The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm's validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

Mobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamforming techniques. Adaptive algorithms allow for improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during two severe weather events in Oklahoma. Several digital beamforming methods were tested and analyzed, producing unique, simultaneous multibeam measurements using the AIR.

On 20 May 2013, the cities of Newcastle, Oklahoma City, and Moore, Oklahoma, were impacted by a longtrack violent tornado that was rated as an EF5 on the enhanced Fujita scale by the National Weather Service. Despite a relatively sustained long track, damage surveys revealed a number of small-scale damage indicators that hinted at storm-scale processes that occurred over short time periods. The University of Oklahoma (OU) Advanced Radar Research Center's PX-1000 transportable, polarimetric, X-band weather radar was operating in a single-elevation PPI scanning strategy at the OU Westheimer airport throughout the duration of the tornado, collecting high spatial and temporal resolution polarimetric data every 20 s at ranges as close as 10 km and heights below 500 m AGL. This dataset contains the only known polarimetric radar observations of the Moore tornado at such high temporal resolution, providing the opportunity to analyze and study finescale phenomena occurring on rapid time scales. Analysis is presented of a series of debris ejections and rear-flank gust front surges that both preceded and followed a loop of the tornado as it weakened over the Moore Medical Center before rapidly accelerating and restrengthening to the east. The gust front structure, debris characteristics, and differential reflectivity arc breakdown are explored as evidence for a ''failed occlusion'' hypothesis. Observations are supported by rigorous hand analysis of critical storm attributes, including tornado track relative to the damage survey, sudden track shifts, and a directional debris ejection analysis. A conceptual description and illustration of the suspected failed occlusion process is provided, and its implications are discussed.

The progression of phased array weather observations, research, and planning over the past decade has led to significant advances in development efforts for future weather radar technologies. However, numerous challenges still remain for large-scale deployment. The eventual goal for phased array weather radar technology includes the use of active arrays, where each element would have its own transmit/receive module. This would lead to significant advantages; however, such a design must be capable of utilizing low-power, solid-state transmitters at each element in order to keep costs down. To provide acceptable sensitivity, as well as the range resolution needed for weather observations, pulse compression strategies are required. Pulse compression has been used for decades in military applications, but it has yet to be applied on a broad scale to weather radar, partly because of concerns regarding sensitivity loss caused by pulse windowing. A robust optimization technique for pulse compression waveforms with minimalistic windowing using a genetic algorithm is presented. A continuous nonlinear frequency-modulated waveform that takes into account transmitter distortion is shown, both in theory and in practical use scenarios. Measured pulses and weather observations from the Advanced Radar Research Center’s dual-polarized PX-1000 transportable radar, which utilizes dual 100-W solid-state transmitters, are presented. Both stratiform and convective scenarios, as well as dual-polarization observations, are shown, demonstrating significant improvement in sensitivity over previous pulse compression methods.

A tornado outbreak occurred in central Oklahoma on 10 May 2010, including two tornadoes with enhanced Fujita scale ratings of 4 (EF-4). Tragically, three deaths were reported along with significant property damage. Several strong and violent tornadoes occurred near Norman, Oklahoma, which is a major hub for severe storms research and is arguably one of the best observed regions of the country with multiple Doppler radars operated by both the federal government and the University of Oklahoma (OU). One of the most recent additions to the radars in Norman is the high-resolution OU Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME). As the name implies, the radar is used as a platform for research and education in both science and engineering studies using polarimetric radar. To facilitate usage of the system by students and faculty, OU-PRIME was constructed adjacent to the National Weather Center building on the OU research campus. On 10 May 2010, several tornadoes formed near the campus while OU researchers were operating OU-PRIME in a sector scanning mode, providing polarimetric radar data with unprecedented resolution and quality. In this article, the environmental conditions leading to the 10 May 2010 outbreak will be described, an overview of OU-PRIME will be provided, and several examples of the data and possible applications will be summarized. These examples will highlight supercell polarimetric signatures during and after tornadogenesis, and they will describe how the polarimetric signatures are related to observations of reflectivity and velocity.

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.

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Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.