Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations

Type Proceedings paper
Date 2020
Language English
Author(s) Beauchamp Maxime1, Fablet Ronan1, Ubelmann Clement2, Ballarotta Maxime3, Chapron BertrandORCID4
Affiliation(s) 1 : IMT Atlantique Bretagne Pays Loire, Brest, France.
2 : Ocean Next, Grenoble, France.
3 : Collecte Localisat Satellites CLS, Ramonville St Agne, France.
4 : IFREMER, Plouzane, France.
Meeting 10th International Conference on Climate Informatics (CI), ELECTR NETWORK, SEP 20-25, 2020
Source CI2020: Proceedings of the 10th International Conference on Climate InformaticsSeptember 2020. ISBN: 978-1-4503-8848-1. pp. 22–29
DOI 10.1145/3429309.3429313
Abstract Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on a small region, part of the GULFSTREAM and mainly driven by energetic mesoscale dynamics. Based on an Observation System Simulation Experiment (OSSE), we will use the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new NN-based end-to-end learning framework for the representation of spatio-temporal irregulary-sampled data. We evaluate how some of these methods are a significant improvements, particularly by catching up the small scales ranging up to 30-40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and (agreggated) along-track nadir observations.
Full Text
File Pages Size Access
Author's final draft 8 3 MB Open access
Corrigendum 7 3 MB Open access
Top of the page

How to cite 

Beauchamp Maxime, Fablet Ronan, Ubelmann Clement, Ballarotta Maxime, Chapron Bertrand (2020). Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations. CI2020: Proceedings of the 10th International Conference on Climate InformaticsSeptember 2020. ISBN: 978-1-4503-8848-1. pp. 22–29. https://archimer.ifremer.fr/doc/00739/85150/