Chinese Science Bulletin

, Volume 59, Issue 9, pp 896–903 | Cite as

Entrainment-mixing parameterization in shallow cumuli and effects of secondary mixing events

  • Chun-Song Lu
  • Yan-Gang Liu
  • Sheng-Jie Niu
Article Atmospheric Science


Parameterization of entrainment-mixing processes in cumulus clouds is critical to improve cloud parameterization in models, but is still at its infancy. For this purpose, we have lately developed a formulation to represent a microphysical measure defined as homogeneous mixing degree in terms of a dynamical measure defined as transition scale numbers, and demonstrated the formulation with measurements from stratocumulus clouds. Here, we extend the previous work by examining data from observed cumulus clouds and find positive correlations between the homogeneous mixing degree and transition scale numbers. These results are similar to those in the stratocumulus clouds, but proved valid for the first time in observed cumulus clouds. The empirical relationships can be used to parameterize entrainment-mixing processes in two-moment microphysical schemes. Further examined are the effects of secondary mixing events on the relationships between homogeneous mixing degree and transition scale numbers with the explicit mixing parcel model. The secondary mixing events are found to be at least partially responsible for the larger scatter in the above positive correlations based on observations than that in the previous results based on numerical simulations without considering secondary mixing events.


Entrainment mixing Cumulus Homogeneous/inhomogeneous mixing Observation Model 



This research was supported by the National Natural Science Foundation of China (41030962, 41305120, 41375138, 41275151, 41075029, 41375137, 41305034); the Natural Science Foundation of Jiangsu Province, China (BK20130988, BK2012860); the Specialized Research Fund for the Doctoral Program of Higher Education (20133228120002); the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (13KJB170014); China Meteorological Administration Special Public Welfare Research Fund (GYHY201406007); the Open Funding from National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics; the Open Funding from Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, China (KDW1102, KDW1104, KDW1201); the Open Funding from Key Laboratory of Meteorological Disaster of Ministry of Education, China (KLME1305, KLME1205, KLME1107); the Qing-Lan Project for Cloud-Fog-Precipitation-Aerosol Study in Jiangsu Province, China; a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; the U.S. Department of Energy’s (DOE) Earth System Modeling (ESM) program via the FASTER project ( and Atmospheric System Research (ASR) program. We appreciate the helpful discussions about the RACORO data with Andrew Vogelmann, Haf Jonsson, Greg McFarquhar, Glenn Diskin, Gunnar Senum and Hee-Jung Yang. We also thank Steven Krueger and Timothy Wagner for their help with the EMPM model.


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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and Technology (NUIST)NanjingChina
  2. 2.Atmospheric Sciences DivisionBrookhaven National Laboratory (BNL)UptonUSA
  3. 3.National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsChinese Academy of SciencesBeijingChina
  4. 4.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and Technology (NUIST)NanjingChina

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