A typical routine in wind field resource assessment, at the most basic level, consists of first to third order statistics of times series data. The quality of the time series data can range between 0.05 to 600 seconds. More often than not the frequency of data will be the latter of the two since it is the cumulative power over long periods of time that define the financial return from turbines and thus high-resolution data is deemed unnecessary. It is now evident that such coarse time series data are no longer sufficient for a representa- tive assessment of the wind and that estimations based on such data are associated with inaccurate power curve pre- diction and turbine damage. In particular it has been sug- gested that such problems are due to a lack of understand- ing of the somewhat intermittent nature of the wind velocity fields and the small-scale fluctuations thus associated. In order to address this there has been a significant increase in research involving coupled mesoscale-microscale mod- els and stochastic downscaling methods. Our contribution is a demonstration that a good knowledge of small-scale variability is essential for a better understanding of the at- mospheric boundary layer. We discuss the applicability of the stochastic anisotropic multifractal model to the complex conditions of wind farm potential and operational sites.