Open AccessNoise Reduction of Atmospheric Emitted Radiance Interferometer (AERI) Observations Using Principal Component Analysis
Author(s) -
David D. Turner,
Robert O. Knuteson,
Henry E. Revercomb,
Chaomei Lo,
R. G. Dedecker
Publication year - 2006
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech1906.1
Subject(s) - radiance , principal component analysis , remote sensing , noise (video) , environmental science , filter (signal processing) , mathematics , geology , statistics , computer science , artificial intelligence , image (mathematics) , computer vision
A principal component noise filter has been applied to ground-based high-spectral-resolution infrared radiance observations collected by the Atmospheric Emitted Radiance Interferometers (AERIs) deployed by the Atmospheric Radiation Measurement (ARM) program. The technique decomposes the radiance observations into their principal components, selects the ones that describe the most variance in the data, and reconstructs the data from these components. An empirical function developed for chemical analysis is utilized to determine the number of principal components to be used in the reconstruction of the data. Statistical analysis of the noise-filtered minus original radiance data, as well as side-by-side analysis of data from two AERI systems utilizing different temporal sampling, demonstrates the ability of the noise filter using this empirical function to retain most of the atmospheric signal above the AERI noise level in the filtered data. The noise filter is applied to data collected at ARM’s tropical, midlatitude, and Arctic sites, demonstrating that the random variability in the data is reduced by 5% to over 450%, depending on the spectral element and location of the instrument. A seasonal analysis of the number of principal components required by the noise filter for each site shows a strong seasonal dependence in the atmospheric variability at the Arctic and midlatitude sites but not at the tropical site.