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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.