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James M. Kurdzo and Robert D. Palmer

Abstract

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.

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John Y. N. Cho and James M. Kurdzo

ABSTRACT

A monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash flood–based benefit of $316 million (M) yr−1. The remaining benefit pools are more modest ($13 M yr−1 for coverage improvement and $69 M yr−1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network’s effectiveness in supporting the flash flood warning decision process.

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John Y. N. Cho and James M. Kurdzo

Abstract

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement of two key radar parameters—fraction of vertical space observed and cross-range horizontal resolution—leads to better tornado warning performance as characterized by tornado detection probability and false-alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In addition, lower false-alarm ratios save costs by cutting down on work and personal time lost while taking shelter. The model is run on the existing contiguous U.S. weather radar network as well as hypothetical future configurations. Results show that the current radars provide a tornado-based benefit of ~$490 million (M) yr−1. The remaining benefit pool is about $260M yr−1, split roughly evenly between coverage- and rapid-scanning-related gaps.

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John Y. N. Cho and James M. Kurdzo

Abstract

An econometric geospatial benefit model for nontornadic thunderstorm wind casualty reduction is developed for meteorological radar network planning. Regression analyses on 22 years (1998–2019) of storm event and warning data show, likely for the first time, a clear dependence of nontornadic severe thunderstorm warning performance on radar coverage. Furthermore, nontornadic thunderstorm wind casualty rates are observed to be negatively correlated with better warning performance. In combination, these statistical relationships form the basis of a cost model that can be differenced between radar network configurations to generate geospatial benefit density maps. This model, applied to the current contiguous U.S. weather radar network, yields a benefit estimate of $207 million (M) yr−1 relative to no radar coverage at all. The remaining benefit pool with respect to enhanced radar coverage and scan update rate is about $36M yr−1. Aggregating these nontornadic thunderstorm wind results with estimates from earlier tornado and flash flood cost reduction models yields a total benefit of $1.12 billion yr−1 for the present-day radars and a remaining radar-based benefit pool of $778M yr−1.

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James M. Kurdzo, Emily F. Joback, Pierre-Emmanuel Kirstetter, and John Y. N. Cho

Abstract

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation [R(A)] and reflectivity–differential reflectivity [R(Z, Z DR)] QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.

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James M. Kurdzo, David J. Bodine, Boon Leng Cheong, and Robert D. Palmer

Abstract

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 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.

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James M. Kurdzo, Boon Leng Cheong, Robert D. Palmer, Guifu Zhang, and John B. Meier

Abstract

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.

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Casey B. Griffin, David J. Bodine, James M. Kurdzo, Andrew Mahre, and Robert D. Palmer

Abstract

On 27 May 2015, the Atmospheric Imaging Radar (AIR) collected high-temporal resolution radar observations of an EF-2 tornado near Canadian, Texas. The AIR is a mobile, X-band, imaging radar that uses digital beamforming to collect simultaneous RHI scans while steering mechanically in azimuth to obtain rapid-update weather data. During this deployment, 20°-by-80° (elevation × azimuth) sector volumes were collected every 5.5 s at ranges as close as 6 km. The AIR captured the late-mature and decaying stages of the tornado. Early in the deployment, the tornado had a radius of maximum winds (RMW) of 500 m and exhibited maximum Doppler velocities near 65 m s−1. This study documents the rapid changes associated with the dissipation stages of the tornado. A 10-s resolution time–height investigation of vortex tilt and differential velocity is presented and illustrates an instance of upward vortex intensification as well as downward tornado decay. Changes in tornado intensity over periods of less than 30 s coincided with rapid changes in tornado diameter. At least two small-scale vortices were observed being shed from the tornado during a brief weakening period. A persistent layer of vortex tilt was observed near the level of free convection, which separated two layers with contrasting modes of tornado decay. Finally, the vertical cross correlation of vortex intensity reveals that apart from the brief instances of upward vortex intensification and downward decay, tornado intensity was highly correlated throughout the observation period.

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Roger M. Wakimoto, Zachary Wienhoff, Howard B. Bluestein, David J. Bodine, and James M. Kurdzo

Abstract

A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρ hv) and differential radar reflectivity (Z DR) immediately after the tornado exited the damaged area rated EF3. Low ρ hv and Z DR occurred near the surface where debris loading was the greatest. The 10th percentile of ρ hv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρ hv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

Open access
James M. Kurdzo, Betty J. Bennett, David J. Smalley, and Michael F. Donovan

Abstract

The dual-polarization upgrade to the WSR-88D network of weather radars included the addition of a hydrometeor classification algorithm (HCA) to the Open Radar Product Generator (ORPG). The HCA product categorizes each Level-III radar range–azimuth cell into one of 10 classifications or marks it as “unknown.” However, not all target types fall under the 10 classification options. For this reason, multiple studies have examined adding new classes to the operational HCA. In this study, a new “inanimate” class is developed for additional hydrometeor classification in the ORPG and is described in detail. This class encompasses nonweather phenomena such as chaff (the primary motivation of this work), sea clutter, and some types of combustion debris and radio frequency interference. The design process is detailed, including human truthing, data selection, and optimization with a genetic algorithm. Multiple case examples are presented and analyzed, both qualitatively and quantitatively. Quantification is put into perspective with previous studies for a better understanding of the algorithm’s performance and impact. A discussion of applications, including subclassing, chaff detection, and implementation as an “aviation” classification algorithm in the ORPG, is presented.

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