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The need for spectrum and the impact on weather observations

Summary

One of the most significant challenges—and potential opportunities—for the scientific community is society's insatiable need for the radio spectrum. Wireless communication systems have profoundly impacted the world's economies and its inhabitants. Newer technological uses in telemedicine, Internet of Things, streaming services, intelligent transportation, etc., are driving the rapid development of 5G/6G (and beyond) wireless systems that demand ever-increasing bandwidth and performance. Without question, these wireless technologies provide an important benefit to society with the potential to mitigate the economic divide across the world. Fundamental science drives the development of future technologies and benefits society through an improved understanding of the world in which we live. Often, these studies require use of the radio spectrum, which can lead to an adversarial relationship between ever evolving technology commercialization and the quest for scientific understanding. Nowhere is this contention more acute than with atmospheric remote sensing and associated weather forecasts (Saltikoff et al. 2016; Witze 2019), which was the theme for the virtual Workshop on Spectrum Challenges and Opportunities for Weather Observations held in November 2020 and hosted by the University of Oklahoma. The workshop focused on spectrum challenges for remote sensing observations of the atmosphere, including active (e.g., weather radars, cloud radars) and passive (e.g., microwave imagers, radiometers) systems for both spaceborne and ground-based applications. These systems produce data that are crucial for weather forecasting—we chose to primarily limit the workshop scope to forecasts up to 14 days, although some observations (e.g., satellite) cover a broader range of temporal scales. Nearly 70 participants from the United States, Europe, South America, and Asia took part in a concentrated and intense discussion focused not only on current radio frequency interference (RFI) issues, but potential cooperative uses of the spectrum ("spectrum sharing"). Equally important to the workshop's international makeup, participants also represented different sectors of the community, including academia, industry, and government organizations. Given the importance of spectrum challenges to the future of scientific endeavor, the U.S. National Science Foundation (NSF) recently began the Spectrum Innovation Initiative (SII) program, which has a goal to synergistically grow 5G/6G technologies with crucial scientific needs for spectrum as an integral part of the design process. The SII program will accomplish this goal in part through establishing the first nationwide institute focused on 5G/6G technologies and science. The University of California, San Diego (UCSD), is leading an effort to compete for NSF SII funding to establish the National Center for Wireless Spectrum Research. As key partners in this effort, the University of Oklahoma (OU) and The Pennsylvania State University (PSU) hosted this workshop to bring together intellectual leaders with a focus on impacts of the spectrum revolution on weather observations and numerical weather prediction.
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Summary

One of the most significant challenges—and potential opportunities—for the scientific community is society's insatiable need for the radio spectrum. Wireless communication systems have profoundly impacted the world's economies and its inhabitants. Newer technological uses in telemedicine, Internet of Things, streaming services, intelligent transportation, etc., are driving the rapid development of...

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Towards the next generation operational meteorological radar

Summary

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA's future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.
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Summary

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA's future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these...

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A neural network approach for waveform generation and selection with multi-mission radar

Published in:
2019 IEEE Radar Conf., 22-26 April 2019.

Summary

Nonlinear frequency modulated (NLFM) pulse compression waveforms have become a mainstream methodology for radars across multiple sectors and missions, including weather observation, target tracking, and target detection. NLFM affords the ability to generate a low-sidelobe autocorrelation function and matched filter while avoiding aggressive amplitude modulation, resulting in more power incident on the target. This capability can lead to significantly lower system design costs due to the possibility of sensitivity gains on the order of 3 dB or more compared with traditional, amplitude-modulated linear frequency modulated (LFM) waveforms. Generation of an optimal NLFM waveform, however, can be an arduous task, and may involve complex optimization and non-closed-form solutions. For a multimission or cognitive radar, which may utilize a wide combination of frequencies, pulse lengths, and amplitude modulations (among other factors), this could lead to an extremely large waveform table for selection. This paper takes a neural network approach to this problem by optimizing a set of over 100 waveforms spanning a wide space and using the results to interpolate the waveform possibilities to a higher resolution. A modified form of a previous NLFM method is combined with a four-hidden-layer neural network to show the integrated and peak range sidelobes of the generated waveforms across the model training space. The results are applicable to multi-mission and cognitive radars that need precise waveform specifications in rapid succession. The expected waveform generation times are addressed and quantified, and the potential applicability to multi-mission and cognitive radars is discussed.
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Summary

Nonlinear frequency modulated (NLFM) pulse compression waveforms have become a mainstream methodology for radars across multiple sectors and missions, including weather observation, target tracking, and target detection. NLFM affords the ability to generate a low-sidelobe autocorrelation function and matched filter while avoiding aggressive amplitude modulation, resulting in more power incident...

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