A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations

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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Heather M. Grams NOAA/NWS/Radar Operations Center, Norman, Oklahoma

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Carrie Langston 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

Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

© 2018 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: Steven M. Martinaitis, steven.martinaitis@noaa.gov

Abstract

Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

© 2018 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: Steven M. Martinaitis, steven.martinaitis@noaa.gov
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  • 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
  • Blake, E. S., 2016: Hurricane Paine. NOAA/National Hurricane Center Tropical Cyclone Rep., 13 pp., http://www.nhc.noaa.gov/data/tcr/EP172016_Paine.pdf.

  • Bringi, V. N., M. A. Rico-Ramirez, and M. Thurai, 2011: Rainfall estimation with an operational polarimetric C-band radar in the United Kingdom: Comparison with a gauge network and error analysis. J. Hydrometeor., 12, 935954, https://doi.org/10.1175/JHM-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., and R. A. A. Franks, 1991: The evaporation of frontal and other stratiform precipitation. Quart. J. Roy. Meteor. Soc., 117, 10571080, https://doi.org/10.1002/qj.49711750109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cocks, S. B., J. Zhang, S. M. Martinaitis, Y. Qi, B. Kaney, and K. Howard, 2017: MRMS QPE performance east of the Rockies during the 2014 warm season. J. Hydrometeor., 18, 761775, https://doi.org/10.1175/JHM-D-16-0179.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comstock, K. K., R. Wood, S. E. Yuter, and C. S. Bretherton, 2004: Reflectivity and rain rate in and below drizzling stratocumulus. Quart. J. Roy. Meteor. Soc., 130, 28912918, https://doi.org/10.1256/qj.03.187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cui, B., Z. Toth, Y. Zhu, and D. Hou, 2012: Bias correction for global ensemble forecast. Wea. Forecasting, 27, 396410, https://doi.org/10.1175/WAF-D-11-00011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feingold, G., 1993: Parameterization of evaporation of rainfall for use in general circulation models. J. Atmos. Sci., 50, 34543467, https://doi.org/10.1175/1520-0469(1993)050<3454:POTEOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodrich, D. C., J.-M. Faures, D. A. Woolhiser, L. J. Lane, and S. Sorooshian, 1995: Measurement and analysis of small-scale convective storm rainfall variability. J. Hydrol., 173, 283308, https://doi.org/10.1016/0022-1694(95)02703-R.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gori, E. G., and J. Joss, 1980: Changes of shape of raindrop size distributions simultaneously observed along a mountain slope. J. Rech. Atmos., 14, 239300.

    • Search Google Scholar
    • Export Citation
  • Grams, H., J. Zhang, and K. Elmore, 2014: Automated identification of enhanced rainfall rates using the near-storm environment for radar precipitation estimates. J. Hydrometeor., 15, 12381254, https://doi.org/10.1175/JHM-D-13-042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregory, D., 1995: A consistent treatment of the evaporation of rain and snow for use in large-scale models. Mon. Wea. Rev., 123, 27162732, https://doi.org/10.1175/1520-0493(1995)123<2716:ACTOTE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrison, D. L., S. J. Driscoll, and M. Kitchen, 2000: Improving precipitation estimates from weather radar using quality control and correction techniques. Meteor. Appl., 7, 135144, https://doi.org/10.1017/S1350482700001468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and L. J. Donner, 1990: A scheme for parameterizing ice water content in general circulation models. J. Atmos. Sci., 47, 18651877, https://doi.org/10.1175/1520-0469(1990)047<1865:ASFPIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Z., and R. C. Srivastava, 1995: Evolution of raindrop size distribution by coalescence, breakup, and evaporation: Theory and observations. J. Atmos. Sci., 52, 17611783, https://doi.org/10.1175/1520-0469(1995)052<1761:EORSDB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalogiros, J., M. N. Anagnostou, E. A. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2014: Evaluation of a new polarimetric algorithm for rain-path attenuation correction of X-band radar observations against disdrometer. IEEE Trans. Geosci. Remote Sens., 52, 13691380, https://doi.org/10.1109/TGRS.2013.2250979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kinzer, G. D., and R. Gunn, 1951: The evaporation, temperature and thermal relaxation-time of freely falling waterdrops. J. Meteor., 8, 7183, https://doi.org/10.1175/1520-0469(1951)008<0071:TETATR>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2010: The impact of evaporation on polarimetric characteristics or rain: Theoretical model and practical implications. J. Appl. Meteor. Climatol., 49, 12471267, https://doi.org/10.1175/2010JAMC2243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leary, C. A., and R. A. Houze Jr., 1979: Melting and evaporation of hydrometeors in precipitation from the anvil clouds of deep tropical convection. J. Atmos. Sci., 36, 669679, https://doi.org/10.1175/1520-0469(1979)036<0669:MAEOHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levin, Z., G. Feingold, and S. Tzivion, 1991: The evolution of raindrop spectra: Comparisons between modeled and observed spectra along a mountain slope in Switzerland. J. Appl. Meteor., 30, 893900, https://doi.org/10.1175/1520-0450(1991)030<0893:TEORSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and R. C. Srivastava, 2001: An analytical solution for raindrop evaporation and its application to radar rainfall measurements. J. Appl. Meteor., 40, 16071616, https://doi.org/10.1175/1520-0450(2001)040<1607:AASFRE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • List, R. E., 1951: Smithsonian Meteorological Tables. 6th ed. Smithsonian Institution, 527 pp.

  • Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penide, G., V. V. Kumar, A. Protat, and P. T. May, 2013: Statistics of drop size distribution parameters and rain rates for stratiform and convective precipitation during the North Australian wet season. Mon. Wea. Rev., 141, 32223237, https://doi.org/10.1175/MWR-D-12-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qi, Y., and J. Zhang, 2017: A physically based two-dimensional seamless reflectivity mosaic for radar QPE in the MRMS system. J. Hydrometeor., 18, 13271340, https://doi.org/10.1175/JHM-D-16-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qi, Y., J. Zhang, and P. Zhang, 2013a: A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates. Quart. J. Roy. Meteor. Soc., 139, 22332240, https://doi.org/10.1002/qj.2095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qi, Y., J. Zhang, P. Zhang, and Q. Cao, 2013b: VPR correction of bright band effects in radar QPEs using polarimetric radar observations. J. Geophys. Res. Atmos., 118, 36273633, https://doi.org/10.1002/jgrd.50364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qi, Y., S. Martinaitis, J. Zhang, and S. Cocks, 2016: A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system. J. Hydrometeor., 17, 16751691, https://doi.org/10.1175/JHM-D-15-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. R., 1971: The effect of variable target reflectivity on weather radar measurements. Quart. J. Roy. Meteor. Soc., 97, 154167, https://doi.org/10.1002/qj.49709741203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. R., and M. K. Yau, 1996: A Short Course in Cloud Physics. 3rd ed. Butterworth-Heinemann, 290 pp.

  • Rosenfeld, D., and Y. Mintz, 1988: Evaporation of rain falling from convective clouds as derived from radar measurements. J. Appl. Meteor., 27, 209215, https://doi.org/10.1175/1520-0450(1988)027<0209:EORFFC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., D. Atlas, and D. B. Wolff, 1992: Beamwidth effects on Z–R relations and area-integrated rainfall. J. Appl. Meteor., 31, 454464, https://doi.org/10.1175/1520-0450(1992)031<0454:BEOZRR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., D. B. Wolff, and D. Atlas, 1993: General probability-matched relations between radar reflectivity and rain rate. J. Appl. Meteor., 32, 5072, https://doi.org/10.1175/1520-0450(1993)032<0050:GPMRBR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schlesinger, M. E., J.-E. Oh, and D. Rosenfeld, 1988: A parameterization of evaporation of rainfall. Mon. Wea. Rev., 116, 18871895, https://doi.org/10.1175/1520-0493(1988)116<1887:APOTEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C. J., 1986: The reduction of errors caused by bright bands in quantitative rainfall measurements made using radar. J. Atmos. Oceanic Technol., 3, 129141, https://doi.org/10.1175/1520-0426(1986)003<0129:TROECB>2.0.CO;2.

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

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., 1988: Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically Based Modelling and Simulation of Climate and Climate Change: Part 1, M. E. Schlesinger, Ed., NATO ASI Series, Vol. 243, Springer, 433–462, https://doi.org/10.1007/978-94-009-3041-4_10.

    • Crossref
    • Export Citation
  • Tang, L., J. Zhang, C. Langston, J. Krause, K. 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
  • Thériault, J. M., R. Rasmussen, K. Ikeda, and S. Landolt, 2012: Dependence of snow gauge collection efficiency on snowflake characteristics. J. Appl. Meteor. Climatol., 51, 745762, https://doi.org/10.1175/JAMC-D-11-0116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • U.K. Meteorological Office, 1964: Aspirated Psychrometer Readings, Degrees Celsius. Part III, Hygrometric Tables, 2nd ed. H. M. Stationary Office, 90 pp.

  • Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distribution. J. Climate Appl. Meteor., 22, 17641775, https://doi.org/10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, X., R. Evaristo, S. Troemel, P. Saavedra, C. Simmer, and A. Ryzhkov, 2016: Radar observations of evaporation and implications for quantitative precipitation and cooling rate estimation. J. Atmos. Oceanic Technol., 33, 17791792, https://doi.org/10.1175/JTECH-D-15-0244.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., B. R. Nelson, A. A. Bradley, J. A. Smith, C. D. Peters-Lidard, A. Kruger, and M. L. Baeck, 1999: An evaluation of NEXRAD precipitation estimates in complex terrain. J. Geophys. Res., 104, 19 69119 703, https://doi.org/10.1029/1999JD900123.

    • 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., Y. Qi, D. Kingsmill, and K. Howard, 2012a: Radar-based quantitative precipitation estimation for the cool season in complex terrain: Case studies from the NOAA Hydrometeorological Testbed. J. Hydrometeor., 13, 18361854, https://doi.org/10.1175/JHM-D-11-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., Y. Qi, K. Howard, C. Langston, and B. Kaney, 2012b: Radar quality index (RQI)—A combined measure of 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, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621637, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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