Intercomparison of Data-Driven and Learning-Based Interpolations of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations

• Published in: Remote Sensing
• Main Authors: Maxime Beauchamp, Ronan Fablet, Clément Ubelmann, Maxime Ballarotta, Bertrand Chapron
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: data-driven and learning-based approaches; interpolation; benchmarking; Nadir and SWOT altimetric satellite data; sea surface height (SSH); Science; Q; North Atlantic
• Online Access: https://doi.org/10.3390/rs12223806; https://doaj.org/article/06b4e100ec6a4fc1967834d828f13a42

Over the last few years, a very active field of research has aimed 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 two small <math display="inline"><semantics><mrow><mn>10</mn><mo>°</mo><mo>×</mo><mn>10</mn><mo>°</mo></mrow></semantics></math> GULFSTREAM and <math display="inline"><semantics><mrow><mn>8</mn><mo>°</mo><mo>×</mo><mn>10</mn><mo>°</mo></mrow></semantics></math> OSMOSIS regions, part of the North Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale dynamics, while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on observation system simulation experiments (OSSE), we used a 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 (Surface Water Ocean Topography) mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new neural networks-based end-to-end learning framework for the representation of spatio-temporal irregularly-sampled data. The main objective of this paper consists of providing a thorough intercomparison exercise with appropriate benchmarking metrics to assess whether ...