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Influence of turbulence on the drop growth in warm clouds, Part II: Sensitivity studies with a spectral bin microphysics and a Lagrangian cloud model

机译:湍流对暖云中液滴生长的影响,第二部分:利用光谱箱微物理学和拉格朗日云模型进行的敏感性研究

摘要

Raindrops in warm clouds grow faster than predicted by classical cloud models. One of the possible reasons for this discrepancy is the influence of cloud turbulence on the coagulation process. In Part I (Siewert et al., 2014) of this paper series, a turbulent collision kernel has been derived from wind tunnel experiments and direct numerical simulations (DNS). Here we use this new collision kernel to investigate the influence of turbulence on coagulation and rain formation using two models of different complexity: a one-dimensional model called RAINSHAFT (height as coordinate) with cloud microphysics treated by a spectral bin model (BIN) and a large-eddy simulation (LES) model with cloud microphysics treated by Lagrangian particles (a so called Lagrangian Cloud Model, LCM). Simulations are performed for the case of no turbulence and for two situations with moderate and with extremely strong turbulence. The idealized 0- and 1-dimensional runs show, that large drops grow faster in the case turbulence is taken into account in the cloud microphysics, as was also found by earlier investigations of other groups. For moderate turbulence intensity, the acceleration is only weak, while it is more significant for strong turbulence. From the model intercomparison it turns out, that the BIN model produced large drops much faster than the LCM, independent of turbulence intensity. The differences are larger than those due to a variation in turbulence intensities. The diverging rate of formation of large drops is due to the use of different growth models for the coagulation process, i.e. the quasi-stochastic model in the spectral BIN model and the continuous growth model in LCM. From the results of this model intercomparison it is concluded, that the coagulation process has to be improved in future versions of the LCM. The LES-LCM model was also applied to the simulation of a single 3-D cumulus cloud. It turned out, that the effect of turbulence on drop formation was even smaller as the turbulence within the cloud was weaker than prescribed in the idealized cases. In summary, the use of the new turbulent collision kernel derived in Part I does enhance rain formation under typical turbulence conditions found in natural clouds but the effect is not very striking. © 2015 The authors.
机译:暖云中的雨滴增长速度快于传统云模型所预测的速度。这种差异的可能原因之一是云湍流对凝结过程的影响。在本系列论文的第一部分(Siewert等,2014)中,从风洞实验和直接数值模拟(DNS)中得出了湍流碰撞核。在这里,我们使用这个新的碰撞核通过两个复杂程度不同的模型来研究湍流对凝结和降雨形成的影响:一个称为RAINSHAFT(高度为坐标)的一维模型,由光谱箱模型(BIN)处理的云微观物理模型和一个由拉格朗日粒子处理过的具有云微观物理学的大涡模拟(LES)模型(所谓的拉格朗日云模型LCM)。在没有湍流的情况下以及在中等湍流和极强湍流的两种情况下进行仿真。理想的0维和1维运行表明,在云微观物理学中考虑到湍流的情况下,大液滴的生长速度更快,这也可以通过其他小组的早期研究发现。对于中等湍流强度,加速度仅较弱,而对于较强湍流则更重要。从模型的比较中可以看出,BIN模型产生的大液滴比LCM快得多,而与湍流强度无关。由于湍流强度的变化,差异大于差异。大液滴形成的发散速率是由于在凝血过程中使用了不同的生长模型,即光谱BIN模型中的准随机模型和LCM中的连续生长模型。从模型比较的结果可以得出结论,在未来版本的LCM中必须改进凝血过程。 LES-LCM模型还用于模拟单个3-D积云。事实证明,湍流对液滴形成的影响甚至更小,因为云中的湍流比理想情况下的规定弱。总而言之,使用第一部分中得出的新的湍流碰撞核确实会增强自然云中典型的湍流条件下的降雨形成,但效果并不十分显着。 ©2015作者。

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