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.

将分析一个独特的数据集，该数据集由2013年1月从平流层气球平台观察到的极性层云（PMC）的高分辨率光学图像组成。事实证明，PMC是中层和较低热层（MLT）的出色动力学示踪剂。其亮度分布中的复杂形态结构可观察到能量和动量通过MLT中的大气重力波（GW）的沉积后果。这一高分辨率数据集的光学图像为最小的尺度（大约一米和几公里之间）的不稳定性和湍流动力学提供了一个独特的窗口。通过此分析，将在单个图像中确定的形态特征与数值模型中预测的形态特征进行比较，以识别这些小尺度上数据中存在的关键动力学特征。 尽管这些图像中可用的视野有限，但对该数据集的分析将使能够识别显示一系列动态的功能，从而提供过渡量表和伴随GW破裂或开尔文 - 赫尔姆霍尔特稳定性的证据。它还将识别在本地雷诺数字上限制界限的特征，从而识别背景湍流级别。该项目将为研究生，本科和高中生提供研究机会。