The Next-Generation Multimission U.S. Surveillance Radar Network

The U.S. Government operates seven distinct radar networks, providing weather and aircraft surveillance for public weather services, air traffic control, and homeland defense. In this paper, we describe a next-generation multimission phased-array radar (MPAR) concept that could provide enhanced weather and aircraft surveillance services with potentially lower life cycle costs than multiple single-function radar networks. We describe current U.S. national weather and aircraft surveillance radar networks and show that by reducing overlapping airspace coverage, MPAR could reduce the total number of radars required by approximately one-third. A key finding is that weather surveillance requirements dictate the core parameters of a multimission radar—airspace coverage, aperture size, radiated power, and angular resolution. Aircraft surveillance capability can be added to a phased array weather radar at low incremental cost because the agile, electronically steered beam would allow the radar to achieve the much more rapid scan update rates needed for aircraft volume search missions, and additionally to support track modes for individual aircraft targets. We describe an MPAR system design that includes multiple transmit–receive channels and a highly digitized active phased array to generate independently steered beam clusters for weather, aircraft volume search, and aircraft track modes. For each of these modes, we discuss surveillance capability improvements that would be realized relative to today's radars. The Federal Aviation Administration (FAA) has initiated the development of an MPAR “preprototype” that will demonstrate critical subsystem technologies and multimission operational capabilities. Initial subsystem designs have provided a solid basis for estimating MPAR costs for comparison with existing, mechanically scanned operational surveillance radars.United States. Federal Aviation Administration (FA8721-05-C-0002

Applications in Low-Power Phased Array Weather Radars

Low-cost X-band radars are an emerging technology that offer significant advantages over traditional systems for weather remote sensing applications. X-band radars provide enhanced angular resolution at a fraction of the aperture size compared to larger, lower frequency systems. Because of their low cost and small form factor, these radars can now be integrated into more research and commercial applications. This work presents research and development activities using a low-cost, X-band (9410 MHz) Phase-Tilt Radar. The phase-tilt design is a novel phased array architecture that allows for rapid electronic scanning in azimuth and mechanical tilting in elevation, as a compromise between cost and performance. This work focuses on field studies and experiments in three meteorological applications. The first stage of research focuses on the real-world application of phased array radars in forest fire monitoring and observation. From April to May 2013, a phase-tilt radar was deployed to South Australia and underwent a field campaign to make polarimetric observations of prescribed burns within and around the Adelaide Hills region. Measurements show the real-time evolution of the smoke plume dynamics at a spatial and temporal resolution that has never before been observed with an X-band radar. This dissertation will perform data analysis on results from this field campaign. Results are compared against existing work, theories, and approaches. In the second stage of research, field experiments are performed to assess the data quality of X-band phased array radars. Specifically, this research focuses on the measurement of and techniques to improve the variance of weather product estimators for dual-polarized systems. Variability in the radar products is a complicated relationship between the radar system specifications, scanning strategy, and the physics governing precipitation. Here, the variance of the radar product estimators is measured using standard radar scanning strategies employed in traditional mechanical antenna systems. Results are compared against adaptive scan strategies such as beam multiplexing and frequency diversity. This work investigates the improvement that complex scanning strategies offer in dual-polarized, X-band phased array radar systems. In the third stage of research, simulations and field experiments are conducted to investigate the performance benefits of adaptive scanning to optimize the data quality of radar returns. This research focuses on the development and implementation of a waveform agile and adaptive scanning strategy to improve the quality of weather product estimators. Active phased array radars allow radar systems to quickly vary both scan pointing angles and waveform parameters in response to real-time observations of the atmosphere. As an evolution of the previous research effort, this work develops techniques to adaptively change the scan pointing angles, transmit and matched filter waveform parameters to achieve a desired level of data quality. Strategies and techniques are developed to minimize the error between observed and desired data quality measures. Simulation and field experiments are performed to assess the quality of the developed strategies

On the Potential of Adaptive Beamforming for Phased-Array Weather Radar

As the Weather Surveillance Radar 1988 Doppler network reaches the end of its expected life, a network of multifunction phased-array radars (MPAR) supporting both aircraft and weather surveillance missions has been proposed. A phased-array system should match the sensitivity, spatial resolution, and data quality of the WSR-88D while having a update time of 60 seconds for weather surveillance. Since an MPAR system must complete both weather and aircraft surveillance missions, the update time reduction provided by having multiple faces is insufficient to achieve the desired 60 second update time for weather surveillance. Therefore, it is likely that multiple simultaneous beams would be needed per face to meet the timeline requirements. An approach to achieve multiple receive beams is to use a spoiled transmit beam and to form a cluster of simultaneous receive beams. However, a significant challenge for this approach is the potential of high sidelobe levels in the two-way radiation pattern, which can result in significantly biased estimates of the radar variables in situations where the signal power has large spatial variation. This dissertation proposes an adaptive beamspace algorithm designed for phased-array weather radar that utilizes a spoiled transmit beam and a cluster of simultaneous receive beams to achieve the desired timeline. Taking advantage of the adaptive algorithm's ability to automatically adjust sidelobe levels to match the scene, the high-sidelobe problem associated with a spoiled transmit beam is mitigated. Through extensive simulations, it is shown that adaptive beamspace processing can produce accurate and calibrated estimates of weather radar variables. Furthermore, it is demonstrated that the adaptive beamspace algorithm can automatically reject interference signals and reduce their impact on the radar-variable estimates. Additionally, it is shown that, despite higher sidelobe levels, the adaptive beamspace algorithm can perform similarly to a conventional system based on a dish antenna in terms of biases when reflectivity gradients are present. Finally, the adaptive beamspace algorithm is shown to compare favorably to some alternative solutions that can also achieve the desired MPAR timeline requirement while preserving data quality

Quantitative Analysis of Rapid-Scan Phased Array Weather Radar Benefits and Data Quality Under Various Scan Conditions

Currently, NEXRAD provides weather radar coverage for the contiguous United States. It is believed that a replacement system for NEXRAD will be in place by the year 2040, where a major goal of such a system is to provide improved temporal resolution compared to the 5-10-min updates of NEXRAD. In this dissertation, multiple projects are undertaken to help achieve the goals of improved temporal resolution, and to understand possible scanning strategies and radar designs that can meet the goal of improved temporal resolution while either maintaining (or improving) data quality. Chapter 2 of this dissertation uses a radar simulator to simulate the effect of various scanning strategies on data quality. It is found that while simply reducing the number of pulses per radial decreases data quality, other methods such as beam multiplexing and radar imaging/digital beamforming offer significant promise for improving data quality and/or temporal resolution. Beam multiplexing is found to offer a speedup factor of 1.7-2.9, while transmit beam spoiling by 10 degrees in azimuth can offer speedup factors up to ~4 in some regions. Due to various limitations, it is recommended that these two methods be used judiciously for rapid-scan applications. Chapter 3 attempts to quantify the benefits of a rapid-scan weather radar system for tornado detection. The first goal of Chapter 3 is to track the development of a common tornado signature (tornadic debris signature, or TDS) and relate it to developments in tornado strength. This is the first study to analyze the evolution of common tornado signatures at very high temporal resolution (6 s updates) by using a storm-scale tornado model and a radar emulator. This study finds that the areal extent of the TDS is correlated with both debris availability and with tornado strength. We also find that significant changes in the radar moment variables occur on short (sub-1-min) timescales. Chapter 3 also shows that the calculated improvement in tornado detection latency time (137-207 s) is greater than that provided by theory alone (107 s). Together, the two results from Chapter 3 emphasize the need for sub-1-min updates in some applications such as tornado detection. The ability to achieve these rapid updates in certain situations will likely require a combination of advanced scanning strategies (such as those mentioned in Chapter 2) and adaptive scanning. Chapter 4 creates an optimization-based model to adaptively reallocate radar resources for the purpose of improving data quality. This model is primarily meant as a proof of concept to be expanded to other applications in the future. The result from applying this model to two real-world cases is that data quality is successfully improved in multiple areas of enhanced interest, at the expense of worsening data quality in regions where data quality is not as important. This model shows promise for using adaptive scanning in future radar applications. Together, these results can help the meteorological community understand the needs, challenges, and possible solutions to designing a replacement system for NEXRAD. All of the techniques studied herein either rely upon (or are most easily achieved by) phased array radar (PAR), which further emphasizes the utility of PAR for achieving rapid updates with sufficient data quality. It is hoped that the results in this dissertation will help guide future decisions about requirements and design specifications for the replacement system for NEXRAD

The Status of the ACRF Millimeter Wave Cloud Radars (MMCRs), the Path Forward for Future MMCR Upgrades, the Concept of 3D Volume Imaging Radar and the UAV Radar

The United States (U.S.) Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) operates millimeter wavelength cloud radars (MMCRs) in several climatological regimes. The MMCRs, are the primary observing tool for quantifying the properties of nearly all radiatively important clouds over the ACRF sites. The first MMCR was installed at the ACRF Southern Great Plains (SGP) site nine years ago and its original design can be traced to the early 90s. Since then, several MMCRs have been deployed at the ACRF sites, while no significant hardware upgrades have been performed. Recently, a two-stage upgrade (first C-40 Digital Signal Processors [DSP]-based, and later the PC-Integrated Radar AcQuisition System [PIRAQ-III] digital receiver) of the MMCR signal-processing units was completed. Our future MMCR related goals are: 1) to have a cloud radar system that continues to have high reliability and uptime and 2) to suggest potential improvements that will address increased sensitivity needs, superior sampling and low cost maintenance of the MMCRs. The Traveling Wave Tube (TWT) technology, the frequency (35-GHz), the radio frequency (RF) layout, antenna, the calibration and radar control procedure and the environmental enclosure of the MMCR remain assets for our ability to detect the profile of hydrometeors at all heights in the troposphere at the ACRF sites

Signal Processing Techniques and Concept of Operations for Polarimetric Rotating Phased Array Radar

The Weather Surveillance Radar 1988 Doppler (WSR-88D) network has been operational for over 30 years and is still the primary observational instrument employed by the National Weather Service (NWS) forecasters to support their critical mission of issuing severe weather warnings and forecasts in the United States. Nevertheless, the WSR-88Ds have exceeded their engineering design lifespan and are projected to reach the end of operational lifetime by 2040. Technological limitations may prevent the WSR-88D to meet demanding functional requirements for future observational needs. The National Oceanic and Atmospheric Administration (NOAA) has started considering radar systems with advanced capabilities for the eventual replacement of the WSR-88D. Unique and flexible capabilities offered by Phased Array Radar (PAR) technology support the required enhanced weather surveillance strategies that are envisioned to improve the weather radar products, making PAR technology an attractive candidate for the next generation of weather radars. If PAR technology is to replace the operational WSR-88D, important decisions must be made regarding the architecture that will be needed to meet the functional requirements. A four-faced planar PAR (4F-PAR) is expected to achieve the requirements set forth by NOAA and the NWS, but deploying and maintaining an operational network of these radars across the U.S. will likely be unaffordable. A more affordable alternative radar system is based on a single-face Rotating PAR (RPAR) architecture, which is capable of exceeding the functionality provided by the WSR-88D network. This dissertation is focused on exploring advanced RPAR scanning techniques in support of meeting future radar functional requirements. A survey of unique RPAR capabilities is conducted to determine which ones could be exploited under an RPAR Concept of Operations (CONOPS). Three capabilities are selected for further investigation: beam agility, digital beamforming, and dwell flexibility. The RPARs beam agility is exploited to minimize the beam smearing that results from the rotation of the antenna system over the collection of samples in the coherent processing interval. The use of digital beamforming is investigated as a possible way to reduce the scan time and/or the variance of estimates. The RPAR's dwell flexibility capability is explored as a possible way to tailor the scan to meteorological observations with the goal of improving data quality. Three advanced RPAR scanning techniques are developed exploiting these capabilities, and their performance in support of meeting the radar functional requirements is quantified. The proposed techniques are implemented on the Advanced Technology Demonstrator (ATD), a dual-polarization RPAR system at the National Severe Storms Laboratory (NSSL) in Norman, OK. Data collection experiments are conducted with the ATD to demonstrate the performance of the proposed techniques for dual-polarization observations. Results are verified by quantitatively comparing fields of radar-variable estimates produced using the proposed RPAR techniques with those produced by a well-known collocated WSR-88D radar simultaneously collecting data following an operational Volume Coverage Pattern (VCP). The techniques introduced are integrated to operate simultaneously, and used to design an RPAR CONOPS that can complete a full volume scan in about one minute, while achieving other demanding functional requirements. It is expected that the findings in this dissertation will provide valuable information that can support the design of the future U.S. weather surveillance radar network

Forecaster Warning Decision Making with Rapidly-Updating Radar Data

Phased-array radar is being considered as a potential future replacement technology for the current operational Weather Surveillance Radar 1988 Doppler system. One of the most notable differences in these weather radar systems is the temporal resolution. With phased-array radar collecting volumetric updates 4–6 times more frequently, the operational impacts of rapidly-updating radar data on forecasters’ warning decision processes must be assessed. The Phased Array Radar Innovative Sensing Experiment (PARISE) was therefore designed to examine forecasters’ warning performance and related warning decision processes during use of ~1-min radar updates in simulated real-time warning operation scenarios. While the 2010, 2012, and 2013 PARISE studies reported encouraging findings for forecasters’ use of these data, each of these studies were limited in terms of sample size and the chosen methods. Additionally, important research questions that had not yet been explored remained unanswered. To address these limitations and investigate new research questions, thirty National Weather Service forecasters were invited to the NOAA Hazardous Weather Testbed to participate in the 2015 PARISE. Participating forecasters completed three components of this study: 1) the traditional experiment, 2) an eye-tracking experiment, and 3) a focus group. The first component was designed to build on previous work by assessing and comparing forecasters’ warning performance and related cognitive workload when using 1-min, 2-min, and 5-min phased-array radar updates during simulated warning operations. This traditional experiment was comprised of nine weather events that varied in terms of weather threat. Next, forecasters’ eye movement data were observed as they each worked a single weather event with either 1-min or 5-min phased-array radar updates. This work was motivated by an eye-tracking pilot study, in which a forecaster’s eye movement data was found to correspond meaningfully to their retrospective recall data that described their warning decision process. The 2015 PARISE eye-tracking experiment allowed for an objective analysis of how forecasters interacted with a radar display and warning interface for a single weather event, and more specifically, supported an investigation of whether radar update speed impacts how forecasters distribute their attention. Lastly, six focus groups were conducted to enable forecasters to share their experiences on their use of rapidly-updating phased-array radar data during the experiment. The findings from the focus groups provide motivation for the integration of rapidly-updating radar data into the forecast office and highlight some important considerations for successful use of these data during warning operations. The work presented in this dissertation was approved by the University of Oklahoma’s Office of Human Research Participant Protection Institutional Review Board under projects #5226 and #5580

EXPLORING THE CAPABILITIES OF THE AGILE BEAM PHASED ARRAY WEATHER RADAR

Weather radar researchers have long been eager to exploit the capabilities of phased array antennas, but high cost and technical complexity have postponed their widespread use in radar meteorology. With the aging of the current network of operational Doppler weather radars, the possibility of replacing them with phased array radars has renewed interest in applying this technology to weather radar research. The main focus of this research is the "agile beam" or electronic scanning capability of phased array antennas. Three research areas that take advantage of this agile beam capability are addressed in this work: spectral characterization of ground clutter with phased array radar data, staggered PRT beam multiplexing (SBMX), and rapid weather detection. Most of the research on ground clutter filtering has been applied to rotating antennas, but the agile beam capability of the phased array allows the collection of data with a stationary antenna. Studying the characteristics of ground clutter spectra for a stationary antenna could lead to new techniques and improvements for clutter filtering with phased arrays. Ground clutter data were collected under varying wind conditions, foliage levels, and terrain types. The shapes of the ground clutter spectra are then characterized using a novel quadratic clutter model, and the dependence of the model parameters on different conditions is explored. The model is then applied to the examination of clutter width and the time series simulation of ground clutter. SBMX takes advantage of the ability of the phased array to scan the beam in a different direction on a pulse-to-pulse basis which can save time by collecting samples that are nearly independent. SBMX is compared to two conventional scanning strategies to assess its performance using both simulations and real data. It performs well at high signal-to-noise ratios and narrow spectrum widths, but the staggered PRT strategy performs comparably to SBMX, takes less time, and has proven strategies for clutter filtering. The last area of research, rapid weather detection, looks at the use of beam multiplexing to improve the detection of weather signatures. A simple beam multiplexing strategy outperforms a contiguous pulse strategy because the probability of detection of weather signatures is constant for beam multiplexing while the probability of detection for contiguous pulses decreases at narrow spectrum widths. The effects of beam broadening on the scanning strategies are also examined