Frequency Distribution of Wind Speed Near the Surface

Stewart, Dorathy A.; Essenwanger, Oskar M.

doi:10.1175/1520-0450(1978)017<1633:fdowsn>2.0.co;2

Cited by 97 publications

(43 citation statements)

References 12 publications

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“…The two-parameter Weibull distribution (Weibull 1951;Stewart and Essenwanger 1978) has long been established as a useful probability distribution for representing wind speeds (Justus et al 1978;Conradsen et al 1984). The Weibull distribution, with scale parameter a .…”

Section: A Weibull Distributionmentioning

confidence: 80%

“…Since the transfer function is monotone, this relationship is also shared by the quantiles of the log-transformed variables (Stewart and Essenwanger 1978); a similar result was demonstrated by Haas et al (2014). The validity of this relationship can be tested by making quantilequantile plots of the log-transformed variable.…”

Section: Transforming Variables To Have Different Weibull Distribumentioning

confidence: 99%

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A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

et al. 2014

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Reliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How can model-simulated variables best be adjusted to make them more realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution?This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950-2010) and regional climate model simulations (1995-2005 and 2045-55 at 12-and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters.A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir-Simpson categories-for example, an increase from 21% (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) to 27%-37% (2045-55) for category 3 or above events and an increase from 1.6% (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) to 2.8%-9.8% (2045-55) for category 5 events.

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Section: A Weibull Distributionmentioning

confidence: 80%

Section: Transforming Variables To Have Different Weibull Distribumentioning

confidence: 99%

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

et al. 2014

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“…However, the wind power forecast is relatively complex, and the forecast errors cannot be avoided. Generally, the wind speed can be approximated as the Gamma distribution [30], inverse Gaussian [31], log-normal [32], and Weibull [33][34][35][36]. Alternatively, copula theory has recently been applied to wind speed and wind power as a way of modeling nonlinear dependence structures [37].…”

Section: Wind Power Generation Modelmentioning

confidence: 99%

Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Yang

et al. 2017

Energies

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This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme.

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“…The Weibull model has been an especially useful tool for wind power analysis (Hennessey, 1977). Stewart and Essenwanger (1978) examined the Weibull distribution, and Luna and Church (1974) examined the log-normal distribution as models for the distribution of wind speeds independent of direction.…”

Section: Introductionmentioning

confidence: 99%

Surface Wind Speed Distributions

Avara¹,

Miers²

1992

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Frequency Distribution of Wind Speed Near the Surface

Cited by 97 publications

References 12 publications

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Surface Wind Speed Distributions

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