Prospective students

Position #1

We will develop a remote sensing algorithm to investigate the vertical structure of mixed-phase cloud properties using airborne lidar (and radar) observations.  Lidar remote sensing capabilities have been underutilized due to difficulties in interpreting lidar signals into characteristics of mixed-phase clouds.  Therefore, the lidar measurements have been used to diagnose the cloud phase based on empirical knowledge.  Our group has theoretical and computational capabilities to interpret lidar signals into microphysical properties of liquid and ice crystals (e.g., right figure).  By taking full advantage of our theoretical knowledge and observational resources offered by the UW King Air (UWKA) facility, we will investigate the characteristics of the vertical structure of the mixed-phase clouds and the atmospheric parameters critical to supercooled droplet glaciation.  This project is supported by the PI’s startup fund, including a small-scale field campaign to be conducted by the next-generation UWKA Research Aircraft sometime between Fall 2025 to Spring 2026. The student will have the opportunity to participate in the field campaign.

Position #2

We will improve the cloud and precipitating hydrometeor type classification using the A-Train active sensor observations with a physics-based approach and provide a global climatology of cloud and precipitating hydrometeors to understand global radiative forcing by these hydrometeors.  A-Train radar and lidar observations (i.e., right figure) have provided an unprecedented 16 years of record on global and vertically resolved observations.   We will build a new A-Train-based remote sensing algorithm without relying on empirical knowledge to improve the classification of cloud and precipitating hydrometeors.  The outcomes of the project will provide solid validation datasets for global climate modeling communities.  This project will be supported by the PI’s startup fund till Summer 2026 and will be funded by an external grant afterward.