Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2183-2022
https://doi.org/10.5194/gmd-15-2183-2022
Development and technical paper
 | 
15 Mar 2022
Development and technical paper |  | 15 Mar 2022

DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations

Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers

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Latest update: 13 Jul 2024
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Short summary
Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.