Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7349-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Aug 2026
Development and technical paper |
| 07 Aug 2026
| 07 Aug 2026
A continuous implicit neural representation framework with gradient regularization for sea surface height reconstruction from satellite altimetry
Related authors
Geosci. Model Dev., 16, 2939–2955, 2023
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Ajayi, A., Le Sommer, J., Chassignet, E., Molines, J.-M., Xu, X., Albert, A., and Cosme, E.: Spatial and temporal variability of the North Atlantic eddy field from two kilometric-resolution ocean models, J. Geophys. Res.-Oceans, 125, e2019JC015827, https://doi.org/10.1029/2019JC015827, 2020. a
Beauchamp, M., Febvre, Q., Georgenthum, H., and Fablet, R.: 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry, Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023, 2023 a
Camargo, C. M. L., Riva, R. E. M., Hermans, T. H. J., Schütt, E. M., Marcos, M., Hernandez-Carrasco, I., and Slangen, A. B. A.: Regionalizing the sea-level budget with machine learning techniques, Ocean Sci., 19, 17–41, https://doi.org/10.5194/os-19-17-2023, 2023. a
Denvil-Sommer, A., Buitenhuis, E. T., Kiko, R., Lombard, F., Guidi, L., and Le Quéré, C.: Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning, Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, 2023. a
Fablet, R., Beauchamp, M., Drumetz, L., and Rousseau, F.: Joint interpolation and representation learning for irregularly sampled satellite-derived geophysical fields, Frontiers in Applied Mathematics and Statistics, 7, 655224, https://doi.org/10.3389/fams.2021.655224, 2021. a
