Collaborative Research: Integrating Models and Observations to Assess Effects of Turbulence on Warm Rain Initiation

合作研究：整合模型和观测来评估湍流对暖雨产生的影响

• 批准号: 1139746
• 负责人: Patrick Chuang
• 金额: $ 36.06万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1139746&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Models; Observations

While the "warm rain" process--in which ice-phase microphysics play little or no role in development of precipitation-size particles--is thought to account for as much as one-third of all tropical precipitation, yet a number of uncertainties remain regarding its exact mechanisms. In particular, the classic theory of condensational growth followed by collision-coalescence accompanying gravitational settling is unable to explain the oft-observed rapid development of rain in clouds confined to temperatures warmer than 0 degC. It has gradually become recognized that turbulence could play a critical role in accelerating this process, but realizable predictions have been difficult to achieve because quantitative research approaches are lacking. Recent advances in theory and computational capacity have enabled more quantitative assessment of turbulence effects on the collision-coalescence rate, and several preliminary parameterizations of turbulent collection have been developed. The goals of this research are to (a) incorporate representation of realistic turbulence-driven collection (i.e. droplet growth) in a large-eddy simulation (LES) model of cloud behavior using a bin microphysical scheme, and subsequently (b) to evaluate resulting predictions using existing in situ observations of real clouds to identify those conditions under which this newly-represented process affects and improves model predictions. Owing to their long lifecycle and a large amount of high-quality observational data available to facilitate real world-model comparisons, marine stratocumulus clouds will be emphasized. A hybrid direct numerical simulation (DNS) approach will be used to develop more accurate parameterization of turbulent collision-coalescence in conditions of low-to-intermediate mean flow dissipation rates, and statistical methods to incorporate such a parameterization into the LES framework will be explored. This approach will be complemented by phenomenological modeling of Reynolds number effects on airflow, and in so doing the LES model will be improved to allow for differing cloud entrainment mixing scenarios. The intellectual merit of this study will center on a more accurate and systematic evolution of the effects of turbulence on realistic stratocumulus conditions and improved assessment of our current ability to represent such effects quantitatively in a predictive mode. Broader Impacts of the effort will include development of findings that should ultimately be applicable to other types of clouds (e.g., more vigorous cumulus) and improved quantitative representation for warm-rain development in more coarse-grained numerical weather prediction and climate models, as well as through enhanced collaboration across the cloud microphysics and computational sciences. This setting will provide a vibrant and multifaceted education and training ground for a mix of undergraduate and graduate students at the two involved institutions.

虽然“暖雨”过程-其中冰相微物理学在降水大小颗粒的发展中几乎或根本没有发挥作用-被认为占所有热带降水的三分之一，但关于其确切机制仍然存在一些不确定性。 特别是，凝聚增长的经典理论，其次是碰撞合并伴随着重力沉降是无法解释的经常观察到的快速发展的雨在云局限于温度高于0摄氏度。 人们逐渐认识到，湍流可以在加速这一过程中发挥关键作用，但由于缺乏定量研究方法，很难实现可实现的预测。 理论和计算能力的最新进展，使更多的定量评估湍流对碰撞聚结率的影响，和湍流收集的几个初步参数化已经开发出来。 本研究的目标是（a）将现实的连续性驱动的收集（即液滴生长）的表示在一个大涡模拟（LES）模式的云行为使用箱微物理方案，随后（B），以评估所产生的预测使用现有的现场观测真实的云，以确定这些条件下，这个新的代表过程影响和改善模型预测。 由于海洋层积云的生命周期长，而且有大量高质量的观测数据可供用于真实的世界模式的比较，因此将着重于海洋层积云。 一个混合直接数值模拟（DNS）的方法将被用来开发更准确的参数化的湍流碰撞合并的条件下，低到中等的平均流量耗散率，并将这种参数化的LES框架的统计方法进行了探索。 这种方法将补充雷诺数对气流的影响的现象学建模，并在这样做的LES模型将得到改进，以允许不同的云夹带混合的情况。 这项研究的智力价值将集中在一个更准确和系统的演变的影响，湍流对现实的层积云条件和改进的评估，我们目前的能力，以定量代表这种影响的预测模式。 这项工作的更广泛影响将包括开发最终适用于其他类型云的研究结果（例如，在更粗粒度的数值天气预报和气候模型中，以及通过加强云微物理学和计算科学之间的合作，改进了暖雨发展的定量表示。 这种设置将提供一个充满活力的和多方面的教育和培训的基础，在两个相关机构的本科生和研究生的组合。