Hydrometeor partitioning ratios for dual-frequency space-borne and polarimetric ground-based radar observations

Abstract. Conventional radar-based hydrometeor classification (HMC) algorithms identify the dominant hydrometeor type within a resolved radar volume, while more recent techniques allow estimation of the proportions of individual hydrometeor classes (hydrometeor partitioning ratios, HPRs) within a mixture. These newer algorithms (HMC_P^DP) are based on dual-polarization (DP) measurements from ground-based radars (GR), while similar algorithms do not yet exist for space-borne radars (SR) with dual-frequency (DF) capabilities. This study has three objectives, (1) to evaluate HPR retrievals, (2) to exploit the combination of DF SR and DP GR for estimating HPR based on satellite DF observations (HPR_k^DF) and (3) to further improve HPR_k^DP estimates based on GR DP observations. To achieve these, DP 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 DF and DP observations and several hundred GR sub-volumes classified with the standard HMC. The latter are used to calculate quasi-HPRs (qHPRs). qHPRs and averaged DF and DP variables serve as basis for the HPR_k^DF and HPR_k^DP retrievals, which in turn are evaluated with the qHPRs. The vertical distributions of HPR_k^DF and HMC_P^DP products are in good agreement. Furthermore, the estimated HPRs show for most hydrometeor classes high correlations with the qHPRs and confirm the overall good performance of the algorithms. However, HMC_P^DP performance is superior to HMC_P^DF. In both DF and DP space, snow HPRs are underestimated, graupel HPRs are overestimated, and HPRs for big drops show only low correlations.