## 1 Introduction  
Thanks to the ocean surface remote sensing data acquired by different altimetric missions (TOPEX/Poseidon, ERS-1, ERS-2, Geosat Follow-On, Jason-1, Envisat and OSTM/Jason-2), our understanding of the ocean circulation has been considerably improved over the last few decades. However, currently, the range of scales over 150 km remains inaccessible to altimetric-derived products because of the limited number of altimetric missions and their spatio-temporal sampling [1]. In this context, a very active field of research now consists of taking advantage of the large amount of data and large number numerical simulations available to overcome these limits of conventional altimetric products, which motivate complementary developments combining high resolution remote sensing and numerical simulations.  
Over the lastfew years, purely data-driven and artificial intelligence (AI)-based algorithms have been proposed [2; 3; 4; 5; 6] to deal with problems directly related to data assimilation and operational oceanography. More specifically, promising preliminary results have been seen for the sea surface reconstruction and prediction from partial and noisy satellite observations.  
In this paper, we propose an intercomparison exercise of several data-driven and learning-based approaches to help with the reconstruction of altimetric fields. As a baseline the DUACS operational processing tool based on well established optimal interpolation (OI) techniques will be considered [7]. In Section 2, we present the case study and its dataset, developed within the BOOST-SWOT project framework ([[https://meom-group.github.io/projects/boost-swot](https://meom-group.github.io/projects/boost-swot)]([https://meom-group.github.io/projects/boost-swot](https://meom-group.github.io/projects/boost-swot))): the NATL60 high resolution deterministic ocean simulation of the North Atlantic [8] is used as a reference to simulate sea surface height (SSH) along-track observations collected by four nadir, which is typically representative of the current observational altimetric capabilities. As an additional feature for the upcoming 2021 SWOT mission, pseudo-SWOT wide-swath observations also following realistic orbits were generated based on the NATL60 simulation. In Section 3, we present the data-driven approaches used in the intercomparison: (1) AnDA, a purely data-driven data assimilation scheme combining a patch-based analog forecasting operator with Kalman-based ensemble data assimilation; (2) VE-DINEOF, an EOF-based iterative method to interpolate in space and time the missing data; and (3) learning-based innovative end-to-end learning techniques that aim to learn jointly the neural network (NN) representation of the dynamics coupled with a NN-based solver of the targeted minimization problem. In Section 4, we provide a detailed evaluation of the results obtained over two small regions, GULFSTREAM and OSMOSIS, both parts of the North Atlantic basin and labeled with very different energetic dynamics. The GULFSTREAM area is mainly driven by mesoscale processes with large eddies, and high energy levels associated with high temporal variability. On the other hand, OSMOSIS is less energetic and the SSH spatial gradient on this domain lets finer structures appear at scales \(<\)100 km, making its reconstruction challenging too. Regarding the SWOT sampling over the two domains, it is regular in OSMOSIS with daily SWOT observations available, whereas the GULFSTREAM region can have several consecutive days without any SWOT observations.  
Last, a discussion based on the evaluation was engaged in to give synthetic key results and additional insights for future related works.