Surface Wind-Speed Statistics Modelling: Alternatives to the Weibull Distribution and Performance Evaluation

Drobinski, Philippe; Coulais, Corentin; Jourdier, Bénédicte

doi:10.1007/s10546-015-0035-7

Cited by 40 publications

(33 citation statements)

References 60 publications

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“…However, the use of the Weibull distribution to describe the wind-speed probability is based on empirical rather than physical justifications and it is not always the most appropriate distribution for the measurements (Tuller and Brett 1984;Drobinski et al 2015). For practical use, a less appealing, but theoretically better justified distribution, has been suggested by Sardeshmukh and Sura (2009).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…In this article we use wind surface measurements (10 m a.g.l) from the global NOAA ISD Lite database (Smith et al, 2011), already used in Drobinski et al (2015) and Vautard et al (2010). This compilation of observations from operational weather stations is the best publicly available dataset over the considered region.…”

Section: Wind-speed Datamentioning

confidence: 99%

“…We use two distributions for the wind speed: the commonly used Weibull distribution and the Rayleigh-Rice distribution, defined in Drobinski et al (2015). We note f their probability density functions (PDF) and F their cumulative distribution function (CDF).…”

Section: Wind-speed Statistic Modelsmentioning

confidence: 99%

“…Despite the widespread use of the Weibull distribution, it has been shown to not accurately model all kinds of observed distributions (see, e.g., Drobinski et al, 2015) and many other distributions have been proposed to model the wind-speed statistics, especially mixed distributions. For a review, see, e.g., Carta et al (2009) and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, this method is only referred to in Pryor et al (2004) and for a completely different subject of application. As a comparison to the Weibull, we also consider a more complex mixed distribution, the Rayleigh-Rice distribution suggested in Drobinski et al (2015). It has been chosen instead of a mixture of Weibull distributions (e.g., Carta et al, 2009), as Baïle et al (2011) report a better description of the tails of the distributions by the Rice-like distribution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Errors in wind resource and energy yield assessments based on the Weibull distribution

Jourdier

Drobinski

2017

Ann. Geophys.

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Abstract. The methodology used in wind resource assessments often relies on modeling the wind-speed statistics using a Weibull distribution. In spite of its common use, this distribution has been shown to not always accurately model real wind-speed distributions. Very few studies have examined the arising errors in power outputs, using either observed power productions or theoretical power curves. This article focuses on France, using surface wind measurements at 89 locations covering all regions of the country. It investigates how statistical modeling using a Weibull distribution impacts the prediction of the wind energy content and of the power output in the context of an annual energy production assessment. For this purpose it uses a plausible power curve adapted to each location. Three common methods for fitting the Weibull distribution are tested (maximum likelihood, first and third moments, and the Wind Atlas Analysis and Application Program (WAsP) method). The first two methods generate large errors in the production (mean absolute error around 5 %), especially in the southern areas where the goodness of fit of the Weibull distribution is poorer. The production is mainly overestimated except at some locations with bimodal wind distributions. With the third method, the errors are much lower at most locations (mean absolute error around 2 %). Another distribution, a mixed Rayleigh-Rice distribution, is also tested and shows better skill at assessing the wind energy yield.

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Aeolian saltation on Mars at low wind speeds

2017

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Laboratory experiments indicate that the fluid threshold friction speed, u*tf, required to initiate fully developed aeolian saltation is much higher on Mars than on Earth. A discrepancy exists between Mars climate models that do not predict winds this strong and observations that sand‐sized particles are indeed moving. This paper describes how wind friction speeds well below u*tf, but above the impact threshold, u*ti, required to sustain saltation, can initiate sustained saltation on Mars, but at relatively low flux. Numerical experiments indicate that a sand grain on Mars mobilized sporadically between u*ti and u*tf will develop, over fetch lengths longer than generally available within low‐pressure wind tunnels, trajectories capable of splashing grains that propagate saltation and collectively form a cluster of saltating grains that migrate downwind together. The passage of a saltation cluster should leave behind a narrow zone of affected surface grains. The cumulative effect of many clusters represents a low‐flux phenomenon that should produce slow changes to aeolian bedforms over periods in which winds remain close to u*ti and never or rarely reach u*tf. Field evidence from small impact ripples along rover traverses is consistent with effects of saltation at these low friction speeds, without obvious evidence for events ≥u*tf. The potential utility of this grain mobility process is that it can operate entirely at more common winds well below u*tf and so help explain widespread sand movements observed on Mars wherever evidence might be mostly absent for u*tf being exceeded.

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Surface Wind-Speed Statistics Modelling: Alternatives to the Weibull Distribution and Performance Evaluation

Cited by 40 publications

References 60 publications

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

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

Errors in wind resource and energy yield assessments based on the Weibull distribution

Aeolian saltation on Mars at low wind speeds

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