Single-Particle Polarization Tomography Enables Highly Accurate Phase Identification of Cloud Particles

Abstract. The phase state of cloud particles and their transformation processes are fundamental to understanding cloud and precipitation formation, variations in radiative energy budgets, and the evolution of severe weather systems. At present, for small-scale cloud particles smaller than 50 μm, conventional identification methods based on morphological features are readily constrained by imaging resolution and feature overlap, making high-accuracy phase discrimination challenging. To address this issue, we propose a polarization tomographic imaging method that integrates digital holography and polarimetric imaging, enabling the simultaneous acquisition of morphological parameters of individual particles within a particle ensemble, including three-dimensional coordinates, particle size, area, perimeter, the major and minor axes of the minimum-area bounding rectangle, and circularity, together with polarization parameters. Through observation experiments in an ice cloud chamber, liquid droplet and ice crystal samples smaller than 50 μm were analyzed. By combining morphological and polarization parameters, accurate identification of liquid droplets and ice crystals in mixed-phase particles was achieved, with an accuracy of 98.33 %, representing an improvement of 4.16 percentage points over the conventional circularity-based identification method. In addition, a continuous 10 s observation window during the late stage of mixed-phase cloud glaciation in the ice cloud chamber experiment was selected to calculate liquid water content, the number concentrations of liquid droplets and ice crystals, and their particle-number fractions. The results show that the droplet number concentration and liquid water content decrease with time, whereas the relative fraction of ice crystals gradually increases, reflecting the microphysical process of liquid water conversion into ice in a mixed-phase environment. This method can not only effectively improve the accuracy of phase identification for small-scale cloud particles but also provide information on the microphysical evolution of cloud particles, offering important support for studies of cloud microphysical processes, improvement of model parameterizations, weather modification, and early warning of severe weather events.