Understanding how much water is stored in snow is important for managing water supplies and tracking climate change. Radar systems that send and receive microwave signals can detect changes in snow depth and structure, especially when they use two different frequencies like C- and Ku-band. In our lab, we built SNOWWI, a dual-frequency airborne radar system, to study snow in detail from the air.

My research focuses on building a computer model that simulates how radar waves bounce off snow layers based on their physical properties—like grain size, density, and depth—without needing actual radar measurements. I’m working with the SMRT (Snow Microwave Radiative Transfer) model, which was originally made for satellite-based observations, and extending it to better handle active radar signals. In doing so, I’ve identified and begun addressing some technical gaps, such as how the model handles multiple scattering and shallow snow layers.

This work will help improve our ability to estimate snow water content and depth using physics-based simulations, making it especially useful in areas where collecting radar data is difficult or not possible.