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Mechanistic Models of the Wind Speed Probability Distribution

While most studies of the wind speed pdf have been empirical, recent efforts have been made to develop physically based models ofpm.(u, v) and p„,(w). Through this approach, the pdfs are expressed in terms of dynamically meaningful parameters rather than abstract statistical parameters. For example, Monahan used an idealized slab model of the sea surface boundary layer momentum budget driven by fluctuating large-scale pressure gradients!91 to express pw(w) in terms of the boundary layer depth, boundary layer top entrainment velocity, surface drag coefficient, and pressure gradient statistics.191 He et al. used a generalized version of this model181 to demonstrate that surface buoyancy fluxes and the character of the land surface have important influences on the pdf of wind speed.181 A subsequent analysis suggested that the long positive tail of the nighttime wind speed pdf is a result of intermittent turbulent mixing at the top of the normally quiescent nocturnal boundary layer.1201 Much more work remains to be done on this problem; the mechanistic study of surface wind pdfs remains in its infancy.

Conclusions

The wind speed probability distribution is a central ingredient in studies of wind hazards, wind energy assessment, and fluxes between the atmosphere and the underlying surface. While the structure ofpw(w) can be constrained by some fundamental general requirements, it is not known to be fully characterized by any single family of distributions. Characterizations of pw(w) can be empirical (by parametric distributions such as the Weibull), or derived from representations of the joint distribution of the vector wind components. An emerging area of study seeks to develop physically based models of pw(w). These approaches offer distinct and complementary insights into the pdf of surface winds.

References

  • 1. Wilks, D.S. Statistical Methods in the Atmospheric Sciences; Academic Press: 2005.
  • 2. Burton, T.; Jenkins, N.; Sharpe, D.; Bossanyi, E. Wind Energy Handbook; Wiley: Chichester, UK, 2011.
  • 3. Holton, J.R. An Introduction to Dynamic Meteorology; Academic Press: 2004.
  • 4. Peixoto, J.P.; Oort, A.H. Physics of Climate; American Institute of Physics: New York, 1992.
  • 5. Arya, S.P. Introduction to Micrometeorology, Academic Press: 2001.
  • 6. Stull, R.B. An Introduction to Boundary Layer Meteorology; Kluwer: Dordrecht, 1997.

7. Sura, P. Extreme climate events. In Encyclopedia of Natural Resources;

  • 8. He, Y.; Monahan, A.H.; Jones, C.G.; Dai, A.; Biner, S.; Caya, D.; Winger, K. Land surface wind speed probability distributions in North America: Observations, theory, and regional climate model simulations. J. Geophys. Res. 2010,115, doi: 10.1029/2008JD010708.
  • 9. Monahan, A.H. The probability distribution of sea surface wind speeds. Part I: Theory and sea winds observations. J. Clim. 2006,19, 497-520.
  • 10. Dai, A.; Deser, C. Diurnal and semidiurnal variations in global surface wind and divergence fields. J. Geophys. Res. 1999,104, 31109-31125.
  • 11. Barthelmie, R.J.; Grisogono, B.; Pryor, S.C. Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea. J. Geophys. Res. 1996,101, 21327-21337.
  • 12. Romero-Centeno, R.; Zavala-Hidalgo, J.; Gallegos, A.; O’Brien, J.J. Isthmus of Tehuantepec wind climatology and ENSO signal. J. Clim. 2003,16, 2628-2639.
  • 13. Jimenez, P.A.; Dudhia, J.; Navarro, J. On the surface wind probability density function over complex terrain. Geophys. Res. Lett. 2011,38, L22803, doi:10.1029/2011GL049669.
  • 14. Carta, J.A.; Ramirez, P.; Velazquez, S. A review of wind speed probability distributions used in wind energy analysis. Case studies in the Canary Islands. Ren. Sust. Energy Rev. 2009,13,933-955.
  • 15. Monahan, A.H. Empirical models of the probability distribution of sea surface wind speeds. J. Clim. 2007,20, 5798-5814.
  • 16. Hennessey, J.P. Some aspects of wind power statistics. J. Appl. Meteor. 1977,16,119-128.
  • 17. Li, M.; Li, X. MEP-type distribution function: a better alternative to Weibull function for wind speed distributions. Ren. Energy 2005,30,1221-1240.
  • 18. Morrissey, M.L.; Greene, J.S. Tractable analytic expressions for the wind speed probability density functions using expansions of orthogonal polynomials. J. App. Meteor. Clim. 2012,51,1310-1320.
  • 19. Justus, C.G.; Hargraves, W.R.; Mikhail, A.; Graber, D. Methods for estimating wind speed frequency distributions. J. Appl. Meteor. 1978,17, 350-353.
  • 20. Monahan, A.H.; He, Y.; McFarlane, N.; Dai, A. The probability distribution of land surface wind speeds. J. Clim. 2011,24, 3892-3909.
  • 21. Brown, B.J.; Katz, R.W.; Murphy, A.H. Time series models to simulate and forecast wind speed and wind power. J. Clim. Appl. Meteor. 1984,23,1184-1195.
  • 22. Johnson, R.A.; Wehrly, T.E. Some angular-linear distributions and related regression models. J. Am. Stat. Assoc. 1978, 73, 602-606.
  • 23. Carta, J. A.; Ramirez, P.; Bueno, C. A joint probability density function of wind speed and direction for wind energy analysis. Energy Conversation. Manag. 2008, 49,1309-1320.
  • 24. Brooks, C.E.P.; Durst, C.S.; Carruthers, N. Upper winds over the world. Part I, The frequency distribution of winds at a point in the free air. Q. J. Roy. Met. Soc. 1946, 72, 55-73.
  • 25. Crutcher, H.L. On the standard vector-deviation wind rose. J. Meteor. 1957,14, 28-33.
  • 26. Davies, M. Non-circular normal wind distributions. Quart. J. Roy. Meteor. Soc. 1958,84, 277-279.
  • 27. Rice, S.O. Mathematical analysis of random noise (part 2). Bell Syst. Tech. J. 1945, 24, 46-156.
  • 28. Weil, H. The distribution of radial error. Ann. Math. Stat. 1954,25,168-170.

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