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

云对地球的能量平衡很重要，同时也是气候和天气的调节器。在气候和天气预报中，云的形成、云量、降水量等的估计都是通过数值模拟完成的。尽管在计算上取得了进展，但在可预见的未来，用于天气和气候预测的真实大气模拟的特点是，很大一部分能量、热量和水蒸气通量等将无法解决，必须进行建模。该项目的目标是在加州大学伯克利分校、斯坦福大学和NCAR科学家的合作下，为湿大气大涡模拟(LES)模拟程序中的次网格和次过滤器尺度开发、验证和向社区提供改进的、更接近物理现实的湍流模型。两个新的亚格子尺度(SGS)模式集(一个使用动力学方法，另一个使用线性代数模式)将用于云模拟的所有元素，即动量、热量、水的液体和汽相、重力、微物理中的预报方程等。这些SGS闭包在以前的边界层模拟中改善了平均场和高阶统计量，但尚未应用于云。SGS模型将在显式过滤和重建框架内构建，这将减少数值误差，并提供更物理的湍流应力表示。智力优势：该项目旨在为未分解的场(包括动量、热量、水蒸气、液体和其他标量通量)提供显著改进的模型，以加深对正在建模的物理过程的理解，并在真实云的测试用例模拟中验证这些模型。另一个目标是澄清在大气模拟中使用这种子程序的有效性，即大气模拟中的网格分辨率区，在该区域中，对流热力等流动特征被部分解析，部分被分网格。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关闭在隐蔽地中的作用在很大程度上仍然没有被探索，特别是在云层的情况下。在前人对SGS模式研究的基础上，本研究将(1)为湿大气建立新的SGS方程组，(2)将它们应用于先验检验，然后(3)进行场尺度情况的模拟，包括晴朗对流边界层、有无降水的信风积云、浅层积云和深部对流。这些模拟将被设置来评估方程组的性能的准确性和效率，并评估模型在TI(或灰色地带)的性能。更广泛的影响：有两个领域的更广泛的影响。首先，这项工作的成功完成将改进对模拟中云的生成和演变的预测。由于这项工作所依据的规范在国际上得到了广泛的应用，这将是社区的一大好处。鉴于对云形成和行为的准确预测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模型也将很容易传输到其他代码，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的建模专业知识以及NCAR的建模和微物理学专业知识。此外，斯坦福大学的一名本科生将参与该项目，以补充博士后研究员的工作。

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.