Weibull Statistics of Wind Speed over the Ocean

Pavía, Edgar G.; O’Brien, James J.

doi:10.1175/1520-0450(1986)025<1324:wsowso>2.0.co;2

Cited by 131 publications

(70 citation statements)

References 12 publications

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“…positive skewness and non-negative property. It is commonly described by the Weibull distribution (Pavia and O'Brien, 1986;Seguro and Lambert, 2000). Its density distribution is given as…”

Section: Dbs For Rh and W : Methods Developmentmentioning

confidence: 99%

Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

Yang

Gardelin

Olsson

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract.As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

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“…positive skewness and non-negative property. It is commonly described by the Weibull distribution (Pavia and O'Brien, 1986;Seguro and Lambert, 2000). Its density distribution is given as…”

Section: Dbs For Rh and W : Methods Developmentmentioning

confidence: 99%

Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

Yang

Gardelin

Olsson

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…Several researchers used the two-parameter Weibull distribution to fit wind speed frequency distributions (Elliott et al, 1987;Schwartz and Elliot, 2001;Dorvlo, 2002;Lun and Lam, 2000;Pavia and O'Brien, 1986;Chang et al, 2003;Ucar and Balo, 2009;Pryor and Barthelmie, 2010;Zaharim et al, 2009;Eskin et al, 2008).…”

Section: Implications Of Weibull Distributionmentioning

confidence: 99%

Characterization of wind power resource in the United States

Gunturu

Schlosser

2012

Atmos. Chem. Phys.

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Abstract. Wind resource in the continental and offshore United States has been reconstructed and characterized using metrics that describe, apart from abundance, its availability, persistence and intermittency. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) boundary layer flux data has been used to construct wind profile at 50 m, 80 m, 100 m, 120 m turbine hub heights. The wind power density (WPD) estimates at 50 m are qualitatively similar to those in the US wind atlas developed by the National Renewable Energy Laboratory (NREL), but quantitatively a class less in some regions, but are within the limits of uncertainty. The wind speeds at 80 m were quantitatively and qualitatively close to the NREL wind map. The possible reasons for overestimation by NREL have been discussed. For long tailed distributions like those of the WPD, the mean is an overestimation and median is suggested for summary representation of the wind resource.The impact of raising the wind turbine hub height on metrics of abundance, persistence, variability and intermittency is analyzed. There is a general increase in availability and abundance of wind resource but there is an increase in intermittency in terms of level crossing rate in low resource regions.

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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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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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Weibull Statistics of Wind Speed over the Ocean

Cited by 131 publications

References 12 publications

Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

Characterization of wind power resource in the United States

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

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