The Vertical Structure of Low-Frequency Atmospheric Flows

低频大气流的垂直结构

• 批准号: 9114073
• 负责人: Julia Nogues-Paegle
• 金额: $ 35.1万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-01-15 至 1995-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9114073&HistoricalAwards=false
• 关键词: Vertical; Structure; Low; Frequency; Atmospheric

The Global Weather Experiment (GWE) of early 1979 deployed augmented observational systems that offered, for the first time, truly global coverage at least on the largest (planetary wave) scales of the atmospheric circulation. Analysis techniques were subsequently developed that merged these observations with the output of continually running numerical models to obtain data sets consistent with the physical and dynamical laws built into the models. Research with these data sets has provided new insights on planetary-scale circulation features which vary slowly over intra- seasonal to seasonal periods. Dr. Nogues-Paegle will use a 10-year data set, which was prepared with state-of-the-art techniques for assimilating observations into numerical model solutions, to look at the vertical structure of such circulation features. The vertical structure detected will provide an indication as to the relative roles of local and remote energy sources in the excitation and maintenance of the quasi-stationary waves composing these features. The results should guide the development of general circulation models so they simulate seasonal and intraseasonal changes more accurately. Such improved predictions will have enhanced credibility and utility in the framing of environmental policy and economic resource allocation decisions.

1979年初的全球天气实验部署了增强观测系统，首次至少在大气环流的最大(行星波)尺度上提供了真正的全球覆盖。随后开发了分析技术，将这些观测结果与连续运行的数值模型的输出相结合，以获得与模型中内置的物理和动力学规律一致的数据集。对这些数据集的研究为行星尺度的环流特征提供了新的见解，这些特征在季节内到季节期间变化缓慢。诺格斯-佩格尔博士将使用一个为期10年的数据集来研究这种环流特征的垂直结构，该数据集是用最先进的观测技术准备的，用于将观测结果同化为数值模式解。探测到的垂直结构将表明本地和远程能源在激发和维持构成这些特征的准静止波方面的相对作用。这些结果将指导大气环流模式的发展，以便它们能够更准确地模拟季节和季节内的变化。这种改进的预测将提高在制定环境政策和经济资源分配决策方面的可信度和实用性。