State Dependent Sensitivity of Spaceborne Radar to Ice Cloud Microphysical Assumptions

It is well known that radar measurements of clouds are sensitive to the assumptions made about the particle size distribution and particle shape. While models of the shape of liquid drops are relatively straightforward, representation of ice is far more complicated, involving a vast diversity of crystal habits and degrees of accumulated frozen liquid drops (rime). Ice crystal shape complexity introduces additional ambiguity into the relationship between radar measurements and cloud properties (mass and number distribution). While the fact that there is sensitivity is well understood, the dependence of the sensitivity of radar to the cloudy state is not. In particular, it is not clear whether the degree and characteristics of the uncertainty may vary as a function of the cloud type observed.

This presentation will show results from a study of simulated spaceborne (W, Ka, Ku band; e.g., CloudSat, INCUS, and GPM) radar reflectivity sensitivity to changes in cloud microphysical property assumptions for a large database of convective systems simulated with a high resolution (O ~ 100 meter horizontal grid spacing) numerical model. The model used is the Regional Atmospheric Modeling System (RAMS), which includes a realistic bin-emulating two-moment bulk microphysical scheme. RAMS has been used to simulate convective systems over land and ocean that range from shallow to deep and isolated to organized, forming in environments with a large range of shear and instability. Radar reflectivity is simulated from the RAMS output using a model that includes detailed ice crystal scattering properties. An uncertainty quantification infrastructure that is coupled with a flexible parallel computing toolkit allows for quantification of radar sensitivity to changes in microphysical assumptions, and for the uncertainties to be characterized as a function of convective storm type, development phase, and environment.