MRMS QPE Performance during the 2013/14 Cool Season

Stephen B. Cocks Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Steven M. Martinaitis Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Brian Kaney Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Jian Zhang NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Kenneth Howard NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

A recently implemented operational quantitative precipitation estimation (QPE) product, the Multi-Radar Multi-Sensor (MRMS) radar-only QPE (Q3RAD), mosaicked dual-polarization QPE, and National Centers for Environmental Prediction (NCEP) stage II QPE were evaluated for nine cool season precipitation events east of the Rockies. These automated, radar-only products were compared with the forecaster quality-controlled NCEP stage IV product, which was considered as the benchmark for QPE. Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) 24-h accumulation data were used to evaluate product performance while hourly automated gauge data (quality controlled) were used for spatial and time series analysis. Statistical analysis indicated all three radar-only products had a distinct underestimate bias, likely due to the radar beam partially or completely overshooting the predominantly shallow winter precipitation systems. While the forecaster quality-controlled NCEP stage IV estimates had the best overall performance, Q3RAD had the next best performance, which was significant as Q3RAD is available in real time whereas NCEP stage IV estimates are not. Stage II estimates exhibited a distinct tendency to underestimate gauge totals while dual-polarization estimates exhibited significant errors related to melting layer challenges.

Corresponding author address: Stephen B. Cocks, National Severe Storms Laboratory, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: stephen.cocks@noaa.gov

Abstract

A recently implemented operational quantitative precipitation estimation (QPE) product, the Multi-Radar Multi-Sensor (MRMS) radar-only QPE (Q3RAD), mosaicked dual-polarization QPE, and National Centers for Environmental Prediction (NCEP) stage II QPE were evaluated for nine cool season precipitation events east of the Rockies. These automated, radar-only products were compared with the forecaster quality-controlled NCEP stage IV product, which was considered as the benchmark for QPE. Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) 24-h accumulation data were used to evaluate product performance while hourly automated gauge data (quality controlled) were used for spatial and time series analysis. Statistical analysis indicated all three radar-only products had a distinct underestimate bias, likely due to the radar beam partially or completely overshooting the predominantly shallow winter precipitation systems. While the forecaster quality-controlled NCEP stage IV estimates had the best overall performance, Q3RAD had the next best performance, which was significant as Q3RAD is available in real time whereas NCEP stage IV estimates are not. Stage II estimates exhibited a distinct tendency to underestimate gauge totals while dual-polarization estimates exhibited significant errors related to melting layer challenges.

Corresponding author address: Stephen B. Cocks, National Severe Storms Laboratory, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: stephen.cocks@noaa.gov
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  • Berkowitz, D. S., Schultz J. A. , Vasiloff S. , Elmore K. L. , Payne C. D. , and Boettcher J. B. , 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.]

  • Cunningham, J. G., Zittel W. D. , Lee R. R. , and Ice R. L. , 2013: Methods for identifying systematic differential reflectivity 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.]

  • Elmore, K. L., Flamig Z. L. , Lakshmanan V. , Kaney B. T. , Farmer V. , Reeves H. D. , and Rothfusz L. P. , 2014: MPING: Crowd-sourcing weather reports for research. Bull. Amer. Meteor. Soc., 95, 13351342, doi:10.1175/BAMS-D-13-00014.1.

    • Search Google Scholar
    • Export Citation
  • Fiebrich, C. A., Morgan C. R. , McCombs A. G. , Hall P. K. Jr., and McPherson R. A. , 2010: Quality assurance procedures for mesoscale meteorological data. J. Atmos. Oceanic Technol., 27, 15651582, doi:10.1175/2010JTECHA1433.1.

    • Search Google Scholar
    • Export Citation
  • Giangrande, S., and Ryzhkov A. , 2008: Estimation of rainfall based on the results of polarimetric echo classification. J. Appl. Meteor., 47, 24452462, doi:10.1175/2008JAMC1753.1.

    • Search Google Scholar
    • Export Citation
  • Goodison, B. E., and Yang D. , 1996: In situ measurements of solid precipitation in high latitudes: The need for correction. Proc. Workshop on the ACSYS Solid Precipitation Climatology Project, WMO/TD-739, WCRP-93, Reston, VA, World Climate Research Programme, 3–17.

  • Gourley, J. J., Kaney B. , and Maddox R. A. , 2003: Evaluating the calibrations of radars: A software approach. Preprints, 31st Int. Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 459–462. [Available online at https://ams.confex.com/ams/32BC31R5C/techprogram/paper_64171.htm.]

  • Groisman, P. Ya., and Legates D. R. , 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc., 75, 215227, doi:10.1175/1520-0477(1994)075<0215:TAOUSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hoban, N. P., Cunningham J. G. , and Zittel D. , 2014: Estimating Systematic WSR-88D differential reflectivity biases using Bragg Scattering. 30th Conf. on Environmental Information Processing Technologies, Atlanta, GA, Amer. Meteor. Society, 2. [Available online at https://ams.confex.com/ams/94Annual/webprogram/Paper237404.html.]

  • Kim, D., Nelson B. , and Seo D. J. , 2009: Characteristics of reprocessed Hydrometeorological Automated Data System (HADS) hourly precipitation data. Wea. Forecasting, 24, 12871296, doi:10.1175/2009WAF2222227.1.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P. E., Gourley J. J. , Hong Y. , Zhang J. , Moazamigoodarzi S. , Langston C. , and Arthur A. , 2015: Probabilistic precipitation rate estimates with ground-based radar networks. Water Resour. Res., 51, 14221442, doi:10.1002/2014WR015672.

    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., Villarini G. , and Smith J. A. , 2010: Radar-rainfall uncertainties: Where are we after thirty years of effort? Bull. Amer. Meteor. Soc., 91, 8794, doi:10.1175/2009BAMS2747.1.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Mitchell K. E. , 2005: The NCEP stage II/ IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/pdfpapers/83847.pdf.]

  • Martinaitis, S. M., 2008: Effects of multi-sensor radar and rain gauge data on hydrologic modeling in relatively flat terrain. M. S. thesis, Dept. of Meteorology, Florida State University, 99 pp.

  • Martinaitis, S. M., Cocks S. , Qi Y. , Kaney B. , Zhang J. , and Howard K. , 2015: Understanding winter precipitation impacts on automated gauge observations within a real-time system. J. Hydrometeor., 16, 23452363, doi:10.1175/JHM-D-15-0020.1.

    • Search Google Scholar
    • Export Citation
  • Marzen, J., and Fuelberg H. E. , 2005: Developing a high resolution precipitation dataset for Florida hydrologic studies. 19th Conf. on Hydrology, New Orleans, LA, Amer. Meteor. Soc., J9.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83718.htm.]

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

    • Search Google Scholar
    • Export Citation
  • Radar Operations Center, 2015: Guidance on adaptable parameters. WSR-88D Operator Handbook, Vol. 1, RPG, NOAA, U.S. Department of Commerce. [Available online at https://www.roc.noaa.gov/wsr88d/Program/OperationsManuals.aspx.]

  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, doi:10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., Giangrande S. E. , Melnikov V. M. , and Schuur T. J. , 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 11381155, doi:10.1175/JTECH1772.1.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., Diederich M. , Zhang P. , and Simmer C. , 2014: Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. J. Atmos. Oceanic Technol., 31, 599619, doi:10.1175/JTECH-D-13-00038.1.

    • Search Google Scholar
    • Export Citation
  • Sieck, L. C., Burges S. J. , and Steiner M. , 2007: Challenges in obtaining reliable measurements of point rainfall. Water Resour. Res., 43, W01420, doi:10.1029/2005WR004519.

    • Search Google Scholar
    • Export Citation
  • Smith, J. A., Seo D. J. , Baeck M. L. , and Hudlow M. D. , 1996: An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res., 32, 20352046, doi:10.1029/96WR00270.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., Smith J. A. , Burges S. J. , Alonso C. V. , and Darden R. W. , 1999: Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation. Water Resour. Res., 35, 24872503, doi:10.1029/1999WR900142.

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

    • Search Google Scholar
    • Export Citation
  • Wang, Y., Zhang P. , Ryzhkov A. , Zhang J. , and Chang P. , 2014: Utilization of specific attenuation for tropical rainfall estimation in complex terrain. J. Hydrometeor., 15, 22502266, doi:10.1175/JHM-D-14-0003.1.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and Brandes E. A. , 1979: Radar measurement of rainfall: A summary. Bull. Amer. Meteor. Soc., 60, 10481058, doi:10.1175/1520-0477(1979)060<1048:RMORS>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2012: Radar Quality Index (RQI)—A combined measure for beam blockage and VPR effects in a national network. Weather Radar and Hydrology, R. J. Moore, S. J. Cole, and A. J. Illingworth, Eds., IAHS Publ. 351, 388–393.

  • Zhang, J., and Coauthors, 2014: Initial operating capabilities of quantitative precipitation estimates in the Multi-Radar Multi-System. 28th Conf. of Hydrology, Atlanta, GA, Amer. Meteor. Soc., 5.3. [Available online at https://ams.confex.com/ams/94Annual/webprogram/Paper240487.html.]

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

    • Search Google Scholar
    • Export Citation
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