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Previous work has shown that differential reflectivity ZDR observations from National Weather Service dual-polarization Doppler weather radars (WSR-88Ds) provide accurate estimates of convective boundary layer (CBL) depth when compared with depth estimates from 0000 UTC rawinsonde observations. We extend this work by launching small rawinsondes, called Windsonds, to study ZDR signals throughout the daytime hours. Results show that it can be difficult to identify CBL depth from ZDR alone when biological scatterers are absent. The exploration of other radar variables leads to the use of azimuthal ZDR variance to help in identifying CBL characteristics. A variable that combines both ZDR and azimuthal ZDR variance, called DVar, allows for improved signal identification using the quasi-vertical profile (QVP) method. Furthermore, the QVP channel width is found to be closely tied to the overall entrainment zone (EZ) structure. Results show that the centers and vertical extents of channels of reduced DVar in QVPs correlate well with sounding-observed CBL depth and EZ depth, respectively, across all stages of CBL development and in both clear and cloud-topped CBLs. The QVP approach tends to fail in identifying CBL and EZ depths when the vertical gradient in moisture above the CBL is small. Additionally, we compare the observed EZ depth to various EZ parameterizations and show that the parameterizations generally underestimate EZ depth. We conclude that the ability of WSR-88Ds to sample the CBL should be leveraged to increase our knowledge of CBL properties. Significance Statement: The boundary layer is the lowest layer of Earth’s atmosphere and influences many weather-related phenomena. During the day, sunlight warms the surface and the convective boundary layer (CBL) forms. Even though CBL characteristics are important for accurate weather forecasts, current methods of observing the CBL are severely lacking. This study investigates the potential of using dual-polarization weather radars to expand CBL observations. We also evaluate how well simplified CBL models predict certain CBL characteristics and how they could be improved in the future.