent decades.

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