Multimodal Unsupervised Spatio-Temporal Interpolation of Satellite Ocean Altimetry Maps

Archambault, Théo; Filoche, Arthur; Charantonnis, Anastase; Béréziat, Dominique

doi:10.5220/0011620100003417

Cited by 8 publications

(31 citation statements)

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“…However, it is also possible to train directly on observations, by applying the observation operator H ssh on the generated map Xssh before computing the loss (see Equations 5,6,7). Filoche et al (2022) performed the interpolation with SSH observations only, and, using the same principle, Archambault et al (2023) showed that it was possible to overfit SSH images starting from SST only and constraining on SSH observations. Both these methods are fitted on one (or a small number) of examples and must therefore be refitted in order to be applied to unseen data.…”

Section: Loss and Regularizationmentioning

confidence: 99%

“…In order to enhance the quality of the SSH reconstruction and sea surface current estimation, using additional physical information such as the Sea Surface Temperature (SST) has been demonstrated to be beneficial (Ciani et al, 2020;Thiria et al, 2023;S. A. Martin et al, 2023;Archambault et al, 2023;Fablet et al, 2023). SST motion is linked to ocean circulation (Isern-Fontanet et al, 2006), and therefore to SSH, as heat is transported by currents in an advection dynamic.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, Fablet et al (2021) performed an efficient supervised SSH interpolation on one year of OSSE data and extended their study using SST showing increased performance (Fablet et al, 2023). On the other hand, Archambault et al (2023); S. A. Martin et al (2023) trained a neural network using only observations.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite

Archambault,

Filoche,

Charantonis

et al. 2023

Preprint

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Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements of Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced by the altimetry community using linear Optimal Interpolations (OI) such as the widely-used Data Unification and Altimeter Combination System (DUACS). However, OI is known for producing overly smooth fields and thus misses some mesostructures and eddies. On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection. We design a realistic twin experiment to emulate the satellite observations of SSH and SST to evaluate interpolation methods. We introduce a deep learning network able to use SST information, and a trainable in two settings: one where we have no access to ground truth during training and one where it is accessible. Our investigation involves a comparative analysis of the aforementioned network when trained using either supervised or unsupervised loss functions. We assess the quality of SSH reconstructions and further evaluate the network’s performance in terms of eddy detection and physical properties. We find that it is possible, even in an unsupervised setting to use SST to improve reconstruction performance compared to SST-agnostic interpolations. We compare our reconstructions to DUACS’s and report a decrease of 41\% in terms of root mean squared error.

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Section: Loss and Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite

Archambault,

Filoche,

Charantonis

et al. 2023

Preprint

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“…Nardelli et al [7] use simulated data from the Mediterranean Sea [38] to reconstruct SSH and surface currents. In more recent developments, Archambault et al [41] and Martin et al [42,43] present training strategies for learning the SSH field without simulated data by computing loss only along the tracks of SSH observations. Self-supervised or unsupervised methods can help close the domain gap between simulations (often in precise geographical contexts) and real data at a global scale.…”

Section: Introductionmentioning

confidence: 99%

“…Estimation of currents from SST signatures has existed in the literature since the 1980s, including [44][45][46][47]. In the aforementioned studies [7,8,40,41], incorporating highresolution SST images notably enhances SSH reconstruction. However, the SST data are affected by clouds and so the models may not achieve strong performance when the SST product is inaccurate due to high levels of clouds.…”

Section: Introductionmentioning

confidence: 99%

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Kugusheva,

Bull,

Moschos

et al. 2024

Remote Sensing

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Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.

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Training Neural Mapping Schemes for Satellite Altimetry With Simulation Data

Febvre,

Le Sommer,

Ubelmann

et al. 2024

J Adv Model Earth Syst

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Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space‐time interpolation problems. However, the training of state‐of‐the‐art neural schemes on real‐world case‐studies is hindered by the sparse space‐time coverage of the sea surface of real altimetry data set. Here, we introduce an innovative approach that leverages state‐of‐the‐art ocean models to train simulation‐based neural schemes for the mapping of sea surface height and demonstrate their performance on real altimetry data sets. We analyze further how the ocean simulation data set used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy‐present configurations to eddy‐rich ones, forced simulations versus reanalyzes using data assimilation and tide‐free versus tide‐resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5‐altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation‐based 4DVarNets outperform the operational observation‐driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation data set used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy‐rich and tide‐free simulation data sets. It improves the resolved longitudinal scale from 151 km for DUACS and 241 km for GLORYS to 98 km and reduces the root mean square error by 23% and 61%. These results open research avenues for new synergies between ocean modeling and ocean observation using learning‐based approaches.

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Multimodal Unsupervised Spatio-Temporal Interpolation of Satellite Ocean Altimetry Maps

Cited by 8 publications

References 0 publications

Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite

Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Training Neural Mapping Schemes for Satellite Altimetry With Simulation Data

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