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Roles of Drop Size Distribution and Turbulence in Autoconversion Based on Lagrangian Cloud Model Simulations
  • Donggun Oh,
  • Yign Noh
Donggun Oh
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Yign Noh
Yonsei University

Corresponding Author:noh@yonsei.ac.kr

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The roles of the drop size distribution (DSD) and turbulence in the autoconversion rate A are investigated by analyzing Lagrangian cloud model (LCM) data for shallow cumulus clouds. The correlations of DSD and turbulence with other cloud parameters are estimated, and they are applied to parameterize their effects on A. A new parameterization of A is proposed based on it, as A = αqc7/3Nc-1/3H(Rc-Rc0) with α = aNc-X(Rc-Rc0)(1+bε), where qc, Nc, and Rc are the mixing ratio, the number concentration, and the volume mean radius of cloud droplets, respectively. ε is the dissipation rate, Rc0 is the threshold value of Rc, H is the Heaviside step function, and X, a, and b are constants. Here, Nc-X(Rc-Rc0) represents the effect of DSD, via its correlation with Nc and qc, while A ∝ qc7/3Nc-1/3 represents the effect of the gravitational collisional growth for given DSD and turbulence. The correlation between turbulence and DSD makes b larger than expected based on turbulence-induced collision enhancement. The effects of DSD and turbulence and their correlations with qc and Nc explain a wide range of exponent values of qc and Nc in many existing parameterizations of A. The new parameterization is compared with the LCM data and applied to a bulk cloud model (BCM) while clarifying the difference between the cloud droplet mixing processes of the LCM and BCM. The importance of DSD and turbulence in the raindrop formation in shallow cumulus clouds are shown by comparing the results from A with and without these effects.