Effects of Cloud Microphysical Latent Heat on a Heavy Rainstorm in Beijing

  • Chunwei Guo
  • Hui XiaoEmail author
  • Huiling Yang
  • Liang Zhai
  • Xiangchen Kong
Original Article


The latent heat produced by cloud microphysical processes can greatly affect the thermal and dynamic structure of the atmosphere, as well as the development and evolution of clouds and precipitation. In this study, to examine the consequences of different kinds of latent heat produced by microphysical processes, four sensitivity tests were conducted based on the control simulation results of a heavy rainstorm occurred in Beijing on 21 July 2012 using the Weather Research and Forecasting Model (WRF). Without the latent heat absorption of evaporation, the convective cloud system developed stronger, and the accumulated precipitation amount increased. Without the latent heat release of deposition, the transit time of the surface front was delayed; in addition, the convective cloud system developed weakly. The accumulated conversion amounts of microphysical processes and the accumulated rainfall amount in the deposition adiabatic test were far less than those in the other tests. Without the latent heat of melting and freezing, the convective cloud system did not change substantially, and there was only a minor effect on precipitation. Hydrometeor production exhibited some changes related to precipitation in the five tests. The latent heat produced by the convective system varied substantially in the five tests with a change in the latent heat budget.


Convective cloud system Precipitation Hydrometeors Cloud microphysical processes Latent heat 



This work was supported by the National Natural Science Foundation of China (Grant No. 41605110), the Beijing Science and Technology Project (Grant No. Z161100001116098), the National Natural Science Foundation of China (Grant No. 41575037) and the National Key Technologies Research and Development Program of China (Grant Nos. 2016YFE0201900-02, 2014CB441403)


  1. Adams-Selin, R.D., van den Heever, S.C., Johnson, R.H.: Sensitivity of bow-echo simulation to microphysical parameterizations. Weather Forecast. 28, 1188–1209 (2013)CrossRefGoogle Scholar
  2. Bryan, G.H., Morrison, H.: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev. 140, 202–225 (2012)CrossRefGoogle Scholar
  3. Carrio, G.G., Cotton, W.R.: Investigations of aerosol impacts on hurricanes: virtual seeding flights. Atmos. Chem. Phys. 11, 2557–2567 (2011). CrossRefGoogle Scholar
  4. Chen, G., Wu, C., Huang, Y.: The role of near-Core convective and Stratiform heating/cooling in tropical cyclone structure and intensity. J. Atmos. Sci. 75(1), 297–326 (2018). CrossRefGoogle Scholar
  5. Chen, Y., Sun, J., Xu, J., Yang, S., Zong, Z., Chen, T., Fang, Z., Sheng, J.: Analysis and thinking on the extremes of the 21 July 2012 torrential rain in Beijing. Part I: observation and thinking. Meteorol. Monogr. 38, 1255–1266 (2012) (in Chinese)Google Scholar
  6. Cohard, J.M., Pinty, J.P.: A comprehensive two-moment warm microphysical bulk scheme.I: description and tests. Quart. J. Roy. Meteor. Soc. 126, 1815–1842 (2000)CrossRefGoogle Scholar
  7. Fernandez-Gonzalez, S., Wang, P.K., Gascon, E., Valero, F., Sanchez, J.L.: Latent cooling and microphysics effects in deep convection. Atmos. Res. 180, 189–199 (2016)CrossRefGoogle Scholar
  8. Fu, D., Guo, X.: A cloud-resolving study on the role of cumulus merger in MCS with heavy precipitation. Adv. Atmos. Sci. 23, 857–868 (2006)CrossRefGoogle Scholar
  9. Garcia-Ortega, E., Lorenzana, J., Merino, A., Fernandez-Gonzalez, S., Lopez, L., Sanchez, J.L.: Performance of multi-physic sensembles in convective precipitation events over northeastern Spain. Atmos. Res. 190, 55–67 (2017). CrossRefGoogle Scholar
  10. Gilmore, M.S., Straka, J.M.: 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 (2004)CrossRefGoogle Scholar
  11. Guo, C., Xiao, H., Yang, H., Tang, Q.: Simulation of the microphysical processes and effect of latent heat on a heavy rainfall event in Beijing. Atmos. Oceanic Sci. Lett. 7(6), 521–526 (2014)CrossRefGoogle Scholar
  12. Guo, C., Xiao, H., Feng, L., Yang, H., Zhu, Y., Li, Z.: Influence of graupel/hail parameters on the simulation of a convective system over coastal South China in summer. Atmos. Oceanic. Sci. Lett. 8, 283–289 (2015a). CrossRefGoogle Scholar
  13. Guo, C., Xiao, H., Yang, H., Tang, Q.: Observation and modelling analyses of the macro- and microphysical characteristics of a heavy rain storm in Beijing. Atmos. Res. 156, 125–141 (2015b)CrossRefGoogle Scholar
  14. Guo, X., Niino, H., Kimura, R.: Numerical modeling on a hazardous microburst-producing hailstorm. In: Towards Digital Earth Proceeding of the International Symposium on Digital Earth. Science Press, pp. 383–398. Beijing, China (1999)Google Scholar
  15. Hjelmfelt, M.R., Roberts, R.D., Orville, H.D., Chen, J., Kopp, F.: Observational and numerical study of a microburst line-producing storm. J. Atmos. Sci. 46, 2731–2744 (1989)CrossRefGoogle Scholar
  16. Hong, S., Pan, H.: Nonlocal boundary layer vertical diffusion in a medium rage forecast model. Mon. Wea. Rev. 124, 2322–2339 (1996)CrossRefGoogle Scholar
  17. Huang, Y., Cui, X.: Dominant cloud microphysical processes of a torrential rainfall event in Sichuan. China. Adv. Atmos. Sci. 32(3), 389–400 (2015)Google Scholar
  18. Janjic, Z.: The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev. 122, 927–945 (1994)CrossRefGoogle Scholar
  19. Janjic, Z.: Comments on “development and evaluation of a convection scheme for use in climate models”. J. Atmos. Sci. 57, 3686 (2000)CrossRefGoogle Scholar
  20. Khain, A., Rosenfeld, D., Pokrovsky, A.: Aerosol impact on the dynamics and microphysics of deep convective clouds. Q. J. R. Meteorol. Soc. 131(611), 2639–2663 (2005)CrossRefGoogle Scholar
  21. Khain, A., Lynn, B., Dudhia, J.: Aerosol effects on intensity of Landfalling hurricanes as seen from simulations with the WRF model with spectral bin microphysics. J. Atmos. Sci. 67(2), 365–384 (2010)CrossRefGoogle Scholar
  22. Krall, G.M., Cottom, W.R.: Potential indirect effects of aerosol on tropical cyclone intensity: convective fluxes and cold-pool activity. Atmos. Chem. Phys. Discuss. 12, 351–385 (2012). CrossRefGoogle Scholar
  23. Li, G., Wang, Y., Zhang, R.: Implementation of a two moment bulk microphysics scheme to the WRF model to investigate aerosol-cloud interaction. J. Geophys. Res. 113, D15211 (2008). CrossRefGoogle Scholar
  24. Li, J., Wang, G., Lin, W., He, Q., Feng, Y., Mao, J.: Cloud-scale simulation study of typhoon Hagupit (2008) part I: microphysical processes of the inner core and three-dimensional structure of the latent heat budget. Atmos. Res. 120, 170–180 (2013a)CrossRefGoogle Scholar
  25. Li, J., Wang, G., Lin, W., He, Q., Feng, Y., Mao, J.: Cloud-scale simulation study of typhoon Hagupit (2008) part II: impact of cloud microphysical latent heat processes on typhoon intensity. Atmos. Res. 120, 202–215 (2013b)CrossRefGoogle Scholar
  26. Li, J., Wu, K., Li, F., Chen, Y., Huang, Y.: Cloud-scale simulation study on the evolution of latent heat processes of mesoscale convective system accompanying heavy rainfall: the Hainan case. Atmos. Res. 169, 331–339 (2016)CrossRefGoogle Scholar
  27. Li, J., Wu, K., Li, F., Huang, Y., Feng, Y.: Effects of latent heat in various cloud microphysics processes on autumn rainstorms with different intensities on Hainan Island. China. Atmos. Res. 189, 47–60 (2017)Google Scholar
  28. Lim, K.S., Hong, S.Y.: 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 (2010)CrossRefGoogle Scholar
  29. McGee, C.J., van den Heever, S.C.: Latent heating and mixing due to entrainment in tropical deep convection. J. Atmos. Sci. 71, 816–832 (2014)CrossRefGoogle Scholar
  30. Parker, D.J., Thorpe, A.J.: The role of snow sublimation in frontogenesis. Q. J. R. Meteorol. Soc. 121, 763–782 (1995). CrossRefGoogle Scholar
  31. Pattnaik, S., Krishnamurti, T.N.: Impact of cloud microphysical processes on hurricane intensity. Part II: sensitivity experiments. Meteorog. Atmos. Phys. 97, 647–662 (2007). CrossRefGoogle Scholar
  32. Qu, Y., Chen, B., Ming, J., Lynn, B.H., Yang, M.J.: Aerosol impacts on the structure, intensity and precipitation of the Landfalling typhoon Saomai (2006). J. Geophys. Res. Atmos. 122, 11,825–11,842 (2017)CrossRefGoogle Scholar
  33. Seigel, R.B., van den Heever, S.C.: Squall-line intensification via hydrometeor recirculation. J. Atmos. Sci. 70, 2012–2031 (2013). CrossRefGoogle Scholar
  34. Sun, J., Zhao, S., Fu, S., Wang, H., Zheng, L.: Multi-scale characteristics of record heavy rainfall over Beijing area on July 21, 2012. Chin. J. Atmos. Sci. 37(3), 705–718 (2013) (in Chinese)Google Scholar
  35. Tao, W.K., Shi, J.J., Chen, S.S., Lang, S., Lin, P., S-Y Hong, C.P.-L., Hou, A.: The impact of microphysical schemes on hurricane intensity and track. Asia-Pac. J. Atmos. Sci. 47, 1–16 (2011)CrossRefGoogle Scholar
  36. Tao, Y., Qi, Y., Hong, Y.: Numerical simulations of the influence of the graupel fall terminal velocity on cloud system and precipitation development. Acta Meteorologica Sinica. 67(3), 370–381 (2009)Google Scholar
  37. Wang, Y.Q.: An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equation model: TCM3. Part II: model refinements and sensitivity to cloud microphysics parameterization. Mon. Weather Rev. 130, 3022–3036 (2002)CrossRefGoogle Scholar
  38. Wang, D., Li, X., Tao, W., Liu, Y., Zhou, H.: Torrential rainfall processes associated with a landfall of severe tropical storm Bilis (2006): a two-dimensional cloud-resolving modeling study. Atmos. Res. 91, 94–104 (2009)CrossRefGoogle Scholar
  39. Wang, P.K., Lin, H.M., Su, S.H.: The impact of ice microphysical processes on the life span of a midlatitude supercell storm. Atmos. Res. 97, 450–461 (2010)CrossRefGoogle Scholar
  40. Xiao, H., Yin, Y., Chen, Q., Zhao, P.G.: Impact of aerosol and freezing level on orographic clouds: a sensitivity study. Atmos. Res. 176-177, 19–28 (2016)CrossRefGoogle Scholar
  41. Xu, S., Lin, W., Sui, C.: The separation of convective and stratiform precipitation regions of simulated typhoon Chanchu and its sensitivity to the number concentration of cloud droplets. Atmos. Res. 122, 229–236 (2013). CrossRefGoogle Scholar
  42. Yang, M.H., Houze, R.A.: Sensitivity of squall line rear inflow to ice microphysics and environmental humidity. Mon. Wea. Rev. 123, 3175–3193 (1995)CrossRefGoogle Scholar
  43. Yang, M.J., Ching, L.: A modeling study of typhoon Toraji (2001): physical parameterization sensitivity and topographic effect. Ter. Atmos. Oceanic Sci. 16(1), 177–213 (2005). CrossRefGoogle Scholar
  44. Yang, H., Xiao, H., Hong, Y.: A numerical study of aerosol effects on cloud microphysical processes of hailstorm clouds. Atmos. Res. 102, 432–443 (2011)CrossRefGoogle Scholar
  45. Yang, H., H. Xiao, and C. Guo, 2015a: Structure and evolution of a squall line in northern China: a case study. Atmos. Res., 158-159, 139–157Google Scholar
  46. Yang, H., Xiao, H., Guo, C.: Impacts of two ice parameterization schemes on the cloud microphysical processes and precipitation of a severe storm in northern China. Atmos. Oceanic Sci. Lett. 8(5), 301–307 (2015b)Google Scholar
  47. Yang, H., Xiao, H., Guo, C., Wen, G., Tang, Q., Sun, Y.: Comparison of aerosol effects on simulated spring and summer hailstorm clouds. Adv. Atmos. Sci. 34(7), 877–893 (2017). CrossRefGoogle Scholar
  48. Zhang, D., Chen, H.: Importance of the upper-level warm core in the rapid intensification of a tropical cyclone. Geophys. Res. Lett. 39, L02806 (2012)Google Scholar
  49. Zhang, D., Lin, Y., Zhao, P., Yu, X., Wang, S., Kang, H., Ding, Y.: The Beijing extreme rainfall of 21 July 2012:“right results”but for wrong reasons. Geophys. Res. Lett. 40(7), 1426–1431 (2013)CrossRefGoogle Scholar
  50. Zhu, T., Zhang, D.L.: Numerical simulation of hurricane Bonnie (1998). Part II: sensitivity to cloud microphysical processes. J. Atmos. Sci. 63, 109–126 (2006). CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Institute of Urban MeteorologyChina Meteorological AdministrationBeijingChina
  2. 2.Key Laboratory of Cloud-Precipitation Physics and Severe Storms, &Center of disaster Reduction, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Beijing Municipal Weather Forecast CenterBeijingChina
  5. 5.Erdos Meteorological AdministrationErdosChina

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