• Abdella, K., , and N. A. McFarlane, 1996: Parameterization of the surface layer exchange coefficients for atmospheric models. Bound.-Layer Meteor., 80, 223248.

    • Search Google Scholar
    • Export Citation
  • Baas, P., , F. C. Bosveld, , H. Klein Baltink, , and A. A. M. Holtslag, 2009: A climatology of nocturnal low-level jets at Cabauw. J. Appl. Meteor. Climatol., 48, 16271642.

    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., , B. Grisogono, , and S. C. Pryor, 1996: Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea. J. Geophys. Res., 101 (D16), 21 32721 337.

    • Search Google Scholar
    • Export Citation
  • Burton, T., , D. Sharpe, , N. Jenkins, , and E. Bossanyi, 2001: Wind Energy Handbook. John Wiley & Sons, 617 pp.

  • Cakmur, R. V., , R. L. Miller, , and O. Torres, 2004: Incorporating the effect of small-scale circulations upon dust emission in an atmospheric general circulation model. J. Geophys. Res., 109, D07201, doi:10.1029/2003JD004067.

    • Search Google Scholar
    • Export Citation
  • Canuto, V. M., , Y. Cheng, , A. M. Howard, , and I. N. Esau, 2008: Stably stratified flows: A model with No Ri (cr). J. Atmos. Sci., 65, 24372447.

    • Search Google Scholar
    • Export Citation
  • Conradsen, K., , L. B. Nielsen, , and L. P. Prahm, 1984: Review of Weibull statistics for estimation of wind speed distributions. J. Climate Appl. Meteor., 23, 11731183.

    • Search Google Scholar
    • Export Citation
  • Costa, F. D., , I. C. Acevedo, , J. C. M. Mombach, , and G. A. Degrazia, 2011: A simplified model for intermittent turbulence in the nocturnal boundary layer. J. Atmos. Sci., 68, 17141729.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , and C. Deser, 1999: Diurnal and semidiurnal variations in global surface wind and divergence fields. J. Geophys. Res., 104 (D24), 31 10931 125.

    • Search Google Scholar
    • Export Citation
  • Esau, I. N., , and A. Grachev, 2007: Turbulent Prandtl number in stably stratified atmospheric boundary layer: Intercomparison between LES and SHEBA data. e-WindEng,5, 1–17. [Available online at http://ejournal.windeng.net/16/.]

  • Estournal, C., , and D. Guedalia, 1985: Influence of geostrophic wind on atmospheric nocturnal cooling. J. Atmos. Sci., 42, 26952698.

  • Ferrero, E., , L. H. Quan, , and D. Massone, 2011: Turbulence in the stable boundary layer at higher Richardson numbers. Bound.-Layer Meteor., 139, 225240.

    • Search Google Scholar
    • Export Citation
  • Gardiner, C. W., 1997: Handbook of Stochastic Methods for Physics, Chemistry, and the Natural Sciences. 2nd ed. Springer, 442 pp.

  • Gopalakrishnan, S. G., , M. Sharan, , R. T. McNider, , and M. P. Singh, 1998: Study of radiative and turbulent processes in the stable boundary layer under weak wind conditions. J. Atmos. Sci., 55, 954960.

    • Search Google Scholar
    • Export Citation
  • He, Y., , A. H. Monahan, , C. G. Jones, , A. Dai, , S. Biner, , D. Caya, , and K. Winger, 2010: Probability distributions of land surface wind speeds over North America. J. Geophys. Res., 115, D04103, doi:10.1029/2008JD010708.

    • Search Google Scholar
    • Export Citation
  • Hennessey, J. P., Jr, 1977: Some aspects of wind power statistics. J. Appl. Meteor., 16, 119128.

  • Justus, C. G., , W. R. Hargraves, , A. Mikhail, , and D. Graber, 1978: Methods for estimating wind speed frequency distributions. J. Appl. Meteor., 17, 350353.

    • Search Google Scholar
    • Export Citation
  • Kantha, L., , and S. Carniel, 2009: A note on modeling mixing in stably stratified flows. J. Atmos. Sci., 66, 25012505.

  • Lazarus, S. M., , and J. Bewley, 2005: Evaluation of a wind power parameterization using tower observations. J. Geophys. Res., 110, D07102, doi:10.1029/2004JD005614.

    • Search Google Scholar
    • Export Citation
  • Li, J., , and H. W. Barker, 2005: A radiation algorithm with correlated-k distribution. Part I: Local thermal equilibrium. J. Atmos. Sci., 62, 286309.

    • Search Google Scholar
    • Export Citation
  • Mahrt, L., 1989: Intermittency of atmospheric turbulence. J. Atmos. Sci., 46, 7995.

  • Mahrt, L., 1999: Stratified atmospheric boundary layers. Bound.-Layer Meteor., 90, 375396.

  • Mahrt, L., 2010: Variability and maintenance of turbulence in the very stable boundary layer. Bound.-Layer Meteor., 135, 118.

  • McNider, R. T., , D. E. England, , M. J. Friedman, , and X. Shi, 1995: Predictability of the stable atmospheric boundary layer. J. Atmos. Sci., 52, 16021614.

    • Search Google Scholar
    • Export Citation
  • Meillier, Y., , R. Frehlich, , R. Jones, , and B. Balsley, 2008: Modulation of small-scale turbulence by ducted gravity waves in the nocturnal boundary layer. J. Atmos. Sci., 65, 14141427.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2006: The probability distribution of sea surface wind speeds. Part I: Theory and SeaWinds observations. J. Climate, 19, 497520.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2012: Can we see the wind? Statistical downscaling of historical sea surface winds in the subarctic northeast Pacific. J. Climate, 25, 15111528.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., , Y. He, , N. A. McFarlane, , and A. Dai, 2011: The probability distributions of land surface wind speeds. J. Climate, 24, 38923909.

    • Search Google Scholar
    • Export Citation
  • Pavia, E. G., , and J. J. O’Brien, 1986: Weibull statistics of wind speed over the ocean. J. Climate Appl. Meteor., 25, 13241332.

  • Penland, C., 2003: Noise out of chaos and why it won’t go away. Bull. Amer. Meteor. Soc., 84, 921925.

  • Petersen, E. L., , N. G. Mortensen, , L. Landberg, , J. Højstrup, , and H. P. Frank, 1998: Wind power meteorology. Part I: Climate and turbulence. Wind Energy, 1, 222.

    • Search Google Scholar
    • Export Citation
  • Revelle, D. O., 1993: Chaos and “bursting” in the planetary boundary layer. J. Appl. Meteor., 32, 11691180.

  • Stull, R. B., 1997: An Introduction to Boundary Layer Meteorology. Kluwer, 670 pp.

  • Van de Wiel, B., , R. Ronda, , A. Moene, , H. de Bruin, , and A. Holtslag, 2002: Intermittent turbulence and oscillations in the stable boundary layer over land. Part I: A bulk model. J. Atmos. Sci., 59, 942958.

    • Search Google Scholar
    • Export Citation
  • Van de Wiel, B., , A. Moene, , O. Hartogensis, , H. de Bruin, , and A. Holtslag, 2003: Intermittent turbulence in the stable boundary layer over land. Part III: A classification for observations during CASES-99. J. Atmos. Sci., 60, 25092522.

    • Search Google Scholar
    • Export Citation
  • Verseghy, D. L., 1991: CLASS—A Canadian land surface scheme for GCMS. I. Soil model. Int. J. Climatol., 11, 111133.

  • Verseghy, D. L., 2000: The Canadian Land Surface Scheme (CLASS): Its history and future. Atmos.–Ocean, 38, 113.

  • Verseghy, D. L., , N. A. McFarlane, , and M. Lazare, 1993: CLASS—A Canadian Land Surface Scheme for GCMs, II. Vegetation model and coupled runs. Int. J. Climatol., 13, 347370.

    • Search Google Scholar
    • Export Citation
  • von Salzen, K., , N. A. McFarlane, , and M. Lazare, 2005: The role of shallow convection in the water and energy cycles of the atmosphere. Climate Dyn., 25, 671688, doi:10.1007/s00382-005-0051-2.

    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S. S., 2010: Comments on the numerical simulation of homogeneous stably-stratified turbulence. Bound.-Layer Meteor., 136, 161164, doi:10.1007/s10546-010-9484-1.

    • Search Google Scholar
    • Export Citation
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The Influence of Boundary Layer Processes on the Diurnal Variation of the Climatological Near-Surface Wind Speed Probability Distribution over Land

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  • 1 School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
  • | 2 Canadian Centre for Climate Modelling and Analysis, University of Victoria, Victoria, British Columbia, Canada
  • | 3 School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
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Abstract

Knowledge of the diurnally varying land surface wind speed probability distribution is essential for surface flux estimation and wind power management. Global observations indicate that the surface wind speed probability density function (PDF) is characterized by a Weibull-like PDF during the day and a nighttime PDF with considerably greater skewness. Consideration of long-term tower observations at Cabauw, the Netherlands, indicates that this nighttime skewness is a shallow feature connected to the formation of a stably stratified nocturnal boundary layer. The observed diurnally varying vertical structure of the leading three climatological moments of near-surface wind speed (mean, standard deviation, and skewness) and the wind power density at the Cabauw site can be successfully simulated using the single-column version of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (CanAM4) with a new semiempirical diagnostic turbulent kinetic energy (TKE) scheme representing downgradient turbulent transfer processes for cloud-free conditions. This model also includes a simple stochastic representation of intermittent turbulence at the boundary layer inversion. It is found that the mean and the standard deviation of wind speed are most influenced by large-scale “weather” variability, while the shape of the PDF is influenced by the intermittent mixing process. This effect is quantitatively dependent on the asymptotic flux Richardson number, which determines the Prandtl number in stable flows. High vertical resolution near the land surface is also necessary for realistic simulation of the observed fine vertical structure of wind speed distribution.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-11-00321.s1.

Corresponding author address: Norman A. McFarlane, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 3065 STN CSC, Victoria BC V8W 3V6, Canada. E-mail: norm.mcfarlane@ec.gc.ca

Abstract

Knowledge of the diurnally varying land surface wind speed probability distribution is essential for surface flux estimation and wind power management. Global observations indicate that the surface wind speed probability density function (PDF) is characterized by a Weibull-like PDF during the day and a nighttime PDF with considerably greater skewness. Consideration of long-term tower observations at Cabauw, the Netherlands, indicates that this nighttime skewness is a shallow feature connected to the formation of a stably stratified nocturnal boundary layer. The observed diurnally varying vertical structure of the leading three climatological moments of near-surface wind speed (mean, standard deviation, and skewness) and the wind power density at the Cabauw site can be successfully simulated using the single-column version of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (CanAM4) with a new semiempirical diagnostic turbulent kinetic energy (TKE) scheme representing downgradient turbulent transfer processes for cloud-free conditions. This model also includes a simple stochastic representation of intermittent turbulence at the boundary layer inversion. It is found that the mean and the standard deviation of wind speed are most influenced by large-scale “weather” variability, while the shape of the PDF is influenced by the intermittent mixing process. This effect is quantitatively dependent on the asymptotic flux Richardson number, which determines the Prandtl number in stable flows. High vertical resolution near the land surface is also necessary for realistic simulation of the observed fine vertical structure of wind speed distribution.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-11-00321.s1.

Corresponding author address: Norman A. McFarlane, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 3065 STN CSC, Victoria BC V8W 3V6, Canada. E-mail: norm.mcfarlane@ec.gc.ca

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