Precipitation Type Classification Method Using New Indicators for GPM Dual-Frequency Precipitation Radar: Numerical Simulation Experiments for Maritime Ice Clouds

Data from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite shows the promise of discriminating hydrometeor type (e.g. rain and snow) from different scattering features between Ku- and Ka-band waves. Improving the classification method in the current operational algorithm is, however, is still needed, and we explore a method using only radar data without referring a temperature profile assumed. In this communication, we will show the potential capability of new indicators DFR/Ze(Ku), the ratio of differential frequency ratio to the reflectivity from Ku-band wave, and the Ku-band path-integrated reflectivity. The use of these indicators enables the classification method to consider additionally attenuation effects. We use a numerical radar data simulator based on a T-matrix method for this purpose, which is extended to use output data from a 3-D bin microphysical model. This presentation will focus on the performance of a new method compared with a method using DFR itself, using model output simulated for maritime ice clouds. The capability of discriminating wet/dry snow and effects of particle size distribution will be also discussed.