Studies of Flow and Turbulent Mixing over Complex Terrain

本项目的目标是改进和改进大涡模拟方法，以便在大气流动的中尺度计算机模式中纳入湍流和陆面效应。 这项工作包括地表对大气过程的影响的表述，大气运动的局部和区域强迫的定义和表述，关键能量传输现象的解决方案，以及模拟准确性的评估。 主要任务是将（小尺度）湍流模式与（大尺度）中尺度模式联系起来。 为此，采用了两个中尺度计算机代码：高级区域预报系统（ARPS）和天气研究和预报模式（WRF）。 将这些与湍流模型联系起来需要考虑表面边界条件和小于计算网格的速度波动。 通过使用一个高分辨率的陆地表面模块来精确表示陆地表面，该模块描述了植被和土壤类型的许多组合。 湍流模型扩展到更大的域使用最近提出的代数应力模型，占运动小于网格尺寸的影响。 双向交互嵌套网格可以改进气流与地形相互作用的模拟。 最终目标是通过在大尺度模型中准确模拟小尺度现象的影响，制定一种改进地方和区域天气预报的详细方法。 该研究的其他广泛影响包括提高了预测污染物扩散的能力，以及模拟特定环境中的流动的能力，为微气象实地实验的设计提供指导。

The objective of this project is to refine and improve the methods of Large Eddy Simulation (LES) to enable the incorporation of turbulence and land-surface effects in mesoscale computer models of atmospheric flow. The work includes representation of the influence of the land surface on atmospheric processes, definition and representation of local and regional forcing of atmospheric motion, resolution of key energy-transporting phenomena, and evaluation of the accuracy of the simulations. The major task is to link (small-scale) turbulence models to (larger-scale) mesoscale models. For this, two mesoscale computer codes are employed: the Advanced Regional Prediction System (ARPS) and the Weather Research and Forecasting Model (WRF). Linking these to turbulence models requires accounting for surface boundary conditions and for velocity fluctuations smaller than the computational grid. Accurate representation of the land surface is achieved through use of a highly resolved land-surface module that characterizes many combinations of vegetation and soil type. Turbulence models are extended to the larger domain using recently proposed algebraic stress models to account for the effects of motions smaller than the grid size. Two-way interactive nesting grids enable improved simulations of the interaction of airflow with the terrain. The ultimate aim is to develop a detailed methodology for improving local and regional weather predictions by accurately simulating the effects of small-scale phenomena within the larger-scale models. Other broad impacts of the research include improved ability to predict the dispersion of pollutants, as well as the capacity to simulate flow in a given setting to provide guidance for the design of micrometeorological field experiments.