Monin-Obukhov similarity theory (MOST) is widely used in numerical weather prediction to model surface fluxes of momentum, heat, and water vapor. However, MOST is based on assumptions of steady state and horizontally homogeneous turbulence that can lead to prediction errors in and around convective storms. To understand the nature of these errors, we used wind and eddy covariance flux measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains Atmospheric Observatory to evaluate MOST predictions in fair-weather and convective storm environments, specifically those of mesoscale convective systems and air mass thunderstorms. Predicted wind profiles agreed well with observations in fair-weather cases, while in convective storm cases, MOST systematically overestimated shear in cold pools, after gust front passage. Surface layer stability was found to be important in assessing MOST within convective storm environments. The overestimation of wind shear in cold pools suggests the role of non-local fluxes in transferring momentum downward. We discuss reasons for discrepancy and agreement with past studies, and conclude with recommendations to improve prediction of surface winds and fluxes in convective storm simulations.