Current Research Interests
- Meteorological applications for Doppler radar
- Pulse compression methods and optimal waveform design for Doppler weather radar
- Waveform design techniques for dealiasing, isolation, and Doppler tolerance correction
- Hydrometeor classification, fuzzy logic, and machine learning
- Weather radar observations of chaff, sea clutter, and icing
- Spectral efficiency for radar networks
- Radar observations of severe local storms and tornadoes at high spatial and temporal resolutions
- Dual-Doppler analysis, advection correction, and polarimetric analysis of severe local storms
- Radar network design and coverage optimization
Abstracts from Selected Publications and Proceedings of Current and Previous Research Topics
Polarimetric Observations of Chaff using the WSR-88D Network
Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D radars can appear prominently on radar users’ displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for automated algorithms to do the same. The WSR-88D network provides dual-polarimetric capabilities across the United States, leading to the collection of a large database of chaff cases. This database is analyzed to determine the characteristics of chaff in terms of the reflectivity factor and polarimetric variables on large scales. Particular focus is given to the dynamics of differential reflectivity (ZDR) in chaff and its dependence on height. Contrary to radar data observations of chaff for a single event, this study is able to reveal a repeatable and new pattern of radar chaff observations. A discussion regarding the observed characteristics is presented, and hypotheses for the observed ZDR dynamics are put forth.
Quantification of Radar QPE Performance based on SENSR Network Design Possibilities
In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial use. As part of this goal, the participating agencies have developed preliminary performance requirements that not only assume minimum capabilities based on legacy radars, but also recognize the need for enhancements in future radar networks. The relatively low density of the legacy radar networks, especially the WSR-88D network, had led to the goal of enhancing low-altitude weather coverage. With multiple design metrics and network possibilities still available to the SENSR agencies, the benefits of low-altitude coverage must be assessed quantitatively. This study estimates Quantitative Precipitation Estimation (QPE) differences based on network density, array size, and polarimetric bias. These factors create a pareto front of cost-benefit for QPE in a new radar network, and these results can be used to determine appropriate tradeoffs for SENSR requirements. Results of this study are presented in the form of a case example that quantifies errors based on the three chosen variables, along with a description of eventual application to a national network in upcoming expansion of the work.
Development of a New Inanimate Class for the WSR-88D Hydrometeor Classification Algorithm
The current implementation of the Hydrometeor Classification Algorithm (HCA) on the WSR-88D network contains two non-hydrometeor-based classes: ground clutter/anomalous propagation and biologicals. A number of commonly observed non-hydrometeor-based phenomena do not fall into either of these two HCA categories, but often are misclassified as ground clutter, biologicals, unknown, or worse yet, weather hydrometeors. Some of these phenomena include chaff, sea clutter, combustion debris and smoke, and radio frequency interference. In order to address this discrepancy, a new class (nominally named “inanimate”) is being developed that encompasses many of these targets. Using this class, a distinction between non-biological and biological non-hydrometeor targets can be made and potentially separated into sub-classes for more direct identification. A discussion regarding the fuzzy logic membership functions, optimization of membership weights, and class restrictions is presented, with a focus on observations of highly stochastic differential phase estimates in all of the aforementioned targets. Recent attempts to separate the results into sub-classes using a support vector machine are presented, and examples of each target type are detailed. Details concerning eventual implementation into the WSR-88D radar product generator are addressed
Observations of Severe Local Storms and Tornadoes with the Atmospheric Imaging Radar
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 photographic 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° by 1°. Rotation in azimuth allows for 20° by 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.
High-Sensitivity Weather Radar Observations using Optimized Pulse Compression Waveforms
The progression of phased array weather observations, research, and planning over the past decade have 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 antennas in the weather radar field includes the use of active arrays, meaning each element would be its own transmit/receive module. This advancement would lead to significant advantages, however such a design must be capable of utilizing low-powered, solid-state transmitters at each element in order to keep costs down. In order to provide acceptable sensitivity, as well as the range resolution needed for weather observations, pulse compression has been shown as a viable technique for implementation. Pulse compression has been used for decades in military applications, but is yet to be applied on a broad scale to weather radar, due in part to concerns regarding sensitivity loss. A broad, detailed optimization technique for pulse compression waveforms with minimalistic amplitude modulation using a genetic algorithm is presented. A continuous nonlinear frequency modulated waveform which takes into account transmitter distortion is shown, both in theory and in practical use scenarios. Potential cost savings for both phased array and traditional radar systems are discussed. Actual point target and weather observations from the Advanced Radar Research Center’s dual-polarized PX-1000 mobile radar, which utilizes dual 100-Watt solid-state transmitters, are presented. A discussion of practical design strategy differences for different types of transmitters is demonstrated using data from PX-1000 and the ARRC’s Rapid X-band Polarimetric (RaXpol) mobile radar platform. Both stratiform and convective scenarios, as well as dual-polarization observations, are shown, demonstrating significant improvement in sensitivity over previous pulse compression methods.
High-Temporal Resolution Polarimetric X-band Doppler Radar Observations of the 20 May 2013 Moore, Oklahoma Tornado
On 20 May 2013, the cities of Newcastle, Oklahoma City, and Moore, Oklahoma were impacted by a long-track 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 fine-scale 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 re-strengthening 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.
Adaptive Waveform Design for Multi-Sector Array Isolation
Multi-sector arrays have been used for decades, including applications on ships, aircraft, vehicles, and ground-based platforms. In most of these applications, however, spatial isolation generated through the use of strategic sector placement has provided sufficient overall isolation between each sector. The United States has recently been exploring the potential for a multi-function phased array radar (MPAR) network that would provide surveillance, tracking, and detection capabilities for the nation’s weather, terminal weather, and national airspace missions. Several studies have assumed a multi-sector approach on a single platform. With the goal of allowing each sector to independently operate, concerns regarding isolation between the sectors have introduced the desire to gain additional isolation through waveform design. Recent advances in frequency-modulated pulse compression techniques have afforded the ability to maximize sensitivity and sidelobe performance within a given time-bandwidth specification; however, waveform design has the potential to bring numerous other spectral efficiency advancements to the MPAR mission. A generalization of recent waveform design techniques into a multi-sector waveform group is presented. Simulations of a four-sector waveform group are carried out and optimized for minimal interference. The ability to achieve high waveform-based isolation is combined with varying spatial isolations and slight frequency offsets to drastically reduce overall spectrum usage for a multi-sector array.
Optimization of Weather Radar Networks using a Genetic Algorithm
The current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network is approaching 20 years of age, leading researchers to begin exploring new opportunities for a next-generation network in the United States. With a vast list of requirements for a new weather radar network, research has provided various approaches to the design and fabrication of such a network. Additionally, new weather radar networks in other countries, as well as networks on smaller scales, must balance a large number of variables in order to operate in the most effective way possible. To offer network designers an objective analysis tool for such decisions, a coverage optimization technique, utilizing a genetic algorithm with a focus on low-level coverage, is presented. Optimization is achieved using a variety of variables and methods, including the use of climatology, population density, and attenuation due to average precipitation conditions. A method to account for terrain blockage in mountainous regions is also presented. Various combinations of multifrequency radar networks are explored, and results are presented in the form of a coverage-based cost–benefit analysis, with considerations for total network lifetime cost.
Long-Term Temperature Trends in the Upper Atmosphere
Ionospheric ion temperature is an excellent approximation to neutral temperature in the upper atmosphere, especially, for altitudes below 300 km. This analysis of long-term ionospheric ion temperature changes between 100 and 550 km at noon is based on a database of incoherent scatter radar observations spanning more than three solar cycles during 1968–2006 at Millstone Hill and provides direct evidence of long-term changes and their height dependency in the upper atmospheric temperature. A cooling trend at altitudes above 200 km and an apparent warming trend below 200 km are found. The cooling increases with height and shows variability with solar activity. The apparent warming varies with season and solar activity. It may result from the thermal subsidence caused by atmospheric contraction and pressure level change and from the ion temperature overestimation in the F1 region due to ion composition long-term changes. These long-term changes in ion temperature are accompanied by changes in electron density, being lower above the F2 peak and higher below the F2 peak. Electron temperature is accordingly enhanced. All these changes appear to be suggestive of a long-term greenhouse gas effect.