In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.

This letter presents initial weather measurements with a cylindrical polarimetric phased array radar (CPPAR) demonstrator developed at The University of Oklahoma. The overall system specifications, waveform design, beam pattern measurement, and beam-to-beam calibration of the CPPAR demonstrator are presented. The weather observations of convective precipitation are provided, employing a single-beam mechanical scan and commutating beam electronic scan. Measurement results from these two scan modes are compared, and the error statistics are derived and discussed. A new feature of the CPPAR commutating beam electronic scan in clutter detection is observed and explained.

This letter combines a time-domain modeling and simulation method for evaluating the impacts of system modules on polarimetric data quality of phased array weather radars with both theoretical analysis and actual measurements. In the presented phased array radar system simulator (PASIM), the distributed weather returns are modeled by randomly distributed scatterers, and Next-Generation Radar (NEXRAD) Level-II data or user-defined weather scenarios are utilized as weather truth fields. Based on a specially designed patch element, a dual-polarization phased array mobile demonstration system (Ten Panel Demonstrator or TPD) is simulated. In addition, the biases of differential reflectivity, copolar correlation coefficient, and differential phase along beam direction away from the broadside in principal plane and nonprincipal plane are used as data quality metrics. Moreover, theoretical analysis, system simulation, and actual TPD proof of concept measurements in a stratiform precipitation and a convective precipitation are presented, respectively, then similarities and discrepancies between simulations and measurements are compared and explained.

This paper presents clutter detection and mitigation for polarimetric phased array weather radar measurements using machine learning. Three approaches of naive Bayes classifier (NBC), multilayer perceptron (MLP), and convolutional neural network (CNN) are used for clutter detection in the cylindrical polarimetric phased array radar measurements. Results show that CNN achieves the best performance in clutter detection, followed by MLP and NBC. This is because CNN utilizes spatial information of the input images, which has different features for clutter from that for weather. It is also shown that the combination of physics-based discriminants of power ratio and raw radar measurements is more effective in clutter detection than the direct use of raw radar measurements. In addition, CNN is employed for clutter mitigation and its performance is compared with the traditional speckle filter technique. It is demonstrated that CNN outperforms the speckle filter and incorporation of power ratio in the training process could further improve CNN’s performance in clutter mitigation.

This letter presents differences in spectrum width estimates between electronic scans and mechanical scans with a cylindrical polarimetric phased array radar (CPPAR). Theoretical formulation of spectrum width estimates considering the transverse wind, shear, and turbulence within the radar’s resolution volume is described. Cross correlation function between two resolution volumes is derived based on wave scattering by randomly distributed scatterers. Spectrum width estimates of ground clutter and weather scatterers with the CPPAR in electronic scans and mechanical scans are presented and compared. It is shown that instead of antenna rotation, the transverse wind, shear, and turbulence within the radar’s resolution volume, which were previously ignored for narrow beams, contribute the most to the differences in spectrum width estimates between mechanical scans and electronic scans for the CPPAR with a wide beam.

This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the copolar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.