Collaborative Research: Subgrid-scale Models for Large-eddy Simulation of Cloud Formation and Evolution

合作研究：云形成和演化大涡模拟的亚网格尺度模型

• 批准号: 1503885
• 负责人: Robert Street
• 金额: $ 13.27万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1503885&HistoricalAwards=false
• 关键词: Collaborative; Research; Subgrid; scale; Models

Clouds are important to the earth's energy balance and a regulator of both climate and weather. The estimation of cloud formation, cloud cover, precipitation, etc., for both climate and weather prediction is accomplished with numerical simulations. Despite computational advances and for the foreseeable future, simulations of the real atmosphere for weather and climate prediction will feature results in which a significant fraction of the energy, heat and vapor fluxes, etc., will not be resolved and must be modeled. This project's goal is to develop, validate and make available to the community improved and more physically realistic turbulence models for the subgrid- and subfilter-scales in moist-atmosphere large-eddy simulation (LES) simulation codes, in collaboration between UC Berkeley, Stanford University, and NCAR scientists. Two new subgrid-scale (SGS) model sets (one using dynamic methods and one using a linear, algebraic model) will be developed for all elements of a cloud simulation, i.e., momentum, heat, water's liquid and vapor phases, graupel, the prognostic equations in the microphysics, etc. These SGS closures have improved mean fields and higher-order statistics in previous boundary layer simulations, but have yet to be applied to clouds. The SGS models will be constructed within the explicit filtering and reconstruction framework, which reduces numerical errors and provides a more physical representation of turbulent stresses.Intellectual Merit: The project aims to provide significantly improved models for the unresolved fields (including momentum, heat, water vapor, liquid, and other scalar fluxes), to yield deeper understanding of the physical processes being modeled, and to validate those models in test case simulations of realistic clouds. The other goal is to clarify the validity of using such subroutines in the Terra Incognita [TI] / Gray Zone of atmospheric simulations, i.e., the zone of grid resolution in which flow features such as convective thermals are partly resolved and partly sub-grid. This zone is becoming an ever greater challenge as numerical simulations cover more and more length scales. The role of SGS closures in the Terra Incognita is still largely unexplored, particularly in the case of clouds. Building upon prior research on SGS modeling, the research will (1) create new SGS equation sets for the moist atmosphere, (2) apply them in a priori tests and then (3) carry out simulations of field-scale situations covering clear convective boundary layers, trade-wind cumulus with and without precipitation, shallow cumulus, and deep convection. These simulations will be set up to assess the performance of the equation sets for their accuracy and efficiency and to assess model performance in the TI (or Gray Zone).Broader Impacts: There are two domains of broader impacts. First, successful completion of this work will yield improved predictions of cloud generation and evolution in the simulations. Because the code on which the work is based is widely used internationally, this will be a major benefit to the community. Given that accurate prediction of cloud formation and behavior is a critical element in weather and climate prediction and, in particular, rainfall, the work has the potential for significant impact across the weather domain. Previous experience suggests that the new SGS models will be easily transported to other codes as well, which will further broaden the impact of this work. Second, this project aims through its collaboration with NCAR to give broad and high quality training to a postdoctoral researcher, who will benefit from the exposure to the modeling expertise at Berkeley and Stanford and the modeling and microphysics expertise at NCAR. In addition, a Stanford undergraduate student will work on the project to complement the work of the postdoctoral researcher.

云对地球的能量平衡很重要，也是气候和天气的调节器。 云的形成、云量、降水等的估计，气候和天气预报都是通过数值模拟来完成的。 尽管计算技术取得了进步，但在可预见的未来，用于天气和气候预测的真实的大气模拟结果的特点是，大部分能量、热量和蒸汽通量等，将无法解决，必须建模。 该项目的目标是开发、验证和向社区提供改进的和物理上更真实的湍流模型，用于大气压大涡模拟（LES）模拟代码中的次网格和子过滤器尺度，该项目由加州大学伯克利分校、斯坦福大学和NCAR科学家合作。 将为云模拟的所有要素开发两套新的亚网格尺度（SGS）模型（一套使用动态方法，另一套使用线性代数模型），即，动量，热量，水的液体和蒸汽相，霰，在微物理学的预测方程等，这些SGS封闭改善了平均场和高阶统计在以前的边界层模拟，但尚未被应用到云。SGS模型将在显式滤波和重建框架内构建，这将减少数值误差，并提供湍流应力的更物理表示。该项目旨在为尚未解决的领域提供显著改进的模型（包括动量、热量、水蒸气、液体和其他标量通量），以更深入地理解所模拟的物理过程，并在真实云的测试案例模拟中验证这些模型。 另一个目标是澄清在Terra Incognita [TI] / Gray Zone大气模拟中使用此类子程序的有效性，即，网格分辨率的区域，其中对流热气流等流动特征部分被分辨，部分为亚网格。 随着数值模拟覆盖越来越多的长度尺度，这一区域正成为一个越来越大的挑战。 SGS封闭在未知领域中的作用在很大程度上仍未被探索，特别是在云的情况下。 在先前对SGS建模的研究基础上，该研究将（1）为潮湿大气创建新的SGS方程组，（2）将其应用于先验测试，然后（3）对场尺度情况进行模拟，包括清晰的对流边界层，有降水和无降水的信风积云，浅积云和深对流。这些模拟将被设置为评估方程组的准确性和效率的性能，并评估模型在TI（或灰色地带）的性能。 首先，这项工作的成功完成将改进模拟中云生成和演变的预测。 由于这项工作所依据的准则在国际上得到广泛使用，这将对社区产生重大利益。 鉴于准确预测云的形成和行为是天气和气候预测的关键因素，特别是降雨，这项工作有可能对整个天气领域产生重大影响。 以往的经验表明，新的SGS模型也将很容易转移到其他代码，这将进一步扩大这项工作的影响。 其次，该项目旨在通过与NCAR的合作，为博士后研究人员提供广泛和高质量的培训，他们将受益于伯克利和斯坦福大学的建模专业知识以及NCAR的建模和微观物理专业知识。此外，一名斯坦福大学的本科生将从事该项目，以补充博士后研究人员的工作。