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Turbulence, condensation, and liquid water transport in numerically simulated nonprecipitating stratocumulus clouds

机译:数值模拟的非平流层积云中的湍流,凝结和液态水传输

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Condensation and turbulent liquid water transport in stratocumulus clouds involve complicated interactions between turbulence dynamics and cloud microphysical processes, and play essential roles in defining the cloud structure. This work aims at understanding this dynamical-microphysical interaction and providing information necessary for parameterizations of the ensemble mean condensation rate and turbulent fluxes of liquid water variables in a coupled turbulence-microphysics model. The approach is to simulate nonprecipitating stratocumulus clouds with a coupled large eddy simulation and an explicit bin-microphysical model, and then perform a budget analysis for four liquid water variables: mean liquid water content, turbulent liquid water flux, mean cloud droplet number concentration, and the number density flux. The results show that the turbulence contribution to the mean condensation rate comes from covariance of the integral cloud droplet radius and supersaturation, which enhances condensation in turbulent updrafts and reduces evaporation in the downdrafts. Turbulent liquid water flux results from a close balance between turbulence dynamics and microphysical processes. Consequently, the flux can be parameterized in terms of the common diffusive downgradient formulation, fluxes of conservative thermodynamic variables, the turbulence mixing timescale, and the condensation timescale, which is determined by the droplet spectrum. The results also suggest that the condensation timescale regulates the turbulence fields, as does the number concentration, because it affects the condensation fluctuation, which is highly correlated with the turbulence vertical motion. A saturation adjustment cloud model, which diagnoses liquid water content at its equilibrium level, instantly condenses (evaporates) all available water vapor (liquid water) surplus. Consequently, there is likely to be a systematic difference between the turbulence field resolved with this type of model and that with a supersaturation-based cloud scheme for which a finite condensation timescale applies. [References: 41]
机译:层积云中的冷凝水和湍流的液态水输送涉及湍流动力学和云微物理过程之间的复杂相互作用,并且在定义云结构中起着至关重要的作用。这项工作旨在了解这种动力学-微观物理的相互作用,并提供了在湍流-微观物理耦合模型中参数化集合平均冷凝率和液态水变量湍流通量的必要信息。该方法是通过耦合的大涡模拟和显式的bin-microphysical模型来模拟非降水平流云,然后对四个液态水变量进行预算分析:四个液态水含量,湍流液态水通量,平均云滴数浓度,和数密度通量结果表明,湍流对平均凝结速率的贡献来自于积分云滴半径和过饱和度的协方差,这增强了湍流上升气流中的凝结并减少了下降气流中的蒸发。湍流的液态水通量来自湍流动力学和微观物理过程之间的紧密平衡。因此,可以根据常见的扩散降梯度公式,保守的热力学变量的通量,湍流混合时间尺度和冷凝时间尺度(由液滴谱确定)来对通量进行参数化。结果还表明,凝结时间尺度与湍流场数一样,也能调节湍流场,因为它会影响凝结波动,而凝结波动与湍流垂直运动高度相关。诊断饱和状态下的液态水含量的饱和度调整云模型会立即冷凝(蒸发)所有可用的水蒸气(液态水)剩余。因此,用这种类型的模型解析的湍流场与采用有限凝结时间尺度的基于过饱和度的云方案的湍流场之间可能存在系统的差异。 [参考:41]

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