From super-droplets to synthetic radar observations: applying a radar simulator to SDM using bin-type data

Radar simulators enable quantitative evaluation of cloud resolving models by translating predicted hydrometeor populations into synthetic radar observations (e.g., reflectivity) comparable to measurements. Most existing simulators are designed for Eulerian bulk or bin microphysics schemes and require gridded size-distribution information that is not directly available from Lagrangian particle-based approarches such as the Super-Droplet Method (SDM). Here we assess the feasibility and current limitations of driving an existing radar simulator using SDM output through a bin-type conversion. Within each model grid cell, super-droplets are categiorized based on phase and size to construct particle size distributions on fixed diameter bins. The resulting simulations capture liquid-phase (rain) signatures reasonably well, indicating that the binning approarch preserves key information needed for warm-rain radar signals. In contrast, simulated radar signatures associated with ice particles remain more uncertain, largely because additional assumptions are required during conversion and scattering calculations (e.g., ice particle habit and density), and because some super-droplet information is not yet fully utilized in the simulator interface. We discuss how the rich attributes carried by super-droplets can be leveraged to better constrain ice particle properties and to develop a more direct, standardized pathway from SDM to radar-simulator-ready inputs, enabling more robust radar-based evaluation of small-scale cloud microphysical processes.