hide
Free keywords:
-
Abstract:
Aggregation or riming processes are highly relevant for precipitation formation and cloud evolution. Their quantitative characterization is however challenging and requires, e.g., knowledge about the fall velocity of particles and a potential presence of supercooled liquid droplets. In this study we present an approach that utilizes polarimetric and Doppler-spectral retrievals from observations of a scanning polarimetric (Slanted Linear Depolarization Ratio (SLDR)-mode) cloud Doppler radar of type MIRA-35, to derive the vertical evolution of hydrometeor shape between top and base of mixed-phase cloud systems with the goal to differentiate riming and aggregation processes. The underlying dataset was acquired in the framework of a 3-year field experiment, “Dynamic Aerosols Clouds and Precipitation Observation in the Pristine Environment in the South Ocean” (DACAPO-PESO) at the southern hemispheric midlatitude site of Punta Arenas, Chile (53°S, 71°W). The central algorithm is the determination of the vertical distribution of particle shape (VDPS) retrieval, which uses Range Height Indicator (RHI) scans from 30° to 90° elevation angle of SLDR and cross-correlation coefficient, which are sensible to the shape and orientation of particles, respectively. From the elevation dependency of these two parameters the microphysical parameter polarizability ratio (i.e., density-weighted aspect ratio) is derived as a function of height. In a final step, the VDPS method is combined with machine-learning-based spectral techniques like VOODOO, a new method for detecting supercooled liquid layers, to differentiate riming and aggregation processes.
