Multi-Fractal Analysis of Nocturnal Boundary Layer Time Series from the Boulder Atmospheric Observatory

Abstract

Lime series from a nocturnal boundary layer are analyzed using fractal techniques. The behavior of the self-affine fractal dimension, D and, is found to drop during a gravity wave train and rise with turbulence. D and, is proposed as a time series conditional sampling criterion for distinguishing waves from turbulence. Only weak correlations are found between D and, bulk turbulence measures such as Brunt-Vaisala frequency, Richardson number and buoyancy length. The advantages of analysis over turbulent kinetic energy (TKE), its component variances, FFT spectra, and self-similar fractals are also discussed in terms of local versus global basis functions dimensional suitability, noise, algorithm complexity, and other factors. D and was found to be the only measure capable of reliably distinguishing the wave from turbulence. Fractals, Gravity waves, Turbulence, Boundary layer meteorology, Time series.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1992
Accession Number
ADA248347

Entities

People

  • Alex J. Decaria

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Boundary Layer
  • Buoyancy
  • Detectors
  • Energy
  • Energy Transfer
  • Frequency
  • Gravity Waves
  • Kinetic Energy
  • Layers
  • Mainframe Computers
  • Measurement
  • Meteorology
  • Observatories
  • Richardson Number
  • Spectra
  • Turbulence

Fields of Study

  • Physics

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Regression Analysis.
  • Wave Propagation and Nonlinear Chaotic Dynamics.