• Berkowitz, D. S., J. A. Schultz, S. Vasiloff, K. L. Elmore, C. D. Payne, and J. B. Boettcher, 2013: Status of dual pol QPE in the WSR-88D network. 27th Conf. on Hydrology, Austin, TX, Amer. Meteor. Soc., 2.2. [Available online at https://ams.confex.com/ams/93Annual/webprogram/Paper221525.html.]

  • Boodoo, S., D. Hudak, N. Donaldson, and M. Leduc, 2010: Application of dual-polarization radar melting-layer detection algorithm. J. Appl. Meteor. Climatol, 49, 17791793, doi:10.1175/2010JAMC2421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., and K. Ikeda, 2004: Freezing level estimations with polarimetric radar. J. Appl. Meteor., 43, 15411553, doi:10.1175/JAM2155.1.

  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar. Cambridge University Press, 636 pp.

    • Crossref
    • Export Citation
  • Doviak, R. J., and D. Zrnić, 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Giangrande, S. E., J. M. Krause, and A. V. Ryzhkov, 2008: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. J. Appl. Meteor. Climatol, 47, 13541364, doi:10.1175/2007JAMC1634.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keränen, R., L. C. Alku, and J. Selzler, 2015: Estimation of melting layer altitudes from dual-polarization weather radar observations. 37th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 1.88. [Available online at https://ams.confex.com/ams/37RADAR/webprogram/Paper275961.html.]

  • Krause, J., V. Lakshmanan, and A. Ryzhkov, 2013: Improving detection of the melting layer using dual-polarization radar, NWP model data, and object identification techniques. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 262. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228651.html.]

  • Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007: The warning decision support system–integrated information. Wea. Forecasting, 22, 596612, doi:10.1175/WAF1009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lundquist, J. D., P. J. Neiman, B. Martner, A. B. White, D. J. Gottas, and F. M. Ralph, 2008: Rain versus snow in the Sierra Nevada, California: Comparing Doppler profiling radar and surface observations of melting level. J. Hydrometeor., 9, 194211, doi:10.1175/2007JHM853.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., 2008: Assessment of radar signal attenuation caused by the melting hydrometeor layer. IEEE Trans. Geosci. Remote Sens., 46, 10391047, doi:10.1109/TGRS.2008.915757.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., 2010: Evaluating polarimetric X-band radar rainfall estimators during HMT. J. Atmos. Oceanic Technol., 27, 122134, doi:10.1175/2009JTECHA1318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., K. A. Clark, and D. E. Kingsmill, 2007: A polarimetric radar approach to identify rain, melting-layer, and snow regions for applying corrections to vertical profiles of reflectivity. J. Appl. Meteor. Climatol, 46, 154166, doi:10.1175/JAM2508.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minder, J. R., and D. E. Kingsmill, 2013: Mesoscale variations of the atmospheric snow line over northern Sierra Nevada: Multiyear statistics, case study, and mechanics. J. Atmos. Sci., 70, 916938, doi:10.1175/JAS-D-12-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schuur, T. J., A. V. Ryzhkov, and J. Krause, 2014: A new melting layer detection algorithm that combines polarimetric radar–based detection with thermodynamic output from numerical models. Eighth European Conf. on Radar in Meteorology and Hydrology, Garmisch-Partenkirchen, Germany, DWD-DLR, P05. [Available online at http://www.pa.op.dlr.de/erad2014/programme/ExtendedAbstracts/135_Schuur.pdf.]

  • White, A. B., D. J. Gottas, E. T. Strem, F. M. Ralph, and P. J. Neiman, 2002: An automated brightband height detection algorithm for use with Doppler radar spectral moments. J. Atmos. Oceanic Technol., 19, 687697, doi:10.1175/1520-0426(2002)019<0687:AABHDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B., D. J. Gottas, A. F. Henkel, P. J. Neinman, F. M. Ralph, and S. I. Gutman, 2010: Developing a performance measure for snow-level forecasts. J. Hydrometeor., 11, 739753, doi:10.1175/2009JHM1181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B., and Coauthors, 2013: A twenty-first-century California observing network for monitoring extreme weather events. J. Atmos. Oceanic Technol., 30, 15851603, doi:10.1175/JTECH-D-12-00217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolfensberger, D., D. Scipion, and A. Berne, 2016: Detection and characterization of the melting layer based on polarimetric radar scans. Quart. J. Roy. Meteor. Soc., 142, 108124, doi:10.1002/qj.2672.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Snow-Level Estimates Using Operational Polarimetric Weather Radar Measurements

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  • 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 2 NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 3 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado
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Abstract

Scanning polarimetric measurements from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems are evaluated for the retrievals of snow-level (SL) heights, which are located below the 0°C isotherm and represent the altitude within the melting layer (ML) where snow changes to rain. The evaluations are conducted by intercomparisons of the SL estimates obtained from the Beale Air Force Base WSR-88D unit (KBBX) during a wet season 6-month period (from October 2012 to March 2013) and robust SL height measurements hSL from a high-resolution vertically pointing Doppler snow-level profiler deployed near Oroville, California. It is shown that a mean value height measurement hL3 between the estimates of the ML top and bottom, which can be derived from the WSR-88D level-III (L3) ML products, provides relatively unbiased estimates of SL heights with a standard deviation of about 165 m. There is little azimuthal variability in derived values of hL3, which is, in part, due to the use of higher radar beam tilts and azimuthal smoothing of the level-III ML products. Height estimates hrho based on detection of the ML minima of the copolar cross-correlation coefficient ρhv calculated from the WSR-88D level-II products are slightly better correlated with profiler-derived SL heights, though they are biased low by about 113 m with respect to hSL. If this bias is accounted for, the standard deviation of the ρhv minima–based SL estimates is generally less than 100 m. Overall, the results of this study indicate that, at least for closer radar ranges (up to ~13–15 km), the operational radar polarimetric data can provide snow-level estimates with a quality similar to those from the dedicated snow-level radar profilers.

© 2017 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 e-mail: Sergey Y. Matrosov, sergey.matrosov@noaa.gov

Abstract

Scanning polarimetric measurements from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems are evaluated for the retrievals of snow-level (SL) heights, which are located below the 0°C isotherm and represent the altitude within the melting layer (ML) where snow changes to rain. The evaluations are conducted by intercomparisons of the SL estimates obtained from the Beale Air Force Base WSR-88D unit (KBBX) during a wet season 6-month period (from October 2012 to March 2013) and robust SL height measurements hSL from a high-resolution vertically pointing Doppler snow-level profiler deployed near Oroville, California. It is shown that a mean value height measurement hL3 between the estimates of the ML top and bottom, which can be derived from the WSR-88D level-III (L3) ML products, provides relatively unbiased estimates of SL heights with a standard deviation of about 165 m. There is little azimuthal variability in derived values of hL3, which is, in part, due to the use of higher radar beam tilts and azimuthal smoothing of the level-III ML products. Height estimates hrho based on detection of the ML minima of the copolar cross-correlation coefficient ρhv calculated from the WSR-88D level-II products are slightly better correlated with profiler-derived SL heights, though they are biased low by about 113 m with respect to hSL. If this bias is accounted for, the standard deviation of the ρhv minima–based SL estimates is generally less than 100 m. Overall, the results of this study indicate that, at least for closer radar ranges (up to ~13–15 km), the operational radar polarimetric data can provide snow-level estimates with a quality similar to those from the dedicated snow-level radar profilers.

© 2017 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 e-mail: Sergey Y. Matrosov, sergey.matrosov@noaa.gov
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