Feasibility of Physics-Based Retrievals of Microphysical Properties of Mixed-Phase Clouds from Airborne Radar-Lidar Observation

Mixed-phase clouds (MPC) pose significant challenges in climate projections due to the complex physical processes involved and the limited understanding of these processes. In particular, the spatial heterogeneity of the microphysical properties of MPCs is hypothesized to play a critical role in the longevity of MPCs. Therefore, there is a pressing need for the observations of the spatial structure of MPCs. Airborne radar and lidar remote sensing observations offer high-resolution vertical profiles of the backscattering signals from MPCs. However, the quantitative estimation of the MPC microphysical properties has still been a challenge. This is in part because of the difficulty of a quantitative interpretation of lidar signals into microphysical properties of MPCs. Due to complex scattering mechanisms at the backscattering, current radar and lidar remote sensing methods rely on empirical backscattering models, which involve significant uncertainty. In this study, we explore advanced light-scattering theory and its computational capabilities that incorporate the coherent backscattering mechanism to develop a physics-based radar-lidar remote sensing method for airborne measurements. The attenuated backscattering coefficients and volume depolarization ratios at 355 nm from lidar measurements and effective radar reflectivity factors at 95 GHz from radar measurements can be related to the microphysical properties of MPCs using the bulk optical property models, allowing the development of a physics-based radar-lidar remote sensing algorithm.

In this presentation, I will briefly describe the theoretical background of the physics-based algorithm and show synthetic experiments to evaluate the performance of the radar-lidar-based remote sensing method for the microphysical property retrievals in MPCs. If time allows, I will show an example of the retrievals of the microphysical properties in MPCs based on airborne radar-lidar observations made by the University of Wyoming King Air (UWKA) research aircraft.