In this study, triple-frequency radar observations (Ku-, Ka- and W-band) from OLYMPEX are used to discriminate particle types to create a multi-frequency Hydrometeor Characterization Algorithm (MFHCA). First, an evaluation of this algorithm relative to ground-based dual-polarization Hydrometeor Classification Algorithms (DPHCA) is performed using coincident in-situ microphysical data, aircraft multi-frequency radar observations and ground-based dual-polarization radar measurements. Case studies of collocated in-situ particle images evaluate the retrieval of aggregate locations derived from the MFHCA and DPHCA. Profiles of the relative fraction of aggregates derived from MFHCA as a function of temperature quantify regions of ice crystal growth and aggregation. As a complement to the particle shape retrieval, dual-frequency and triple-frequency empirical retrievals of bulk effective density (ρ) and mean mass dimension (Dmm) are constructed from the OLYMPEX in-situ dataset and applied to the entire radar dataset. The empirical retrievals of ρ and Dmm are used to evaluate and constrain the assumptions of the retrieval of ice mass and ice mass flux, with the goal to improve spaceborne retrievals of ice-phase precipitation. Retrieved profiles of aggregates, ρ, and Dmm characterize snow and ice over both the ocean and complex topography and are compared against in-situ profiles of ρ and Dmm to better characterize the hydrometeor properties occurring in landfalling Pacific Northwest midlatitude cyclones