# The effect of the generalized extreme value distribution parameter estimation methods in extreme wind speed prediction

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## Abstract

The modeling and prediction of extreme values of geophysical variables, such as wind, ocean surface waves, sea level, temperature and river flow, has always been a field of main concern for engineers and scientists. The analysis of extreme wind speed particularly plays an important role in natural disasters’ preparedness, prevention, mitigation and management and in various ocean, environmental and civil engineering applications, such as the design of offshore platforms and coastal marine structures, coastal management, wind climate analysis and structural safety. The block maxima (BM) approach is fundamental for extreme value analysis. BM method is closely related to the generalized extreme value (GEV) distribution, which unifies the three asymptotic extreme value distributions into a single one. The most common methods used for the estimation of the GEV parameters are maximum likelihood (ML) and probability weighted moments methods. In this work, several very common as well some less known estimation methods are firstly assessed through a simulation analysis. The results of the analysis showed that the maximum product of spacings (MPS), the elemental percentile (EP), the ordinary entropy method and, in a lesser degree, the ML methods seem to be, in general, superior to the other examined methods with respect to bias, mean squared error and variance of the estimated parameters. The effects of the estimation methods have been also assessed with respect to the *n-*year design values of real wind speed measurements. The obtained results suggest that the MPS and EP methods, which are rather unknown to the engineering community, describe adequately well the extreme quantiles of the wind speed data samples.

## Keywords

Extreme wind speed Return period GEV parameter estimation methods Block maxima method## Notes

### Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 287844 for the project “Towards COast to Coast NETworks of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential” (COCONET). The research leading to these results has been also funded from the Greek General Secretariat for Research and Technology for the project “National programme for the utilization of offshore wind potential in the Aegean Sea: preparatory actions” (AVRA). The authors wish to thank Mrs D. Sifnioti and Mrs F. Karathanasi for their help in editing the manuscript and Mr. M. Stamos for his assistance in computational issues.

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