Many studies have performed microphysical retrievals using radars of different frequencies, platforms, and methodologies. However, little is known about the consistency of retrievals derived from different radar platforms (i.e., airborne or spaceborne vs. ground-based) and their methodologies. This study is the first to directly compare snow mass-weighted mean diameter (Dm) retrievals from both nadir-pointing airborne multi-frequency radars and ground-based polarimetric range-height indicator radar scans along the same airborne flight track with a resolution

𝐴𝐴 of ;(10 m). Dm retrievals between each method produced mean absolute errors of 0.49, 0.74, and 0.93 mm where the largest differences were between the ground and aircraft retrievals. A triple-frequency analysis suggests the possibility that snow aggregates were generally composed of needles. These results can be used as a benchmark for comparing retrieval methodologies and highlight the continued uncertainty regarding the optimal approach for ice microphysical retrievals. Plain Language Summary Both airborne and ground-based weather radar measurements can be used to estimate snowflake characteristics such as their mean size. However, it is difficult to compare the radar-estimated mean sizes between these platforms because of the vast methodological differences in how these sizes are estimated and the spatial and temporal differences resulting from each radar's geographical location. This study is the first to directly compare snowflake size retrievals using ground-based mobile radar scans that collected data along the same path as an overhead-passing aircraft, thus directly minimizing any error resulting from matching values between radars aboard the aircraft and the ground radar. We also used the aircraft radars' multiple frequencies to investigate what types of particles were dominant throughout the storm. Through this multi-frequency analysis, we found that it is possible that snow particles were aggregates of thin needles that were stuck together. These results can be used by other researchers and forecasters to understand how snowflake size estimates can vary between different platforms and methodologies.