Inferring the Properties of Snow in Southern Ocean Shallow Convection and Frontal Systems using Dual Polarization C-Band Radar.


We use dual polarization C-Band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the Austral Summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual polarization observables, we estimate ice water content (𝑞𝑖), the mass-mean particle size (Dm), and the exponent of the mass-dimensional relationship (bm) that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve 𝑞𝑖, Dm, and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multi-cellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors.