Wind power meteorology. Part I: climate and turbulence

Petersen, E. Ladewig; Mortensen, Niels Gylling; Landberg, L.; Højstrup, Jørgen; Frank, Helmut

doi:10.1002/(sici)1099-1824(199804)1:1+<25::aid-we4>3.3.co;2-4

Cited by 67 publications

(19 citation statements)

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“…The standard empirically based pdf used for studies of surface wind speeds is the Weibull distribution (e.g., Petersen et al 1998a;Burton et al 2001). As discussed in the appendix, this distribution is characterized by a particular relationship between statistical moments: the skewness is a decreasing function of the ratio of the mean to the standard deviation (positive when this ratio is small, near zero when the ratio is intermediate in size (;3-4), and negative when the ratio is large).…”

Section: Probability Distribution Of Land Surface Winds: Global Obsermentioning

confidence: 99%

The Probability Distribution of Land Surface Wind Speeds

Monahan

McFarlane

et al. 2011

Journal of Climate

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The probability density function (pdf) of land surface wind speeds is characterized using a global network of observations. Daytime surface wind speeds are shown to be broadly consistent with the Weibull distribution, while nighttime surface wind speeds are generally more positively skewed than the corresponding Weibull distribution (particularly in summer). In the midlatitudes, these strongly positive skewnesses are shown to be generally associated with conditions of strong surface stability and weak lower-tropospheric wind shear. Long-term tower observations from Cabauw, the Netherlands, and Los Alamos, New Mexico, demonstrate that lower-tropospheric wind speeds become more positively skewed than the corresponding Weibull distribution only in the shallow (;50 m) nocturnal boundary layer. This skewness is associated with two populations of nighttime winds: (i) strongly stably stratified with strong wind shear and (ii) weakly stably or unstably stratified with weak wind shear. Using an idealized two-layer model of the boundary layer momentum budget, it is shown that the observed variability of the daytime and nighttime surface wind speeds can be accounted for through a stochastic representation of intermittent turbulent mixing at the nocturnal boundary layer inversion.

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Section: Probability Distribution Of Land Surface Winds: Global Obsermentioning

confidence: 99%

The Probability Distribution of Land Surface Wind Speeds

Monahan

McFarlane

et al. 2011

Journal of Climate

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“…We note here that our assumption in scenario 2 ensures that the TKE of the volume of air passing through the rotor is up to 5 m 2 s −2 more than the background value. In reality, turbulence decreases with distance downwind from the rotor [ Petersen et al , 1998]. Taylor [1983] has presented the horizontal turbulence intensity data in the wake of a single 12 m tall turbine with 17 m rotor diameter located in Aldborough, UK.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Can large wind farms affect local meteorology?

2004

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[1] The RAMS model was used to explore the possible impacts of a large wind farm in the Great Plains region on the local meteorology over synoptic timescales under typical summertime conditions. A wind turbine was approximated as a sink of energy and source of turbulence. The wind farm was created by assuming an array of such turbines. Results show that the wind farm significantly slows down the wind at the turbine hub-height level. Additionally, turbulence generated by rotors create eddies that can enhance vertical mixing of momentum, heat, and scalars, usually leading to a warming and drying of the surface air and reduced surface sensible heat flux. This effect is most intense in the early morning hours when the boundary layer is stably stratified and the hub-height level wind speed is the strongest due to the nocturnal low-level jet. The impact on evapotranspiration is small.

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“…Unfortunately, long‐term measurements of homogenous time series of gust speed are rare (Klawa and Ulbrich, ; Wan et al , ; Augter and Roos, ). If they are available, then variations in land cover at and around the measuring sites often reduce their spatial http://www.dict.cc/englisch-deutsch/representativity.html (Wieringa, ; Petersen et al , ; Landberg et al , ).…”

Section: Introductionmentioning

confidence: 99%

Modelling monthly near‐surface maximum daily gust speed distributions in Southwest Germany

Jung

Schindler

2016

Intl Journal of Climatology

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This study deals with high spatial resolution modelling of near-surface gust speed distributions in a complex region. The database on which gust speed distributions were modelled includes 69 daily maximum gust speed time series provided by the German Weather Service (DWD) for the period 1979-2013. Comparability of the time series was achieved by gap filling, homogenisation, detrending and measuring height correction. Monthly empirical cumulative distribution functions (CDF emp ) were fitted by 48 continuous theoretical cumulative distribution functions (CDF). The best-fitting CDF was identified by evaluating the Kolmogorov-Smirnov statistic (D), probability-probability (P-P) plots and quantile-quantile (Q-Q) plots. The five-parameter Wakeby distribution (WK5) was found to be the most appropriate CDF for all months. Therefore, monthly WK5-parameters were modelled on a 50 × 50 m resolution grid by an LSBoost approach based on sector-and distance-limited predictors of surface roughness and terrain-related variables (curvature, elevation, topographic exposure) as well as on ERA-Interim reanalysis wind speed data available at the 850 hPa pressure level (U 850hPa ). The final, modelled WK5-distributions (WK5 mod ) yield monthly quantiles (F distr ) of daily gust speed values and return periods (RP distr ) of near-surface gust speed up to 10 years.

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Wind power meteorology. Part I: climate and turbulence

Cited by 67 publications

References 0 publications

The Probability Distribution of Land Surface Wind Speeds

The Probability Distribution of Land Surface Wind Speeds

Can large wind farms affect local meteorology?

Modelling monthly near‐surface maximum daily gust speed distributions in Southwest Germany

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