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. Correct, timely and meaningful interpretation of polarimetric weather radarobservations requires an accurate understanding of hydrometeors and theirassociated microphysical processes along with well-developed techniques thatautomatize their recognition in both the spatial and temporal dimensions ofthe data. This study presents a novel technique for identifying differenttypes of hydrometeors from quasi-vertical profiles (QVPs). In this newtechnique, the hydrometeor types are identified as clusters belonging to ahierarchical structure. The number of different hydrometeor types in thedata is not predefined, and the method obtains the optimal number of clustersthrough a recursive process. The optimal clustering is then used to labelthe original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-banddual-polarization Doppler weather radar (NXPol) show that the techniqueprovides stable and consistent results. Comparison with available airbornein situ measurements also indicates the value of this novel method forproviding a physical delineation of radar observations. Although thisdemonstration uses NXPol data, the technique is generally applicable tosimilar multivariate data from other radar observations.

Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method