Probability distributions of land surface wind speeds over North America

• Published in: Journal of Geophysical Research. B. Solid Earth 2010-02, Vol.115 (D4), p.n/a
• Main Authors: He, Yanping, Monahan, Adam Hugh, Jones, Colin G., Dai, Aiguo, Biner, Sebastien, Caya, Daniel, Winger, Katja
• Format: Article
• Language: English
• Subjects: Atmospheric boundary layer; Atmospheric models; Atmospheric sciences; Buoyancy; Buoyancy flux; Climate; Climate change; Climate models; Diurnal variations; Earth; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fluctuations; Flux; Geophysics; Land; Land cover; land surface wind probability distribution; Mean; Night; Night-time; Nighttime; Probability density function; Probability density functions; RCM; Regional climate models; Regional climates; Regions; Risk assessment; Skewness; Standard deviation; Statistical analysis; Stratification; surface drag and buoyancy; Surface wind; Time of use; Weather stations; Weibull distribution; Weibull distribution and wind energy; Wind power; Wind speed
• ISSN: 0148-0227; 2169-897X; 2156-2202; 2169-8996
• DOI: 10.1029/2008JD010708

Knowledge of the probability distributions of surface wind speeds (SWS) is essential for surface flux estimation, wind power estimation, and wind risk assessments. The two‐parameter Weibull distribution is the most widely used empirical distribution for SWS. This study considers the probability density function (PDF) of 3‐hourly observations from 720 weather stations over North America for the period 1979–1999. The PDF of SWS is classified by season, time of day, and land surface type. The Weibull PDF is characterized by a particular relationship between the mean, standard deviation, and skewness. While the moments of the observed daytime SWS PDF are found to collapse around this Weibull relationship, the observed nighttime PDF has a broader range of values and is significantly more skewed than the Weibull PDF over rough surfaces. An idealized model shows that SWS skewness has a much greater rate of change with both the mean and standard deviation of surface buoyancy flux under conditions of stable stratification than that of unstable stratification. This result suggests that surface buoyancy flux plays an important role in generating diurnal variation of SWS PDF. Two global reanalyses products (ERA‐40 and NCEP‐NCAR) and three regional climate models (RCMs) (Rossby Centre Atmospheric Model version 3 (RCA3), limited area version of Global Environmental Multiscale Model (GEM‐LAM), and Canadian Regional Climate Model, version 4 (CRCM4)) all have a less skewed nighttime PDF and a more narrow range of the normal wind speed during day and night. Among them, two of the RCMs capture the observed SWS differences across different land cover types, and only one of the RCMs produces the observed seasonal peak of SWS PDF.