Advertisement

Journal of Meteorological Research

, Volume 33, Issue 1, pp 1–17 | Cite as

Role of Differences in Surface Diurnal–Nocturnal Thermodynamics over Complex Terrain in a Squall Line Process

  • Wei Tao
  • Xuexing Qiu
  • Ruijiao Wu
  • Kun ZhouEmail author
Special Collection on Weather and Climate under Complex Terrain and Variable Land Surfaces: Observations and Numerical Simulations
  • 6 Downloads

Abstract

Squall lines frequently invade the Yangtze–Huaihe River region (YHR), where the complex terrain of rivers, lakes, and mountains plays an important role in the initiation and maintenance of convection. The surface heat flux not only varies with surface conditions, but also changes between day and night. Coupled with the terrain forcing, such diurnal–nocturnal thermodynamic differences shift the low-level baroclinity, and thus further complicate the convective activities. To investigate the integrated impact of diurnal–nocturnal thermodynamic differences on the development of squall lines over complex terrain including disasters that might ensue, numerical modeling experiments on a squall line in July 2014 were performed by forcing a squall line to pass the YHR separately at daytime and nighttime. The results show that the low-level instability during the day is much larger than that during the night, and is determined predominantly by the shortwave heating of the surface. Specifically, the solar radiation enhances the temperature gradient between the warmland ahead of the squall line and the convectively generated cold pool in the region around Chaohu Lake and the Yangtze River. Such low-level baroclinity sets preconditions in the environment towards the occurrence of deep convection. The increased precipitation and the evaporation of rain in the daytime also enhance the cold pool and the associated downdraft, which further intensify the squall line. Meanwhile, the valley breeze is intensified during the day. Such scenarios promote convection that extends the squall line and the associated heavy precipitation and wind gusts southward. This research may have significant implications for enhancing the squall line prediction capability in the YHR and improving our understanding of the physical mechanisms of convective activities over complex terrain.

Key words

squall line diurnal–nocturnal thermodynamic difference (DNTD) baroclinity complex terrain 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We wish to thank Editor Huqiang Zhang and the two anonymous reviewers for their very careful reviews and constructive comments, which have greatly improved both content and presentation of this paper. Wei Tao thanks Dingling Zhong for her assistance with Fig. 13.

Supplementary material

13351_2019_8052_MOESM1_ESM.pdf (558 kb)
Role of Differences in Surface Diurnal–Nocturnal Thermodynamic over Complex Terrain in a Squall Line Process

References

  1. Alfaro, D. A., 2017: Low-tropospheric shear in the structure of squall lines: Impacts on latent heating under layer-lifting ascent. J. Atmos. Sci., 74: 229–248, doi: 10.1175/JAS-D-16-0168.1.CrossRefGoogle Scholar
  2. Alfaro, D. A., and M. Khairoutdinov, 2015: Thermodynamic constraints on the morphology of simulated midlatitude squall lines. J. Atmos. Sci., 72: 3116–3137, doi: 10.1175/JAS-D-14-0295.1.CrossRefGoogle Scholar
  3. Barker, D. M., W. Huang, Y. R. Guo, et al., 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132: 897–914, doi: 10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.CrossRefGoogle Scholar
  4. Barthlott, C., and D. J. Kirshbaum, 2013: Sensitivity of deep convection to terrain forcing over Mediterranean islands. Quart. J. Roy. Meteor. Soc., 139: 1762–1779, doi: 10.1002/qj.2089.CrossRefGoogle Scholar
  5. Benjamin, T. B., 1968: Gravity currents and related phenomena. J. Fluid Mech., 31: 209–248, doi: 10.1017/S0022112068000 133.CrossRefGoogle Scholar
  6. Cai, F., and Y. N. Pan, 2010: A numerical simulation study of surface flux impacts on the development of a squall line. J. Trop. Meteor., 26: 105–110, doi: 10.3969/j.issn.1004-4965.2010. 01.016. (in Chinese)Google Scholar
  7. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129: 569–585, doi: 10.1175/1520-0493 (2001)129<0569:CAALSH>2.0.CO;2.Google Scholar
  8. Chen, M. X., and Y. C. Wang, 2012: Numerical simulation study of interactional effects of the low-level vertical wind shear with the cold pool on a squall line evolution in North China. Acta Meteor. Sinica, 70: 371–386, doi: 10.11676/qxxb2012.033. (in Chinese)CrossRefGoogle Scholar
  9. Chen, X. C., K. Zhao, J. Z. Sun, et al., 2016: Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case. Adv. Atmos. Sci., 33: 1106–1119, doi: 10.1007/s00376-016-5290-0.CrossRefGoogle Scholar
  10. Clark, D. B., C. M. Taylor, and A. J. Thorpe, 2004: Feedback between the land surface and rainfall at convective length scales. J. Hydrometeor., 5: 625–639, doi: 10.1175/1525-7541(2004)005<0625:FBTLSA>2.0.CO;2.CrossRefGoogle Scholar
  11. Collins, W. D., P. J. Rasch, B. A. Boville, et al., 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Technical Note NCAR/TN-464+STR. Boulder, 1–214, doi: 10.5065/D63N21CH.Google Scholar
  12. Frame, J., and P. Markowski, 2006: The interaction of simulated squall lines with idealized mountain ridges. Mon. Wea. Rev., 134: 1919–1941, doi: 10.1175/MWR3157.1.CrossRefGoogle Scholar
  13. French, A. J., and M. D. Parker, 2010: The response of simulated nocturnal convective systems to a developing low-level jet. J. Atmos. Sci., 67: 3384–3408, doi: 10.1175/2010JAS3329.1.CrossRefGoogle Scholar
  14. Hong, S. Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132: 103–120, doi: 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.CrossRefGoogle Scholar
  15. Hu, M., M. Xue, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134: 675–698, doi: 10.1175/MWR3092.1.Google Scholar
  16. Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122: 927–945, doi:1 0.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.CrossRefGoogle Scholar
  17. Jirak, I. L., W. R. Cotton, and R. L. McAnelly, 2003: Satellite and radar survey of mesoscale convective system development. Mon. Wea. Rev., 131: 2428–2449, doi: 10.1175/1520-0493 (2003)131<2428:SARSOM>2.0.CO;2.CrossRefGoogle Scholar
  18. 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
  19. Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77: 437–472, doi: 10.1175/1520-0477(1996)077<0437:TNY RP>2.0.CO;2.CrossRefGoogle Scholar
  20. Letkewicz, C. E., and M. D. Parker, 2010: Forecasting the maintenance of mesoscale convective systems crossing the Appalachian Mountains. Wea. Forecasting, 25: 1179–1195, doi: 10.1175/2010WAF2222379.1.CrossRefGoogle Scholar
  21. Meng, Z. Y., and F. Q. Zhang, 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136: 522–540, doi: 10.1175/2007MWR 2106.1.CrossRefGoogle Scholar
  22. Meng, Z. Y., and F. Q. Zhang, 2008b: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVAR in a month-long experiment. Mon. Wea. Rev., 136: 3671–3682, doi: 10.1175/2008 MWR2270.1.CrossRefGoogle Scholar
  23. Meng, Z. Y., F. Q. Zhang, P. Markowski, et al., 2012: A modeling study on the development of a bowing structure and associated rear inflow within a squall line over south China. J. Atmos. Sci., 69: 1182–1207, doi: 10.1175/JAS-D-11-0121.1.CrossRefGoogle Scholar
  24. Meng, Z. Y., D. C. Yan, and Y. J. Zhang, 2013: General features of squall lines in East China. Mon. Wea. Rev., 141: 1629–1647, doi: 10.1175/MWR-D-12-00208.1.CrossRefGoogle Scholar
  25. Moncrieff, M. W., and C. H. Liu, 1999: Convection initiation by density currents: Role of convergence, shear, and dynamical organization. Mon. Wea. Rev., 127: 2455–2464, doi: 10.1175/1520-0493(1999)127<2455:CIBDCR>2.0.CO;2.CrossRefGoogle Scholar
  26. National Centers for Environmental Prediction (NCEP), National Weather Service (NWS), NOAA, et al., 2000: NCEP FNL Operational Model Global Tropospheric Analyses, Continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Available online at 10.5065/D6M043C6. Accessed on 25 June 2017.Google Scholar
  27. NCAR, 2016: User’s Guide for the Advanced Research WRF (ARW) Modeling System Version 3.8. National Center for Atmospheric Research, 434 pp. Available online at www2. mmm.ucar.edu/wrf/users/docs/user_guide_V3.8/contents.html.Google Scholar
  28. Noh, Y., W. G. Cheon, S. Y. Hong, et al., 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound-Layer Meteor., 107: 401–427, doi: 10.1023/A:1022146015946.CrossRefGoogle Scholar
  29. Oberthaler, A. J., and P. M. Markowski, 2013: A numerical simulation study of the effects of anvil shading on quasi-linear convective systems. J. Atmos. Sci., 70: 767–793, doi: 10.1175/JAS-D-12-0123.1.CrossRefGoogle Scholar
  30. Parker, M. D., and R. H. Johnson, 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128: 3413–3436, doi: 10.1175/1520-0493(2001)129<3413:O MOMMC>2.0.CO;2.CrossRefGoogle Scholar
  31. Parker, M. D., and R. H. Johnson, 2004: Simulated convective lines with leading precipitation. Part I: Governing dynamics. J. Atmos. Sci., 61: 1637–1655, doi: 10.1175/1520-0469(2004) 061<1637:SCLWLP>2.0.CO;2.Google Scholar
  32. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120: 1747–1763, doi: 10.1175/1520-0493 (1992)120<1747:TNMCSS>2.0.CO;2.CrossRefGoogle Scholar
  33. Peters, K., and C. Hohenegger, 2017: On the dependence of squall-line characteristics on surface conditions. J. Atmos. Sci., 74: 2211–2228, doi: 10.1175/JAS-D-16-0290.1.CrossRefGoogle Scholar
  34. Qiu, X. X., and F. Q. Zhang, 2016: Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations. Sci. China Earth Sci., 59: 518–532, doi: 10.1007/s11430-015-5224-1.CrossRefGoogle Scholar
  35. Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45: 463–485, doi: 10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.CrossRefGoogle Scholar
  36. Rudari, R., D. Entekhabi, and G. Roth, 2004: Terrain and multiple-scale interactions as factors in generating extreme precipitation events. J. Hydrometeor., 5: 390–404, doi: 10.1175/1525-7541(2004)005<0390:TAMIAF>2.0.CO;2.CrossRefGoogle Scholar
  37. Schenkman, A. D., M. Xue, A. Shapiro, et al., 2011: The analysis and prediction of the 8–9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139: 224–246, doi: 10.1175/2010MWR3336.1.CrossRefGoogle Scholar
  38. Schlemmer, L., and C. Hohenegger, 2014: The formation of wider and deeper clouds as a result of cold-pool dynamics. J. Atmos. Sci., 71: 2842–2858, doi: 10.1175/JAS-D-13-0170.1.CrossRefGoogle Scholar
  39. Sever, G., and Y.-L. Lin, 2017: Dynamical and physical processes associated with orographic precipitation in a conditionally unstable uniform flow: Variation in basic wind speed. J. Atmos. Sci., 74: 449–466, doi: 10.1175/JAS-D-16-0077.1.CrossRefGoogle Scholar
  40. Shen, X. Y., S. J. Yue, J. Liu, et al., 2016: Effects of latent heating and surface heat fluxes on a squall line process. J. Meteor. Sci., 36: 709–720, doi: 10.3969/2016jms.0013. (in Chinese)Google Scholar
  41. Skamarock, W. C., J. B. Klemp, J. Dudhia, et al., 2008: A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR. Boulder, 1–113, doi: 10.5065/D68S4MVH.Google Scholar
  42. Sun, W. Y., and Y. Ogura, 1979: Boundary-layer forcing as a possible trigger to a squall-line formation. J. Atmos. Sci., 36: 235–254, doi: 10.1175/1520-0469(1979)036<0235:BLFAAP> 2.0.CO;2.CrossRefGoogle Scholar
  43. Takemi, T., 2007: Environmental stability control of the intensity of squall lines under low-level shear conditions. J. Geophys. Res. Atmos., 112 doi: 10.1029/2007JD008793.Google Scholar
  44. Tawfik, A. B., and P. A. Dirmeyer, 2014: A process-based framework for quantifying the atmospheric preconditioning of surface-triggered convection. Geophys. Res. Lett., 41: 173–178, doi: 10.1002/2013GL057984.CrossRefGoogle Scholar
  45. Weckwerth, T. M., 2000: The effect of small-scale moisture variability on thunderstorm initiation. Mon. Wea. Rev., 128: 4017–4030, doi: 10.1175/1520-0493(2000)129<4017:TEOS SM>2.0.CO;2.CrossRefGoogle Scholar
  46. Weckwerth, T. M., L. J. Bennett, L. J. Miller, et al., 2014: An observational and modeling study of the processes leading to deep, moist convection in complex terrain. Mon. Wea. Rev., 142: 2687–2708, doi: 10.1175/MWR-D-13-00216.1.CrossRefGoogle Scholar
  47. Weisman, M. L., 1993: The genesis of severe, long-lived bow echoes. J. Atmos. Sci., 50: 645–670, doi: 10.1175/1520-0469 (1993)050<0645:TGOSLL>2.0.CO;2.CrossRefGoogle Scholar
  48. Weisman, M. L., J. B. Klemp, and R. Rotunno, 1988: Structure and evolution of numerically simulated squall lines. J. Atmos. Sci., 45: 1990–2013, doi: 10.1175/1520-0469(1988)045<1990: SAEONS>2.0.CO;2.CrossRefGoogle Scholar
  49. Wheatley, D. M., and R. J. Trapp, 2008: The effect of mesoscale heterogeneity on the genesis and structure of mesovortices within quasi-linear convective systems. Mon. Wea. Rev., 136: 4220–4241, doi: 10.1175/2008MWR2294.1.CrossRefGoogle Scholar
  50. Wolters, D., C. C. van Heerwaarden, J. V.-G. de Arellano, et al., 2010: Effects of soil moisture gradients on the path and the intensity of a West African squall line. Quart. J. Roy. Meteor. Soc., 136: 2162–2175, doi: 10.1002/qj.712.CrossRefGoogle Scholar
  51. Zhang, F. Q., Z. Y. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134: 722–736, doi: 10.1175/MWR3101.1.Google Scholar
  52. Zhang, Y., Z. Y. Meng, P. J. Zhu, et al., 2016: Mesoscale modeling study of severe convection over complex terrain. Adv. Atmos. Sci., 33: 1259–1270, doi: 10.1007/s00376-016-5221-0.CrossRefGoogle Scholar
  53. Zheng, L. L., J. H. Sun, X. L. Zhang, et al., 2013: Organizational modes of mesoscale convective systems over central East China. Wea. Forecasting, 28: 1081–1098, doi: 10.1175/WAFD-12-00088.1.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Anhui Meteorological ObservatoryChina Meteorological AdministrationHefeiChina

Personalised recommendations