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

虽然“暖雨”过程——其中冰相微物理在降水大小颗粒的发展中几乎没有作用——被认为占所有热带降水的三分之一，但其确切机制仍然存在许多不确定性。特别是，经典的凝聚增长理论伴随着重力沉降的碰撞合并，无法解释在温度高于0摄氏度的云中经常观测到的雨的快速发展。人们逐渐认识到，湍流可能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，难以实现可实现的预测。理论和计算能力的最新进展使湍流对碰撞合并率的影响能够进行更定量的评估，并且已经开发了几个初步的湍流收集参数化。本研究的目标是：(a)使用bin微物理方案在云行为的大涡模拟（LES）模型中纳入实际湍流驱动收集（即液滴生长）的表示，随后(b)使用现有的真实云的原位观测来评估结果预测，以确定这种新表示的过程影响和改进模型预测的条件。由于海洋层积云的生命周期长，且有大量高质量的观测数据可用于真实世界模式的比较，因此将重点介绍海洋层积云。混合直接数值模拟（DNS）方法将用于在低至中等平均流动耗散率条件下开发更精确的湍流碰撞-合并参数化，并将探索将这种参数化纳入LES框架的统计方法。这种方法将通过雷诺数对气流影响的现象学建模加以补充，这样，LES模型将得到改进，以允许不同的云夹带混合场景。这项研究的智力价值将集中在更准确和系统地演变湍流对实际层积云条件的影响，并改进我们目前在预测模式中定量表示这种影响的能力的评估。这项工作的更广泛的影响将包括发展最终应适用于其他类型云（例如，更有活力的积云）的发现，以及在更粗粒度的数值天气预报和气候模型中改进暖雨发展的定量表示，以及通过加强云微物理学和计算科学之间的合作。这种环境将为两所院校的本科生和研究生提供一个充满活力和多方面的教育和培训场所。

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.