• Andrieu, H., and J. D. Creutin, 1995: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method. Part I: Formulation. J. Appl. Meteor., 34, 225239, https://doi.org/10.1175/1520-0450(1995)034<0225:IOVPOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arkin, P. A., A. V. R. Krishna Rao, and R. R. Kelkar, 1989: Large-scale precipitation and outgoing longwave radiation from INSAT-1B during the 1986 southwest monsoon season. J. Climate, 2, 619628, https://doi.org/10.1175/1520-0442(1989)002<0619:LSPAOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bliss, N. T., R. Bond, J. Kepner, H. Kim, and A. Reuther, 2006: Interactive grid computing at Lincoln laboratory. Lincoln Lab. J., 16, 165216.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 664 pp.

  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519, https://doi.org/10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brotzge, J. A., and Coauthors, 2018: Development of a statewide, multiuse surface and vertical profiling network: An overview of the New York state mesonet. 22nd Conf. on IOAS-AOLS, Austin, TX, Amer. Meteor. Soc., 7.6, https://ams.confex.com/ams/98Annual/webprogram/Paper333289.html.

  • Cho, J.-Y.-N., 2015: Revised Multifunction Phased Array Radar (MPAR) network siting analysis. MIT Lincoln Laboratory Project Rep. ATC-425, 84 pp., https://www.ll.mit.edu/sites/default/files/publication/doc/2018-05/Cho_2015_ATC-425.pdf.

  • Cho, J.-Y.-N., and J. M. Kurdzo, 2019: Weather radar network benefit model for tornadoes. J. Appl. Meteor. Climatol., 58, 971987, https://doi.org/10.1175/JAMC-D-18-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cho, J.-Y.-N., and J. M. Kurdzo, 2020a: Weather radar network benefit model for flash flood casualty reduction. J. Appl. Meteor. Climatol., 59, 589604, https://doi.org/10.1175/JAMC-D-19-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cho, J.-Y.-N., and J. M. Kurdzo, 2020b: Weather radar network benefit model for nontornadic thunderstorm wind casualty cost reduction. Wea. Climate Soc., 12, 789804, https://doi.org/10.1175/WCAS-D-20-0063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ciach, G. J., 2003: Local random errors in tipping-bucket rain gauge measurements. J. Atmos. Oceanic Technol., 20, 752759, https://doi.org/10.1175/1520-0426(2003)20<752:LREITB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cocks, S. B., S. M. Martinaitis, B. Kaney, J. Zhang, and K. Howard, 2016: MRMS QPE performance during the 2013/14 cool season. J. Hydrometeor., 17, 791810, https://doi.org/10.1175/JHM-D-15-0095.1.

    • 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
  • Cocks, S. B., L. Tang, Y. Wang, J. Zhang, A. Ryzhkov, P. Zhang, and K. W. Howard, 2018: MRMS precipitation estimates using specific attenuation. 32nd Conf. on Hydrology, Austin, TX, Amer. Meteor. Soc., 77, https://ams.confex.com/ams/98Annual/webprogram/Paper335167.html.

  • Cocks, S. B., and Coauthors, 2019: A prototype quantitative precipitation estimation algorithm for operational S-band polarimetric radar utilizing specific attenuation and specific differential phase. Part II: Performance verification and case study analysis. J. Hydrometeor., 20, 9991014, https://doi.org/10.1175/JHM-D-18-0070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delbert, W., C. Haonan, V. Chandrasekar, C. Robert, C. Carroll, R. David, M. Sergey, and Z. Yu, 2017: Evaluation of multisensor quantitative precipitation estimation in Russian River Basin. J. Hydrol. Eng., 22, E5016002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001422.

    • Search Google Scholar
    • Export Citation
  • Dotzek, N., and T. Fehr, 2003: Relationship between precipitation rates at the ground and aloft—A modeling study. J. Appl. Meteor., 42, 12851301, https://doi.org/10.1175/1520-0450(2003)042<1285:RBPRAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. Dover Publications, 481 pp.

  • Doviak, R. J., V. Bringi, A. Ryzhkov, A. Zahrai, and D. Zrnić, 2000: Considerations for polarimetric upgrades to operational WSR-88D radars. J. Atmos. Oceanic Technol., 17, 257278, https://doi.org/10.1175/1520-0426(2000)017<0257:CFPUTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. E., and E. R. Ducot, 2006: Corridor integrated weather system. Lincoln Lab. J., 16, 5980.

  • Fan, R. E., P. H. Chen, and C. J. Lin, 2005: Working set selection using second order information for training support vector machines. J. Mach. Learn. Res., 6, 18891918.

    • Search Google Scholar
    • Export Citation
  • Gesch, D. B., K. L. Verdin, and S. K. Greenlee, 1999: New land surface digital elevation model covers the Earth. Eos, Trans. Amer. Geophys. Union, 80, 6970, https://doi.org/10.1029/99EO00050.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giangrande, S. E., and A. V. Ryzhkov, 2005: Calibration of dual-polarization radar in the presence of partial beam blockage. J. Atmos. Oceanic Technol., 22, 11561166, https://doi.org/10.1175/JTECH1766.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., R. A. Maddox, K. W. Howard, and D. W. Burgess, 2002: An exploratory multisensor technique for quantitative estimation of stratiform rainfall. J. Hydrometeor., 3, 166180, https://doi.org/10.1175/1525-7541(2002)003<0166:AEMTFQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., and Coauthors, 2017: The FLASH project: Improving the tools for flash flood monitoring and prediction across the United States. Bull. Amer. Meteor. Soc., 98, 361372, https://doi.org/10.1175/BAMS-D-15-00247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Habib, E., W. F. Krajewski, and A. Kruger, 2001: Sampling errors of tipping-bucket rain gauge measurements. J. Hydrol. Eng., 6, 159166, https://doi.org/10.1061/(ASCE)1084-0699(2001)6:2(159).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herman, G. R., and R. S. Schumacher, 2016: Extreme precipitation in models: An evaluation. Wea. Forecasting, 31, 18531879, https://doi.org/10.1175/WAF-D-16-0093.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hershfield, D. M., 1961: Rainfall frequency atlas of the United States: For durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Weather Bureau Tech. Paper 40, 65 pp., http://www.nws.noaa.gov/oh/hdsc/PF_documents/TechnicalPaper_No40.pdf.

  • Ho, C. H., and C. J. Lin, 2012: Large-scale linear support vector regression. J. Mach. Learn. Res., 13, 33233348.

  • Hong, Y., and J. J. Gourley, 2015: Radar Hydrology: Principles, Models, and Applications. CRC Press, 182 pp.

  • Huuskonen, A., E. Saltikoff, and I. Holleman, 2014: The operational weather radar network in Europe. Bull. Amer. Meteor. Soc., 95, 897907, https://doi.org/10.1175/BAMS-D-12-00216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janowiak, J. E., A. Gruber, C. R. Kondragunta, R. E. Livezey, and G. J. Huffman, 1998: A comparison of the NCEP–NCAR reanalysis precipitation and the GPCP rain gauge–satellite combined dataset with observational error considerations. J. Climate, 11, 29602979, https://doi.org/10.1175/1520-0442(1998)011<2960:ACOTNN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., H. Andrieu, G. Delrieu, and B. Boudevillain, 2010: Identification of vertical profiles of reflectivity for correction of volumetric radar data using rainfall classification. J. Appl. Meteor. Climatol., 49, 21672180, https://doi.org/10.1175/2010JAMC2369.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurdzo, J. M., and R. D. Palmer, 2012: Objective optimization of weather radar networks for low-level coverage using a genetic algorithm. J. Atmos. Oceanic Technol., 29, 807821, https://doi.org/10.1175/JTECH-D-11-00076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurdzo, J. M., E. F. Clemons, J. Y. N. Cho, P. L. Heinselman, and N. Yussouf, 2018a: Quantification of radar QPE performance based on SENSR network design possibilities. IEEE Radar Conf. (RadarConf18), Oklahoma City, OK, IEEE, 169–174, https://ieeexplore.ieee.org/document/8378551.

    • Crossref
    • Export Citation
  • Kurdzo, J. M., E. R. Williams, D. J. Smalley, B. J. Bennett, D. C. Patterson, M. S. Veillette, and M. F. Donovan, 2018b: Polarimetric observations of chaff using the WSR-88D network. J. Appl. Meteor. Climatol., 57, 10631081, https://doi.org/10.1175/JAMC-D-17-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurdzo, J. M., Y. Wen, C. M. Kuster, J. Y. N. Cho, and T. J. Schuur, 2020: Investigating the impact of radar observation height on streamflow modeling: The 31 May 2013 El Reno/Oklahoma City, OK flash flood case. 36th Conf. on Environmental Information Processing Technologies, Boston, MA, Amer. Meteor. Soc., 12B.4, https://ams.confex.com/ams/2020Annual/webprogram/Paper367153.html.

  • Mandapaka, P. V., and U. Germann, 2010: Radar-rainfall error models and ensemble generators. Rainfall: State of the Science, Geophys. Monogr., Vol. 191, Amer. Geophys. Union, 247–264, https://doi.org/10.1029/2010GM001003.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., F. M. Ralph, P. J. Neiman, and A. B. White, 2014: Quantitative assessment of operational weather radar rainfall estimates over California’s Northern Sonoma County using HMT-West data. J. Hydrometeor., 15, 393410, https://doi.org/10.1175/JHM-D-13-045.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 17971818, https://doi.org/10.1175/2009BAMS2507.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michelson, M., W. Shrader, and J. Wieler, 1990: Terminal Doppler weather radar. Microwave J., 33, 139148.

  • Miller, J., R. Frederick, and R. Tracey, 1973: Colorado. Vol. III, Precipitation-Frequency Atlas of the Western United States, NOAA Atlas 2, National Weather Service, 48 pp., https://mhfd.org/wp-content/uploads/2019/12/NOAA_Atlas_2_Precipitation_Frequency_Vol_3_Colorado-1.pdf.

  • Mishra, A. K., 2013: Effect of rain gauge density over the accuracy of rainfall: A case study over Bangalore, India. Springerplus, 2, 311, https://doi.org/10.1186/2193-1801-2-311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Climatic Data Center, 2019: 80-year list of severe weather fatalities. NOAA Doc., https://www.weather.gov/media/hazstat/80years.pdf.

  • National Weather Service, 2018: The Common Operations and Development Environment (CODE) for the WSR-88D open RPG. NOAA, https://www.weather.gov/code88d/.

  • 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
  • Pavlovic, S., and Coauthors, 2013: NOAA Atlas 14: Updated precipitation frequency estimates for the United States. 2013 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H52B-07.

  • 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
  • Ryzhkov, A. V., and D. S. Zrnić, 2019: Radar Polarimetry for Weather Observations. Springer, 486 pp., https://doi.org/10.1007/978-3-030-05093-1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., S. E. Giangrande, and T. J. Schuur, 2005b: Rainfall estimation with a polarimetric prototype of WSR-88D. J. Appl. Meteor., 44, 502515, https://doi.org/10.1175/JAM2213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., T. J. Schurr, D. W. Burgess, and D. Zrnić, 2005c: Polarimetric tornado detection. J. Appl. Meteor., 44, 557570, https://doi.org/10.1175/JAM2235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnić, 2005d: The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809824, https://doi.org/10.1175/BAMS-86-6-809.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Secrest, G., 2016: ZDR calibration update. NOAA Doc., 9 pp., https://www.roc.noaa.gov/WSR88D/PublicDocs/TAC/2016/ZDRCalibrationUpdate_TAC2016Mar.pdf.

  • Sene, K., 2012: Flash Floods: Forecasting and Warning. Springer, 395 pp.

  • 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, 20352045, https://doi.org/10.1029/96WR00270.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snow, J., 2017: Memorandum: Recommendation of R(A) technique for QPE. NOAA Radar Operations Center Doc., 1 p., https://www.roc.noaa.gov/WSR88D/PublicDocs/TAC/2017/February2017NEXRADTAC-Specific%20Attenuation%20QPE%20Decision.pdf

  • Stailey, J. E., and K. D. Hondl, 2016: Multifunction phased array radar for aircraft and weather surveillance. Proc. IEEE, 104, 649659, https://doi.org/10.1109/JPROC.2015.2491179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc., 90, 14871500, https://doi.org/10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., P. G. Bashor, and V. L. McDowell, 2010: Comparison of rain gauge measurements in the Mid-Atlantic region. J. Hydrometeor., 11, 553565, https://doi.org/10.1175/2009JHM1137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vapnik, V., 1995: The Nature of Statistical Learning Theory. Springer, 201 pp.

  • Wang, Y., S. Cocks, L. Tang, A. Ryzhkov, P. Zhang, J. Zhang, and K. Howard, 2019: A prototype quantitative precipitation estimation algorithm for operational S-band polarimetric radar utilizing specific attenuation and specific differential phase. Part I: Algorithm description. J. Hydrometeor., 20, 985997, https://doi.org/10.1175/JHM-D-18-0071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, M. E., J. Y. N. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 17391752, https://doi.org/10.1175/BAMS-88-11-1739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, M. E., K. D. Hondl, M. J. Istok, and R. E. Saffle, 2018: NOAA’s spectrum efficient national surveillance radar (SENSR) research program. 34th Conf. on Environmental Information Processing Technologies, Austin, TX, Amer. Meteor. Soc., 10.1, https://ams.confex.com/ams/98Annual/webprogram/Paper337206.html.

  • Xu, H., C.-Y. Xu, H. Chen, Z. Zhang, and L. Li, 2013: Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. J. Hydrol., 505, 112, https://doi.org/10.1016/j.jhydrol.2013.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., 2016: Weather Radar Polarimetry. CRC Press, 323 pp.

  • 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, K. Howard, C. Langston, and B. Kaney, 2011a: Radar quality index (RQI)—A combined measure of beam blockage and VPR effects in a national network. Proc. Eighth Int. Symp. on Weather Radar and Hydrology, Exeter, United Kingdom, Royal Meteor. Soc., 388–393.

  • Zhang, J., and Coauthors, 2011b: National mosaic and multi-sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, https://doi.org/10.1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • 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. Eighth European Conf. on Radar in Meteorology and Hydrology (ERAD 2014), Garmisch-Partenkirchen, Germany, DWD and DLR, DAC.P12, https://www.roc.noaa.gov/WSR88D/PublicDocs/Publications/132_Zittel.pdf.

  • Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766, https://doi.org/10.1175/BAMS-88-11-1753.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., R. Doviak, G. Zhang, and A. Ryzhkov, 2010a: Bias in differential reflectivity due to cross coupling through the radiation patterns of polarimetric weather radars. J. Atmos. Oceanic Technol., 27, 16241637, https://doi.org/10.1175/2010JTECHA1350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., G. Zhang, and R. J. Doviak, 2010b: Bias correction and Doppler measurement for polarimetric phased-array radar. IEEE Trans. Geosci. Remote Sens., 49, 843853, https://doi.org/10.1109/TGRS.2010.2057436.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 48 48 16
Full Text Views 21 21 5
PDF Downloads 28 28 6

Geospatial QPE Accuracy Dependence on Weather Radar Network Configurations

View More View Less
  • 1 Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts
  • 2 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • 3 School of Civil Engineering and Environmental Sensing, University of Oklahoma, Norman, Oklahoma
  • 4 Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma
  • 5 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
© Get Permissions
Restricted access

Abstract

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation [R(A)] and reflectivity–differential reflectivity [R(Z, ZDR)] QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.

© 2020 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: James M. Kurdzo, james.kurdzo@ll.mit.edu

Abstract

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation [R(A)] and reflectivity–differential reflectivity [R(Z, ZDR)] QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.

© 2020 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: James M. Kurdzo, james.kurdzo@ll.mit.edu
Save