Journal of Meteorological Research

, Volume 29, Issue 1, pp 82–92 | Cite as

An attempt to improve Kessler-type parameterization of warm cloud microphysical conversion processes using CloudSat observations

  • Jinfang Yin (尹金方)
  • Donghai Wang (王东海)
  • Guoqing Zhai (翟国庆)


Improvements to the Kessler-type parameterization of warm cloud microphysical conversion processes (also called autoconversion) are proposed based on a large number of CloudSat observations between June 2006 and April 2011 over Asian land areas. The emphasis is given to the vertical distribution of liquid water content (LWC), particularly, the threshold values of LWC for autoconversion. The results warrant a new approach to the numerical parameterization of autoconversion in warm clouds. One feature of this new approach is that the autoconversion threshold, which has been treated as a constant in previous parameterization schemes, is diagnosed as a function of altitude by using a relationship between LWC and height (H) derived from CloudSat observations: \(LWC_{dig} = - 500.0\ln \left( {\frac{H} {{9492.2}}} \right)\). Under this framework, the threshold LWC decreases with increasing H, allowing autoconversion to occur in clouds with low LWC (approximately 0.3 g m−3) at levels above 5.5 km. Autoconversion rates calculated based on the new parameterization are compared to those calculated based on several commonly used parameterization schemes over a range of LWCs from 0.01 to 1.0 g m−3. The new scheme provides reasonable simulations of autoconversion at various vertical levels.

Key words

autoconversion microphysical parameterization threshold of autoconversion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Austin, R. T., and G. L. Stephens, 2001: Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat. 1: Algorithm formulation. J. Geophys. Res., 106, 28233–28242.CrossRefGoogle Scholar
  2. Beheng, K. D., 1994: A parameterization of warm cloud microphysical conversion processes. Atmos. Res., 33, 193–206.CrossRefGoogle Scholar
  3. Berry, E. X., 1968: Modification of the warm rain process. Preprints, First National Conf. on Weather Modification, Albany, NY, Amer. Meteor. Soc., 81–88.Google Scholar
  4. Berry, E. X., and R. L. Reinhardt, 1974: An analysis of cloud drop growth by collection. Part II: Single initial distributions. J. Atmos. Sci., 31, 1825–1831.CrossRefGoogle Scholar
  5. Carrioó, G. G., and L. Levi, 1995: On the parameterization of autoconversion: Effects of small-scale turbulent motions. Atmos. Res., 38, 21–27.CrossRefGoogle Scholar
  6. Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan, 80, 99–118.CrossRefGoogle Scholar
  7. Cotton, W. R., 1972: Numerical simulation of precipitation development in supercooled cumuli-Part I. Mon. Wea. Rev., 100, 757–763.CrossRefGoogle Scholar
  8. Deng, Z. Z., C. S. Zhao, Q. Zhang, et al., 2009: Statistical analysis of microphysical properties and the parameterization of effective radius of warm clouds in Beijing area. Atmos. Res., 93, 888–896.CrossRefGoogle Scholar
  9. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.CrossRefGoogle Scholar
  10. Eguchi, N., T. Hayasaka, and M. Sawada, 2014: Maritime-continental contrasts in the properties of low-level clouds: A case study of the summer of the 2003 Yamase, Japan, cloud event. Adv. Meteor., 548091, doi: 10.1155/2014/548091.Google Scholar
  11. Franklin, C. N., 2008: A warm rain microphysics parameterization that includes the effect of turbulence. J. Atmos. Sci., 65, 1795–1816.CrossRefGoogle Scholar
  12. Fritsch, J. M., and R. E. Carbone, 2004: Improving quantitative precipitation forecasts in the warm season, A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955–965.CrossRefGoogle Scholar
  13. Gettelman, A., H. Morrison, C. R. Terai, et al., 2013: Microphysical process rates and global aerosol-cloud interactions. Atmos. Chem. Phys., 13, 9855–9867.CrossRefGoogle Scholar
  14. Ghosh, S., and P. R. Jonas, 1999: On the application of the classic Kessler and Berry schemes in large eddy simulation models with a particular emphasis on cloud autoconversion, the onset time of precipitation and droplet evaporation. Ann. Geophys., 16, 628–637.CrossRefGoogle Scholar
  15. Guo, H., Y. Liu, and J. E. Penner, 2008: Does the threshold representation associated with the autoconversion process matter? Atmos. Chem. Phys., 8, 1225–1230.CrossRefGoogle Scholar
  16. Hou Tuanjie, Hu Zhaoxia, and Lei Hengchi, 2011: A study of the structure and microphysical processes of a precipitating stratiform cloud in Jilin. Acta Meteor. Sinica, 69, 508–520. (in Chinese)Google Scholar
  17. Hsieh, W. C., H. Jonsson, L. P. Wang, et al., 2009: On the representation of droplet coalescence and autoconversion, evaluation using ambient cloud droplet size distributions. J. Geophys. Res., 114. doi: 10.1029/2008JD010502Google Scholar
  18. Hu Zhijin, 1979: On the conditions of warm rain formation in cumulus clouds. Acta Meteor. Sinica, 37, 72–79. (in Chinese)Google Scholar
  19. Hu Zhijin, Yan Caifan, and Wang Yubin, 1987: Numerical simulation of rain and seeding processes in warm layer clouds. Acta Meteor. Sinica, 41, 79–88. (in Chinese)Google Scholar
  20. Iacobellis, S. F., and R. C. J. Somerville, 2006: Evaluating parameterizations of the autoconversion process using a single-column model and atmospheric radiation measurement program measurements. J. Geophys. Res., 111, D02203, doi: 10.1029/2005JD006296.Google Scholar
  21. Jiang, J. H., H. Su, C. X. Zhai, et al., 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J. Geophys. Res., 117, D14105, doi: 10.1029/2011JD017237.CrossRefGoogle Scholar
  22. Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Boston, Meteorological Monographs, Amer. Meteor. Soc., 10 pp.Google Scholar
  23. Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229–243.CrossRefGoogle Scholar
  24. Kochtubajda, B., 1995: The microstructure of selected, small, isolated, cumulus clouds near Red Deer, Alberta. Atmos. Res., 35, 253–270.CrossRefGoogle Scholar
  25. Kong, F., and M. K. Yau, 1997: An explicit approach to microphysics in MC2. Atmosphere-Ocean, 35, 257–291.CrossRefGoogle Scholar
  26. Kubar, T. L., E. W. Duane, and J.-L. Li, 2011: Boundary layer and cloud structure controls on tropical low cloud cover using A-Train satellite data and ECMWF analyses. J. Climate, 24, 194–215.CrossRefGoogle Scholar
  27. Lee, S., B. H. Kahn, and J. Teixeira, 2010: Characterization of cloud liquid water content distributions from CloudSat. J. Geophys. Res., 115, D20203, doi: 10.1029/2009JD013272.CrossRefGoogle Scholar
  28. Li, J.-L. F., D. E. Waliser, and C. Wood, et al., 2011: Comparisons of satellites liquid water estimates to ECMWF and GMAO analyses, 20th century IPCC AR4 climate simulations, and GCM simulations. Geophys. Res. Lett., 38, L24807, doi: 10.1029/2011GL049956.Google Scholar
  29. Liu, Y. G., and P. H. Daum, 2004: Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations. J. Atmos. Sci., 61, 1539–1548.CrossRefGoogle Scholar
  30. Liu, Y. G., P. H. Daum, R. McGraw, et al., 2006: Parameterization of the autoconversion process. Part II: Generalization of Sundqvist-type parameterizations. J. Atmos. Sci., 63, 1103–1109.CrossRefGoogle Scholar
  31. Manton, M. L., and W. R. Cotton, 1977: Formulation of approximate equations for modeling moist deep convection on the mesoscale. Ph. D. dissertation, Atmospheric Science, Colorado State University, 62 pp.Google Scholar
  32. Marchand, R., G. G. Mace, T. Ackerman, et al., 2008: Hydrometeor detection using Cloudsat-An earthorbiting 94-GHz cloud radar. J. Atmos. Ocea. Tech., 25, 519–533.CrossRefGoogle Scholar
  33. Miles, N. L., J. Verlinde, and E. E. Clothiaux, 2000: Cloud droplet size distributions in low-level stratiform clouds. J. Atmos. Sci., 57, 295–311.CrossRefGoogle Scholar
  34. Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of super-cooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071–1107.CrossRefGoogle Scholar
  35. Rotstayn, L. D., and Y. G. Liu, 2005: A smaller global estimate of the second indirect aerosol effect. Geophys. Res. Lett., 32, L05708, doi: 10.1029/2004GL021922.Google Scholar
  36. Rutledge, S. A., and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41, 2949–2972.CrossRefGoogle Scholar
  37. Schultz, P., 1995: An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123, 3331–3343.CrossRefGoogle Scholar
  38. Seifert, A., and K. D. Beheng, 2001: A double-moment parameterization for simulating autoconversion, accretion, and selfcollection. Atmos. Res., 59, 265–281.CrossRefGoogle Scholar
  39. Seifert, A., L. Nuijens, and B. Stevens, 2010: Turbulence effects on warm-rain autoconversion in precipitating shallow convection. Quart. J. Roy. Meteor. Soc., 136, 1753–1762.CrossRefGoogle Scholar
  40. Silverman, B. A., and M. Glass, 1973: A numerical simulation of warm cumulus clouds: Part I. Parameterized vs non-parameterized microphysics. J. Atmos. Sci., 30, 1620–1637.CrossRefGoogle Scholar
  41. Simpson, J., and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471–489.CrossRefGoogle Scholar
  42. Skamarock, W. C., J. B. Klemp, J. Dudhia, et al., 2008: A Description of the Advanced Research WRF version 3. Boulder, Colorado, USA. Scholar
  43. Squires, P., 1958: The microstructure and colloidal stability of warm clouds. Part I: The relation between structure and stability. Tellus, 10, 256–261.CrossRefGoogle Scholar
  44. Stephens, G. L., D. G. Vane, S. Tanelli, et al., 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, doi: 10.1029/2008JD009982.Google Scholar
  45. Tao, W.-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4, 35–72.Google Scholar
  46. Tao, W.-K., S. Lang, X. Zeng, et al., 2014: The Goddard Cumulus Ensemble model (GCE): Improvements and applications for studying precipitation processes. Atmos. Res., 143, 392–424.CrossRefGoogle Scholar
  47. Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519–542.CrossRefGoogle Scholar
  48. Tisler, P., and H. Savijärvi, 2002: On the parameterization of precipitation in warm clouds. Atmos. Res., 63, 163–176.CrossRefGoogle Scholar
  49. Vaillancourt, P. A., A. Tremblay, S. G. Cober, et al. 2000: Evaluation of a mixed-phase cloud scheme’s ability of forecasting supercooled liquid water in clouds. Proc. 13th Int. Conf. Clouds and Precip, Reno, NV, 14–18 August, Amer. Meteor. Soc., 586–589.Google Scholar
  50. Wang, Z., and K. Sassen, 2001: Cloud type and macrophysical property retrieval using multiple remote sensors. J. Appl. Meteor., 40, 1665–1682.CrossRefGoogle Scholar
  51. Wolde, M., and G. Vali, 2002: Cloud structure and crystal growth in nimbostratus. Atmos. Res., 61, 49–74.CrossRefGoogle Scholar
  52. Wood, R., and P. N. Blossey, 2005: Comments on “Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations.” J. Atmos. Sci., 62, 3003–3006.CrossRefGoogle Scholar
  53. Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 2373–2423.CrossRefGoogle Scholar
  54. Xie Xiaoning and Liu Xiaodong, 2015: Aerosol-cloudprecipitation interactions in WRF model: Sensitivity to autoconversion parameterization. J. Meteor. Res., 29, 72–81, doi: 10.1007/s13351-014-4065-8.CrossRefGoogle Scholar
  55. Xu Huanbin and Wang Siwei, 1985: A numerical model of hail-bearing convective cloud (I): Biparameter evolution of size distribution of raindrops, frozen raindrops, and hailstones. Acta Meteor. Sinica, 43, 13–25. (in Chinese)Google Scholar
  56. Yang, S., and X. Zou, 2012: Assessments of cloud liquid water contributions to GPS radio occultation refractivity using measurements from COSMIC and CloudSat. J. Geophys. Res., 117, D06219, doi: 10.1029/2011JD016452.Google Scholar
  57. Yin, J. F., D. H. Wang, and G. Q. Zhai, 2011: Long-term in-situ measurements of the cloud-precipitation microphysical properties over East Asia. Atmos. Res., 102, 206–217.CrossRefGoogle Scholar
  58. Yin Jinfang, Wang Donghai, Zhai Guoqing, et al., 2013a: Observational characteristics of cloud vertical pro-files over the continent of East Asia from the Cloud-Sat data. Acta Meteor. Sinica, 27, 26–39.CrossRefGoogle Scholar
  59. Yin, J. F., D. H. Wang, and G. Q. Zhai, 2013b: A comparative study of cloud-precipitation microphysical properties between East Asia and other regions. J. Meteor. Soc. Japan, 91, 507–526.CrossRefGoogle Scholar
  60. Yin, J. F., D. H. Wang, G. Q. Zhai, et al., 2014: An investigation into liquid water content versus cloud number concentration in the stratiform clouds over North China. Atmos. Res., 139, 137–143.CrossRefGoogle Scholar
  61. Zhang Dianguo, Guo Xueliang, Gong Dianli, et al., 2011: The observational results of the clouds microphysical structure based on the data obtained by 23 sorties between 1989 and 2008 in Shandong Province. Acta Meteor. Sinica, 69, 195–207. (in Chinese)Google Scholar
  62. Zhao Yanfeng, Wang Donghai, and Yin Jinfang, 2014: A study on cloud microphysical characteristics over the Tibetan Plateau using CloudSat data. J. Trop. Meteor., 30, 239–248. (in Chinese)Google Scholar
  63. Zhong Lingzhi, Liu Liping, Chen Lin, et al., 2010: A potential application of a millimeter wavelength radar to studying the cloud physics mechanism for ice and snow weather. Acta Meteor. Sinica, 68, 705–716. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jinfang Yin (尹金方)
    • 1
  • Donghai Wang (王东海)
    • 1
  • Guoqing Zhai (翟国庆)
    • 2
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.Department of Earth ScienceZhejiang UniversityHangzhouChina

Personalised recommendations