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Geospatial QPE accuracy dependence on weather radar network configurations

October 27, 2020
  |
Journal Article
Author:
James M. Kurdzo
…
Published in:
J. Appl. Meteor. Climatol., Vol. 59, No. 1, 2020, pp. 1773-92.
Topic:
radar
R&D area:
R&D group:

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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Geospatial QPE accuracy dependence on weather radar network configurations

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

September 28, 2020
  |
Journal Article
Author:
John Y. Cho
…
Published in:
Wea. Climate Soc., Vol. 12, No. 4, October 2020, pp. 789-804.
Topic:
radar
R&D area:
R&D group:

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 nontornadic thunderstorm wind casualty cost reduction

Weather radar network benefit model for flash flood casualty reduction

April 1, 2020
  |
Journal Article
Author:
John Y. Cho
…
Published in:
J. Appl. Meteor. Climatol., Vol. 59, No. 4, April 2020, pp. 589-604.
Topic:
radar
R&D area:
R&D group:

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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Weather radar network benefit model for flash flood casualty reduction

Monetized weather radar network benefits for tornado cost reduction

August 29, 2019
  |
Project Report
Author:
John Y. Cho
…
Published in:
MIT Lincoln Laboratory Report NOAA-35
Topic:
modeling
R&D area:
R&D group:

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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Monetized weather radar network benefits for tornado cost reduction

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

April 22, 2019
  |
Conference Paper
Author:
James M. Kurdzo
…
Published in:
2019 IEEE Radar Conf., 22-26 April 2019.
R&D area:
R&D group:

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

Weather radar network benefit model for tornadoes

February 21, 2019
  |
Journal Article
Author:
John Y. Cho
…
Published in:
J. Appl. Meteor. Climatol., 22 April 2019, doi:10.1175/JAMC-D-18-0205.1.
Topic:
radar
R&D area:
R&D group:

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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Weather radar network benefit model for tornadoes

Quantification of radar QPE performance based on SENSR network design possibilities

April 23, 2018
  |
Conference Paper
Author:
James M. Kurdzo
…
Published in:
2018 IEEE Radar Conf., RadarConf, 23-27 April 2018.
Topic:
modeling
R&D area:
R&D group:

Summary

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 lays the groundwork for estimating 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 will eventually be used to determine appropriate tradeoffs for SENSR requirements. Results of this study are presented in the form of two case examples that quantify errors based on polarimetric bias and elevation, along with a description of eventual application to a national network in upcoming expansion of the work.
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Summary

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...

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Quantification of radar QPE performance based on SENSR network design possibilities

Command and control for multifunction phased array radar

October 1, 2017
  |
Journal Article
Author:
Mark E. Weber
…
Published in:
IEEE Trans. Geosci. Remote Sens., Vol. 55, No. 10, October 2017, pp. 5899-5912.
Topic:
algorithms
R&D area:
R&D group:

Summary

We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and utilizes techniques for increasing scan rate where feasible. After reviewing the candidate MPAR architectures and relevant previous research, we describe a specific C2 framework that is consistent with a demonstrated active array architecture using overlapped subarrays to realize multiple, concurrent receive beams. Analysis of recently articulated requirements for near-airport and national-scale aircraft surveillance indicates that with this architecture, 40–60% of the MPAR scan timeline would be available for the high-fidelity weather observations currently provided by the Weather Service Radar (WSR-88D) network. We show that an appropriate use of subarray generated concurrent receive beams, in concert with previously documented, complementary techniques to increase the weather scan rate, could enable MPAR to perform full weather volume scans at a rate of 1 per minute. Published observing system simulation experiments, human-in-the-loop studies and radar-data assimilation experiments indicate that high-quality weather radar observations at this rate may significantly improve the lead time and reliability of severe weather warnings relative to current observation capabilities.
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Summary

We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and...

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Command and control for multifunction phased array radar

A new radio frequency interference filter for weather radars

July 1, 2017
  |
Journal Article
Author:
John Y. Cho
Published in:
J. Atmos. Ocean. Technol., Vol. 34, No. 7, 1 July 2017, pp. 1393-1406.
Topic:
algorithms
R&D area:
R&D group:

Summary

A new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations and real weather radar data with RFI are used to perform algorithm comparisons. Results are consistent with theoretical considerations and show the 2D RFI filter to be a promising addition to the signal processing arsenal against interference with weather radars. Increased computational burden is the only drawback relative to filters currently used by operational systems.
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Summary

A new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations...

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A new radio frequency interference filter for weather radars

The threat to weather radars by wireless technology

July 1, 2016
  |
Journal Article
Author:
Elena Saltikoff
…
Published in:
Amer. Meteor. Soc., Vol. 97, No. 7, 1 July 2016, pp. 1159-67, doi: 10.1175/BAMS-D-15-00048.1.
Topic:
radar
R&D area:
R&D group:

Summary

Wireless technology, such as local area telecommunication networks and surveillance cameras, causes severe interference for weather radars, because they use the same operational radio frequencies. One or two disturbances can be removed from the radar image, but the number and power of the interfering wireless devices are growing all over the world, threatening that one day the radars could not be used at all. Some agencies have already changed or are considering changing frequency bands, but now even other bands are under threat. Use of equipment at radio frequencies is regulated by laws and international agreements. Technologies have been developed for peaceful co-existence. If wireless devices use these technologies to protect weather radars, their data transmission capabilities become limited, so it is tempting to violate the regulations. Hence, it is an important task for the worldwide weather community to involve themselves in the radio-frequency management process and work in close contact with their National Radio Authorities to ensure that meteorological interests be duly taken into account in any decision making process toward the future usage of wireless devices.
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Summary

Wireless technology, such as local area telecommunication networks and surveillance cameras, causes severe interference for weather radars, because they use the same operational radio frequencies. One or two disturbances can be removed from the radar image, but the number and power of the interfering wireless devices are growing all over...

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The threat to weather radars by wireless technology
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