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Article
Article Type:
Research Article

Comparison of Objective Supercell Identification Techniques Using an Idealized Cloud Model

Jason Naylor Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

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Matthew S. Gilmore Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

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Richard L. Thompson Storm Prediction Center, Norman, Oklahoma

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Roger Edwards Storm Prediction Center, Norman, Oklahoma

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Robert B. Wilhelmson Department of Atmospheric Sciences, and the National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, Urbana–Champaign, Illinois

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Print Publication:
01 Jul 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00209.1
Page(s):
2090–2102
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Abstract

The accuracy, reliability, and skill of several objective supercell identification methods are evaluated using 113 simulations from an idealized cloud model with 1-km horizontal grid spacing. Horizontal cross sections of vorticity and radar reflectivity at both mid- and low levels were analyzed for the presence of a supercell, every 5 min of simulation time, to develop a “truth” database. Supercells were identified using well-known characteristics such as hook echoes, inflow notches, bounded weak-echo regions (BWERs), and the presence of significant vertical vorticity.

The three objective supercell identification techniques compared were the Pearson correlation (PC) using an analysis window centered on the midlevel storm updraft; a modified Pearson correlation (MPC), which calculates the PC at every point in the horizontal using a small 3 km × 3 km analysis window; and updraft helicity (UH). Results show that the UH method integrated from 2 to 5 km AGL, and using a threshold value of 180 m2 s−2, was equally as accurate as the MPC technique—averaged from 2 to 5 km AGL and using a minimum updraft threshold of 7 m s−1 with a detection threshold of 0.3—in discriminating between supercells and nonsupercells for 1-km horizontal grid spacing simulations. At courser resolutions, the UH technique performed best, while the MPC technique produced the largest threat scores for higher-resolution simulations. In addition, requiring that the supercell detection thresholds last at least 20 min reduced the number of false alarms.

Corresponding author address: Jason Naylor, Dept. of Atmospheric Science, Clifford Hall Room 400, University of North Dakota, 4149 University Ave. Stop 9006, Grand Forks, ND 58202-9006. E-mail: jason.naylor@und.edu

Abstract

The accuracy, reliability, and skill of several objective supercell identification methods are evaluated using 113 simulations from an idealized cloud model with 1-km horizontal grid spacing. Horizontal cross sections of vorticity and radar reflectivity at both mid- and low levels were analyzed for the presence of a supercell, every 5 min of simulation time, to develop a “truth” database. Supercells were identified using well-known characteristics such as hook echoes, inflow notches, bounded weak-echo regions (BWERs), and the presence of significant vertical vorticity.

The three objective supercell identification techniques compared were the Pearson correlation (PC) using an analysis window centered on the midlevel storm updraft; a modified Pearson correlation (MPC), which calculates the PC at every point in the horizontal using a small 3 km × 3 km analysis window; and updraft helicity (UH). Results show that the UH method integrated from 2 to 5 km AGL, and using a threshold value of 180 m2 s−2, was equally as accurate as the MPC technique—averaged from 2 to 5 km AGL and using a minimum updraft threshold of 7 m s−1 with a detection threshold of 0.3—in discriminating between supercells and nonsupercells for 1-km horizontal grid spacing simulations. At courser resolutions, the UH technique performed best, while the MPC technique produced the largest threat scores for higher-resolution simulations. In addition, requiring that the supercell detection thresholds last at least 20 min reduced the number of false alarms.

Corresponding author address: Jason Naylor, Dept. of Atmospheric Science, Clifford Hall Room 400, University of North Dakota, 4149 University Ave. Stop 9006, Grand Forks, ND 58202-9006. E-mail: jason.naylor@und.edu
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  • Brooks, H. E., C. A. Doswell III, and L. J. Wicker, 1993: STORMTIPE: A forecasting experiment using a three-dimensional cloud model. Wea. Forecasting, 8, 352–362.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606–618.

    • Search Google Scholar
    • Export Citation

  • Browning, K. A., 1964: Airflow and precipitation trajectories within severe local storms which travel to the right of the winds. J. Atmos. Sci., 21, 634–639.

    • Search Google Scholar
    • Export Citation

  • Browning, K. A., and F. A. Ludlam, 1962: Airflow in convective storms. Quart. J. Roy. Meteor. Soc., 88, 117–135.

  • Browning, K. A., and R. J. Donaldson, 1963: Airflow and structure of a tornadic storm. J. Atmos. Sci., 20, 533–545.

  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 2917–2928.

    • Search Google Scholar
    • Export Citation

  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 2394–2416.

    • Search Google Scholar
    • Export Citation

  • Bunkers, M. J., M. R. Hjelmfelt, and P. L. Smith, 2006: An observational examination of long-lived supercells. Part I: Characteristics, evolution, and demise. Wea. Forecasting, 21, 673–688.

    • Search Google Scholar
    • Export Citation

  • Clark, T. L., 1979: Numerical simulations with a three-dimensional cloud model: Lateral boundary condition experiments and multicellular severe storm simulations. J. Atmos. Sci., 36, 2191–2215.

    • Search Google Scholar
    • Export Citation

  • Davies, J. M., and R. H. Johns, 1993: Some wind and instability parameters associated with strong and violent tornadoes 1. Wind shear and helicity. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 573–582.

  • Doswell, C. A., III, 2001: Severe convective storms—An overview. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 1–26.

  • Doswell, C. A., III, and D. W. Burgess, 1993: Tornadoes and tornadic storms: A review of conceptual models. The Tornado: Its Structure, Dynamics, Prediction,and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 161–172.

  • Droegemeier, K. K., S. M. Lazarus, and R. Davies-Jones, 1993: The influence of helicity on numerically simulated convective storms. Mon. Wea. Rev., 121, 2005–2029.

    • Search Google Scholar
    • Export Citation

  • Fujita, T., 1958: Mesoanalysis of the Illinois tornadoes of 9 April 1953. J. Meteor., 15, 288–296.

  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation and evolution sensitivity in simulated deep convective storms: Comparisons between liquid-only and simple ice and liquid phase microphysics. Mon. Wea. Rev., 132, 1897–1916.

    • Search Google Scholar
    • Export Citation

  • Glickman, T. S., 2000: Glossary of Meteorology. 2nd ed. Amer. Meteor. Soc., 855 pp.

  • Kain, J. S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2006: Examination of convective-allowing configurations of the WRF model for the prediction of severe convective weather: The SPC/NSSL spring program 2004. Wea. Forecasting, 21, 167–181.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931–952.

    • Search Google Scholar
    • Export Citation

  • Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 1070–1096.

    • Search Google Scholar
    • Export Citation

  • Klimowski, B. A., M. J. Bunkers, M. R. Hjelmfelt, and J. N. Covert, 2003: Severe convective windstorms over the northern high plains of the United States. Wea. Forecasting, 18, 502–519.

    • Search Google Scholar
    • Export Citation

  • Knupp, K. R., J. R. Stalker, and E. W. McCaul Jr., 1998: An observational and numerical study of a mini-supercell storm. Atmos. Res., 49, 35–63.

    • Search Google Scholar
    • Export Citation

  • Lemon, L. R., 1977: New severe thunderstorm radar identification techniques and warning criteria: A preliminary report. NOAA Tech. Memo. NWS NSSFC-1, NTIS-PB-273049, 60 pp.

  • Lemon, L. R., and C. A. Doswell III, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 106, 48–61.

    • Search Google Scholar
    • Export Citation

  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Marwitz, J. D., 1972: The structure and motion of severe hailstorms. Part I: Supercell storms. J. Appl. Meteor., 11, 166–179.

  • McCaul, E. W., Jr., 1991: Buoyancy and shear characteristics of hurricane–tornado environments. Mon. Wea. Rev., 119, 1954–1978.

  • McCaul, E. W., Jr., and M. L. Weisman, 1996: Simulations of shallow supercell storms in landfalling hurricane environments. Mon. Wea. Rev., 124, 408–429.

    • Search Google Scholar
    • Export Citation

  • Moller, A. R., C. A. Doswell III, M. P. Foster, and G. R. Woodall, 1994: The operational recognition of supercell thunderstorm environments and storm structures. Wea. Forecasting, 9, 327–324.

    • Search Google Scholar
    • Export Citation

  • Newton, C. W., and S. Katz, 1958: Movement of large convective rainstorms in relation to winds aloft. Bull. Amer. Meteor. Soc., 39, 129–136.

    • Search Google Scholar
    • Export Citation

  • Rotunno, R., and J. Klemp, 1985: On the rotation and propagation of simulated supercell thunderstorms. J. Atmos. Sci., 42, 271–292.

  • Smith, P. L., Jr., C. G. Myers, and H. D. Orville, 1975: Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation. J. Appl. Meteor., 14, 1156–1165.

    • Search Google Scholar
    • Export Citation

  • Sobash, R. A., J. S. Kain, D. R. Bright, A. R. Dean, M. C. Coniglio, and S. J. Weiss, 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convective-allowing model forecasts. Wea. Forecasting, 26, 714–728.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487–1499.

    • Search Google Scholar
    • Export Citation

  • Stout, G. E., and F. A. Huff, 1953: Radar records Illinois tornadogenesis. Bull. Amer. Meteor. Soc., 34, 281–284.

  • Thompson, R. L., 1998: Eta model storm-relative winds associated with tornadic and nontornadic supercells. Wea. Forecasting, 13, 125–137.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the rapid update cycle. Wea. Forecasting, 18, 1243–1261.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102–115.

    • Search Google Scholar
    • Export Citation

  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504–520.

    • Search Google Scholar
    • Export Citation

  • Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 2479–2498.

    • Search Google Scholar
    • Export Citation

  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23, 407–437.

    • Search Google Scholar
    • Export Citation

  • Wicker, L. J., M. P. Kay, and M. P. Foster, 1997: STORMTIPE-95: Results from a convective storm forecast experiment. Wea. Forecasting, 12, 388–398.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Academic Press, 627 pp.

  • Ziegler, C. L., E. R. Mansell, J. M. Straka, D. R. MacGorman, and D. W. Burgess, 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 1738–1766.

    • Search Google Scholar
    • Export Citation
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