Utilizing Spaceborne Radars to Retrieve Dry Snowfall

A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Z(e)-S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (; 3-4 dBZ(e)), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZ(e). The average conditional global snowfall rate is calculated to be about 0.28 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 multifrequency 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). It is estimated that because of its higher detectability threshold, only about 7%-1% of the near-surface radar reflectivity values and about 17%-4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.