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首页> 外文期刊>Atmospheric chemistry and physics >Macroscopic impacts of cloud and precipitation processes on maritime shallow convection as simulated by a large eddy simulation model with bin microphysics
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Macroscopic impacts of cloud and precipitation processes on maritime shallow convection as simulated by a large eddy simulation model with bin microphysics

机译：宏观影响对箱微型药物大型涡仿真模型模拟云和降水过程的宏观影响

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This paper discusses impacts of cloud and precipitation processes on macrophysical properties of shallow convective clouds as simulated by a large eddy model applying warm-rain bin microphysics. Simulations with and without collision-coalescence are considered with cloud condensation nuclei (CCN) concentrations of 30, 60, 120, and 240 mg(-1). Simulations with collision-coalescence include either the standard gravitational collision kernel or a novel kernel that includes enhancements due to the small-scale cloud turbulence. Simulations with droplet collisions were discussed in Wyszogrodzki et al. (2013) focusing on the impact of the turbulent collision kernel. The current paper expands that analysis and puts model results in the context of previous studies. Despite a significant increase of the drizzle/rain with the decrease of CCN concentration, enhanced by the effects of the small-scale turbulence, impacts on the macroscopic cloud field characteristics are relatively minor. Model results show a systematic shift in the cloudtop height distributions, with an increasing contribution of deeper clouds for stronger precipitating cases. We show that this is consistent with the explanation suggested in Wyszogrodzki et al. (2013); namely, the increase of drizzle/rain leads to a more efficient condensate offloading in the upper parts of the cloud field. A second effect involves suppression of the cloud droplet evaporation near cloud edges in low-CCN simulations, as documented in previous studies (e.g., Xue and Feingold, 2006). We pose the question whether the effects of cloud turbulence on drizzle/rain formation in shallow cumuli can be corroborated by remote sensing observa-tions, for instance, from space. Although a clear signal is extracted from model results, we argue that the answer is negative due to uncertainties caused by the temporal variability of the shallow convective cloud field, sampling and spatial resolution of the satellite data, and overall accuracy of remote sensing retrievals.
机译：本文探讨了云和沉淀过程对浅对流云的宏观物理特性的影响，其施加温雨箱微型药物的大型涡流模拟。用云缩合核（CCN）浓度为30,60,120和240mg（-1），考虑具有和不具有碰撞聚结的模拟。采用碰撞结合的模拟包括标准引力碰撞核或新的内核，包括由于小规模云湍流引起的增强。在Wyszogrodzki等人中讨论了具有液滴冲突的模拟。 （2013）专注于湍流碰撞内核的影响。目前的论文扩展了该分析并将模型结果放在先前研究的背景下。尽管在CCN浓度的降低减少了毛毛雨/雨量的显着增加，但通过小规模湍流的影响增强，尽管提高了小规模湍流的影响，但对宏观云场特征的影响是相对较小的。模型结果显示了Cloudtop Height分布中的系统偏移，对于更强的沉淀案件，更深的云的贡献越来越大。我们表明这与Wyszogrodzki等人的解释一致。 （2013）;即，毛毛雨/雨的增加导致云场的上部更有效的冷凝水卸载。第二次效果涉及抑制低CCN模拟中云边缘附近的云液滴蒸发，如先前的研究中所述（例如，薛和费丁，2006年）。我们提出了云湍流对浅层模糊的影响云湍流的影响，可以通过远程感测观察，例如从空间来证实。尽管从模型结果中提取了清晰的信号，但我们认为答案是由于由浅对流云场的时间变异性引起的不确定性，卫星数据的采样和空间分辨率以及遥感检索的总体精度引起的不确定因素。

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