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. Radar-based hydrometeor classification typically comes downto determining the dominant type of hydrometeor populating a given radarsampling volume. In this paper we address the subsequent problem of inferringthe secondary hydrometeor types present in a volume – the issue ofhydrometeor de-mixing. The present study relies on the semi-supervisedhydrometeor classification proposed by Besic et al. ( 2016 ) but neverthelessresults in solutions and conclusions of a more general character andapplicability. In the first part, oriented towards synthesis, a bin-basedde-mixing approach is proposed, inspired by the conventional coherent andlinear decomposition methods widely employed across different remote-sensingdisciplines. Intrinsically related to the concept of entropy, introduced inthe context of the radar hydrometeor classification in Besic et al. ( 2016 ) , theproposed method, based on the hypothesis of the reduced random interferencesof backscattered signals, estimates the proportions of different hydrometeortypes in a given radar sampling volume, without considering the neighboringspatial context. Plausibility and performances of the method are evaluatedusing C- and X-band radar measurements, compared with hydrometeor propertiesderived from a Multi-Angle Snowflake Camera instrument. In the second part,we examine the influence of the potential residual random interferencecontribution in the backscattering from different hydrometeors populating aradar sampling volume. This part consists in adapting and testing thetechniques commonly used in conventional incoherent decomposition methods tothe context of weather radar polarimetry. The impact of the residualincoherency is found to be limited, justifying the hypothesis of the reducedrandom interferences even in a case of mixed volumes and confirming theapplicability of the proposed bin-based approach, which essentially relies onfirst-order statistics.

Unraveling hydrometeor mixtures in polarimetric radar measurements