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: 0724088
• 负责人: Gabriel Katul
• 金额: $ 8.04万
• 依托单位: Duke University
• 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=0724088&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.

统计学家和大气微气象学家之间的 CMG 研究合作旨在进一步解释和探索大气边界层湍流领域的一些基本问题。具体来说，该方法将研究多尺度统计方法的使用，强调通过分析小波分解系数的分布得出的理论成分。本质上以反问题的方式，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 熵度量的低维特征分析），以帮助估计未知的基础时空-不同系统中的规模信息。将研究阔叶林、松林、草地和裸露土壤景观的广泛纵向数据集。这项研究将有助于解决的更大物理问题是将湍流大气过程的高频（秒到分钟）强迫与它们的生态和气候后果可能只能在几年到几十年的时间尺度上显现出来。