Height variation of wind speed and wind distributions statistics

A B S T R A C T Multi-scale variability of winds in the complex terrain of southwestern Norway is investigated using up to 20 yr of observations from nine automatic weather stations and reanalysis data. Significant differences between the large-and local-scale winds are found. These differences are mainly governed by the large-scale topography of Southern Norway. Winds from the southeast and statically stable flow from the northwest are found to be significantly reduced at the ground level due to large-scale wake and blocking effects. Southwesterly and northeasterly winds are orographically enhanced. At a local scale, there are differences in the wind speed distributions between the surface stations, both in space and time. These differences can to a large extent be quantified in terms of the Weibull distribution function and associated with the respective geographical locations as discretised in four characteristic surface categories: offshore, inland, coast and mountain. The inland category is found to be associated with relatively low but variable wind speeds, whereas the coastal and offshore locations are dominated by more steady and stronger winds. The mountain wind speed distribution is fundamentally different from the others; it shares the variability with the inland locations but the higher average wind speed with the other categories.

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“…Justus and Mikhail, 1976;Hennessey, 1977;Pavia and O'Brien, 1986;Wei, 2010). The function can be expressed as follows:…”

Section: Wind Speed Distributionmentioning

confidence: 99%

Multi-scale variability of winds in the complex topography of southwestern Norway

Jonassen¹,

Ólafsson

Reuder³

et al. 2012

Tellus A: Dynamic Meteorology and Oceanography

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“…In connection with the development of wind power after the energy crisis in 1973, Justus and Mikhail (1976) suggested a parametrization of the wind-speed probability distribution in terms of the Weibull distribution, described by its scale and shape parameters. The parametrization was based on a universal power law of the vertical wind-speed profile proposed by Zimmer et al (1975), but was limited to heights of 100 m and was soon found (Doran and Verholek 1978) to lead to significant errors.…”

Section: Introductionmentioning

confidence: 99%

Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Gryning

Floors

Peña

et al. 2015

Boundary-Layer Meteorol

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Wind-speed observations from tall towers are used in combination with observations up to 600 m in altitude from a Doppler wind lidar to study the long-term conditions over suburban (Hamburg), rural coastal (Høvsøre) and marine (FINO3) sites. The variability in the wind field among the sites is expressed in terms of mean wind speed and Weibull distribution shape-parameter profiles. The consequences of the carrier-to-noise-ratio (CNR) threshold-value choice on the wind-lidar observations are revealed as follows. When the wind-lidar CNR is lower than a prescribed threshold value, the observations are often filtered out as the uncertainty in the wind-speed measurements increases. For a pulsed heterodyne Doppler lidar, use of the traditional -22 dB CNR threshold value at all measuring levels up to 600 m results in a ≈7 % overestimation in the long-term mean wind speed over land, and a ≈12 % overestimation in coastal and marine environments. In addition, the height of the profile maximum of the shape parameter of the Weibull distribution (so-called reversal height) is found to depend on the applied CNR threshold; it is found to be lower at small CNR threshold values. The reversal height is greater in the suburban (high roughness) than in the rural (low roughness) area. In coastal areas the reversal height is lower than that over land and relates to the internal boundary layer that develops downwind from the coastline. Over the sea the shape parameter increases towards the sea surface. A parametrization of the vertical profile of the shape parameter fits well with observations over land, coastal regions and over the sea. An applied model for the dependence of the reversal height on the surface roughness is in good agreement with the observations over land.

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“…Most often the wind speed at a meteorology station is measured along a tower at different elevations, and it is desired to be able to find the wind profile at this station for further wind loadings or energy calculations. Some researchers have employed the Weibull pdf for empirical wind speed relative frequency distribution (histogram), and a set of formulas are derived for the extrapolation of the Weibull pdf parameters [15][16][17][18]. In general, two-parameter Weibull pdf of wind speed, P(V ), is given as,…”

Section: Weibull Distribution Parameter Extrapolationmentioning

confidence: 99%

“…The k value can best be estimated by using the approximate relationship for (14) as given by Justus and Mikhail [17], that is:…”

Section: Weibull Distribution Parameter Extrapolationmentioning

confidence: 99%

Wind Velocity Vertical Extrapolation by Extended Power Law

Şen

Altunkaynak

Erdik

2012

Advances in Meteorology

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Wind energy gains more attention day by day as one of the clean renewable energy resources. We predicted wind speed vertical extrapolation by using extended power law. In this study, an extended vertical wind velocity extrapolation formulation is derived on the basis of perturbation theory by considering power law and Weibull wind speed probability distribution function. In the proposed methodology not only the mean values of the wind speeds at different elevations but also their standard deviations and the cross-correlation coefficient between different elevations are taken into consideration. The application of the presented methodology is performed for wind speed measurements at Karaburun/Istanbul, Turkey. At this location, hourly wind speed measurements are available for three different heights above the earth surface.

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Height variation of wind speed and wind distributions statistics

Cited by 276 publications

References 2 publications

Multi-scale variability of winds in the complex topography of southwestern Norway

Multi-scale variability of winds in the complex topography of southwestern Norway

Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Wind Velocity Vertical Extrapolation by Extended Power Law

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