Accurate classification of ice particles in clouds is essential for improving the understanding of cloud microphysics and improving weather and climate models.
This thesis investigates the use of spectral polarimetry in millimetre-wavelengths, combined with a Discrete Dipole Approximation (DDA) and Gaussian Mixture Model (GMM) scattering database, to classify ice particles through fuzzy logic. Utilizing a dual-wavelength (94 and 35 GHz), dual-polarized cloud radar installed in Cabauw, this study analyses two non-precipitating ice cloud events. Spectral polarimetric variables, including differential reflectivity (Z_DR), Slanted Linear Depolarization Ratio (SLDR), backscattering phase (φ_bs), and Dual Spectral Ratio (DSR), were derived from radar measurements and compared with modelled values from the scattering database. Results indicated that different ice particle types exhibited distinct polarimetric characteristics, but a lot of overlap between particles remained.

A fuzzy logic classifier was developed, incorporating both 1D and 2D membership functions to improve differentiability between particle types. Adding temperature and liquid water path as variables was necessary to distinguish between branched planar, aggregates and graupel particles. The classification results were mostly consistent and as expected, though there was a high dependence on temperature, suggesting areas for further refinement. Through fuzzy logic outputs Q and Q-gap, the most probable type of ice particles is identified and a first assessment on the quality of this identification is given.

This study demonstrates that combining spectral polarimetric variables with an advanced scattering database has potential to improve the classification of ice particles. In particular, the proposed technique could allow the classification of possible different ice particle types for each radar observation volume. The method lays the basis for future developments in cloud microphysics and radar-based ice particle classification.