Orthonormal wavelet expansions are applied to atmospheric surface layer velocity measurements that exhibited about three decades of inertial subrange energy spectrum. A direct relation between the nth order structure function and the wavelet coefficients is derived for intermittency investigations. This relation is used to analyze power-law deviations from the classical Kolmogrov theory in the inertial subrange. The local nature of the orthonormal wavelet transform in physical space aided the identification of events contributing to inertial subrange intermittency buildup. By suppressing these events, intermittency effects on the statistical stucture of the inertial subrange is eliminated. The suppression of intermittency on the nth order structure function is carried out via a conditional wavelet sampling scheme. The conditional sampling scheme relies on an indicator function that identifies the contribution of large dissipation events in the wavelet space-scale domain. The conditioned wavelet statistics reproduce the Kolmogrov scaling in the inertial subrange and resulted in a zero intermittency factor. A relation between Kolmogrov's theory and Gaussian statistics is also investigated. Intermittency resulted in non-Gaussian statistics for the inertial subrange scales.