关于云中湍流凝结的理想化模型研究
On idealized models of turbulent condensation in clouds
AI总结：
本研究分析了云中湍流凝结的理想化模型，发现其预测的滴谱依赖空间采样尺度且存在虚假拓宽、局部未必宽分布的问题，提出了评估这类模型优缺点的框架，为云大涡模拟的次网格参数化提供参考。
摘要：
各类微物理模型试图通过近似表征湍流环境中凝结过程导致的随机滴增长，来解释云中宽滴谱（DSD）的形成机制。本研究分析了特定的理想化模型，这类模型中滴增长条件的变化主要源于携带滴的空气的湍流垂直速度的变化，例如在绝热气块中运行的随机涡跳模型（stochastic eddy hopping model）以及某些类直接数值模拟（DNS-like）模型。研究表明，这类模型产生的滴尺寸统计量沿垂直方向具有空间非均匀性，导致其预测的滴谱（DSD）依赖于DSD的空间采样尺度Δ。在这些模型中，Δ与滴的湍流扩散空间范围（由类布朗运动偏移近似）隐含相关，因此随时间以t^(1/2)的规律增长。这会导致虚假的DSD持续拓宽，因为滴半径平方的标准差随时间的增长（同样遵循t^(1/2)规律）本质上源于采样尺度Δ的增大。此外，本文讨论的模型所预测的DSD不一定具有局部宽分布特征（即大、小滴在足够小的体积内可能并未充分混合），因此并不一定意味着重力诱导的滴碰并概率会升高。在为次网格参数化构建坚实物理基础的工作中，本研究提出了一个用于阐释理想化模型优缺点的框架，指明了如何在云的大涡模拟中，将这类模型作为湍流凝结的次网格表征进行合理评估与使用。
英文摘要：
Various microphysical models attempt to explain the occurrence of broad droplet size distributions (DSD) in clouds through approximate representations of the stochastic droplet growth by condensation in a turbulent environment. This work analyzes specific idealized models, where the variability of droplet growth conditions arises primarily from variability in the turbulent vertical velocity of the air carrying these droplets. Examples are the stochastic eddy hopping model operating in adiabatic parcels and certain types of DNS-like models. We show that such models produce droplet size statistics that are spatially inhomogeneous along the vertical direction, causing the predicted DSD to depend on the DSD spatial sampling scale $Δ$. In these models, $Δ$ is implicitly related to the spatial extent of the droplets turbulent diffusion (approximated by Brownian-like excursions) and thus grows like $t^{1/2}$. This leads to spurious continuous DSD broadening, as the growth in time (also like $t^{1/2}$) of the standard deviation of droplet squared radius arises essentially from the growth of the sampling scale $Δ$. Also, the DSDs predicted by the models discussed here are not necessarily locally broad (in the sense that large and small droplets might not be well-mixed in sufficiently small volumes) and thus do not necessarily indicate enhanced probabilities of gravity-induced droplet coagulation. In the effort to build a firm physical basis for subgrid parametrizations, this study presents a framework to explain the merits and limitations of idealized models, indicating how to assess and use them wisely as a subgrid representation of turbulent condensation in large-eddy simulations of clouds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
