While some airport operations such as ground de-icing rely on liquid water-equivalent based measurements, other operations such as runway plowing rely on snow depth. Since the Automated Surface Observing Stations (ASOS) do not measure snow depth, it must be done by a human observer or through various modeling techniques. Snow depth modeling though can easily evolve into a complex problem dependent upon many different variables. Aside from modeling, various Ultrasonic Snow Depth Sensor (USDS) have been developed to directly measure the depth. Measurements of the USDS, however, can possess large magnitudes of variability. Previously established snow depth algorithms using liquid-equivalent snow gauge measurements have demonstrated good correlation when compared to the USDS. Efforts made to further the preliminary results established by Galloway et al. 2006, are discussed in this paper. Additionally, a larger database of snowfall events will be examined and the analysis presented here.