Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.: On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109,
https://doi.org/10.5194/os-15-1091-2019, 2019.
a
Ballarotta, M., Ubelmann, C., Rogé, M., Fournier, F., Faugère, Y.,
Dibarboure, G., Morrow, R., and Picot, N.: Dynamic Mapping of Along-Track
Ocean Altimetry: Performance from Real Observations, J. Atmos.
Ocean. Tech., 37, 1593–1601,
https://doi.org/10.1175/JTECH-D-20-0030.1,
2020.
a,
b
Barbastathis, G., Ozcan, A., and Situ, G.: On the use of deep learning for
computational imaging, Optica, 6, 921–943,
https://doi.org/10.1364/OPTICA.6.000921,
2019.
a
Barth, A., Alvera-Azcárate, A., Licer, M., and Beckers, J.-M.: DINCAE 1.0: a
convolutional neural network with error estimates to reconstruct sea surface
temperature satellite observations, Geoscientific Model Development
Discussions, 2019, 1–21,
https://doi.org/10.5194/gmd-2019-128, 2019.
a,
b
Beauchamp, M.: GF (four nadirs) application of 4DVarNet-SSH, Youtube [video],
https://youtube.com/shorts/QKXukB_Rd5E, last access: 26 August 2022a. a
Beauchamp, M.:
GF (one SWOT plus four nadirs) application of 4DVarNet-SSH, Youtube [video],
https://youtube.com/shorts/i91Z1pMm4gY,
last access: 26 August 2022b. a
Beauchamp, M.:
OSMOSIS (four nadirs) application of 4DVarNet-SSH, Youtube [video],
https://youtube.com/shorts/Pxcsd0Afco0,
last access: 26 August 2022c. a
Beauchamp, M.: OSMOSIS (one SWOT plus four nadirs) application of 4DVarNet-SSH, Youtube [video],
https://youtube.com/shorts/HbVSJFtdG6Q,
last access: 26 August 2022d. a
Beauchamp, M., Fablet, R., Ubelmann, C., Ballarotta, M., and Chapron, B.:
Intercomparison of Data-Driven and Learning-Based Interpolations of
Along-Track Nadir and Wide-Swath SWOT Altimetry Observations, Remote Sensing,
12, 3806,
https://doi.org/10.3390/rs12223806, 2020.
a,
b,
c,
d,
e
Beauchamp, M., Amar, M. M., Febvre, Q., and Fablet, R.: End-to-End Learning of
Variational Interpolation Schemes for Satellite-Derived SSH Data, in: 2021
IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, 7418–7421,
https://doi.org/10.1109/IGARSS47720.2021.9554800, 2021.
a,
b
Beauchamp, M., Thompson, J., Georgenthum, H., Febvre, Q., and Fablet, R.:
Learning Neural Optimal Interpolation Models and Solvers, arXiv [preprint],
https://doi.org/10.48550/ARXIV.2211.07209, 2022.
a,
b
Beckers, J. M. and Rixen, M.: EOF Calculations and Data Filling from
Incomplete Oceanographic Datasets, J. Atmos. Ocean.
Tech., 20, 1839–1856, 2003b. a
Benkiran, M., Ruggiero, G., Greiner, E., Le Traon, P.-Y., Rémy, E., Lellouche,
J. M., Bourdallé-Badie, R., Drillet, Y., and Tchonang, B.: Assessing the
Impact of the Assimilation of SWOT Observations in a Global High-Resolution
Analysis and Forecasting System Part 1: Methods, Front. Mar. Sci.,
8,
https://doi.org/10.3389/fmars.2021.691955, 2021.
a,
b
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in
the geosciences: An overview of methods, issues, and perspectives, WIREs
Clim. Change, 9, e535,
https://doi.org/10.1002/wcc.535, 2018.
a,
b
Chelton, D. B., Ries, J., Haines, B. J., Fu, L.-L., and Callahan, P. S.:
Satellite Altimetry, in: International Geophysics, edited by: Cazenave, A.
and Fu, L.-L., vol. 69 of Satellite Altimetry and Earth SciencesA
Handbook of Techniques and Applications, 1–ii, Academic Press,
http://www.sciencedirect.com/science/article/pii/S0074614201801467 (last access: 11 April 2023),
2001. a
Delepoulle, A., evanmason, Clément, CoriPegliasco, Capet, A., Troupin, C., and Koldunov, N.:
AntSimi/py-eddy-tracker: v3.6.1,
Zenodo [code],
https://doi.org/10.5281/zenodo.7197432,
2022.
a
Dufau, C., Orsztynowicz, M., Dibarboure, G., Morrow, R., and Le Traon, P.-Y.:
Mesoscale resolution capability of altimetry: Present and future, J.
Geophys. Res.-Oceans, 121, 4910–4927,
https://doi.org/10.1002/2015JC010904,
2016.
a
Esteban-Fernandez, D.: SWOT project mission performance and error budget
document, Tech. rep., JPL D-79084, NASA, 2014. a
Fablet, R. and Chapron, B.: Multimodal learning-based inversion models for the
space-time reconstruction of satellite-derived geophysical fields, ArXiv [preprint],
https://doi.org/10.48550/ARXIV.2203.10640, 2022.
a,
b,
c
Fablet, R., Drumetz, L., and Rousseau, F.: End-to-end learning of optimal
interpolators for geophysical dynamics, CI 2019: 9th International Workshop on Climate Informatics, Paris, France,
https://imt-atlantique.hal.science/hal-02285701 (last access: 11 April 2023), 2019. a
Fablet, R., Drumetz, L., and Rousseau, F.: Joint learning of variational
representations and solvers for inverse problems with partially-observed
data, arXiv [preprint],
https://doi.org/10.48550/arXiv.2006.03653, 2020.
a,
b
Fablet, R., Beauchamp, M., Drumetz, L., and Rousseau, F.: Joint Interpolation
and Representation Learning for Irregularly Sampled Satellite-Derived
Geophysical Fields, Front. Appl. Math. Stat., 7, 655224,
https://doi.org/10.3389/fams.2021.655224, 2021.
a,
b,
c,
d
Febvre, Q., Fablet, R., Sommer, J. L., and Ubelmann, C.: Joint Calibration and
Mapping of Satellite Altimetry Data Using Trainable Variational Models, in:
ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), 1536–1540,
https://doi.org/10.1109/ICASSP43922.2022.9746889, 2022.
a
Gaultier, L., Ubelmann, C., and Fu, L.-L.: The Challenge of Using Future
SWOT Data for Oceanic Field Reconstruction, J. Atmos.
Ocean. Tech., 33, 119–126,
https://doi.org/10.1175/JTECH-D-15-0160.1, 2015.
a,
b
hgeorgenthum, Febvre, Q., maxbeauchamp, Fablet, R., Carpentier, B., and MMAMAR: CIA-Oceanix/4dvarnet-core: Release for 4DVarNet-MM-SSH code (4dvarnet-mm-ssh-tgrs-2022), Zenodo [code],
https://doi.org/10.5281/zenodo.7503266, 2023.
a,
b,
c
Le Guillou, F., Metref, S., Cosme, E., Ubelmann, C., Ballarotta, M., Verron,
J., and Le Sommer, J.: Mapping altimetry in the forthcoming SWOT era by
back-and-forth nudging a one-layer quasi-geostrophic model, Earth and Space
Science Open Archive [preprint], p. 15,
https://doi.org/10.1002/essoar.10504575.1, 2020.
a,
b,
c,
d,
e,
f
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system, Ocean Sci., 14, 1093–1126,
https://doi.org/10.5194/os-14-1093-2018, 2018.
a
Lguensat, R., Tandeo, P., Aillot, P., and Fablet, R.: The Analog Data
Assimilation, Mon. Weather Rev., 145, 4093–4107,
https://doi.org/10.1175/MWR-D-16-0441.1, 2017.
a,
b,
c
Li, Z., Archer, M., Wang, J., and Fu, L.-L.: Formulation and demonstration of an extended-3DVAR multi-scale data assimilation system for the SWOT altimetry era, Ocean Sci. Discuss. [preprint],
https://doi.org/10.5194/os-2021-89, 2021.
a
Lopez-Radcenco, M., Pascual, A., Gomez-Navarro, L., Aissa-El-Bey, A., Chapron,
B., and Fablet, R.: Analog Data Assimilation of Along-Track Nadir
and Wide-Swath SWOT Altimetry Observations in the Western
Mediterranean Sea, IEEE J. Sel. Top. Appl., 12, 2530–2540,
https://doi.org/10.1109/JSTARS.2019.2903941, 2019.
a
Manucharyan, G. E., Siegelman, L., and Klein, P.: A Deep Learning Approach to
Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep
Currents in Geostrophic Ocean Turbulence, J. Adv. Model.
Earth Sy., 13, e2019MS001965,
https://doi.org/10.1029/2019MS001965, 2021.
a
Mason, E., Pascual, A., and McWilliams, J. C.: A New Sea Surface Height–Based Code for
Oceanic Mesoscale Eddy Tracking, J. Atmos. Ocean.
Tech., 31, 1181–1188,
https://doi.org/10.1175/JTECH-D-14-00019.1, 2014.
a
Metref, S., Cosme, E., Le Guillou, F., Le Sommer, J., Brankart, J.-M., and
Verron, J.: Wide-Swath Altimetric Satellite Data Assimilation With
Correlated-Error Reduction, Front. Mar. Sci., 6, 822,
https://doi.org/10.3389/fmars.2019.00822, 2020.
a
Ngodock, H., Carrier, M., Souopgui, I., Smith, S., Martin, P., Muscarella, P.,
and Jacobs, G.: On the direct assimilation of along-track sea-surface height
observations into a free-surface ocean model using a weak constraints
four-dimensional variational (4D-Var) method, Q. J. Roy.
Meteor. Soc., 142, 1160–1170,
https://doi.org/10.1002/qj.2721, 2015.
a
Pauthenet, E., Bachelot, L., Balem, K., Maze, G., Tréguier, A.-M., Roquet, F., Fablet, R., and Tandeo, P.: Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks, Ocean Sci., 18, 1221–1244,
https://doi.org/10.5194/os-18-1221-2022, 2022.
a
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and WOO, W.-C.:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation
Nowcasting, in: Advances in Neural Information Processing Systems, edited by:
Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R., vol. 28,
Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf (last access: 11 April 2023),
2015.
a
Taburet, G., Sanchez-Roman, A., Ballarotta, M., Pujol, M.-I., Legeais, J.-F., Fournier, F., Faugere, Y., and Dibarboure, G.: DUACS DT2018: 25 years of reprocessed sea level altimetry products, Ocean Sci., 15, 1207–1224,
https://doi.org/10.5194/os-15-1207-2019, 2019.
a,
b,
c,
d
Tandeo, P., Ailliot, P., Bocquet, M., Carrassi, A., Miyoshi, T., Pulido, M.,
and Zhen, Y.: A Review of Innovation-Based Methods to Jointly Estimate Model
and Observation Error Covariance Matrices in Ensemble Data Assimilation,
Mon. Weather Rev., 148, 3973–3994,
https://doi.org/10.1175/mwr-d-19-0240.1, 2020.
a,
b
Ubelmann, C., Cornuelle, B., and Fu, L.-L.: Dynamic Mapping of Along-Track
Ocean Altimetry: Method and Performance from Observing System Simulation
Experiments, J. Atmos. Ocean. Tech., 33, 1691–1699,
https://doi.org/10.1175/JTECH-D-15-0163.1, 2016.
a,
b
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, L., and Polosukhin, I.: Attention is All you Need, in: Advances in
Neural Information Processing Systems, edited by: Guyon, I., Luxburg, U. V.,
Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R.,
vol. 30, Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (last access: 11 April 2023),
2017. a
Vient, J.-M., Fablet, R., Jourdin, F., and Delacourt, C.: End-to-End Neural
Interpolation of Satellite-Derived Sea Surface Suspended Sediment
Concentrations, Remote Sensing, 14, 4024,
https://doi.org/10.3390/rs14164024, 2022.
a
