CAREER: Advancing the Understanding of Turbulence-Microphysics Interactions in Clouds Through Multiscale Numerical Modeling

云对地球系统中的许多过程至关重要，包括降水形成、水资源和气候反馈;云的作用是当前和未来气候不确定性的主要来源。天气和气候的计算机模型往往不能准确地捕捉到温度、湿度和风速的随机湍流波动对云滴形成和增长的影响。这些湍流波动可以影响云的寿命，降水形成和气候反馈，因此对于天气和气候预测的准确建模非常重要。该项目的中心目标是了解湍流与云滴形成和增长之间的小尺度相互作用，并改进这些过程在天气和气候预报模型中的表现。该项目的教育部分旨在通过遵循真实教学原则制定课程计划，增加物理学和其他STEM领域的本科生对大气科学的参与。这些课程将由教学密集型的俄克拉荷马州大学的教师进行试点，旨在提高不同本科生对地球科学的兴趣。尽管湍流与云中微物理过程之间的小尺度相互作用很重要，但这些相互作用在大涡模拟和天气及气候预报模式中的表现仍然很差。利用直接数值模拟（DNS）捕捉湍流中数百万拉格朗日液滴的形成和生长，研究人员将开发新的亚网格尺度模型，用于精确捕捉主导物理学的双折射-微物理相互作用。这些次网格尺度模型将根据DNS和实验室云室数据进行验证;由此产生的大涡模拟将被用来研究湍流和微观物理之间的相互作用在多大程度上影响云的寿命、降水的形成、降水量，和其他大型-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

Clouds are critical for many processes in the Earth system, including precipitation formation, water resources, and climate feedbacks; the role of clouds is a major source of uncertainty in present and future climate. Computer models of weather and climate often fail to accurately capture the effects of random, turbulent fluctuations of temperature, humidity, and wind speed on cloud droplet formation and growth. These turbulent fluctuations can impact cloud lifetimes, precipitation formation, and climate feedbacks and therefore are important to model accurately for both weather and climate prediction. The central aims of this project are to understand the small-scale interactions between turbulence and cloud droplet formation and growth and to improve the representation of these processes in weather and climate forecasting models. The educational component of this project is intended to increase engagement of undergraduates from physics and other STEM fields in atmospheric science through the development of lesson plans following principles of authentic instruction. These lessons will be piloted by instructors at teaching-intensive Oklahoma universities and are intended to promote interest in the geosciences among a diverse pool of undergraduate students. Despite the importance of small-scale interactions between turbulence and microphysical processes in clouds, these interactions remain poorly represented in both large eddy simulations and weather and climate forecasting models. Using direct numerical simulations (DNS) that capture the formation and growth of millions of Lagrangian droplets in a turbulent flow, the researchers will develop new subgrid-scale models for turbulence-microphysics interactions that accurately capture the governing physics. These subgrid-scale models will be validated against DNS and laboratory cloud chamber data; the resulting large eddy simulations will be used to investigate the extent to which interactions between turbulence and microphysics—at spatial scales larger than those attainable in DNS—influence cloud lifetimes, precipitation formation, albedo, and other large-scale cloud properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.