| Description |
Through the analysis of scanning polarimetric W-band cloud radar data collected during STORMVEX, an algorithm has been developed to both identify and parameterize various ice crystal habits present within mixed-phase clouds. Armed with a unique dataset, the development of the algorithm took advantage of a slant 45° linear depolarization ratio (SLDR) measurement that was made as a function of the radar elevation angle when in range height indicator (RHI) scanning mode. This measurement technique proved to be invaluable in that it limited the influence of the particle's maximum dimension on the measured depolarization, which instead became more a function of the ice particle's shape. Validated through in situ measurements; pristine dendrites, lightly rimed dendrites, rimed stellar crystals, aggregates of dendrites, columns, and graupel particles were identified and matched with specific SLDR signatures. With a known ice particle habit and SLDR signature, the ice particle habit identification segment of the newly developed algorithm was then applied to the entire dataset consisting of 38,190 individual scans, in order to identify ice particle habits at a combined 849,745 range-heights and scanning angles. Through this analysis and the use of a chi-square test statistic, the predominant ice particle habit could be determined. Of primary interest in this study were the parameterizations of the ice particle mass and radar backscatter cross section. Through the modeling of the chosen ice particle habit as an oblate spheroid, these parameterizations were carried out in part by relying on previously published empirical studies as well as T-matrix scattering calculations of oblate spheroids composed of an ice/air mixture. Due to the computational expense of Tmatrix calculations, however, a new T-matrix scaling factor was derived from the Clausius-Mossotti relation, which relates the refractive index of a material to its polarizability. With this scaling factor, new T-matrix results could be found, still functions of ice particle mass and shape. Using this new parameterization scheme, a radar-based cloud microphysical property retrieval algorithm was then executed for two cases and compared to generic parameterizations. Results show that the potential difference in the retrieved microphysical properties for the generic versus the ice particle habit-based parameterization could be as high as a factor of two. |
