Weather radars have become indispensable to meteorologists and the general public for both understanding and awareness of high-impact weather events and as part of the operational warning infrastructure. In the U.S., the operational weather radar network is composed of approximately 160 WSR-88D radars, which are S-band, dish-based, polarimetric Doppler radars. This work reports on the development of a fully digital phased array weather radar that is being used to assess the potential of such technology as a replacement for the WSR-88D radars. The “Horus” radar is a truck-based, S-band, fully digital polarimetric phased array radar. Fully digital systems hold promise for meeting some of the greatest technical challenges facing the meteorological community, such as the effective integration of dual-polarization capability with phased array beam agility. This paper describes the fully digital Horus phased array weather radar that was recently completed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU). An overview of the advantages and challenges facing fully digital arrays for weather observations is provided along with potential mitigation strategies. Initial weather observations with Horus are given with the goal of assessing the radar scanning capabilities and most importantly the polarimetric quality. Finally, a vision for the future of next-generation weather radar operations is given with an eye toward leveraging the scalable design of Horus for high-resolution weather observations.

The recently installed S-band phased-array radar (PAR) at the National Weather Radar Testbed (NWRT) offers fast and flexible beam steering through electronic beam forming. This capability allows the implementation of a novel scanning strategy termed beam multiplexing (BMX), with the goal of providing fast updates of weather information with high statistical accuracy. For conventional weather radar the data acquisition time for a sector scan or a volume coverage pattern (VCP) can be reduced by increasing the antenna’s rotation rate to the extent that the pedestal allows. However, statistical errors of the spectral moment estimates will increase due to the fewer samples that are available for the estimation. BMX is developed to exploit the idea of collecting independent samples and maximizing the usage of radar resources. An improvement factor is introduced to quantify the BMX performance, which is defined by the reduction in data acquisition time using BMX when the same data accuracy obtained by a conventional scanning strategy is maintained. It is shown theoretically that a fast update without compromising data quality can be achieved using BMX at small spectrum widths and a high signal-to-noise ratio (SNR). Applications of BMX to weather observations are demonstrated using the PAR, and the results indicate that an average improvement factor of 2–4 can be obtained for SNR higher than 10 dB.

Weather radar technologies and instrumentation play a vital role in early warning of severe weather. For example, the annual impacts of adverse weather on the U.S. national highway system and roads are staggering: 7,400 weather-related deaths and 1.5 million weather-related crashes [1]. In addition, US$4.2 billion is lost each year as a result of air traffic delays attributed to weather. Research on high-impact weather is broadly motivated by society's need to improve the prediction of these weather events. The research approaches to accomplish this goal vary significantly with the inherent predictability of the weather system. For example, the current forecast approaches for issuing warnings of short-lived events, such as tornadoes and flash floods, are primarily based on observations with a focus on advanced Doppler radar measurements.

Important requirements for a future generation of weather surveillance radars include improvements in data quality and more rapid update of volumetric data. Phased array radar (PAR) is a candidate technology capable of providing the required functionality. The rotating PAR (RPAR) is a potential architecture that could improve the capabilities of the current parabolic-reflector-based US Weather Surveillance Radar—1988 Doppler (WSR-88D) operational network and is more affordable than other candidate PAR architectures. However, RPAR concept of operations that support observational needs has to be developed. The Distributed Beams (DB) technique introduced in this article provides a way to either reduce the scan times or to reduce the variance of radar-variable estimates by azimuthally spoiling the transmit beam while receiving multiple digital beams as the radar rotates in azimuth. Specifically, the rotation speed of the pedestal is derived from the duration of the coherent processing interval (CPI) to produce the desired spatial sampling. This results in beams from subsequent CPIs in approximately the same directions, which increases the number of available data samples for processing. The increased number of available samples can be coherently processed to reduce the variance of estimates. Alternatively, by reducing the number of samples per CPI and increasing the RPAR’s rotation rate, the scan time can be reduced without increasing the variance of estimates. Results presented demonstrate both applications of the DB technique for dual-polarization observations. Given that this technique makes use of spoiled transmit beams, its benefits come at the expense of degraded angular resolution (beamwidth and sidelobe levels), and reduced sensitivity compared with the use of pencil beams. The technique could be implemented as part of an RPAR concept of operations to meet requirements for the future weather surveillance network if certain tradeoffs are accounted for in the radar design process.