Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods

Abstract

Measurements of optical turbulence time series data using unattended instruments over long time intervals inevitably lead to data drop-outs or degraded signals. We present a comparison of methods using both Principal Component Analysis, which is also known as the Karhunen-Loeve decomposition, and ARIMA that seek to correct for these event-induced and mechanically-induced signal drop-outs and degradations. We report on the quality of the correction by examining the Intrinsic Mode Functions generated by Empirical Mode Decomposition. The data studied are optical turbulence parameter time series from a commercial long path length optical anemometer/scintillometer, measured over several hundred metres in outdoor environments.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA472169

Entities

People

  • C. O. Font
  • E. Oh
  • G. C. Gilbreath
  • H. Nazari
  • J. L. Chang

Organizations

  • University of Puerto Rico at Mayaguez

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Atmospheric Motion
  • Data Analysis
  • Data Science
  • Data Sets
  • Decomposition
  • Eigenvalues
  • Environment
  • Factor Analysis
  • Information Science
  • Instrumentation
  • Intervals
  • Measurement
  • Physics
  • Puerto Rico
  • Statistics
  • Time Intervals
  • Turbulence

Fields of Study

  • Physics

Readers

  • Educational Psychology
  • Radar Systems Engineering.
  • Statistical inference.