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Variation in entrainment rate and relationship with cloud microphysical properties on the scale of 5 m

  • Article
  • Earth Sciences
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Science Bulletin

Abstract

This paper focuses on the variability in entrainment rate in individual cumulus clouds using the entrainment rate estimated on the scale of 5 m in 186 shallow cumulus clouds from eight aircraft flights, using in situ observations from the RACORO field campaign (the routine atmospheric radiation measurement aerial facility clouds with low optical water depths optical radiative observations) over the atmospheric radiation measurement Southern Great Plains site, USA. The result shows that the mean entrainment rate of all the 186 clouds systematically decreases from the cloud edge to the cloud center. Further analysis of the fluctuation of entrainment rate shows that the probability density function of entrainment rate in each flight can be fitted by the lognormal, gamma, or Weibull distributions virtually equally well, with the Weibull distribution being the best. The parameter “standard deviation” in the lognormal distribution is weakly negatively correlated, and the other parameters in the three distributions are positively correlated with relative humidity in the entrained dry air and dilution effect, respectively. Entrainment rate is negatively correlated with droplet concentration, droplet size, and liquid water content, but positively correlated with relative dispersion. The effect of entrainment rate on the spectral shape of cloud droplet size distribution is examined and linked to the systems theory on the cloud droplet size distribution.

摘要

云在全球辐射平衡和气候变化中起着重要作用。气候模式中的积云参数化方案影响降水和气候等的数值模拟研究。夹卷率是积云参数化中一个重要的物理量,但夹卷率的估算存在很大的不确定性,关于夹卷率概率密度分布函数的观测研究鲜有报道。本文利用积云飞机观测资料,发现从云的边界到云的中心,夹卷率递减。夹卷率的概率密度分布函数可以用对数正态分布、伽玛分布或者威布尔分布拟合,其中威布尔分布的效果最好。夹卷率与云滴数浓度、半径和含水量负相关,与离散度正相关。夹卷率对云滴谱的影响显著,总体而言,这与云滴谱的系统理论是一致的。

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (41305120, 91337215); the Research Foundation for Environmental Protection of Jiangsu Province (2013042); the Natural Science Foundation of Jiangsu Province, China (BK20130988); the Specialized Research Foundation 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 Foundation (GYHY201406007); the Open Funding from State Key Laboratory of Severe Weather (2013LASW-B06); the Open Funding from Key Laboratory of Meteorological Disaster of Ministry of Education, China (KLME1305); the Qing Lan Project; a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; the US Department of Energy’s (DOE) Earth System Modeling (ESM) program via the FASTER project (www.bnl.gov/faster) and Atmospheric System Research (ASR) Program. Data used in this article are from the US Department of Energy ARM Aerial Facility’s RACORO Campaign. We appreciate the helpful discussions about the data with Drs. Andrew Vogelmann, Gunnar Senum, Seong Soo Yum, Haf Jonsson, Greg McFarquhar, and Hee-Jung Yang. We also appreciate Dr. Glenn Diskin’s help on the data from DLH.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Science, Nanjing, 210036, China

    Muning Cheng

  2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    Muning Cheng & Chunsong Lu

  3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chunsong Lu

  4. Biological, Environmental and Climate Science Department, Brookhaven National Laboratory, Upton, NY, 11973, USA

    Yangang Liu

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  1. Muning Cheng
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Correspondence to Chunsong Lu.

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Cheng, M., Lu, C. & Liu, Y. Variation in entrainment rate and relationship with cloud microphysical properties on the scale of 5 m. Sci. Bull. 60, 707–717 (2015). https://doi.org/10.1007/s11434-015-0737-8

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