Broadening of Modeled Cloud Droplet Spectra Using Bin Microphysics in an Eulerian Spatial Domain
Broadening of Modeled Cloud Droplet Spectra Using Bin Microphysics in an Eulerian Spatial Domain
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使用欧拉空间域中的宾微物理拓宽模拟云滴光谱
DOI：
10.1175/jas-d-18-0055.1
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发表时间：
2018
影响因子：
3.1
通讯作者：
Z. Lebo
中科院分区：
文献类型：
--
作者：
H. Morrison;M. Witte;G. Bryan;J. Harrington;Z. Lebo
本研究在具有分档微物理过程的欧拉动力学模型中，对凝结增长和输送过程中的液滴尺寸分布（DSD）特征进行了研究。采用了一系列建模框架，包括气块模型、一维（1D）模型和三维大涡模拟（LES）。一维模型（仅包含垂直平流和凝结增长）中的分档DSD几乎与LES中的一样宽，并且与层积云观测到的DSD宽度相符。这些DSD比气块框架内的拉格朗日微物理计算得到的DSD宽得多，气块框架内的计算作为一维测试的数值基准。相比之下，对于不考虑欧拉输送的上升气块，分档模型得到的DSD与拉格朗日微物理基准相似。这些结果表明，与垂直平流相关的数值扩散是一维模型和LES中DSD变宽的关键因素。与先前研究表明的在实际云中使DSD变宽的物理混合过程不同，这种由垂直数值扩散导致的DSD变宽是不符合物理实际的。有人提出，在具有分档微物理过程的典型LES配置中，由垂直数值扩散导致的人为DSD变宽弥补了不同液滴群体水平变异性和混合的体现不足，或者弥补了对其他使DSD变宽的机制（如巨云凝结核的增长）的忽略。这些结果使人们对具有分档微物理过程的欧拉动力学模型研究DSD变宽的物理机制的能力产生怀疑，尽管它们可能合理地模拟了DSD的整体特征。
This study investigates droplet size distribution (DSD) characteristics from condensational growth and transport in Eulerian dynamical models with bin microphysics. A hierarchy of modeling frameworks is utilized, including parcel, one-dimensional (1D), and three-dimensional large-eddy simulation (LES). The bin DSDs from the 1D model, which includes only vertical advection and condensational growth, are nearly as broad as those from the LES and in line with observed DSD widths for stratocumulus clouds. These DSDs are much broader than those from Lagrangian microphysical calculations within a parcel framework that serve as a numerical benchmark for the 1D tests. In contrast, the bin-modeled DSDs are similar to the Lagrangian microphysical benchmark for a rising parcel in which Eulerian transport is not considered. These results indicate that numerical diffusion associated with vertical advection is a key contributor to broadening DSDs in the 1D model and LES. This DSD broadening from vertical numerical diffusion is unphysical, in contrast to the physical mixing processes that previous studies have indicated broaden DSDs in real clouds. It is proposed that artificial DSD broadening from vertical numerical diffusion compensates for underrepresented horizontal variability and mixing of different droplet populations in typical LES configurations with bin microphysics, or the neglect of other mechanisms that broaden DSDs such as growth of giant cloud condensation nuclei. These results call into question the ability of Eulerian dynamical models with bin microphysics to investigate the physical mechanisms for DSD broadening, even though they may reasonably simulate overall DSD characteristics.
东南太平洋层积云中的液滴浓度和光谱展宽
影响因子：
3.1
作者：
Snider, Jefferson R.;Leon, David;Wang, Zhien
通讯作者：
Wang, Zhien
影响因子：
3.1
作者：
F. Hoffmann;Y. Noh;S. Raasch
通讯作者：
F. Hoffmann;Y. Noh;S. Raasch
影响因子：
56.9
作者：
Beals, Matthew J.;Fugal, Jacob P.;Stith, Jeffrey L.
通讯作者：
Stith, Jeffrey L.