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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 13, issue 3
Geosci. Model Dev., 13, 1609–1622, 2020
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 1609–1622, 2020
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 27 Mar 2020

Model description paper | 27 Mar 2020

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Alexander Barth et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alexander Barth on behalf of the Authors (25 Feb 2020)  Author's response    Manuscript
ED: Publish as is (28 Feb 2020) by Patrick Jöckel
Publications Copernicus
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Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network....
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