Dependence of the Wind Profile Power Law on Stability for Various Locations

Touma, Jawad S.

doi:10.1080/00022470.1977.10470503

Cited by 143 publications

(68 citation statements)

References 1 publication

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“…I did not account for stability effects in the height corrections (e.g. Touma 1977) because data were not available to implement such corrections.…”

Section: Introductionmentioning

confidence: 99%

Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990

Klink¹

1999

Clim. Res.

108

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“…I did not account for stability effects in the height corrections (e.g. Touma 1977) because data were not available to implement such corrections.…”

Section: Introductionmentioning

confidence: 99%

Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990

Klink¹

1999

Clim. Res.

108

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“…Low wind speed values mostly occur when the atmosphere is stably stratified [22]. Thus, the influence of U 850hPa on U 100m,mod is rather small.…”

Section: Predictor Variable Selection and Importancementioning

confidence: 99%

“…It was demonstrated by [11] that the power law performs well compared to similar wind speed extrapolation methods. According to [22], the accuracy of the power law increases when stratification effects and the influence of the wind speed are considered. Therefore, the Hellmann exponent (E) was computed on a daily basis.…”

Section: Wind Speed Extrapolationmentioning

confidence: 99%

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High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung

2016

Energies

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Abstract:In this paper a methodology is presented that can be used to model the annual wind energy yield (AEY mod ) on a high spatial resolution (50 mˆ50 m) grid based on long-term (1979-2010) near-surface wind speed (U S ) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEY mod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The U S values were extrapolated to the height 100 m (U 100m,emp ) above ground level (AGL) by the Hellman power law. All U 100m,emp time series were then converted to empirical cumulative distribution functions (CDF emp ). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDF emp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level (U 850hPa ), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance (PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U 100m,emp and the modeled wind speed at 100 m AGL (U 100m,mod ), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEY mod .

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“…In places where trees or structures obstruct the near-surface wind, the use of a constant 1/7 exponent may yield quite erroneous estimates because it was derived for turbulent flow over a flat plate, and the logarithmic wind profile is preferred (Counihan 1975, Touma 1977. In the neutral atmospheric surface layer where turbulence is generated primarily in a mechanical way, logarithmic wind profile is described by the relation (WMO 1981): Wind speed distribution can be denoted by a plot of the wind speed probability distribution function to reveal the character of this apparently random variation.…”

Section: Techniques Of Assessing Wind Resource Characteristicsmentioning

confidence: 99%

Survey of wind power potential for wind-based electricity at Makambako, Iringa Tanzania

Kainkwa¹

2009

Tanz. J. Sci.

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Dependence of the Wind Profile Power Law on Stability for Various Locations

Cited by 143 publications

References 1 publication

Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990

Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Survey of wind power potential for wind-based electricity at Makambako, Iringa Tanzania

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