Cao, B. J., X. Y. Yang, J. Wen, Q. Hu, and Z. Y. Zhu, 2024: Large eddy simulation of vertical structure and size density of deep layer clouds. Adv. Atmos. Sci., 41(8), 1629−1642, https://doi.org/10.1007/s00376-023-3134-2.
Citation: Cao, B. J., X. Y. Yang, J. Wen, Q. Hu, and Z. Y. Zhu, 2024: Large eddy simulation of vertical structure and size density of deep layer clouds. Adv. Atmos. Sci., 41(8), 1629−1642, https://doi.org/10.1007/s00376-023-3134-2.

Large Eddy Simulation of Vertical Structure and Size Density of Deep Layer Clouds

  • In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of −1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection, but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.
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