Role of Differences in Surface Diurnal–Nocturnal Thermodynamics over Complex Terrain in a Squall Line Process
- 6 Downloads
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 wordssquall line diurnal–nocturnal thermodynamic difference (DNTD) baroclinity complex terrain
Unable to display preview. Download preview PDF.
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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