Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM

摘要

Profile classification is a critical module in the microphysics retrieval algorithm for the dual-frequency precipitation radar (DPR) that will be onboard the Global Precipitation Measurement (GPM) Core satellite. Hydrometeor profile characterization (HPC or melting region detection) is an important part of profile classification. To accomplish this classification, characteristics of measured dual-frequency ratio $DFR_{m}$, defined as the difference between measured reflectivity at two frequency channels (Ku- and Ka-bands), were studied for different hydrometeor phases. This paper shows that a $DFR_{m}$ profile can be used to detect the frozen, mixed-phase, and liquid regions. An HPC model is developed in this paper for DPR profile classification using $DFR_{m}$ and its range variability along the height. Data collected by the Second Generation Airborne Precipitation Radar (APR-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. Signatures of Doppler velocity, as well as the linear depolarization ratio at Ku-band, available for APR-2 data, are used for cross-validation purpose. Comparison of the melting layer top and bottom between the HPC model and the velocity-based estimates shows that they compare well, with a 2% bias. The performance of the HPC method at GPM-DPR observation resolution is evaluated and is shown to be applicable to observation at GPM-DPR resolution. It can be inferred from the analysis presented that the methodology developed in this paper using $DFR_{m}$ is a good candidate for HPC for GPM-DPR.