Studying altocumulus with ice virga using ground-based active and passive remote sensors

Mixed-phase clouds are still poorly understood, though studies have indicated that their parameterization in general circulation models is critical for climate studies. Most of the knowledge of mixed-phase clouds has been gained from in situ measurements, but reliable remote sensing algorithms to study mixed-phase clouds extensively are lacking. A combined active and passive remote sensing approach for studying supercooled altocumulus with ice virga, using multiple remote sensor observations, is presented. Precipitating altocumulus clouds are a common type of mixed-phase clouds, and their easily identifiable structure provides a simple scenario to study mixed-phase clouds. First, ice virga is treated as an independent ice cloud, and an existing lidar-radar algorithm to retrieve ice water content and general effective size profiles is applied. Then, a new iterative approach is used to retrieve supercooled water cloud properties by minimizing the difference between atmospheric emitted radiance interferometer (AERI)-observed radiances and radiances, calculated using the discrete-ordinate radiative transfer model at 12 selected wavelengths. Case studies demonstrate the capabilities of this approach in retrieving radiatively important microphysical properties to characterize this type of mixed-phase cloud. The good agreement between visible optical depths derived from lidar measurement and those estimated from retrieved liquid water path and effective radius provides a closure test for the accuracy of mainly AERI-based supercooled water cloud retrieval.