Boundary-Layer Meteorology

, Volume 163, Issue 2, pp 179–201 | Cite as

A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer

  • G. C. Efthimiou
  • D. Hertwig
  • S. Andronopoulos
  • J. G. Bartzis
  • O. Coceal
Research Article

Abstract

Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind-comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behaviour of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model’s basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of near-surface wind speeds shows a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on knowledge of only a few simple flow statistics (mean wind speed, wind-speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence.

Keywords

Atmospheric surface layer Beta distribution Direct numerical simulation Extreme wind speeds Probability density function Weibull distribution Wind tunnel 

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Environmental Research Laboratory, INRASTESNCSR DemokritosAgia ParaskeviGreece
  2. 2.Department of MeteorologyUniversity of ReadingReadingUK
  3. 3.Department of Mechanical EngineeringUniversity of Western MacedoniaKozaniGreece
  4. 4.Department of Meteorology, National Centre for Atmospheric ScienceUniversity of ReadingReadingUK

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