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.