CEDAR: Analysis of High Resolution Stratospheric Observations of Polar Mesospheric Clouds

CEDAR：极地中层云的高分辨率平流层观测分析

• 批准号: 1452183
• 负责人: Amber Miller
• 金额: $ 23.77万
• 依托单位: Columbia 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: 2015-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452183&HistoricalAwards=false
• 关键词: CEDAR; Analysis; Resolution; Stratospheric; Observations

A unique data set consisting of high-resolution optical images of Polar Mesospheric Clouds (PMCs) observed from a stratospheric balloon platform in January of 2013 will be analyzed. PMCs have proven to be excellent tracers of dynamics in the mesosphere and lower thermosphere (MLT). Complex morphological structures in their brightness distribution provide observable consequences of the deposition of energy and momentum by atmospheric gravity waves (GW) in the MLT. This high-resolution data set of optical images provides a unique window into the poorly understood instability and turbulent dynamics on the smallest scales (between roughly a meter and a few km). Through this analysis, morphological features identified in individual images will be compared with those predicted in numerical models in order to identify key dynamical features present in the data on these small scales. Despite the limited field of view available in these images, the analysis of this data set will enable the identification of features that show a range of dynamics, providing evidence for transitional scales and instability accompanying GW breaking or Kelvin-Helmholtz instabilities. It will also enable the identification of features that place bounds on the local Reynolds number, and therefore the background turbulence level. This project will provide research opportunities for graduate, undergraduate, and high school students.

将分析由2013年1月从平流层气球平台观测到的极地中层云(PMC)的高分辨率光学图像组成的独特数据集。私营军营公司已被证明是中间层和低热层(MLT)动力学的极好示踪剂。其亮度分布的复杂形态结构提供了大气重力波(GW)在MLT中沉积能量和动量的明显后果。这一高分辨率的光学图像数据集提供了一个独特的窗口，可以在最小的尺度(大约一米到几公里之间)了解鲜为人知的不稳定性和湍流动力学。通过这种分析，将在单个图像中识别的形态特征与在数值模型中预测的形态特征进行比较，以便在这些小尺度上识别数据中存在的关键动力学特征。尽管这些图像的视野有限，但对该数据集的分析将能够识别显示一系列动力学的特征，为伴随GW破裂或Kelvin-Helmholtz不稳定性而产生的过渡尺度和不稳定性提供证据。它还将允许识别对局部雷诺数以及背景湍流水平施加限制的特征。该项目将为研究生、本科生和高中生提供研究机会。