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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 12, issue 4
Atmos. Meas. Tech., 12, 2567–2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 2567–2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 May 2019

Research article | 02 May 2019

Neural network radiative transfer for imaging spectroscopy

Brian D. Bue et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (25 Mar 2019)  Author's response
ED: Publish subject to technical corrections (26 Mar 2019) by Lars Hoffmann
AR by Brian Bue on behalf of the Authors (27 Mar 2019)  Author's response    Manuscript
Post-review adjustments
AA: Author's adjustment | EA: Editor approval
AA by Brian Bue on behalf of the Authors (17 Apr 2019)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Apr 2019) by Lars Hoffmann
Publications Copernicus
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Short summary
Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere. These measurements rely on computationally expensive radiative transfer models (RTMs). Spectrometers produce too much data to process with RTMs directly, requiring approximations that trade accuracy for speed. We demonstrate that neural networks can quickly emulate RTM calculations more accurately than current approaches, enabling the application of more sophisticated RTMs than current methods permit.
Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere....
Citation