Airborne radar-lidar remote sensing for characterizing the microphysical properties of mixed-phase clouds

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 plays a critical role in the longevity of MPCs. Therefore, there is a pressing need for the observations of the spatial structure of MPCs. Active sensor remote sensing observations offer high-resolution vertical profiles of MPCs that could not be achieved by airborne in-situ measurements. In particular, polarimetric lidars discriminate between liquid and ice particles by utilizing the empirical thresholds of the depolarization ratios. However, the uncertainty of the empirical backscattering properties prevents us from a quantitative understanding of the microphysical properties in MPCs. In this study, we have developed the bulk backscattering property models for MPCs, which are incorporated into a radar-lidar-based remote sensing framework to perform numerical retrieval experiments for the characterization of the microphysical properties of MPCs. The synergistic use of 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 significantly enhances the information content of MPCs, demonstrating the potential of radar-lidar remote sensing approach based on a physics-based algorithm for characterizing MPC properties. 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.