Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2119-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2119-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Apr 2023
Development and technical paper |
| 19 Apr 2023
| 19 Apr 2023
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
Maxime Beauchamp
CORRESPONDING AUTHOR
IMT Atlantique Bretagne-Pays de la Loire, 655 Av. du Technopôle, 29280 Plouzané, France
Quentin Febvre
IMT Atlantique Bretagne-Pays de la Loire, 655 Av. du Technopôle, 29280 Plouzané, France
Hugo Georgenthum
IMT Atlantique Bretagne-Pays de la Loire, 655 Av. du Technopôle, 29280 Plouzané, France
Ronan Fablet
IMT Atlantique Bretagne-Pays de la Loire, 655 Av. du Technopôle, 29280 Plouzané, France
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Cited
15 citations as recorded by crossref.
- Improved Surface Currents from Altimeter-Derived and Sea Surface Temperature Observations: Application to the North Atlantic Ocean D. Ciani et al. 10.3390/rs16040640
- Evaluation of the sub-annual sea level anomalies in the continental shelf of the Southwestern Atlantic and their relation to wind variability M. Juhl et al. 10.1007/s10236-024-01621-y
- Spatially-distributed parameter identification by physics-informed neural networks illustrated on the 2D shallow-water equations H. Boulenc et al. 10.1088/1361-6420/adb0e7
- Regional daily sea level maps from Multi-mission Altimetry using Space–time Window Kriging M. Juhl et al. 10.1016/j.asr.2025.04.014
- Unsupervised super-resolution data assimilation using conditional variational autoencoders with estimating background covariances via super-resolution Y. Yasuda & R. Onishi 10.1063/5.0263474
- Local Ocean Wave Field Estimation Using a Deep Generative Model of Wave Buoys P. Han et al. 10.1109/TGRS.2023.3334304
- Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms D. Ciani et al. 10.5194/os-21-199-2025
- Scale-Aware Neural Calibration for Wide Swath Altimetry Observations Q. Febvre et al. 10.1109/TGRS.2024.3363503
- Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. 10.1109/TGRS.2023.3268006
- Integrating wide-swath altimetry data into Level-4 multi-mission maps M. Ballarotta et al. 10.5194/os-21-63-2025
- VarDyn: Dynamical Joint‐Reconstructions of Sea Surface Height and Temperature From Multi‐Sensor Satellite Observations F. Le Guillou et al. 10.1029/2024MS004689
- Predicting particle catchment areas of deep-ocean sediment traps using machine learning T. Picard et al. 10.5194/os-20-1149-2024
- Multimodal learning–based reconstruction of high-resolution spatial wind speed fields M. Zambra et al. 10.1017/eds.2024.34
- Block-Circulant Approximation of the Precision Matrix for Assimilating SWOT Altimetry Data M. Yaremchuk et al. 10.3390/rs16111954
- Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi 10.1029/2023MS003658
14 citations as recorded by crossref.
- Improved Surface Currents from Altimeter-Derived and Sea Surface Temperature Observations: Application to the North Atlantic Ocean D. Ciani et al. 10.3390/rs16040640
- Evaluation of the sub-annual sea level anomalies in the continental shelf of the Southwestern Atlantic and their relation to wind variability M. Juhl et al. 10.1007/s10236-024-01621-y
- Spatially-distributed parameter identification by physics-informed neural networks illustrated on the 2D shallow-water equations H. Boulenc et al. 10.1088/1361-6420/adb0e7
- Regional daily sea level maps from Multi-mission Altimetry using Space–time Window Kriging M. Juhl et al. 10.1016/j.asr.2025.04.014
- Unsupervised super-resolution data assimilation using conditional variational autoencoders with estimating background covariances via super-resolution Y. Yasuda & R. Onishi 10.1063/5.0263474
- Local Ocean Wave Field Estimation Using a Deep Generative Model of Wave Buoys P. Han et al. 10.1109/TGRS.2023.3334304
- Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms D. Ciani et al. 10.5194/os-21-199-2025
- Scale-Aware Neural Calibration for Wide Swath Altimetry Observations Q. Febvre et al. 10.1109/TGRS.2024.3363503
- Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. 10.1109/TGRS.2023.3268006
- Integrating wide-swath altimetry data into Level-4 multi-mission maps M. Ballarotta et al. 10.5194/os-21-63-2025
- VarDyn: Dynamical Joint‐Reconstructions of Sea Surface Height and Temperature From Multi‐Sensor Satellite Observations F. Le Guillou et al. 10.1029/2024MS004689
- Predicting particle catchment areas of deep-ocean sediment traps using machine learning T. Picard et al. 10.5194/os-20-1149-2024
- Multimodal learning–based reconstruction of high-resolution spatial wind speed fields M. Zambra et al. 10.1017/eds.2024.34
- Block-Circulant Approximation of the Precision Matrix for Assimilating SWOT Altimetry Data M. Yaremchuk et al. 10.3390/rs16111954
Latest update: 18 May 2025
Short summary
4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class of algorithms can be used to provide efficient reconstructions of a dynamical system based on single observations. We provide a 4DVarNet application to sea surface height reconstructions based on nadir and future Surface Water and Ocean and Topography data. It outperforms other methods, from optimal interpolation to sophisticated DA algorithms. This work is part of on-going AI Chair Oceanix projects.
4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class...
