Identifying Mixed-Phase Conditions in Deep Convective Clouds with Collocated ARM Vertically Pointing Active Sensors

Mixed-phase conditions in deep convective clouds impact ice nucleation and growth. These microphysical processes are critically important to precipitation efficiency and anvil cloud area and properties, impacting the radiative effects and lifetime of convective cloud systems. Observations of mixed-phase conditions can be difficult to obtain in convection. While lidars can effectively identify the presence of liquid water, they attenuate quickly in liquid clouds, and thus cannot give a full picture of most clouds. Doppler spectra from vertically pointing cloud radars have been effectively used to determine water clouds identified by lidars in high latitude clouds. Here we use ground-based, vertically pointing Ka-band cloud radar Doppler spectra from the Department of Energy’s Atmospheric Radiation Measurement (ARM) facility at the Southern Great Plains (SGP) site along with vertically pointing Raman Lidar measurements to identify phase in convective clouds. Machine learning techniques are used to characterize liquid water from moments of the Doppler spectra. Though neither of these instruments can penetrate heavily precipitating convective cores, they can provide high temporal and vertical resolution of the detection of supercooled liquid water within stratiform and anvil regions and early developing convection. The retrieved mixed-phase cloud dataset from this method will be compared to other precipitation radar datasets and in situ aircraft data during the MC3E field campaign to develop a more comprehensive picture of the phase of convective systems and characterize limitations in this method. The observations shown in this study are a first step towards providing phase partitioning statistics to be used for evaluating model microphysical processes.