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The prediction accuracy of outdoor sound is in large part limited by uncertainties in the state of the atmosphere. These uncertainties can potentially be reduced by inferring scaling parameters of the atmospheric surface layer from wind noise. Screened microphones sense wind noise as a result of mean atmospheric flow, turbulent eddy interaction with the windscreen, and pressure fluctuations within the turbulent flow. Under conditions of terrain homogeneity and atmospheric quasisteadiness, the Monin-Obukhov similarity theory (MOST) states that only a handful of parameters governs the dynamics of the atmospheric surface layer. This study explores the relationships of atmospheric similarity parameters to the acoustic spectrum of wind noise in a convective boundary layer. Ambient noise data collected in a high desert during a 2007 long-range sound propagation experiment are analyzed for the purposes of establishing a nondimensional empirical relationship between the acoustic power spectrum and MOST parameters. Furthermore, this paper examines the consequences of inferring surface-layer scaling parameters with different parameter priors. This study shows that, for minimizing the variance in the inversion, the most important parameter to constrain is the Obukhov length.