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Statistical analysis of the results of minisodar measurements of vertical profiles of wind velocity components in a 5–200 m layer of the atmosphere shows that this problem belongs to the class of robust nonparametric problems of mathematical statistics. In this work, a new consecutive nonparametric method of adaptive pendular truncation is suggested for outlier detection and selection in sodar data. The method is implemented in a censoring algorithm. The efficiency of the suggested algorithm is tested in numerical experiments. The algorithm has been used to calculate statistical characteristics of wind velocity components, including vertical profiles of the first four moments, the correlation coefficient, and the autocorrelation and structure functions of wind velocity components. The results obtained are compared with classical sample estimates.

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In the present work, the kinetic energy of wind outliers taken to mean wind velocity values exceeding a preset value and including wind gusts are compared with the mean wind kinetic energy component retrieved from minisodar measurements using the robust parametric algorithm proposed by the authors. Allowance for the contribution of the wind outliers in the parametric estimates of the wind kinetic energy enables its fine structure to be determined and its effect on light flying objects, high-rise buildings, bridges to be estimated and the energy potentials of wind turbines to be evaluated.

No abstract