Editorial Type:
Article
Article Type:
Research Article

An Objective High-Resolution Hail Climatology of the Contiguous United States

John L. Cintineo Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma

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Travis M. Smith Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma

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Valliappa Lakshmanan Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma

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Harold E. Brooks National Severe Storms Laboratory, Norman, Oklahoma

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Kiel L. Ortega Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Oct 2012
DOI:
https://doi.org/10.1175/WAF-D-11-00151.1
Page(s):
1235–1248
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Abstract

The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.

Current affiliation: Cooperative Institute of Meteorological Satellite Studies, Madison, Wisconsin.

Corresponding author address: John L. Cintineo, CIMSS, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: john.cintineo@ssec.wisc.edu

A comment/reply has been published regarding this article and can be found at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-13-00020.1

Abstract

The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.

Current affiliation: Cooperative Institute of Meteorological Satellite Studies, Madison, Wisconsin.

Corresponding author address: John L. Cintineo, CIMSS, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: john.cintineo@ssec.wisc.edu

A comment/reply has been published regarding this article and can be found at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-13-00020.1

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  • Amburn, S. A., and Wolf P. L. , 1997: VIL density as a hail indicator. Wea. Forecasting, 12, 473–478.

  • Brooks, H. E., and Lee J. , 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. J. Atmos. Res., 67–68, 73–94.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., Doswell C. A. III, and Kay M. P. , 2003: Climatological estimates of local daily tornado probability for the United States. Wea. Forecasting, 18, 626–640.

    • Search Google Scholar
    • Export Citation

  • Cecil, D. J., and Blankenship C. B. , 2012: Towards a global climatology of severe hailstorms as estimated by satellite passive microwave imagers. J. Climate, 25, 687–703.

    • Search Google Scholar
    • Export Citation

  • Changnon, S. A., 1999: Data and approaches for determining hail risk in the contiguous United States. J. Appl. Meteor., 38, 1730–1739.

    • Search Google Scholar
    • Export Citation

  • Changnon, S. A., and Changnon D. , 2000: Long-term fluctuations in hail incidences in the United States. J. Climate, 13, 658–664.

  • Changnon, S. A., Schickedanz P. , and Danford H. , 1967: Hail patterns in Illinois and South Dakota. Preprints, Fifth Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 325–355.

  • Cheresnick, D. R., Basara J. B. , and Mitchell E. D. , 2004: An analysis of severe hail swaths in the southern plains of the United States. Preprints, 22nd Conf. on Severe Local Storms,Hyannis, MA, Amer. Meteor. Soc., P1.34. [Available online at http://ams.confex.com/ams/pdfpapers/103987.pdf.]

  • Cintineo, J. L., Smith T. M. , Lakshmanan V. , and Ansari S. , 2011: An automated system for processing the Multi-Year Reanalysis Of Remotely Sensed Storms (MYRORSS). Preprints, 27th Conf. on Interactive Information Processing Systems, Seattle, WA, Amer. Meteor. Soc., J9.3. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper182332.html.]

  • Davis, S. M., and LaDue J. , 2004: Nonmeteorological factors in warning verification. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P2.7. [Available online at http://ams.confex.com/ams/pdfpapers/81766.pdf.]

  • Doswell, C. A., III, Brooks H. E. , and Kay M. P. , 2005: Climatological estimates of daily local nontornadic severe thunderstorm probability for the United States. Wea. Forecasting, 20, 577–595.

    • Search Google Scholar
    • Export Citation

  • Hales, J. E., Jr., 1993: Biases in the severe thunderstorm data base: Ramifications and solutions. Preprints, 13th Conf. on Weather Forecasting and Analysis, Vienna, VA, Amer. Meteor. Soc., 504–507.

  • Hales, J. E., Jr., and Kelly D. L. , 1985: The relationship between the collection of severe thunderstorm reports and warning verification. Preprints, 14th Conf. on Severe Local Storms, Indianapolis, IN, Amer. Meteor. Soc., 13–16.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

  • Kelly, D. L., Schaefer J. T. , and Doswell C. A. III, 1985: Climatology of nontornadic severe thunderstorm events in the United States. Mon. Wea. Rev., 113, 1997–2014.

    • Search Google Scholar
    • Export Citation

  • Kessinger, C. J., Ellis S. , and Van Andel J. , 2003: The radar echo classifier: A fuzzy logic algorithm for the WSR-88D. Preprints, Third Conf. on Artificial Applications to the Environmental Sciences, Long Beach, CA, Amer. Meteor. Soc., P1.6. [Available online at http://ams.confex.com/ams/pdfpapers/54946.pdf.]

  • Lakshmanan, V., Rabin R. , and DeBrunner V. , 2003: Multiscale storm identification and forecast. J. Atmos. Res., 67, 367–380.

  • Lakshmanan, V., Smith T. M. , Hondl K. , Stumpf G. J. , and Witt A. , 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity, and derived products. Wea. Forecasting, 21, 802–823.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., Fritz A. , Smith T. M. , Hondl K. , and Stumpf G. , 2007a: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288–305.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., Smith T. M. , Stumpf G. J. , and Hondl K. , 2007b: The Warning Decision Support System–Integrated Information. Wea. Forecasting, 22, 596–612.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., Hondl K. D. , and Rabin R. , 2009: An efficient, general-purpose technique for identifying storm cells in geospatial images. J. Atmos. Oceanic Technol., 26, 523–537.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., Zhang J. , and Howard K. , 2010: A technique to censor biological echoes in radar reflectivity data. J. Appl. Meteor. Climatol., 49, 435–462.

    • Search Google Scholar
    • Export Citation

  • Marzban, C., and Witt A. , 2001: A Bayesian neural network for severe-hail size prediction. Wea. Forecasting, 16, 600–610.

  • Mather, G. K., Treddenick D. , and Parsons R. , 1976: An observed relationship between the height of the 45-dBZ contours in storm profiles and surface hail reports. J. Appl. Meteor., 15, 1336–1340.

    • Search Google Scholar
    • Export Citation

  • Mitchell, E. D., and Elmore K. L. , 1998: A technique for identifying regions of high shear associated with mesocyclones and tornadic vortex signatures. Preprints, 14th Conf. on Interactive Information Processing Systems, Phoenix, AZ, Amer. Meteor. Soc., 312–315.

  • Morgan, G. M., Jr., and Summers P. W. , 1982: Hailfall and hailstorm characteristics. Thunderstorms: A Social, Scientific and Technological Documentary, E. Kessler, Ed., Vol. 2, Thunderstorm Morphology and Dynamics, U.S. Government Printing Office, 363–408.

  • Nelson, S. P., and Young S. K. , 1979: Characteristics of Oklahoma hailfalls and hailstorms. J. Appl. Meteor., 18, 339–347.

  • NWS, cited 2011: WFO severe weather products specification. National Weather Service Instruction 10-511. [Available online at http://www.weather.gov/directives/.]

  • Ortega, K. L., Smith T. M. , Stumpf G. J. , Hocker J. , and Lopez L. , 2005: A comparison of multi-sensor hail diagnosis techniques. Preprints, 21st Conf. on Interactive Information Processing Systems, San Diego, CA, Amer. Meteor. Soc., P1.11. [Available online at http://ams.confex.com/ams/pdfpapers/87640.pdf.]

  • Ortega, K. L., Smith T. M. , and Stumpf G. J. , 2006: Verification of multi-sensor, multi-radar hail diagnosis techniques. Preprints, Symp. on the Challenges of Severe Convective Storms, Atlanta, GA, Amer. Meteor. Soc., P1.1. [Available online at http://ams.confex.com/ams/pdfpapers/104885.pdf.]

  • Ortega, K. L., Smith T. M. , Manross K. L. , Scharfenberg K. A. , Witt A. , Kolodziej A. G. , and Gourley J. J. , 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90, 1519–1530.

    • Search Google Scholar
    • Export Citation

  • ROC, cited 2011: NEXRAD coverage below 10,000 feet AGL. [Available online at http://www.roc.noaa.gov/WSR88D/Maps.aspx.]

  • Saltikoff, E., Tuovinen J. , Kotro J. , Kuitunen T. , and Hohti H. , 2010: A climatological comparison of radar and ground observations of hail in Finland. J. Appl. Meteor. Climatol., 49, 101–114.

    • Search Google Scholar
    • Export Citation

  • Schaefer, J. T., Levit J. J. , Weiss S. J. , and McCarthy D. W. , 2004: The frequency of large hail over the contiguous United States. Preprints, 14th Conf. on Applied Meteorology, Seattle, WA, Amer. Meteor. Soc., 3.3. [Available online at http://ams.confex.com/ams/pdfpapers/69834.pdf.]

  • Smith, T. M., and Elmore K. L. , 2004: The use of radial velocity derivatives to diagnose rotation and divergence. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.6. [Available online at http://ams.confex.com/ams/pdfpapers/81827.pdf.]

  • Straka, J., 2009: Cloud and Precipitation Microphysics: Principles and Parameterizations. 1st ed. Cambridge University Press, 406 pp.

  • Stumpf, G. J., Smith T. M. , and Hocker J. , 2004: New hail diagnostic parameters derived by integrating multiple radars and multiple sensors. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P7.8. [Available online at http://ams.confex.com/ams/pdfpapers/81451.pdf.]

  • Torres, S. M., and Curtis C. D. , 2007: Initial implementation of super-resolution data on the NEXRAD network. Preprints, 23rd Int. Conf. on Interactive Information Processing Systems,San Antonio, TX, Amer. Meteor. Soc., 5B.10. [Available online at http://ams.confex.com/ams/pdfpapers/116240.pdf.]

  • Trapp, R. J., Wheatley D. M. , Atkins N. T. , Przybylinski R. W. , and Wolf R. , 2006: Buyer beware: Some words of caution on the use of severe wind reports in postevent assessment and research. Wea. Forecasting, 21, 408–415.

    • Search Google Scholar
    • Export Citation

  • Tuovinen, J. P., Punkka A. J. , Rauhala J. , Hohti H. , and Schultz D. , 2009: Climatology of severe hail in Finland: 1930–2006. Mon. Wea. Rev., 137, 2238–2249.

    • Search Google Scholar
    • Export Citation

  • Waldvogel, A., Federer B. , and Grimm P. , 1979: Criteria for the detection of hail cells. J. Appl. Meteor., 18, 1521–1525.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Elsevier, 627 pp.

  • Wilson, C. J., Ortega K. L. , and Lakshmanan V. , 2009: Evaluating multi-radar, multi-sensor hail diagnosis with high resolution hail reports. Preprints, 25th Conf. on Interactive Information Processing Systems, Phoenix, AZ, Amer. Meteor. Soc., P2.9. [Available online at http://ams.confex.com/ams/pdfpapers/146206.pdf.]

  • Witt, A., Eilts M. D. , Stumpf G. S. , Johnson J. T. , Mitchell E. D. , and Thomas K. W. , 1998a: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.

    • Search Google Scholar
    • Export Citation

  • Witt, A., Eilts M. D. , Stumpf G. S. , Mitchell E. D. , Johnson J. T. , and Thomas K. W. , 1998b: Evaluating the performance of WSR-88D severe storm detection algorithms. Wea. Forecasting, 13, 513–518.

    • Search Google Scholar
    • Export Citation

  • Wyatt, A., and Witt A. , 1997: The effect of population density on ground-truth verification of reports used to score a hail detection algorithm. Preprints, 28th Conf. on Radar Meteorology, Austin, TX, Amer. Meteor. Soc., 368–369.

  • Xie, B., Zhang Q. , and Wang Y. , 2010: Observed characteristics of hail size in four regions in China during 1980–2005. J. Climate, 23, 4973–4982.

    • Search Google Scholar
    • Export Citation
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