An Empirical Investigation of Scaling Behavior in the Atmospheric Turbulence for Understanding the Underlying Cascade Process

TL;DR

This paper empirically examines the scaling behaviors in atmospheric turbulence wind data, comparing them with cascade models, and finds that existing models cannot fully capture the observed scaling properties, offering insights into turbulence processes.

Contribution

It provides an empirical analysis of atmospheric turbulence scaling behaviors and evaluates the effectiveness of common cascade models, highlighting their limitations.

Findings

Both models describe PDF changes but fail to fit scaling behaviors simultaneously.

Scaling behaviors in atmospheric turbulence are not fully captured by standard cascade models.

The study offers new clues for understanding the cascade process in atmospheric turbulence.

Abstract

We study the scaling behaviors in the wind velocity time series collected at the atmospheric surface layer and compare them with two commonly used cascade models, the truncated stable distribution and the log-normal model. Results show that although both models can describe the change of probability density functions from non-Gaussian to Gaussian like distributions with the increase of time scale, they can not fit the scaling behaviors observed in the probability of return and in the moments at the same time. This work provides some clues on the understanding of cascade process in the atmospheric turbulence.