The Impacts of Multi-Lag Moment Processor on a Solid-State Polarimetric Weather Radar

Solid-state weather radars generally require pulse compression and blind range mitigation waveform in order to gain sufficient sensitivity to overcome the low peak power of solid-state transmitters and mitigate the near-range data lost due to the long transmit duty cycle, respectively. At the Advanced Radar Research Center (ARRC) of the University of Oklahoma, we have developed a solid-state polarimetric weather radar, the PX-1000, which uses a long waveform for far range observations and short waveform for blind range filling. It should be emphasized here that we typically use a virtually non-tapered waveform, which fully utilized of the capacity of the solid-state transmitters. One of the consequences of data acquisition using long and short waveforms is the abrupt change of signal-to-noise ratio (SNR) at the transition range from short waveform to long waveform. This effect is manifested into a rapid increase of cross-pol correlation coefficients (RhoHV), which makes subsequent data processing, e.g., data interpretation, automated hydrometeor classification and numerical model assimilations, more challenging. The multi-mag moment processor, recently developed in the ARRC, is less sensitive to SNR due to its underlying concept of fitting the auto- and cross-correlation estimates to the Gaussian functions without using auto-correlation estimates at lag-0. In addition, this algorithm does not depend on noise estimation because the use of lag-0 auto-correlation is avoided. In this work, we focus on the RhoHV estimation. We found that multi-lag moment processor provides superior results in RhoHV estimate compared to the canonical method, especially when the SNR is moderate to low (<20 dB), which is most typical for low-power solid-state weather radars. In this work, several case studies will be presented to illustrate the impacts of multi-lag moment processor in storm events with stratiform rain, mixtures of rain, wet snow and dry snow.