Vertical Mixing and Horizontal Transport for Weak-wind Stable Conditions

弱风稳定条件下的垂直混合和水平输送

• 批准号: 0607842
• 负责人: Larry Mahrt
• 金额: $ 64.81万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0607842&HistoricalAwards=false
• 关键词: Vertical; Mixing; Horizontal; Transport; Weak

New results in the literature have dramatically altered our conceptual framework for the stable nocturnal boundary layer, and also have identified serious inadequacies in our understanding for weak-wind strongly-stratified conditions. Similarity theory, which forms the basis for representation of the turbulent mixing in numerical models, is not supported by recent analyses for weak-wind very-stable conditions. Additional influences not in standard similarity theory include: a) intermittency on a variety of scales, b) downward transport of shear-generated turbulence from above the surface inversion layer, c) significant horizontal advection over even weak surface heterogeneity with strongly-stratified conditions, d) more efficient transport of momentum compared to transport of heat in some situations and e) coupling with small-scale mesoscale motions, such as gravity waves and meandering motions. The PI is evaluating the role of these influences in order to generalize commonly-used similarity theories for turbulent fluxes, and for several alternatives to similarity theory as well. These theories are being expanded to include probability distributions to account for the broad range of turbulence characteristics for a given set of external conditions when unpredictable gravity waves and meandering motions also are present. For this work he will use quality-controlled tower flux data from 20 sites that has been accumulated for a wide variety of surface types and climate regimes. The datasets include five micro-networks of flux towers for examination of the spatial structure of the turbulence and mesoscale motions. Computation of fluxes with a new more careful method is essential for this examination of weak turbulence. Intellectual Merit: The combination of improved analysis techniques and the networks of eddy-correlation measurements yields new insight into the space-time structure of turbulence and mesoscale meandering/gravity waves, and their interaction, for very stable conditions. These analyses improve our understanding of intermittency of turbulence and its influence on the flux-gradient relationship.Broader Impacts: The work will improve formulations of turbulent mixing and mesoscale transport in atmospheric models, particularly for cases of very weak mixing when risk of frost damage and ground fog formation are greatest. These are the cases where models suffer the largest errors in predicted surface variables. The new formulations of turbulent mixing and mesoscale transport improve modeling of dispersion of atmospheric contaminants in weak-wind nocturnal conditions when buildup of contaminant concentrations are greatest. In addition, the massive quality-controlled datasets are being made available to the research community. Two undergraduate students, and an international graduate student or post-doctoral researcher with separate funding, are working with this veteran researcher on this project.

在文献中的新结果已经大大改变了我们的概念框架的稳定的夜间边界层，也已经确定了严重的不足之处，我们的理解弱风强分层条件。相似理论是数值模式中湍流混合表示的基础，但最近对弱风非常稳定条件的分析不支持相似理论。标准相似性理论中没有的其他影响包括：a）各种尺度上的不稳定性，B）从地面逆温层上方向下输送剪切产生的湍流，c）在强分层条件下，即使是弱的地面非均匀性上也有显著的水平平流，d）在某些情况下，与热量输送相比，动量输送更有效，以及e）与小尺度中尺度运动耦合，例如重力波和弯曲运动。PI正在评估这些影响的作用，以推广湍流通量的常用相似性理论，以及相似性理论的几种替代方案。这些理论正在扩展到包括概率分布，以考虑到不可预测的重力波和曲折运动也存在时，对于给定的一组外部条件的湍流特性的广泛范围。在这项工作中，他将使用来自20个地点的质量控制塔通量数据，这些数据是针对各种表面类型和气候状况积累的。数据集包括五个微网络的通量塔的湍流和中尺度运动的空间结构的检查。用一种新的更仔细的方法计算通量对于弱湍流的这种检验是必不可少的。智力优势：改进的分析技术和网络的涡相关测量相结合，产生新的洞察湍流和中尺度弯曲/重力波的时空结构，以及它们之间的相互作用，非常稳定的条件。这些分析提高了我们的理解湍流的不稳定性及其对通量梯度relationship.Broader影响：这项工作将改善配方的湍流混合和中尺度大气模型中的运输，特别是对于非常弱的混合的情况下，霜冻破坏和地面雾形成的风险是最大的。在这些情况下，模型在预测的表面变量中遭受最大的误差。湍流混合和中尺度输送的新配方改善了弱风夜间条件下污染物浓度最大时大气污染物扩散的建模。此外，正在向研究界提供大量质量控制数据集。两名本科生和一名国际研究生或博士后研究员（单独资助）正在与这位资深研究员合作开展该项目。