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.

A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.

Phased array radar holds promise for future weather surveillance technology. It has the potential to improve temporal resolution, and allow significant performance and flexibility improvements compared to traditional reflector-antenna radars. Nevertheless, phased array radars have some drawbacks that need to be addressed. Two critical drawbacks are the variable copolar bias and cross-polar contamination. This article presents a novel technique for reducing the antenna cross-polar contamination on polarimetric measurements. The cross-polar canceller (XPC) technique is intended for fully digital phased array systems, but can also be implemented on systems with sub-array-level digital signals. XPC selects canceller elements strategically throughout the array to transmit cancelling signals. The canceller elements are modulated in amplitude and phase and transmit/receive in the cross-polarization to mitigate cross-polar radiation. The XPC algorithm provides the number of canceller elements, their location, and the complex coefficient per steering angle to maximize the cross-polar isolation. Calculations in this article are derived for the simultaneous transmission and simultaneous reception (STSR) mode, which simultaneously transmits electromagnetic waves in the horizontal ( H ) and vertical ( V ) polarizations. Nevertheless, they are also valid for the alternate transmission simultaneous reception (ATSR) mode. The XPC technique is being implemented in the Horus system, a fully digital, S-band, polarimetric phased array system designed by the Advanced Radar Research Center (ARRC). The analysis presented here are based on a measured Horus embedded element pattern, and a simulated 8×8 array. Preliminary results indicate that XPC is effective at mitigating cross-polar radiation, and significantly improving polarimetric weather measurements.

The 2007 and 2008 spring refractivity experiments at KTLX investigated the potential utility of high-resolution, near-surface refractivity measurements to operational forecasting. During these experiments, forecasters at the Norman, Oklahoma, National Weather Service Forecast Office (NWSFO) assessed refractivity and scan-to-scan refractivity change fields retrieved from the Weather Surveillance Radar-1988 Doppler weather radar near Oklahoma City—Twin Lakes, Oklahoma (KTLX). Both quantitative and qualitative analysis methods were used to analyze the 41 responses from seven forecasters to a questionnaire designed to measure the impact of refractivity fields on forecast operations. The analysis revealed that forecasts benefited from the refractivity fields on 25% of the days included in the evaluation. In each of these cases, the refractivity fields provided complementary information that somewhat enhanced the forecasters’ capability to analyze the near-surface environment and boosted their confidence in moisture trends. A case in point was the ability to track a retreating dryline after its location was obscured by a weak reflectivity bloom caused by biological scatterers. Forecasters unanimously agreed, however, that the impact of this complementary information on their forecasts was too insignificant to justify its addition as an operational dataset. The applicability of these findings to other NWSFOs may be limited to locations with similar weather situations and access to surface data networks like the Oklahoma Mesonet.

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range ($r_{\mathrm{a}}$) is important for surveillance but a high aliasing velocity ($v_{\mathrm{a}}$) is also important to obtain storm dynamics unambiguously. However, the $r_{\mathrm{a}}$ and $v_{\mathrm{a}}$ are inversely related to pulse repetition time. This “Doppler dilemma” is more challenging at shorter wavelengths. The proposed algorithm employs a CNN (convolutional neural network), which is widely used in image classification, to tackle the velocity dealiasing issue. Velocity aliasing can be converted to a classification problem. The velocity field and aliased count can be regarded as the input image and the label, respectively. Through a fit-and-adjust process, the best weights and the biases of the model are determined to minimize a cost function. The proposed method is compared against the traditional region-based method. Both methods show similar performance on mostly filled precipitation. For sparsely filled precipitation; however, the CNN demonstrated better performance since the CNN processes the entire scan at once while the region-based method processes only the limited adjacent area.

A measurement procedure to determine transmitted differential phase between horizontally and vertically polarized radiation of a dual-polarization radar is presented. It is applicable to radars that transmit and receive Simultaneously Horizontally and Vertically (SHV) polarized waves. The method relies only on power measurements and leverages capabilities of polarimetric radars with independent transmitters per polarization to estimate differential phase on transmission. Measurements using this method by ground and airborne systems are conducted, and results are validated using a software-defined radio that provides true phase measurements. The differential phase estimate produced with the proposed method for the PX-1000 (X-band) radar is -105°, while that produced with the software-defined radio is -104.47° ±4.43°. The proposed method with a simple power sensor achieves a precision of ±2.5°.