Ocean Science
Ocean Science
OS
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/os-18-1221-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-1221-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Research article
 | 
25 Aug 2022
Research article |  | 25 Aug 2022

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo

Data sets

Gulf Stream Daily Temperature, Salinity and Mixed Layer Depth fields from Ocean Stratification network (OSnet). Etienne Pauthenet, Loïc Bachelot, Anne-Marie Tréguier, Kevin Balem, Guillaume Maze, Fabien Roquet, Ronan Fablet, and Pierre Tandeo https://doi.org/10.5281/zenodo.6011144

CTD data from Cruise 33AT20120419 M. McCartney https://cchdo.ucsd.edu/cruise/33AT20120419

Model code and software

OSnet Gulf Stream Etienne Pauthenet, Loïc Bachelot, Guillaume Maze, Kevin Balem https://github.com/euroargodev/OSnet-GulfStream

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Short summary
Temperature and salinity profiles are essential for studying the ocean’s stratification, but there are not enough of these data. Satellites are able to measure daily maps of the surface ocean. We train a machine to learn the link between the satellite data and the profiles in the Gulf Stream region. We can then use this link to predict profiles at the high resolution of the satellite maps. Our prediction is fast to compute and allows us to get profiles at any locations only from surface data.
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