The work presented here utilizes the active sensors aboard NASA A-Train satellites to expand this analysis over the entire GIS. The two-channel LIDAR aboard CALIPSO is particularly sensitive to cloud liquid water, and, despite winter darkness and underlying icy surfaces, can discriminate between mixed-phase and fully-glaciated clouds. The millimeter-wavelength cloud profiling radar (CPR) aboard CloudSat is able to simultaneously determine if snowfall is present beneath the cloud. We leverage A-Train derived data products (2B-CLDCLASS-LIDAR, 2B-FLXHR-LIDAR, 2C-PRECIP-COLUMN, and 2C-SNOW-PROFILE) to composite case studies over Summit Station, comparing results from the ground-based and space-borne sensors. We then look over the full GIS, dividing snowfall events into the two regimes and documenting their frequency in time and space and approximating the snowfall rate and accumulation each produce. We use the ERA-I v5 reanalysis product to examine the synoptic scale patterns that dominate for the two regimes.