Abstract. Conventional radar-based hydrometeor classification algorithms identify the dominant hydrometeor type within a resolved radar volume, while newer techniques estimate the proportions of individual hydrometeor classes (hydrometeor partitioning ratios, HPRs) within a mixture. These newer algorithms (HMCP) are based on dual-polarization measurements from ground-based radars (GR), while to date no comparable algorithms for space-borne radars (SR) with dual-frequency capabilities exist. This study (1) further improves HPR estimates based on GR dual-polarization measurements, (2) exploits the combination of dual-frequency SR and dual-polarization GR to introduce HPRs based on dual-frequency observations only, and (3) evaluates GR- and SR-based HPR retrievals. To achieve these objectives, dual-polarization measurements of NEXRAD's GRs are matched with those of the dual-frequency precipitation radar of the Global Precipitation Measurement Core satellite. All matched volumes are represented by averaged dual-frequency and dual-polarization observations and several hundred GR sub-volumes classified with standard hydrometeor classification. The latter are used to calculate quasi-HPRs (qHPRs). qHPRs and averaged dual-frequency and dual-polarization variables of the training dataset are used to derive covariances and centroids for each hydrometeor class. They serve as the basis for dual-frequency and dual-polarization based HPR retrievals within HMCP and are applied to the test dataset. The ensuing evaluation of HPR retrievals is performed with the qHPRs of the test dataset. HPRs show for most hydrometeor classes high correlations with the qHPRs and confirm the overall good HMCP performance. However, dual-polarization based classification performance is superior to dual-frequency ones. Both underestimate snow, overestimate graupel, and result in low correlations for big drops.
Pejcic et al. (Tue,) studied this question.