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Abstract. The probability density functions (pdf's) for the longitudinal and vertical velocities, temperature, their derivatives, and momentum and sensible heat fluxes were measured in the atmospheric surface layer for a wide range of atmospheric stability conditions. The measured pdf's for both the velocity and the temperature fluctuations are near-Gaussian and consistent with corresponding laboratory measurements for nearneutral and stable stability conditions. Hence the first-and second-order moments are sufficient to predict the heat and momentum flux pdf's. The lower-order moments can be estimated from mean meteorological conditions using surface layer similarity theory. For unstable conditions the pdf for temperature is non-Gaussian and is strongly skewed due to local convective thermal plumes. For near-neutral and stable conditions the pdf's for the velocity and temperature longitudinal gradients have long exponential tails, in agreement with findings in laboratory experiments and numerical simulations.

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Similarity models in the inner region of the unstable atmospheric boundary layer (ABL) are generally based on four dimensional parameters: buoyancy, friction velocity, surface heat flux, and the height above the land surface. In the free convection limit the friction velocity can be neglected, thus reducing the measurement needs in practical applications. Some field measurements of the second moment of temperature have indicated that free convection scaling of this statistic may be extended into more dynamic regimes, namely, the "dynamic-convective" and, perhaps, the "dynamic" regions of the ABL. An advantage of this approach is that the sensible heat flux can be estimated without shear stress measurements. This temperature variance similarity model is applied for a wide range of unstably stratified flows over the dry Owens Lake, in southeastern California. The simple free convection model of the temperature variance is accurate for sensible heat flux estimation across the full range of unstable atmospheric stability conditions.

Orthonormal wavelet expansions are applied to atmospheric surface layer velocity and temperature measurements above a uniform bare soil 'surface that exhibit a long inertial subrange energy spectrum. In order to investigate intermittency effects on Kolmogorov's theory, a direct relation between the &h-order structure function and the wavelet coefficients is derived. This relation is used to examine deviations from the classical Kolmogorov theory for velocity and temperature in the inertial subrange. The local nature of the orthonormal wavelet transform in physical space aided the identification of events directly contributing to intermittency buildup at inertial subrange scales. These events occur at edges of large eddies and contaminate the Kolmogorov inertial subrange scaling. By suppressing these events, the statistical structure of the inertial subrange for the velocity and temperature, as described by Kolmogorov's theory, is recovered. The suppression of intermittency on the nth-order structure function is carried out via a conditional wavelet sampling scheme. The conditioned wavelet statistics reproduced the Kolmogorov scaling (up to n = 6) in the inertial subrange and result in a zero intermittency factor. The conditional wavelet statistics for the mixed velocity temperature structure functions are also presented. It was found that the conditional wavelet statistics for these mixed moments result in a thermal intermittency parameter consistent with other laboratory and field measurements. The relationship between Kolmogorov's theory and near-Gaussian statistics for velocity and temperature gradients is also considered.