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Applicability of Neural Networks to Etalon Fringe Filtering in Laser SpectrometersWe present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.
Document ID
20180002873
Document Type
Reprint (Version printed in journal)
Authors
Nicely, J. M. (Universities Space Research Association Columbia, MD, United States)
Hanisco, T. F. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Riris, H. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
May 16, 2018
Publication Date
May 1, 2018
Publication Information
Publication: Journal of Quantitative Spectroscopy & Radiative Transfer
Volume: 211
Subject Category
Instrumentation and Photography
Report/Patent Number
GSFC-E-DAA-TN55652
Funding Number(s)
CONTRACT_GRANT: NNH15CO48B
CONTRACT_GRANT: 0
Distribution Limits
Public
Copyright
Other
Keywords
diode laser spectroscopy
atmospheric retrievals
etalon fringes
Neural networks