• Allerup, P., and H. Madsen, 1980: Accuracy of point precipitation measurements. Nord. Hydrol., 11, 5770, https://doi.org/10.2166/nh.1980.0005.

  • Benjamin, S. G., and et al. , 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., and D. A. Zelinsky, 2018: Hurricane Harvey. NOAA/National Hurricane Center Tropical Cyclone Rep., 77 pp., https://www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf.

  • Chang, W., T. C. Wang, and P. Lin, 2009: Characteristics of the raindrop size distribution and drop shape relation in typhoon systems in the western Pacific from the 2D video disdrometer and NCU C-band polarimetric radar. J. Atmos. Oceanic Technol., 26, 19731993, https://doi.org/10.1175/2009JTECHA1236.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cifelli, R., N. Doesken, P. Kennedy, L. D. Carey, S. A. Rutledge, C. Gimmestad, and T. Depue, 2005: The Community Collaborative Rain, Hail, and Snow network: Informal education for scientists and citizens. Bull. Amer. Meteor. Soc., 86, 10691078, https://doi.org/10.1175/BAMS-86-8-1069.

    • 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., and et al. , 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
  • Daly, C., W. P. Gibson, G. H. Taylor, M. K. Doggett, and J. I. Smith, 2007: Observer bias in daily precipitation measurements at United States cooperative network stations. Bull. Amer. Meteor. Soc., 88, 899912, https://doi.org/10.1175/BAMS-88-6-899.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., and G. R. Essenberg, 2001: Comparative rainfall observations from pit and aboveground rain gauges with and without wind shields. Water Resour. Res., 37, 32533263, https://doi.org/10.1029/2001WR000541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Førland, E. J., and I. Hanssen-Bauer, 2000: Increased precipitation in the Norwegian Arctic: True or false? Climatic Change, 46, 485509, https://doi.org/10.1023/A:1005613304674.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Førland, E. J., and et al. , 1996. Manual for operational correction of Nordic precipitation data. Norwegian Meteorological Institute Rep. 24/96, 66 pp.

    • Crossref
    • Export Citation
  • Fovell, R. G., and A. Gallagher, 2020: Boundary layer and surface verification of the High-Resolution Rapid Refresh, version 3. Wea. Forecasting, 35, 22552278, https://doi.org/10.1175/WAF-D-20-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helms, D., P. Miller, M. Barth, D. Starosta, B. Gordon, S. Schofield, F. Kelly, and S. Koch, 2009: Status update of the transition from research to operations of the Meteorological Assimilation Data Ingest System. 25th Conf. on Int. Interactive Information and Processing Systems, Phoenix, AZ, Amer. Meteor. Soc., 5A.3, https://ams.confex.com/ams/89annual/techprogram/paper_149883.htm.

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

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

    • Search Google Scholar
    • Export Citation
  • Martinaitis, S. M., H. M. Grams, C. Langston, J. Zhang, and K. Howard, 2018: A real-time evaporation correction scheme for radar-derived mosaicked precipitation estimations. J. Hydrometeor., 19, 87111, https://doi.org/10.1175/JHM-D-17-0093.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Medlin, J. M., S. K. Kimball, and K. G. Blackwell, 2007: Radar and rain gauge analysis of the extreme rainfall during Hurricane Danny’s (1997) landfall. Mon. Wea. Rev., 135, 18691888, https://doi.org/10.1175/MWR3368.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nešpor, V., and B. Sevruk, 1999: Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. J. Atmos. Oceanic Technol., 16, 450464, https://doi.org/10.1175/1520-0426(1999)016<0450:EOWIEO>2.0.CO;2.

    • 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, P. Zhang, and Q. Cao, 2013: 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
  • Ryzhkov, A., 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
  • Stewart, S. R., and R. Berg, 2019: Hurricane Florence. NOAA/National Hurricane Center Tropical Cyclone Rep., 98 pp. https://www.nhc.noaa.gov/data/tcr/AL062018_Florence.pdf.

  • 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
  • Tokay, A., P. G. Bashor, E. Habib, and T. Kasparis, 2008: Raindrop size distribution measurements in tropical cyclones. Mon. Wea. Rev., 136, 16691685, https://doi.org/10.1175/2007MWR2122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bataes, T. Pangburn, and C. L. Hanson, 1998: Accuracy of NWS 8″ standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 5468, https://doi.org/10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.

    • 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, K. Howard, C. Langston, and B. Kaney, 2012: Radar Quality Index (RQI) – A combined measure of beam blockage and VPR effects in a national network. IAHS Publ., 351, 388393.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and et al. , 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
  • Zhang, J., L. Tang, S. Cocks, P. Zhang, A. Ryzhkov, K. Howard, C. Langston, and B. Kaney, 2020: A dual-polarization radar synthetic QPE for operations. J. Hydrometeor., 21, 25072521. https://doi.org/10.1175/JHM-D-19-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 196 196 14
Full Text Views 58 58 10
PDF Downloads 86 86 12

The Historic Rainfalls of Hurricanes Harvey and Florence: A Perspective from the Multi-Radar Multi-Sensor 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
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Hurricane Harvey in 2017 generated one of the most catastrophic rainfall events in United States history. Numerous gauge observations in Texas exceeded 1200 mm, and the record accumulations resulted in 65 direct fatalities from rainfall-induced flooding. This was followed by Hurricane Florence in 2018, where multiple regions in North Carolina received over 750 mm of rainfall. The Multi-Radar Multi-Sensor (MRMS) system provides the unique perspective of applying fully automated seamless radar mosaics and locally gauge-corrected products for these two historical tropical cyclone rainfall events. This study investigates the performance of various MRMS quantitative precipitation estimation (QPE) products as it pertains to rare extreme tropical cyclone rainfall events. Various biases were identified in the radar-only approaches, which were mitigated in a new dual-polarimetric synthetic radar QPE approach. A local gauge correction of radar-derived QPE provided statistical improvements over the radar-only products but introduced consistent underestimation biases attributed to undercatch from tropical cyclone winds. This study then introduces a conceptual methodology to bulk correct for gauge wind undercatch across the numerous gauge networks ingested by the MRMS system. Adjusting the hourly gauge observations for wind undercatch resulted in increased storm-total accumulations for both tropical cyclones that better matched independent gauge observations, yet its application across large network collections highlighted the challenges of applying a singular wind undercatch correction scheme for significant wind events (e.g., tropical cyclones) while recognizing the need for increased metadata on gauge characteristics.

© 2021 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

Hurricane Harvey in 2017 generated one of the most catastrophic rainfall events in United States history. Numerous gauge observations in Texas exceeded 1200 mm, and the record accumulations resulted in 65 direct fatalities from rainfall-induced flooding. This was followed by Hurricane Florence in 2018, where multiple regions in North Carolina received over 750 mm of rainfall. The Multi-Radar Multi-Sensor (MRMS) system provides the unique perspective of applying fully automated seamless radar mosaics and locally gauge-corrected products for these two historical tropical cyclone rainfall events. This study investigates the performance of various MRMS quantitative precipitation estimation (QPE) products as it pertains to rare extreme tropical cyclone rainfall events. Various biases were identified in the radar-only approaches, which were mitigated in a new dual-polarimetric synthetic radar QPE approach. A local gauge correction of radar-derived QPE provided statistical improvements over the radar-only products but introduced consistent underestimation biases attributed to undercatch from tropical cyclone winds. This study then introduces a conceptual methodology to bulk correct for gauge wind undercatch across the numerous gauge networks ingested by the MRMS system. Adjusting the hourly gauge observations for wind undercatch resulted in increased storm-total accumulations for both tropical cyclones that better matched independent gauge observations, yet its application across large network collections highlighted the challenges of applying a singular wind undercatch correction scheme for significant wind events (e.g., tropical cyclones) while recognizing the need for increased metadata on gauge characteristics.

© 2021 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
Save