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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2022
Research article |
| 25 Aug 2022
| 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
CORRESPONDING AUTHOR
Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Loïc Bachelot
Ifremer, Univ. Brest, CNRS, IRD, Service Ingénierie des Systèmes d'Information (PDG-IRSI-ISI), IUEM, 29280, Plouzané, France
Kevin Balem
Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Guillaume Maze
Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Anne-Marie Tréguier
Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Fabien Roquet
Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
Ronan Fablet
IMT Atlantique, CNRS UMR Lab-STICC, Brest, France
Pierre Tandeo
IMT Atlantique, CNRS UMR Lab-STICC, Brest, France
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Cited
14 citations as recorded by crossref.
- Reconstruction of subsurface ocean state variables using Convolutional Neural Networks with combined satellite and in situ data P. Smith et al. 10.3389/fmars.2023.1218514
- Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model J. Zhang et al. 10.1016/j.ocemod.2023.102232
- Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model Y. Zhao et al. 10.1016/j.dsr.2023.104179
- CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. 10.3389/fmars.2024.1151868
- Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning H. Su et al. 10.1080/17538947.2024.2332374
- 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. 10.5194/gmd-16-2119-2023
- Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model J. Qi et al. 10.3389/fmars.2023.1181182
- Anthropogenic carbon pathways towards the North Atlantic interior revealed by Argo-O2, neural networks and back-calculations R. Asselot et al. 10.1038/s41467-024-46074-5
- Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks T. Xu et al. 10.3390/jmse11071352
- Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach J. Qi et al. 10.1016/j.dynatmoce.2023.101403
- Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring S. Mukonza & J. Chiang 10.3390/environments10100170
- Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
- Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements B. Champenois & T. Sapsis 10.1016/j.physd.2023.134026
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14 citations as recorded by crossref.
- Reconstruction of subsurface ocean state variables using Convolutional Neural Networks with combined satellite and in situ data P. Smith et al. 10.3389/fmars.2023.1218514
- Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model J. Zhang et al. 10.1016/j.ocemod.2023.102232
- Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model Y. Zhao et al. 10.1016/j.dsr.2023.104179
- CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. 10.3389/fmars.2024.1151868
- Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning H. Su et al. 10.1080/17538947.2024.2332374
- 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. 10.5194/gmd-16-2119-2023
- Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model J. Qi et al. 10.3389/fmars.2023.1181182
- Anthropogenic carbon pathways towards the North Atlantic interior revealed by Argo-O2, neural networks and back-calculations R. Asselot et al. 10.1038/s41467-024-46074-5
- Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks T. Xu et al. 10.3390/jmse11071352
- Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach J. Qi et al. 10.1016/j.dynatmoce.2023.101403
- Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring S. Mukonza & J. Chiang 10.3390/environments10100170
- Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
- Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements B. Champenois & T. Sapsis 10.1016/j.physd.2023.134026
- A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. 10.3389/fmars.2023.1077623
Latest update: 29 Jun 2024
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.
Temperature and salinity profiles are essential for studying the ocean’s stratification, but...
