Journal of Meteorological Research

, Volume 32, Issue 2, pp 157–171 | Cite as

Investigation into the Formation, Structure, and Evolution of an EF4 Tornado in East China Using a High-Resolution Numerical Simulation

  • Dan Yao
  • Haile Xue
  • Jinfang Yin
  • Jisong Sun
  • Xudong Liang
  • Jianping Guo
Regular Articles


Devastating tornadoes in China have received growing attention in recent years, but little is known about their formation, structure, and evolution on the tornadic scale. Most of these tornadoes develop within the East Asian monsoon regime, in an environment quite different from tornadoes in the U.S. In this study, we used an idealized, highresolution (25-m grid spacing) numerical simulation to investigate the deadly EF4 (Enhanced Fujita scale category 4) tornado that occurred on 23 June 2016 and claimed 99 lives in Yancheng, Jiangsu Province. A tornadic supercell developed in the simulation that had striking similarities to radar observations. The violent tornado in Funing County was reproduced, exceeding EF4 (74 m s–1), consistent with the on-site damage survey. It was accompanied by a funnel cloud that extended to the surface, and exhibited a double-helix vorticity structure. The signal of tornado genesis was found first at the cloud base in the pressure perturbation field, and then developed both upward and downward in terms of maximum vertical velocity overlapping with the intense vertical vorticity centers. The tornado’s demise was found to accompany strong downdrafts overlapping with the intense vorticity centers. One of the interesting findings of this work is that a violent surface vortex was able to be generated and maintained, even though the simulation employed a free-slip lower boundary condition. The success of this simulation, despite using an idealized numerical approach, provides a means to investigate more historical tornadoes in China.

Key words

tornado Cloud Model version 1 (CM1) vertical vorticity updraft helicity double-helix structure hazardous wind 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors thank Prof. Yuqing Wang from the University of Hawaii, Prof. Dalin Zhang from the University of Maryland, and Prof. Yali Luo from the Chinese Academy of Meteorological Sciences (CAMS), for their instructive suggestions in revising this manuscript. We are also appreciative of the assistance of Drs. Hongli Wang and Ying Liu from CAMS, and Dr. Lin Zhang from the Meteorological Observation Center of China. Dr. Dan Yao is especially grateful to his Ph.D. degree supervisor, Prof. Zhiyong Meng from Peking University, who guided him onto the road of tornado damage survey and numerical simulation research. The editor and two anonymous reviewers are sincerely thanked for their instructive comments.


  1. Atkins, N. T., K. M. Butler, K. R. Flynn, et al., 2014: An integrated damage, visual, and radar analysis of the 2013 Moore, Oklahoma, EF5 tornado. Bull. Amer. Meteor. Soc., 95, 1549–1561, doi: 10.1175/BAMS-D-14-00033.1.CrossRefGoogle Scholar
  2. Bai, L. Q., Z. Y. Meng, L. Huang, et al., 2017: Integrated damage, visual, and radar analysis of the 2015 Foshan, Guangdong, EF3 Tornado in China produced by the landfalling Typhoon Mujigae (2015). Bull. Amer. Meteor. Soc., 98, 2619–2640, doi: 10.1175/BAMS-D-16-0015.1.CrossRefGoogle Scholar
  3. Bodine, D. J., T. Maruyama, R. D. Palmer, et al., 2016: Sensitivity of tornado dynamics to soil debris loading. J. Atmos. Sci., 73, 2783–2801, doi: 10.1175/JAS-D-15-0188.1.CrossRefGoogle Scholar
  4. Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 2917–2928, doi: 10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.CrossRefGoogle Scholar
  5. Clark, A. J., J. S. Kain, P. T. Marsh, et al., 2012: Forecasting tornado pathlengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea. Forecasting, 27, 1090–1113, doi: 10.1175/WAF-D-11-00147.1.CrossRefGoogle Scholar
  6. Clark, A. J., J. D. Gao, P. T. Marsh, et al., 2013: Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity. Wea. Forecasting, 28, 387–407, doi: 10.1175/WAFD-12-00038.1.CrossRefGoogle Scholar
  7. Coffer, B. E., and M. D. Parker, 2017: Simulated supercells in nontornadic and tornadic VORTEX2 environments. Mon. Wea. Rev., 145, 149–180, doi: 10.1175/MWR-D-16-0226.1.CrossRefGoogle Scholar
  8. Davies-Jones, R., 2015: A review of supercell and tornado dynamics. Atmos. Res., 158–159, 274–291, doi: 10.1016/j.atmosres.2014.04.007.CrossRefGoogle Scholar
  9. Davies-Jones, R., and P. Markowski, 2013: Lifting of ambient air by density currents in sheared environments. J. Atmos. Sci., 70, 1204–1215, doi: 10.1175/JAS-D-12-0149.1.CrossRefGoogle Scholar
  10. Ek, M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi: 10.1029/2002JD003296.CrossRefGoogle Scholar
  11. French, M. M., H. B. Bluestein, I. PopStefanija, et al., 2013: Reexamining the vertical development of tornadic vortex signatures in supercells. Mon. Wea. Rev., 141, 4576–4601, doi: 10.1175/MWR-D-12-00315.1.CrossRefGoogle Scholar
  12. Grasso, L. D., and W. R. Cotton, 1995: Numerical simulation of a tornado vortex. J. Atmos. Sci., 52, 1192–1203, doi: 10.1175/1520-0469(1995)052<1192:NSOATV>2.0.CO;2.CrossRefGoogle Scholar
  13. Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339, doi: 10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2.CrossRefGoogle Scholar
  14. Houser, J. L., H. B. Bluestein, and J. C. Snyder, 2016: A finescale radar examination of the tornadic debris signature and weakecho reflectivity band associated with a large, violent tornado. Mon. Wea. Rev., 144, 4101–4130, doi: 10.1175/MWR-D-15-0408.1.CrossRefGoogle Scholar
  15. Iacono, M. J., J. S. Delamere, E. J. Mlawer, et al., 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi: 10.1029/2008JD009944.CrossRefGoogle Scholar
  16. Kain, J. S., 2004: The Kain Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.CrossRefGoogle Scholar
  17. Kain, J. S., S. J. Weiss, D. R. Bright, et al., 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931–952, doi: 10.1175/WAF2007106.1.CrossRefGoogle Scholar
  18. Klemp, J. B., and R. B. Wilhelmson, 1978: Simulations of right- and left-moving storms produced through storm splitting. J. Atmos. Sci., 35, 1097–1110, doi: 10.1175/1520-0469(1978)035<1097:SORALM>2.0.CO;2.CrossRefGoogle Scholar
  19. Klemp, J. B., R. B. Wilhelmson, and P. S. Ray, 1981: Observed and numerically simulated structure of a mature supercell thunderstorm. J. Atmos. Sci., 38, 1558–1580, doi: 10.1175/1520-0469(1981)038<1558:OANSSO>2.0.CO;2.CrossRefGoogle Scholar
  20. Lim, K. S. S., and S. Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612, doi: 10.1175/2009MWR2968.1.CrossRefGoogle Scholar
  21. Markowski, P. M., 2016: An idealized numerical simulation investigation of the effects of surface drag on the development of near-surface vertical vorticity in supercell thunderstorms. J. Atmos. Sci., 73, 4349–4385, doi: 10.1175/JAS-D-16-0150.1.CrossRefGoogle Scholar
  22. Markowski, P. M., and Y. P. Richardson, 2009: Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research. Atmos. Res., 93, 3–10, doi: 10.1016/j.atmosres.2008.09.015.CrossRefGoogle Scholar
  23. Markowski, P. M., and G. H. Bryan, 2016: LES of laminar flow in the PBL: A potential problem for convective storm simulations. Mon. Wea. Rev., 144, 1841–1850, doi: 10.1175/MWRD-15-0439.1.CrossRefGoogle Scholar
  24. Markowski, P. M., and Y. P. Richardson, 2017: Large sensitivity of near-surface vertical vorticity development to heat sink location in idealized simulations of supercell-like storms. J. Atmos. Sci., 74, 1095–1104, doi: 10.1175/JAS-D-16-0372.1.CrossRefGoogle Scholar
  25. Markowski, P., Y. Richardson, and G. Bryan, 2014: The origins of vortex sheets in a simulated supercell thunderstorm. Mon. Wea. Rev., 142, 3944–3954, doi: 10.1175/MWR-D-14-00162.1.CrossRefGoogle Scholar
  26. Meng, Z. Y., and D. Yao, 2014: Damage survey, radar, and environment analyses on the first-ever documented tornado in Beijing during the heavy rainfall event of 21 July 2012. Wea. Forecasting, 29, 702–724, doi: 10.1175/WAF-D-13-00052.1.CrossRefGoogle Scholar
  27. Meng, Z. Y., D. Yao, L. Q. Bai, et al., 2016: Wind estimation around the shipwreck of Oriental Star based on field damage surveys and radar observations. Sci. Bull., 61, 330–337, doi: 10.1007/s11434-016-1005-2.CrossRefGoogle Scholar
  28. Naylor, J., and M. S. Gilmore, 2014: Vorticity evolution leading to tornadogenesis and tornadogenesis failure in simulated supercells. J. Atmos. Sci., 71, 1201–1217, doi: 10.1175/JAS-D-13-0219.1.CrossRefGoogle Scholar
  29. Naylor, J., M. A. Askelson, and M. S. Gilmore, 2012: Influence of low-level thermodynamic structure on the downdraft properties of simulated supercells. Mon. Wea. Rev., 140, 2575–2589, doi: 10.1175/MWR-D-11-00200.1.CrossRefGoogle Scholar
  30. Nowotarski, C. J., P. M. Markowski, Y. P. Richardson, et al., 2014: Properties of a simulated convective boundary layer in an idealized supercell thunderstorm environment. Mon. Wea. Rev., 142, 3955–3976, doi: 10.1175/MWR-D-13-00349.1.CrossRefGoogle Scholar
  31. Orf, L., R. Wilhelmson, B. Lee, et al., 2017: Evolution of a longtrack violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc., 98, 45–68, doi: 10.1175/BAMS-D-15-00073.1.CrossRefGoogle Scholar
  32. Rasmussen, E. N., 2003: Refined supercell and tornado forecast parameters. Wea. Forecasting, 18, 530–535, doi: 10.1175/1520-0434(2003)18<530:RSATFP>2.0.CO;2.CrossRefGoogle Scholar
  33. Rasmussen, E. N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 1148–1164, doi: 10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.CrossRefGoogle Scholar
  34. Roberts, B., M. Xue, A. D. Schenkman, et al., 2016: The role of surface drag in tornadogenesis within an idealized supercell simulation. J. Atmos. Sci., 73, 3371–3395, doi: 10.1175/JASD-15-0332.1.CrossRefGoogle Scholar
  35. Rotunno, R., 2013: The fluid dynamics of tornadoes. Annu. Rev. Fluid Mech., 45, 59–84, doi: 10.1146/annurev-fluid-011212-140639.CrossRefGoogle Scholar
  36. Rotunno, R., P. M. Markowski, and G. H. Bryan, 2017: “Near ground” vertical vorticity in supercell thunderstorm models. J. Atmos. Sci., 74, 1757–1766, doi: 10.1175/JAS-D-16-0288.1.CrossRefGoogle Scholar
  37. Schenkman, A. D., M. Xue, and M. Hu, 2014: Tornadogenesis in a high-resolution simulation of the 8 May 2003 Oklahoma City supercell. J. Atmos. Sci., 71, 130–154, doi: 10.1175/JAS-D-13-073.1.CrossRefGoogle Scholar
  38. Stensrud, D. J., L. J. Wicker, M. Xue, et al., 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 2–16, doi: 10.1016/j.atmosres.2012.04.004.CrossRefGoogle Scholar
  39. Sun, J. Z., M. Xue, J. W. Wilson, et al., 2014: Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409–426, doi: 10.1175/BAMS-D-11-00263.1.CrossRefGoogle Scholar
  40. Weisman, M. L., and R. Rotunno, 2000: The use of vertical wind shear versus helicity in interpreting supercell dynamics. J. Atmos. Sci., 57, 1452–1472, doi: 10.1175/1520-0469(2000)057<1452:TUOVWS>2.0.CO;2.CrossRefGoogle Scholar
  41. Wicker, L. J., and R. B. Wilhelmson, 1995: Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm. J. Atmos. Sci., 52, 2675–2703, doi: 10.1175/1520-0469(1995)0522<2675:SAAOTD>2.0.CO;2.CrossRefGoogle Scholar
  42. Wurman, J., D. Dowell, Y. Richardson, et al., 2012: The second verification of the origins of rotation in tornadoes experiment: VORTEX2. Bull. Amer. Meteor. Soc., 93, 1147–1170, doi: 10.1175/BAMS-D-11-00010.1.CrossRefGoogle Scholar
  43. Wurman, J., K. Kosiba, and P. Robinson, 2013: In situ, Doppler radar, and video observations of the interior structure of a tornado and the wind-damage relationship. Bull. Amer. Meteor. Soc., 94, 835–846, doi: 10.1175/BAMS-D-12-00114.1.CrossRefGoogle Scholar
  44. Xue, M., M. Hu, and A. D. Schenkman, 2014: Numerical prediction of the 8 May 2003 Oklahoma City tornadic supercell and embedded tornado using ARPS with the assimilation of WSR-88D Data. Wea. Forecasting, 29, 39–62, doi: 10.1175/WAF-D-13-00029.1.CrossRefGoogle Scholar
  45. Xue, M., K. Zhao, M. J. Wang, et al., 2016: Recent significant tornadoes in China. Adv. Atmos. Sci., 33, 1209–1217, doi: 10.1007/s00376-016-6005-2.CrossRefGoogle Scholar
  46. Yao, Y. Q., X. D. Yu, Y. J. Zhang, et al., 2015: Climate analysis of tornadoes in China. J. Meteor. Res., 29, 359–369, doi: 10.1007/s13351-015-4983-0.CrossRefGoogle Scholar
  47. Zhao, K., M. J. Wang, M. Xue, et al., 2017: Doppler radar analysis of a tornadic miniature supercell during the landfall of Typhoon Mujigae (2015) in South China. Bull. Amer. Meteor. Soc., 98, 1821–1831, doi: 10.1175/BAMS-D-15-00301.1.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dan Yao
    • 1
  • Haile Xue
    • 1
  • Jinfang Yin
    • 1
  • Jisong Sun
    • 1
  • Xudong Liang
    • 1
  • Jianping Guo
    • 1
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina

Personalised recommendations