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

这项由一名统计学家和一名大气微气象学家合作进行的CMG研究旨在进一步解释和探讨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。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熵测度的低维特征分析），以帮助估计不同系统中未知的潜在时空尺度信息。将对阔叶林、松林、草地和裸露土壤景观的大量纵向数据集进行调查。这项研究将有助于解决的更大的物理问题是湍流大气过程的高频（秒到分钟）强迫与其生态和气候后果之间的联系，而这些后果可能只能在数年到数十年的时间尺度上揭示出来。

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.