Extreme wind speeds from long-term synthetic records

Abstract This study proposes a novel methodology to create a large sized synthetic dataset of wind velocities and adopts this to discuss the probability distributions commonly used for extreme winds. A large number of long-term time series of mean wind speed are generated by a numerical procedure that faithfully reproduces the macro-meteorological component of wind velocity, while guaranteeing sample functions with random extremes. Through application of this technique, a large sized dataset of synthetic extreme wind observations has been extracted, of a size unprecedented in literature. Commonly applied extreme value (EV) methods are then used to process the dataset produced. In the first instance, the effectiveness of these models is tested to exclude any false effects due to the limited period covered by current wind measurements. Following this, interval estimations of design wind speeds are derived by analyzing EVs from records of different lengths in order to explore the applicability of EV distributions to real situations. The comparison between analytical and numerical results provides many interesting and intriguing points of discussion, and opens the way to new research horizons in EV analysis.

[1]  Ian R. Harris.  Generalised Pareto methods for wind extremes. Useful tool or mathematical mirage , 2005 .

[2]  R. Ian Harris,et al.  XIMIS, a penultimate extreme value method suitable for all types of wind climate , 2009 .

[3]  S. Rice.  Mathematical analysis of random noise , 1944 .

[4]  Andrea Freda,et al.  A pilot study of the wind speed along the Rome–Naples HS/HC railway line. Part 1—Numerical modelling and wind simulations , 2010 .

[5]  D. Young,et al.  Recurrence relations between the P.D.F.'s of order statistics of dependent variables, and some applications. , 1967, Biometrika.

[6]  I. Weissman.  Estimation of Parameters and Large Quantiles Based on the k Largest Observations , 1978 .

[7]  Nicholas J. Cook,et al.  Towards better estimation of extreme winds , 1982 .

[8]  David Walshaw,et al.  Getting the Most From Your Extreme Wind Data: A Step by Step Guide , 1994, Journal of research of the National Institute of Standards and Technology.

[9]  Jean Palutikof,et al.  A review of methods to calculate extreme wind speeds , 1999 .

[10]  Jon A. Peterka,et al.  Improved extreme wind prediction for the United States , 1992 .

[11]  E. Takle,et al.  Note on the Use of Weibull Statistics to Characterize Wind-Speed Data , 1978 .

[12]  J. R. Wallis,et al.  Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .

[13]  R. I. Harris.  The macrometeorological spectrum—a preliminary study , 2008 .

[14]  E. J. Gumbel,et al.  Statistics of Extremes. , 1960 .

[15]  R. I. Harris,et al.  Extreme value analysis of epoch maxima—convergence, and choice of asymptote , 2004 .

[16]  Nicholas J. Cook,et al.  Confidence limits for extreme wind speeds in mixed climates , 2004 .

[17]  Dan Rosbjerg,et al.  The climate of extreme winds at the great belt, Denmark , 1992 .

[18]  R. I. Harris.  Gumbel re-visited - a new look at extreme value statistics applied to wind speeds , 1996 .

[19]  Forrest J. Masters,et al.  Non-Gaussian Simulation: Cumulative Distribution Function Map-Based Spectral Correction , 2003 .

[20]  J. Holmes,et al.  Application of the generalized Pareto distribution to extreme value analysis in wind engineering , 1999 .

[21]  R. I. Harris,et al.  Improvements to the `Method of Independent Storms' , 1999 .

[22]  G. H. Yu,et al.  A distribution free plotting position , 2001 .

[23]  B. J. Vickery,et al.  On the prediction of extreme wind speeds from the parent distribution , 1977 .

[24]  A. Jenkinson.  The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[25]  Kenji Kai,et al.  Spectrum Climatology of the Surface Winds in Japan, Part II: The Diurnal Variation, theSynoptic Fluc , 1987 .

[26]  R. Fisher,et al.  Limiting forms of the frequency distribution of the largest or smallest member of a sample , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.

[27]  Discussion on Generalized extreme gust wind speeds distributions by E. Cheng, C. Yeung , 2003 .

[28]  Richard L. Smith,et al.  Models for exceedances over high thresholds , 1990 .

[29]  Mircea Grigoriu,et al.  Applied non-Gaussian processes : examples, theory, simulation, linear random vibration, and MATLAB solutions , 1995 .

[30]  Giovanni Solari,et al.  The wind forecast for safety management of port areas , 2012 .

[31]  J. Pickands.  Statistical Inference Using Extreme Order Statistics , 1975 .

[32]  Andrea Freda,et al.  A pilot study of the wind speed along the Rome–Naples HS/HC railway line.: Part 2—Probabilistic analyses and methodology assessment , 2010 .

[33]  Giovanni Solari,et al.  Long-term simulation of the mean wind speed , 2011 .

[34]  Jonathan A. Tawn,et al.  An extreme-value theory model for dependent observations , 1988 .

[35]  Nicholas John Cook,et al.  Postscript to “Exact and general FT1 penultimate distributions of extreme wind speeds drawn from tail-equivalent Weibull parents” , 2008 .

[36]  Richard L. Smith.  Extreme value theory based on the r largest annual events , 1986 .

[37]  Mahesh D. Pandey,et al.  THE R LARGEST ORDER STATISTICS MODEL FOR EXTREME WIND SPEED ESTIMATION , 2007 .

[38]  A. Naess,et al.  Estimation of Long Return Period Design Values for Wind Speeds , 1998 .

[39]  Nicholas John Cook,et al.  The Designer's Guide To Wind Loading Of Building Structures , 1986 .

[40]  Anthony C. Davison,et al.  Modelling Excesses over High Thresholds, with an Application , 1984 .

[41]  Stuart Coles,et al.  Directional Modelling of Extreme Wind Speeds , 1994 .

[42]  Nicholas J. Cook,et al.  Exact and general FT1 penultimate distributions of extreme wind speeds drawn from tail-equivalent Weibull parents , 2004 .

[43]  R. I. Harris.  Errors in GEV analysis of wind epoch maxima from Weibull parents , 2006 .

[44]  Lionel Weiss,et al.  Asymptotic inference about a density function at an end of its range , 1971 .

[45]  E. Simiu,et al.  Extreme Wind Distribution Tails: A “Peaks over Threshold” Approach , 1996 .