Utilizing spaceborne radars to retrieve dry snowfall from a global and regional perspective

A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates using derived Ze-S relationships, which were created from backscatter cross-sections of various non-spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (~3 to 4 dBZe), and about 95% of all CPR dry snowfall observations are less than 10 dBZe. The average conditional global snowfall rate is calculated to be about .2 mm h-1, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized.

The potential of future multi-frequency spaceborne radars is evaluated using proxy 35/13.6 GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement Dual-frequency Precipitation Radar (DPR). Due to its higher detectability threshold, only about 8.5%/1.5% of the near-surface radar reflectivity values and 33%/13% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument. However, these potential detection shortcomings can be at least partially mitigated by using CPR-derived snowfall rate distributions.