Dual-PRF Processing Algorithm for Enhanced Polarimetric Measurements in Weather Radar

Range and velocity ambiguities significantly impact weather radar measurements, particularly dual-polarization variables in simultaneous transmission mode. A key challenge is determining the unambiguous range interval from which returns originate, especially in the presence of overlaid returns that obscure polarimetric estimates. This article presents a novel dual-pulse repetition frequency (PRF) processing algorithm that blends long- and short-PRT data to recover polarimetric variables in contaminated regions. Two approaches are proposed: designing pulse sequences that inherently produce similar quality of polarimetric variables or applying adaptive range-averaging techniques when pulse sequences cannot be modified. The performance is demonstrated through both time-series simulations and actual radar observations. Results from real data analysis show high consistency between data from the long- and short-PRT sequences, with Pearson correlation coefficients exceeding 0.81 for all polarimetric variables. These advancements improve the operational utility of dual-PRF batch mode scanning, providing a practical solution for enhanced dual-polarization measurements in weather radar networks.