Hybrid Scan and Joint Signal Processing for A High Efficient MPAR
To effectively utilize the MPAR resources, we propose a hybrid scan strategy and assume four synchronized beams each covering a ninety degree sector in azimuth. This strategy consists of: i) a narrow beam with uniform PRTs at low elevations (< 2 degree) to achieve high resolution, sensitivity, and ground clutter cancellation; ii) a staggered PRT Beam-Multiplexing (SBMX) for middle elevations (2 ~ 10 degrees) to save sampling time; iii) a broad beam at high elevations (> 10 degrees) to reduce the number of beams. This scan strategy enables volume sampling in 30 seconds with a single beam from each sector and it would save MPAR R&D costs because it has fewer simultaneous transmitting beams, but requires fundamental research in signal processing to meet requirements for weather surveillance.
This hybrid scan strategy does not take into account the time needed to collect low-level super-resolution data (Torres and Curtis 2007). To obtain super-resolution weather measurements, we propose sampling the azimuths at half-beamwidth and having the number of transmitted pulses equal to one half of the number used on the current WSR-88D at radials spaced a beamwidth apart. Dwell times of this method are equivalent to those of the WSR-88D legacy operation. To preserve the measurement accuracy of the second order moments and meteorological variables, the samples from adjacent beams are jointly processed. Let sn-1: [s(1), s(2),…, s(M)]n-1, sn: [s(1), s(2),…, s(M)]n, and sn+1: [s(1), s(2),…, s(M)]n+1 be the time-series data at the directions φn-1, φn, and φn+1, respectively. We propose two approaches to obtain high quality data: i) moment average method (MAM), and ii) joint signal processing (JSP). In the MAM approach, second order moments of the collected signal sn are estimated at each direction. Then, the three adjacent sets of these moments are averaged so that the weight of the center ones is higher than the weight of the side ones. Thus obtained moments are combined to estimate the Doppler and polarimetric variables for the center beam. In the JSP approach, the three signals are combined to form a composite sequence S = [sn-1, sn, sn+1] having 3M samples which is suitable for ground clutter mitigation and estimation of Doppler and polarimetric variables.
A comparison between dish-antenna super-resolution beam and PAR JSP beam patterns is made. For example, in the case of conventional super-resolution, 20 samples (40 samples for regular resolution) collected with a dish antenna mechanically scanning over a half beamwidth (0.5 degree) would be processed to obtain moment data, and the effective beam width is 1.11 degrees for the intrinsic beam of 1.0 degree. Note that the super-resolution sequence on the WSR-88D has 40 heavily weighted samples. In the case of PAR JSP operation, 20 samples are collected at each beam direction, separated by a half beamwidth (0.5 degree), and a total of 60 (20:20:20) samples from three adjacent beams are jointly processed to obtain moment estimates. The three beam patterns for each direction (in black) and their composite effective pattern (in blue) are plotted. The resulting super-resolution and the PAR JSP effective patterns (in blue) are compared, along with the intrinsic beam pattern (black) and the legacy regular effective pattern (in red). Because a -70 dB Taylor window is applied, the PAR JSP beam with 60 samples has the same power efficiency of 0.5 as that of the conventional super-resolution of 20 samples (compared with 40 samples over 1.0 degree). Since the PAR JSP beam has a similarly effective pattern as the conventional super-resolution beam, it is expected that the super-resolution data can be obtained with the PAR JSP. This is being tested with the NWRT data (Borowska et al. 2015 this conference).
Borowska, L., G. Zhang, and D. Zrnic, 2015: Demonstration of Super-resolution Measurements with a Phased Array Radar, AMS Annual Meeting, 2015, Phoenix, Az.
Torres, S. M., and C. Curtis, 2006: Design considerations for improved tornado detection using super-resolution data on the NEXRAD network. ERAD
Zhang, G., D. Zrnic, and L. Borowska, 2014: Joint Signal Processing for High Efficiency in MPAR Development and Operation, OU Interlectural Property Disclosure 15NOR003.
Zrnić, D. S.,2014: Speeding the volume coverage suitable for MPAR. Persoanl communication.