Reduction of spatially structured errors in wide-swath altimetric satellite data using data assimilation

Metref, Sammy; Sommer, Julien Le; Poel, Nora; Brankart, Jean-Michel; Verron, Jacques; Navarro, Luis

doi:10.31223/osf.io/fcvb8

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The eddies are 2 times bigger in summer than in winter and their sizes vary geographical consistently with the latitudinal variation of the first Rossby radius of deformation. NATL60 has been used in several studies such as Metref et al (2019Metref et al ( , 2020 The SSH field of this NR is subsampled by realistic synthetic satellite constellations, simulating both nadir (conventional) and SWOT altimetry. Existing simulator tools (Gaultier et al 2016) are used to generate the datasets.…”

Section: Experimental Design a Datamentioning

confidence: 99%

Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model

Guillou

Metref

Ubelmann

et al. 2021

Journal of Atmospheric and Oceanic Technology

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During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields which are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standardDUACS products are created with an optimal interpolation method which is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution.The present paper makes the case that this temporal-spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging, to interpolate altimetric data while respecting quasigeostrophic dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadirs data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

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Section: Experimental Design a Datamentioning

confidence: 99%

Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model

Guillou

Metref

Ubelmann

et al. 2021

Journal of Atmospheric and Oceanic Technology

Self Cite

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“…We direct readers to this work for a detailed understanding of NATL60's capabilities. Additionally, numerous studies have employed the non-extended version of NATL60 for resolving fine-scale dynamical processes (Amores et al, 2018;Fresnay et al, 2018;Metref et al, 2019;Metref et al, 2020).…”

Section: Enatl60 Based Ossementioning

confidence: 99%

CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning

Cutolo,

Pascual,

Ruiz

et al. 2024

Front. Mar. Sci.

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Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for traditional interpolation techniques. To address this, we developed the CLuster Optimal Interpolation Neural Network (CLOINet), which combines the robust mathematical framework of the Optimal Interpolation (OI) scheme with a self-supervised clustering approach. CLOINet efficiently segments remote sensing images into clusters to reveal non-local correlations, thereby enhancing fine-scale oceanic reconstructions. We trained our network using outputs from an Ocean General Circulation Model (OGCM), which also facilitated various testing scenarios. Our Observing System Simulation Experiments aimed to reconstruct deep salinity fields using Sea Surface Temperature (SST) or Sea Surface Height (SSH), alongside sparse in-situ salinity observations. The results showcased a significant reduction in reconstruction error up to 40% and the ability to resolve scales 50% smaller compared to baseline OI techniques. Remarkably, even though CLOINet was trained exclusively on simulated data, it accurately reconstructed an unseen SST field using only glider temperature observations and satellite chlorophyll concentration data. This demonstrates how deep learning networks like CLOINet can potentially lead the integration of modeling and observational efforts in developing an ocean digital twin.

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Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

et al. 2020

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In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60 ∘ simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of 10 4 and 10 3 for summer and winter, respectively. This is encouraging for the processing of the future SWOT data.

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Reduction of spatially structured errors in wide-swath altimetric satellite data using data assimilation

Cited by 7 publications

References 19 publications

Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model

Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model

CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

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