LAUSR

Bright band (BB) is a layer of enhanced radar reflectivity due to hydrometeor melting and coalescence. BB identification is of high importance in radar quantitative precipitation estimation (QPE) and other applications. The dual-frequency precipitation radar (DPR) onboard the core satellite of the Global Precipitation Measurement mission (GPM) enables new investigations of BB characteristics on a global scale. However, the GPM-DPR operational BB identification algorithm based on the vertical profile of reflectivity (VPR) from single-frequency (SF; Ku-band) or dual-frequency (DF; Ku- and Ka-band) observations still has room for improvement. In the current study, an improved GPM-DPR SF BB identification algorithm is presented based on the detection of inflection points within a given range in a VPR. The improved GPM-DPR SF BB identification algorithm decreases the overestimation (underestimation) error of the BB bottom (top) height identified by the GPM-DPR SF algorithm, from 322 m (−345 m) to 182 m (−211 m), compared to the identifications by the GPM-DPR DF algorithm. The GPM-DPR SF will misestimate the depth of the melting layer, and the reflectivity difference between BB bottom (top) and BB peak to about 1.5 (3.5) dB, which will lead to misunderstanding the vertical physical variation of the precipitation particles. The validation through $\rho _{\mathrm {HV}}$ derived from WSR-88D observations in the conterminous United States (CONUS) demonstrates that the GPM-DPR DF algorithm is of higher accuracy, and the improved GPM-DPR SF algorithm performs better than the GPM-DPR SF algorithm. The new algorithm will contribute to hydrometeor phase classification and the studies on BB characteristics.