• Andrić, J., M. R. Kumjian, D. S. Zrnić, J. M. Straka, and V. M. Melnikov, 2013: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study. J. Appl. Meteor. Climatol., 52, 682700, https://doi.org/10.1175/JAMC-D-12-028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bachmann, S. M., and M. Tracy, 2009: Data driven adaptive identification and suppression of ground clutter for weather radar. 25th Conf. on Int. Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 11B.3, https://ams.confex.com/ams/89annual/techprogram/paper_146356.htm.

  • Bachmann, S. M., Y. Al-Rashid, P. Bronecke, R. Palmer, and B. Isom, 2010: Suppression of the wind farm contribution from the atmospheric radar returns. 26th Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Atlanta, GA, Amer. Meteor. Soc., 392, https://ams.confex.com/ams/90annual/techprogram/paper_159956.htm.

  • Balakrishnan, N., and D. S. Zrnić, 1990: Use of polarization to characterize precipitation and discriminate large hail. J. Atmos. Sci., 47, 15251540, https://doi.org/10.1175/1520-0469(1990)047<1525:UOPTCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., S. Weygandt, T. G. Smirnova, M. Hu, S. E. Peckham, J. M. Brown, K. Brundage, and G. S. Manikin, 2009: Assimilation of radar reflectivity data using a diabatic digital filter: Applications to the Rapid Update Cycle and Rapid Refresh and initialization of High Resolution Rapid Refresh forecasts with RUC/RR grids. 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Phoenix, AZ, Amer. Meteor. Soc., 7B.3, https://ams.confex.com/ams/pdfpapers/150469.pdf.

  • Benjamin, S. G., S. Weygandt, C. Alexander, J. M. Brown, T. G. Smirnova, P. Hofmann, E. James, and G. Dimego, 2011: NOAA’s hourly-updated 3km HRRR and RUC/Rapid Refresh—Recent (2010) and upcoming changes toward improving weather guidance for air-traffic management. Second Aviation, Range, and Aerospace Meteorology Special Symp. on Weather–Air Traffic Management Integration, Seattle, WA, Amer. Meteor. Soc., 3.2, https://ams.confex.com/ams/91Annual/webprogram/Paper185659.html.

  • Benjamin, S. G., and Coauthors, 2013: Data assimilation and model updates in the 2013 Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) analysis and forecast systems. NCEP/EMC Meeting, Washington, DC, NCEP/EMC/Model Evaluation Group, http://ruc.noaa.gov/pdf/NCEP_HRRR_RAPv2_6jun2013-Benj-noglob.pdf.

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berenguer, M., D. Sempere-Torres, C. Corral, and R. Sanchez-Diezma, 2006: A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. J. Atmos. Oceanic Technol., 23, 11571180, https://doi.org/10.1175/JTECH1914.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chandrasekar, V., R. Keränen, S. Lim, and D. Moisseev, 2013: Recent advances in classification of observations from dual polarization weather radars. Atmos. Res., 119, 97111, https://doi.org/10.1016/j.atmosres.2011.08.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheong, B. L., R. Palmer, and S. Torres, 2011: Automatic wind turbine identification using level-II data. IEEE Radar Conf., Kansas, MO, IEEE, 271–275, https://doi.org/10.1109/RADAR.2011.5960542.

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

  • Fabry, F., and I. Zawadzki, 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52, 838851, https://doi.org/10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/2007JAMC1634.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., and C. M. Calvert, 2003: Automated detection of the bright band using WSR-88D data. Wea. Forecasting, 18, 585599, https://doi.org/10.1175/1520-0434(2003)018<0585:ADOTBB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., P. Tabary, and J. P. Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol., 24, 14391451, https://doi.org/10.1175/JTECH2035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hood, K., S. Torres, and R. Palmer, 2010: Automatic detection of wind turbine clutter for weather radars. J. Atmos. Oceanic Technol., 27, 18681880, https://doi.org/10.1175/2010JTECHA1437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Isom, B. M., and Coauthors, 2009: Detailed observations of wind turbine clutter with scanning weather radars. J. Atmos. Oceanic Technol., 26, 894910, https://doi.org/10.1175/2008JTECHA1136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krause, J. M., 2016: A simple algorithm to discriminate between meteorological and nonmeteorological radar echoes. J. Atmos. Oceanic Technol., 33, 18751885, https://doi.org/10.1175/JTECH-D-15-0239.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., 2013a: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. J. Oper. Meteor., 1, 226242, https://doi.org/10.15191/nwajom.2013.0119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., 2013b: Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications. J. Oper. Meteor., 1, 243264, https://doi.org/10.15191/nwajom.2013.0120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., J. Zhang, K. Hondl, and C. Langston, 2012: A statistical approach to mitigating persistent clutter in radar reflectivity data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 652662, https://doi.org/10.1109/JSTARS.2011.2181828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., C. Karstens, J. Krause, and L. Tang, 2014: Quality control of weather radar data using polarimetric variables. J. Atmos. Oceanic Technol., 31, 12341249, https://doi.org/10.1175/JTECH-D-13-00073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., and V. Chandrasekar, 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy system, and in situ verification. J. Atmos. Oceanic Technol., 17, 140164, https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>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, 13371350, https://doi.org/10.1175/JTECH2054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nai, F., R. Palmer, and S. Torres, 2011: Wind turbine clutter mitigation using range-Doppler domain signal processing method. 27th Conf. on Interactive Information and Processing Systems, Seattle, WA, Amer. Meteor. Soc., 9.4, https://ams.confex.com/ams/91Annual/webprogram/Paper183727.html.

  • Norin, L., and G. Haase, 2012: Doppler weather radars and wind turbines. Doppler Radar Observations—Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications, J. Bech and J. L. Chau, Eds., InTech, 333–354.

    • Crossref
    • Export Citation
  • Park, H. S., A. V. Ryzhkov, D. S. Zrnić, and K.-E. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24, 730748, https://doi.org/10.1175/2008WAF2222205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., and D. S. Zrnić, 2019: Radar Polarimetry for Weather Observations. Springer Atmospheric Sciences, Springer International Publishing, 486 pp.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, T. M., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 16171630, https://doi.org/10.1175/BAMS-D-14-00173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steiner, M., and J. A. Smith, 2002: Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data. J. Atmos. Oceanic Technol., 19, 673686, https://doi.org/10.1175/1520-0426(2002)019<0673:UOTDRS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tabary, P., A. Le Henaff, G. Vulpiani, J. Parent-du-Chatelet, and J. J. Gourley, 2006: Melting layer characterization and identification with a C-band dual-polarization radar: A long-term analysis. Fourth European Conf. on Radar in Meteorology and Hydrology, Barcelona, Spain, Servei Meteorologic de Catalunya, 17–20.

  • Tang, L., J. Zhang, C. Langston, J. Krause, K. W. Howard, and V. Lakshmanan, 2014: A physically based precipitation–nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system. Wea. Forecasting, 29, 11061119, https://doi.org/10.1175/WAF-D-13-00072.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, L., J. Zhang, C. Langston, and K. Cooper, 2016: Canadian radar quality control in Multi-Radar-Multi-Sensor system. Fifth Aviation, Range and Aerospace Meteorology Special Symp., New Orleans, LA, Amer. Meteor. Soc., 835, https://ams.confex.com/ams/96Annual/webprogram/Paper282745.html.

  • Vogt, R. J., J. R. Reed, T. Crum, J. T. Snow, R. Palmer, B. Isom, and D. W. Burgess, 2007: Impacts of wind farms on WSR-88D operations and policy considerations. 23rd Int. Conf. on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer. Meteor. Soc., 5B.7, https://ams.confex.com/ams/87ANNUAL/techprogram/paper_120352.htm.

  • Vogt, R. J., T. D. Crum, W. Greenwood, E. J. Ciardi, and R. G. Guenther, 2011: New criteria for evaluating wind turbine impacts on NEXRAD radars. American Wind Energy Association Conf. and Exhibition, Anaheim, CA, American Wind Energy Association.

  • Walpole, R. E., R. H. Myers, S. L. Myers, and K. E. Ye, 2016: Probability and Statistics for Engineers and Scientists. 9th ed. Pearson, 816 pp.

    • Search Google Scholar
    • Export Citation
  • 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 moment. J. Atmos. Oceanic Technol., 19, 687697, https://doi.org/10.1175/1520-0426(2002)019<0687:AABHDA>2.0.CO;2.

    • 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, https://doi.org/10.1002/qj.2672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Y. Qi, 2010: A real time algorithm for the correction of brightband effects in radar-derived QPE. J. Hydrometeor., 11, 11571171, https://doi.org/10.1175/2010JHM1201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., S. Wang, and B. Clarke, 2004: WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure. 11th Conf. on Aviation, Aviation, Range and Aerospace Meteorology Special Symp., Hyannis, MA, Amer. Meteor. Soc., P5.4, http://ams.confex.com/ams/11aram22sls/techprogram/programexpanded_229.htm.

  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and A. V. Ryzhkov, 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389406, https://doi.org/10.1175/1520-0477(1999)080<0389:PFWSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 67 67 17
Full Text Views 24 24 5
PDF Downloads 32 32 6

Updates on the Radar Data Quality Control in the MRMS Quantitative Precipitation Estimation System

View More View Less
  • 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • 2 NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
  • 3 NOAA/NWS/Radar Operations Center, Norman, Oklahoma
  • 4 Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois
© Get Permissions
Restricted access

Abstract

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

Corresponding author: Lin Tang, lin.tang@noaa.gov

Abstract

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

Corresponding author: Lin Tang, lin.tang@noaa.gov
Save