CMG Collaborative Research: Multiscale Statistical Methodologies to Unravel Complexities in Atmospheric Turbulence Data

CMG 合作研究：揭示大气湍流数据复杂性的多尺度统计方法

• 批准号: 0724524
• 负责人: Brani Vidakovic
• 金额: $ 24.39万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0724524&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Multiscale; Statistical

This CMG research collaboration between a statisitican and an atmospheric micrometeorologist seeks further explanation and pursuit of a number of basic questions in the area of turbulence in the atmospheric boundary layer. Specifically, the approach will investigate the use of multi-scale statistical methodologies, stressing theoretical components arrived at through analysis of the distribution of wavelet decomposition coefficients. Essentially in the manner of an inverse problem, high dimensional atmospheric turbulence data sets will be used to construct low-dimensional models (including analysis of low dimensional features based on Langevin stochastic differential equations and Tsallis entropy measures) to help estimate unknown underlying space-time-scale information in different systems. Extensive longitudinal data sets available over a hardwood forest, a pine forest, grassland and bare soil scapes will be investigated.Larger physical problems that this research will contribute to is a connection of high-frequency (seconds to minutes) forcings of turbulent atmospheric processes to their ecological and climate consequences which may in turn only revealed on time scales of years to decades.

这个CMG研究合作之间的statistican和大气微气象学家寻求进一步的解释和追求的一些基本问题，在该地区的湍流在大气边界层。具体而言，该方法将调查使用多尺度统计方法，强调理论成分，通过小波分解系数的分布分析。基本上以反问题的方式，高维大气湍流数据集将用于构建低维模型（包括基于Langevin随机微分方程和Tsallis熵测量的低维特征分析），以帮助估计不同系统中未知的潜在时空尺度信息。广泛的纵向数据集可在硬木林，松林，草地和裸露的土壤scapes.Larger物理问题，这项研究将有助于连接的高频（秒到分钟）强迫湍流大气过程的生态和气候的后果，这可能反过来只揭示了几年到几十年的时间尺度。