Editorial Type:
Article
Article Type:
Research Article

Weather Radar Network Benefit Model for Tornadoes

John Y. N. Cho Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts

Search for other papers by John Y. N. Cho in
Current site
Google Scholar
PubMed Close
and
James M. Kurdzo Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts

Search for other papers by James M. Kurdzo in
Current site
Google Scholar
PubMed Close
Print Publication:
01 May 2019
DOI:
https://doi.org/10.1175/JAMC-D-18-0205.1
Page(s):
971–987
Restricted access

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John Y. N. Cho, jync@ll.mit.edu

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John Y. N. Cho, jync@ll.mit.edu
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Ashley, W. S., and S. M. Strader, 2016: Recipe for disaster: How the dynamic ingredients of risk and exposure are changing the tornado disaster landscape. Bull. Amer. Meteor. Soc., 97, 767–786, https://doi.org/10.1175/BAMS-D-15-00150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Barnes, L. R., E. C. Gruntfest, M. H. Hayden, D. M. Schultz, and C. Benight, 2007: False alarms and close calls: A conceptual model of warning accuracy. Wea. Forecasting, 22, 1140–1147, https://doi.org/10.1175/WAF1031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bieringer, P., and P. S. Ray, 1996: A comparison of tornado warning lead times with and without NEXRAD Doppler radar. Wea. Forecasting, 11, 47–52, https://doi.org/10.1175/1520-0434(1996)011<0047:ACOTWL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., and J. Correia Jr., 2018: Long-term performance metrics for National Weather Service tornado warnings. Wea. Forecasting, 33, 1501–1511, https://doi.org/10.1175/WAF-D-18-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brotzge, J., and S. Erickson, 2009: NWS tornado warnings with zero or negative lead times. Wea. Forecasting, 24, 140–154, https://doi.org/10.1175/2008WAF2007076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brotzge, J., and S. Erickson, 2010: Tornadoes without NWS warning. Wea. Forecasting, 25, 159–172, https://doi.org/10.1175/2009WAF2222270.1.

  • Brotzge, J., and W. Donner, 2013: The tornado warning process: A review of current research, challenges, and opportunities. Bull. Amer. Meteor. Soc., 94, 1715–1733, https://doi.org/10.1175/BAMS-D-12-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brotzge, J., K. Hondl, B. Philips, L. Lemon, E. J. Bass, D. Rude, and D. L. Andra, 2010: Evaluation of distributed collaborative adaptive sensing for detection of low-level circulations and implications for severe weather warning operations. Wea. Forecasting, 25, 173–189, https://doi.org/10.1175/2009WAF2222233.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brotzge, J., S. Erickson, and H. Brooks, 2011: A 5-yr climatology of tornado false alarms. Wea. Forecasting, 26, 534–544, https://doi.org/10.1175/WAF-D-10-05004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brotzge, J., S. E. Nelson, R. L. Thompson, and B. T. Smith, 2013: Tornado probability of detection and lead time as a function of convective mode and environmental parameters. Wea. Forecasting, 28, 1261–1276, https://doi.org/10.1175/WAF-D-12-00119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, R. A., and V. T. Wood, 2012a: Simulated vortex detection using a four-face phased-array Doppler radar. Wea. Forecasting, 27, 1598–1603, https://doi.org/10.1175/WAF-D-12-00059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, R. A., and V. T. Wood, 2012b: The tornadic signature: An update. Wea. Forecasting, 27, 525–530, https://doi.org/10.1175/WAF-D-11-00111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, R. A., V. T. Wood, and D. Sirmans, 2002: Improved tornado detection using simulated and actual WSR-88D data with enhanced resolution. J. Atmos. Oceanic Technol., 19, 1759–1771, https://doi.org/10.1175/1520-0426(2002)019<1759:ITDUSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Burgess, D. W., and L. R. Lemon, 1990: Severe thunderstorm detection by radar. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 619–647.

    • Crossref
    • Export Citation

  • Cesario, F. J., 1976: Value of time in recreation benefit studies. Land Econ., 52, 32–41, https://doi.org/10.2307/3144984.

  • Cho, J. Y. N., 2015: Revised Multifunction Phased Array Radar (MPAR) network siting analysis. MIT Lincoln Laboratory Project Rep. ATC-425, 84 pp., https://www.ll.mit.edu/sites/default/files/publication/doc/2018-05/Cho_2015_ATC-425.pdf.

  • Cho, J. Y. N., and M. E. Weber, 2010: Terminal Doppler weather radar enhancements. Proc. 2010 IEEE Radar Conf., Washington, DC, Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/RADAR.2010.5494427.

    • Crossref
    • Export Citation

  • Chrisman, J. N., 2013: Dynamic scanning. NEXRAD Now, No. 22, NOAA/NWS/Radar Operations Center, Norman, OK, 1–3, https://www.roc.noaa.gov/WSR88D/PublicDocs/NNOW/NNow22c.pdf.

  • Chrisman, J. N., 2014: The continuing evolution of dynamic scanning. NEXRAD Now, No. 23, NOAA/NWS/Radar Operations Center, Norman, OK, 8–13, http://www.roc.noaa.gov/WSR88D/PublicDocs/NNOW/NNow23a.pdf.

  • CIESIN, 2017: Population density, v4.10 (2000, 2005, 2010, 2015, 2020). Gridded Population of the World (GPW), v4, NASA Socioeconomic Data and Applications Center, Center for International Earth Science Information Network, Columbia University, Palisades, NY, https://doi.org/10.7927/H4DZ068D.

    • Crossref
    • Export Citation

  • Edwards, R., and H. E. Brooks, 2010: Possible impacts of the enhanced Fujita scale on United States tornado data. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P8.28, http://ams.confex.com/ams/pdfpapers/175398.pdf.

  • FAA, 2016: Spectrum Efficient National Surveillance Radar (SENSR) program announcement. Federal Aviation Administration Solicitation 25214, AAQ-300, https://faaco.faa.gov/index.cfm/announcement/view/25214.

  • Fabry, F., 2015: Radar Meteorology: Principles and Practice. Cambridge University Press, 256 pp.

    • Crossref
    • Export Citation

  • Falk, K. W., 1997: Techniques for issuing severe thunderstorm and tornado warnings with the WSR-88D Doppler radar. NOAA Tech. Memo. NWS SR-185, 38 pp., https://repository.library.noaa.gov/view/noaa/6360.

  • FEMA, 2009: FEMA benefit–cost analysis reengineering (BCAR): Tornado safe room module methodology report, version 4.5. Federal Emergency Management Administration Rep., 75 pp., https://www.fema.gov/media-library-data/20130726-1738-25045-0690/tornadomethodology.pdf.

  • Fricker, T., J. B. Elsner, and T. H. Jagger, 2017: Population and energy elasticity of tornado casualties. Geophys. Res. Lett., 44, 3941–3949, https://doi.org/10.1002/2017GL073093.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gibbs, J. G., 2016: A skill assessment of techniques for real-time diagnosis and short-term prediction of tornado intensity using the WSR-88D. J. Oper. Meteor, 4, 170–181, https://doi.org/10.15191/nwajom.2016.0413.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heinselman, P. L., and S. M. Torres, 2011: High-temporal-resolution capabilities of the National Weather Radar Testbed Phased-Array Radar. J. Appl. Meteor. Climatol., 50, 579–593, https://doi.org/10.1175/2010JAMC2588.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heinselman, P. L., D. L. Priegnitz, K. L. Manross, T. M. Smith, and R. W. Adams, 2008: Rapid sampling of severe storms by the National Weather Radar Testbed Phased Array Radar. Wea. Forecasting, 23, 808–824, https://doi.org/10.1175/2008WAF2007071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heinselman, P. L., D. S. LaDue, D. M. Kingfield, and R. Hoffman, 2015: Tornado warning decisions using phased-array radar data. Wea. Forecasting, 30, 57–78, https://doi.org/10.1175/WAF-D-14-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Istok, M. J., A. Cheek, A. D. Stern, R. E. Saffle, B. R. Klein, N. Shen, and W. M. Blanchard, 2009: Leveraging multiple FAA radars for NWS operations. 25th Int. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 10B.2, https://ams.confex.com/ams/pdfpapers/145466.pdf.

  • Kurdzo, J. M., and R. D. Palmer, 2012: Objective optimization of weather radar networks for low-level coverage using a genetic algorithm. J. Atmos. Oceanic Technol., 29, 807–821, https://doi.org/10.1175/JTECH-D-11-00076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kurdzo, J. M., and Coauthors, 2017: Observations of severe local storms and tornadoes with the atmospheric imaging radar. Bull. Amer. Meteor. Soc., 98, 915–935, https://doi.org/10.1175/BAMS-D-15-00266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lemon, L. R., and C. A. Doswell III, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107, 1184–1197, https://doi.org/10.1175/1520-0493(1979)107<1184:STEAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Manson, S., J. Schroeder, D. Van Riper, and S. Ruggles, 2018: IPUMS National Historical Geographic Information System, version 13.0. University of Minnesota, accessed 11 June 2018, http://doi.org/10.18128/D050.V13.0.

    • Crossref
    • Export Citation

  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 1797–1818, https://doi.org/10.1175/2009BAMS2507.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Michelson, M., W. W. Shrader, and J. G. Wieler, 1990: Terminal Doppler Weather Radar. Microwave J., 33, 139–148.

  • Moran, M. J., and C. Monje, 2016: Guidance on treatment of the economic value of a statistical life (VSL) in U.S. Department of Transportation Analyses—2016 adjustment. Department of Transportation Memo., 13 pp., https://cms.dot.gov/sites/dot.gov/files/docs/2016%20Revised%20Value%20of%20a%20Statistical%20Life%20Guidance.pdf.

  • NOAA, 2018: Natural hazard statistics. NWS Office of Climate, Water, and Weather Services, http://www.nws.noaa.gov/om/hazstats.shtml.

  • NRC, 2002: Weather Radar Technology beyond NEXRAD. National Research Council, National Academy Press, 81 pp., https://doi.org/10.17226/10394.

    • Crossref
    • Export Citation

  • Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in C: The Art of Scientific Computing. 2nd Ed. Cambridge University Press, 994 pp.

  • Ramsdell, J. V., Jr., and J. P. Rishel, 2007: Tornado climatology of the contiguous United States. U.S. Nuclear Regulatory Commission Office of Nuclear Regulatory Research Tech. Rep. NUREG/CR-4461, Rev. 2, 257 pp., https://www.nrc.gov/docs/ML0708/ML070810400.pdf.

  • Schultz, C. J., and Coauthors, 2012: Dual-polarization tornadic debris signatures, Part I: Examples and utility in an operational setting. Electron. J. Oper. Meteor., 13 (9), 120–137, http://nwafiles.nwas.org/ej/pdf/2012-EJ9.pdf.

    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., and D. Sutter, 2005: WSR-88D radar, tornado warnings, and tornado casualties. Wea. Forecasting, 20, 301–310, https://doi.org/10.1175/WAF857.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., and D. Sutter, 2008: Tornado warnings, lead times, and tornado casualties: An empirical investigation. Wea. Forecasting, 23, 246–258, https://doi.org/10.1175/2007WAF2006027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., and D. Sutter, 2009: False alarms, tornado warnings, and tornado casualties. Wea. Climate Soc., 1, 38–53, https://doi.org/10.1175/2009WCAS1005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., and D. Sutter, 2011: Economic and Societal Impact of Tornadoes. Amer. Meteor. Soc., 282 pp.

    • Crossref
    • Export Citation

  • Smith, B. T., R. L. Thompson, J. S. Grams, C. Broyles, and H. E. Brooks, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 1114–1135, https://doi.org/10.1175/WAF-D-11-00115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stailey, J. E., and K. D. Hondl, 2016: Multifunction Phased Array Radar for aircraft and weather surveillance. Proc. IEEE, 104, 649–659, https://doi.org/10.1109/JPROC.2015.2491179.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sutter, D., and S. Erickson, 2010: The time cost of tornado warnings and the savings with storm-based warnings. Wea. Climate Soc., 2, 103–112, https://doi.org/10.1175/2009WCAS1011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Torres, S., and C. Curtis, 2006: Design considerations for improved tornado detection using superresolution data on the NEXRAD network. Preprints, Third European Conf. on Radar Meteorology and Hydrology (ERAD), Barcelona, Spain, Amer. Meteor. Soc., 5B.10, https://ams.confex.com/ams/pdfpapers/116240.pdf.

  • USCB, 2016: B25033: Total population in occupied housing units by tenure by units in structure. 2011–2015 American Community Survey 5-Year Estimates, U. S. Census Bureau, accessed 11 June 2018, http://factfinder2.census.gov.

  • Weber, M. E., J. Y. N. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 1739–1751, https://doi.org/10.1175/BAMS-88-11-1739.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilson, K. A., P. L. Heinselman, C. M. Custer, D. M. Kingfield, and Z. Kang, 2017: Forecaster performance and workload: Does radar update time matter? Wea. Forecasting, 32, 253–274, https://doi.org/10.1175/WAF-D-16-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wood, V. T., and R. A. Brown, 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12, 928–938, https://doi.org/10.1175/1520-0434(1997)012<0928:EORSOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zrnic, D. S., and R. J. Doviak, 1976: Effective antenna pattern of scanning radars. IEEE Trans. Aerosp. Electron. Syst., AES-12, 551–555, https://doi.org/10.1109/TAES.1976.308254.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3048 1573 77
PDF Downloads 1344 230 12