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

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 A...

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Bibliographic Details
Published inarXiv.org
Main Authors M P J L Chang, Nazari, H, Font, C O, Gilbreath, G C, E Oh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.01.2007
Subjects
Autoregressive models
Empirical analysis
Measurement methods
Principal components analysis
Scintillation counters
Time series
Turbulence
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.0701238