Non-Rayleigh scattering applied to hydrometeor size estimation

Multi-frequency radar measurements collected at 2.8 GHz (S-band), 33.12 GHz (Ka-band) and 94.92 GHz (W-band) are processed using a neural network to estimate particle size distributions in ice-phase clouds composed of dry particles. The model data used to train the neural network was generated using the discrete-dipole approximation (DDA), a gamma particle size distribution function and an assumed size-density relationship, which is applied to distributions of both crystals and aggregates. Measurements are presented from the Maritime Continent Thunderstorm Experiment (MCTEX), which was held near Darwin Australia during November and December, 1995. The University of Massachusetts 33.12 GHz/94.92 GHz Cloud Profiling Radar System (CPRS), the NOAA 2.8 GHz profiler and other sensors were clustered near the village of Garden Point, Melville Island where numerous convective storms were observed. The 2.8 GHz radar is essentially non-attenuating over the path lengths considered and the authors use its cloud-top reflectivity values to remove extinction from measurements at the higher frequencies. The authors outline development of a generalized backscattering model for ice particles, describe the neural network used to retrieve particle size distribution parameters, and present results for a stratiform cloud case