Vectorial statistics for the standard deviation of wind direction

The standard deviation of wind direction is an important parameter in atmospheric pollution management. It can be used to calculate the rate of horizontal diffusion and from this the transport and dispersion of air contaminants can be determined. The standard deviation of wind direction cannot be calculated directly from customary linear statistics, mainly because of its periodic nature which makes the zero position arbitrary. Various algorithms have been proposed to estimate its value. The methodologies adopted in meteorology implicitly assume that the wind angle can be treated independently of the wind speed. Such an assumption might not be appropriate in some instances, as will be shown in this work by means of an example. To overcome this limitation, a new algorithm that takes into account both the periodic and the vectorial nature of the wind direction will be proposed. This is done by weighing each sample value with the corresponding wind speed. The results obtained from the new method were compared to those determined from algorithms available in literature using measured data. The comparison indicates that while the behavior is similar, differences do exist. Further investigation indicated that while the differences can be small, they might be physically important.

[1]  D. Sprevak,et al.  The estimation of the parameters of the distribution of wind speed and direction , 1980 .

[2]  N. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[3]  Rudolf O. Weber ESTIMATORS FOR THE STANDARD DEVIATIONS OF LATERAL, LONGITUDINAL AND VERTICAL WIND COMPONENTS , 1998 .

[4]  E. Batschelet Circular statistics in biology , 1981 .

[5]  B. Hicks,et al.  A preliminary multiple resistance routine for deriving dry deposition velocities from measured quantities , 1987 .

[6]  John S. Irwin,et al.  Estimating Plume Dispersion-A Comparison of Several Sigma Schemes , 1983 .

[7]  D. C. Baird,et al.  Experimentation: An Introduction to Measurement Theory and Experiment Design , 1965 .

[8]  K. Essa,et al.  Comparison of some sigma schemes for estimation of air pollutant dispersion in moderate and low winds , 2005 .

[9]  Mario C. Cirillo,et al.  An intercomparison of semiempirical diffusion models under low wind speed, stable conditions , 1992 .

[10]  Rajender Parsad,et al.  ESTIMATION OF PARAMETERS , 2007 .

[11]  T. Koh,et al.  Improved diagnostics for NWP verification in the tropics , 2009 .

[12]  J. A. Carta,et al.  A joint probability density function of wind speed and direction for wind energy analysis , 2008 .

[13]  R. O. Weber,et al.  Estimators for the Standard Deviation of Horizontal Wind Direction , 1997 .

[14]  Y. Mori Evaluation of Several “Single-Pass” Estimators of the Mean and the Standard Deviation of Wind Direction , 1986 .

[15]  K. Musiake,et al.  Observations of Energy Fluxes and Evapotranspiration over Terrestrial Complex Land Covers in the Tropical Monsoon Environment , 2002 .

[16]  K. Clawson,et al.  Project Sagebrush: Revisiting the Value of the Horizontal Plume Spread Parameterσy , 2016 .

[17]  S. Hanna Lateral Turbulence Intensity and Plume Meandering During Stable Conditions. , 1983 .

[18]  E. Erdem,et al.  Comparison of bivariate distribution construction approaches for analysing wind speed and direction data , 2011 .

[19]  Lagrangian Particle Modeling of Dispersion in Light Winds , 2013 .

[20]  P. Farrugia,et al.  Comparative analysis of estimators for wind direction standard deviation , 2006 .

[21]  A framework for the structure of a low windspeed field , 1994 .

[22]  T. J. Lyons,et al.  Principles of Air Pollution Meteorology , 1990 .

[23]  M. Sharan,et al.  Statistical evaluation of sigma schemes for estimating dispersion in low wind conditions , 1996 .

[24]  M. Wesely,et al.  Fluxes of gases and particles above a deciduous forest in wintertime , 1983 .

[25]  Anil Kumar Yadav,et al.  COMPARISON OF SIGMA SCHEMES FOR ESTIMATION OF AIR POLLUTANT DISPERSION IN LOW WINDS , 1995 .

[26]  S. Alessandrini,et al.  Tracer dispersion simulation in low wind speed conditions with a new 2D Langevin equation system , 2006 .

[27]  J. I. Ibarra A New Approach for the Determination of Horizontal Wind Direction Fluctuations , 1995 .

[28]  P. Verma,et al.  Locating air quality monitoring station using wind impact area diagram , 2008, Environmental monitoring and assessment.

[29]  O. M. Essenwanger,et al.  Elements of statistical analysis , 1986 .

[30]  B. Isikwue,et al.  Estimation of horizontal pollution potential and mean ground level concentrations of air pollutants from an elevated source over Makurdi, Nigeria using wind data , 2010 .

[31]  R. J. Yamartino,et al.  A Comparison of Several `Single-Pass' Estimators of the Standard Deviation of Wind Direction. , 1984 .

[32]  P. Farrugia,et al.  On the Algorithms Used to Compute the Standard Deviation of Wind Direction , 2009 .