Editorial Type:
Article
Article Type:
Research Article

Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development

Lindsey M. Richardson Radar Operations Center, National Weather Service, Norman, Oklahoma
Centuria Corporation, Norman, Oklahoma

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Jeffrey G. Cunningham 557th Weather Wing, Offutt Air Force Base, Nebraska

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W. David Zittel Radar Operations Center, National Weather Service, Norman, Oklahoma

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Robert R. Lee Radar Operations Center, National Weather Service, Norman, Oklahoma

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Richard L. Ice Radar Operations Center, National Weather Service, Norman, Oklahoma
557th Weather Wing, Offutt Air Force Base, Nebraska

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Valery M. Melnikov Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
National Severe Storms Laboratory, Norman, Oklahoma

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Nicole P. Hoban Research Experiences for Undergraduates, National Weather Center, Norman, Oklahoma
North Carolina State University, Raleigh, North Carolina

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Joshua G. Gebauer Research Experiences for Undergraduates, National Weather Center, Norman, Oklahoma
University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Mar 2017
DOI:
https://doi.org/10.1175/JTECH-D-16-0030.1
Page(s):
465–478
Restricted access

Abstract

Studies have shown that echo returns from clear-air Bragg scatter (CABS) can be used to detect the height of the convective boundary layer and to assess the systematic differential reflectivity (ZDR) bias for a radar site. However, these studies did not use data from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) or data from a large variety of sites. A new algorithm to automatically detect CABS from any operational WSR-88D with dual-polarization capability while excluding contamination from precipitation, biota, and ground clutter is presented here. Visual confirmation and tests related to the sounding parameters’ relative humidity slope, refractivity gradient, and gradient Richardson number are used to assess the algorithm. Results show that automated detection of CABS in operational WSR-88D data gives useful ZDR bias information while omitting the majority of contaminated cases. Such an algorithm holds potential for radar calibration efforts and Bragg scatter studies in general.

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

Corresponding author e-mail: Lindsey M. Richardson, lindsey.m.richardson@noaa.gov

Abstract

Studies have shown that echo returns from clear-air Bragg scatter (CABS) can be used to detect the height of the convective boundary layer and to assess the systematic differential reflectivity (ZDR) bias for a radar site. However, these studies did not use data from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) or data from a large variety of sites. A new algorithm to automatically detect CABS from any operational WSR-88D with dual-polarization capability while excluding contamination from precipitation, biota, and ground clutter is presented here. Visual confirmation and tests related to the sounding parameters’ relative humidity slope, refractivity gradient, and gradient Richardson number are used to assess the algorithm. Results show that automated detection of CABS in operational WSR-88D data gives useful ZDR bias information while omitting the majority of contaminated cases. Such an algorithm holds potential for radar calibration efforts and Bragg scatter studies in general.

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

Corresponding author e-mail: Lindsey M. Richardson, lindsey.m.richardson@noaa.gov
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Journal of Atmospheric and Oceanic Technology

  • Atlas, D., 1959: Meteorological “angel” echoes. J. Meteor., 16, 6–11, doi:10.1175/1520-0469(1959)016<0006:ME>2.0.CO;2.

  • Balsley, B. B., G. Svensson, and M. Tjernström, 2008: On the scale-dependence of the gradient Richardson number in the residual layer. Bound.-Layer Meteor., 127, 57–72, doi:10.1007/s10546-007-9251-0.

    • Search Google Scholar
    • Export Citation

  • Bean, B. R., and E. J. Dutton, 1966: Radio Meteorology. NBS Monogr., No. 92, U.S. Government Printing Office, 435 pp., doi:10.5962/bhl.title.38144.

    • Crossref
    • Export Citation

  • Cohn, S. A., 1994: Investigations of the wavelength dependence of radar backscatter from atmospheric turbulence. J. Atmos. Oceanic Technol., 11, 225–238, doi:10.1175/1520-0426(1994)011<0225:IOTWDO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cohn, S. A., R. R. Rogers, S. Jascourt, W. L. Ecklund, D. A. Carter, and J. S. Wilson, 1995: Interactions between clear-air reflective layers and rain observed with a boundary layer wind profiler. Radio Sci., 30, 323–341, doi:10.1029/94RS03168.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cowley, J. M., 1995: Diffraction Physics. 3rd ed. North-Holland, 481 pp.

    • Crossref
    • Export Citation

  • Crum, T. D., R. L. Alberty, and D. W. Burgess, 1993: Recording, archiving, and using WSR-88D data. Bull. Amer. Meteor. Soc., 74, 645–653, doi:10.1175/1520-0477(1993)074<0645:RAAUWD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cunningham, J. G., W. D. Zittel, R. R. Lee, and R. L. Ice, 2013: Methods for identifying systematic differential reflectivity (Zdr) biases on the operational WSR-88D network. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 9B.5. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228792.html.]

  • Davison, J. L., R. M. Rauber, and L. Di Girolamo, 2013a: A revised conceptual model of the tropical marine boundary layer. Part II: Detecting relative humidity layers using Bragg scattering from S-band radar. J. Atmos. Sci., 70, 3025–3046, doi:10.1175/JAS-D-12-0322.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Davison, J. L., R. M. Rauber, L. Di Girolamo, and M. A. LeMone, 2013b: A revised conceptual model of the tropical marine boundary layer. Part III: Bragg scattering layer statistical properties. J. Atmos. Sci., 70, 3047–3062, doi:10.1175/JAS-D-12-0323.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Doviak, R. J., and D. S. Zrnić, 2006: Doppler Radar and Weather Observations. 2nd ed. Dover Publications, 562 pp.

  • Gage, K. S., C. R. Williams, W. L. Ecklund, and P. E. Johnston, 1999: Use of two profilers during MCTEX for unambiguous identification of Bragg scattering and Rayleigh scattering. J. Atmos. Sci., 56, 3679–3691, doi:10.1175/1520-0469(1999)056<3679:UOTPDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hardy, K. R., and I. Katz, 1969: Probing the clear atmosphere with high power, high resolution radars. Proc. IEEE, 57, 468–480, doi:10.1109/PROC.1969.7001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hardy, K. R., and H. Ottersten, 1969: Radar investigations of convective patterns in the clear atmosphere. J. Atmos. Sci., 26, 666–672, doi:10.1175/1520-0469(1969)26<666:RIOCPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heinselman, P. L., P. L. Spencer, K. L. Elmore, D. J. Stensrud, R. M. Hluchan, and P. C. Burke, 2009: Radar reflectivity–based estimates of mixed layer depth. J. Atmos. Oceanic Technol., 26, 229–239, doi:10.1175/2008JTECHA1091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoban, N. P., J. G. Cunningham, and W. D. Zittel, 2014: Estimating systematic WSR-88D differential reflectivity (ZDR) biases using Bragg scattering. 30th Conf. on Environmental Information Processing Technologies, Atlanta, GA, Amer. Meteor. Soc., 2. [Available online at http://ams.confex.com/ams/94Annual/webprogram/Manuscript/Paper237404/Final_Extend_Abstract_AMS_Feb2014.pdf.]

  • Ivić, I. R., 2014: On the use of a radial-based noise power estimation technique to improve estimates of the correlation coefficient on dual-polarization weather radars. J. Atmos. Oceanic Technol., 31, 1867–1880, doi:10.1175/JTECH-D-14-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ivić, I. R., C. Curtis, and S. M. Torres, 2013: Radial-based noise power estimation for weather radars. J. Atmos. Oceanic Technol., 30, 2737–2753, doi:10.1175/JTECH-D-13-00008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ivić, I. R., J. C. Krause, O. E. Boydstun, A. E. Daniel, A. D. Free, and W. D. Zittel, 2014: Effects of radial-based noise power estimation on spectral moment estimates. J. Atmos. Oceanic Technol., 31, 2671–2691, doi:10.1175/JTECH-D-14-00048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kaimal, J. C., and Coauthors, 1982: Estimating the depth of the daytime convective boundary layer. J. Appl. Meteor., 21, 1123–1129, doi:10.1175/1520-0450(1982)021<1123:ETDOTD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Knight, C. A., and L. J. Miller, 1993: First radar echoes from cumulus clouds. Bull. Amer. Meteor. Soc., 74, 179–188, doi:10.1175/1520-0477(1993)074<0179:FREFCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Knight, C. A., and L. J. Miller, 1998: Early radar echoes from small warm cumulus: Bragg and hydrometeor scattering. J. Atmos. Sci., 55, 2974–2992, doi:10.1175/1520-0469(1998)055<2974:EREFSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Melnikov, V. M., and D. S. Zrnić, 2007: Autocorrelation and cross-correlation estimators of polarimetric variables. J. Atmos. Oceanic Technol., 24, 1337–1350, doi:10.1175/JTECH2054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Melnikov, V. M., R. J. Doviak, D. S. Zrnić, and D. J. Stensrud, 2011: Mapping Bragg scatter with a polarimetric WSR-88D. J. Atmos. Oceanic Technol., 28, 1273–1285, doi:10.1175/JTECH-D-10-05048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Melnikov, V. M., R. J. Doviak, D. S. Zrnić, and D. J. Stensrud, 2013a: Structures of Bragg scatter observed with the polarimetric WSR-88D. J. Atmos. Oceanic Technol., 30, 1253–1258, doi:10.1175/JTECH-D-12-00210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Melnikov, V. M., D. Zrnić, M. Schmidt, and R. Murnan, 2013b: ZDR calibration issues in the WDR-88Ds: Report on 2013-MOU. NSSL Rep., NOAA, 54 pp. [Available online at http://www.nssl.noaa.gov/publications/wsr88d_reports/WSR88D_ZDRcalib_Report_2013.pdf.]

  • Ottersten, H., 1969: Atmospheric structure and radar backscattering in clear air. Radio Sci., 4, 1179–1193, doi:10.1029/RS004i012p01179.

  • Richardson, L. M., W. D. Zittel, R. R. Lee, V. M. Melnikov, R. L. Ice, and J. G. Cunningham, 2017: Bragg scatter detection by the WSR-88D. Part II: Assessment of ZDR bias estimation. J. Atmos. Oceanic Technol., 34, 479–493, doi:10.1175/JTECH-D-16-0031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Scotti, R. S., and G. M. Corcos, 1969: Measurements on the growth of small disturbances in a stratified shear layer. Radio Sci., 4, 1309–1313, doi:10.1029/RS004i012p01309.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., 2007: Planetary boundary layer and turbulence parameterizations. Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, 138–184.

    • Crossref
    • Export Citation

  • Thorpe, S. A., 1969: Experiments on the stability of stratified shear flows. Radio Sci., 4, 1327–1331, doi:10.1029/RS004i012p01327.

  • University of Wyoming, 2014: Atmospheric soundings. Department of Atmospheric Science, University of Wyoming. Subset used: October 2013–September 2014, accessed 12 October 2014. [Available online at http://weather.uwyo.edu/upperair/sounding.html.]

  • vanZanten, M. C., P. G. Duynkerke, and J. W. M. Cuijpers, 1999: Entrainment parameterization in convective boundary layers. J. Atmos. Sci., 56, 813–828, doi:10.1175/1520-0469(1999)056<0813:EPICBL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 91, Academic Press, 627 pp.

  • WSR-88D ROC, 2008: WSR-88D system specification revision H. NWS Radar Operations Center Rep. ROC/2810000H/0WY55, 160 pp.

  • Zittel, W. D., J. G. Cunningham, R. R. Lee, L. M. Richardson, R. L. Ice, and V. Melnikov, 2014: Use of hydrometeors, Bragg scatter, and sun spikes to determine system ZDR biases in the WSR-88D fleet. Extended Abstracts, Eighth European Conf. on Radar in Meteorology and Hydrology (ERAD 2014), Garmisch-Partenkirchen, Germany, DWD and DLR, DAC.P12. [Available online at https://www.roc.noaa.gov/WSR88D/PublicDocs/Publications/132_Zittel.pdf.]

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