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Radar coverage analysis for the Terminal Precipitation on the Glass Program

Author:
Published in:
MIT Lincoln Laboratory Report ATC-450

Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain analyses conducted over the 146 terminal radar approach control (TRACON) airspaces that are within scope of TPoG to identify and quantify future TPoG benefits, as well as potential operational issues.
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Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain...

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Radar-optimized wind turbine siting

Author:
Published in:
IEEE Trans. Sustain. Energy, Vol. 13, No. 1, January 2022, pp. 403-13.

Summary

A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's orientation relative to a radar site, the impacts on that radar can be minimized. The proposed changes to wind farm siting are relatively minor and do not have a significant effect on wind turbine density. With proper optimization of radar clutter mitigation, radar tracking performance above such wind farms can be significantly increased. Both present-day and potential future or upgraded radar systems are analyzed. The reduction in radar performance due to wind turbine clutter is approximately halved using this method. The developed method is robust with respect to controlled variations in wind turbine placement caused by potential obstructions.
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Summary

A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's...

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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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Geospatial QPE accuracy dependence on weather radar network configurations

Published in:
J. Appl. Meteor. Climatol., Vol. 59, No. 1, 2020, pp. 1773-92.

Summary

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 2015–2017 are processed using both the specific attenuation [R(A)] and reflectivity-differential reflectivity [R(Z,ZDR)] QPE methods and are compared against Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified based on 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 based on 2-year/1-hr return rates are used for a CONUS-wide analysis of QPE errors in extreme rainfall situations. These errors are then re-quantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Finally, 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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Summary

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...

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Weather radar network benefit model for nontornadic thunderstorm wind casualty cost reduction

Author:
Published in:
Wea. Climate Soc., Vol. 12, No. 4, October 2020, pp. 789-804.

Summary

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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Summary

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...

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Weather radar network benefit model for flash flood casualty reduction

Author:
Published in:
J. Appl. Meteor. Climatol., Vol. 59, No. 4, April 2020, pp. 589-604.

Summary

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 ($13M yr-1 for coverage improvement and $69M 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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Summary

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...

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Monetized weather radar network benefits for tornado cost reduction

Author:
Published in:
MIT Lincoln Laboratory Report NOAA-35

Summary

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead 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, including increased lead times, are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In combination, then, it is clearly established that better and faster radar observations reduce tornado casualty rates. Furthermore, lower false alarm ratios save costs by cutting down on people's time lost when taking shelter.
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Summary

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental...

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Weather radar network benefit model for tornadoes

Author:
Published in:
J. Appl. Meteor. Climatol., 22 April 2019, doi:10.1175/JAMC-D-18-0205.1.

Summary

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--lead 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 cost by cutting down on work and personal time lost while taking shelter. The model is run on the existing contiguous United States weather radar network as well as hypothetical future configurations. Results show that the current radars provide a tornado-based benefit of ~$490M per year. The remaining benefit pool is about $260M per year that is roughly split evenly between coverage- and rapid-scanning-related gaps.
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Summary

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--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results...

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Polarimetric observations of chaff using the WSR-88D network

Published in:
J. Appl. Meteor. Climatol., Vol. 57, No. 5, 1 May 2018, pp. 1063-1081.

Summary

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. The 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.
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Summary

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...

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