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

被引:6
作者
Cutolo, Eugenio [1 ,2 ]
Pascual, Ananda [1 ]
Ruiz, Simon [1 ]
Zarokanellos, Nikolaos D. [2 ]
Fablet, Ronan [3 ]
机构
[1] IMEDEA CSIC UIB, Esporles, Spain
[2] Balear Isl Coastal Observing & Forecasting Syst SO, Palma De Mallorca, Spain
[3] IMT Atlantique, CNRS UMR Lab, STICC, INRIA Team Odyssey, Brest, France
关键词
deep-learning; ocean; remote-sensing; SST; SSH; gliders; OSSE; ASSIMILATION; TEMPERATURE; TRANSPORT; SALINITY; CTD;
D O I
10.3389/fmars.2024.1151868
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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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页数:13
相关论文
共 46 条
[1]   Spatial and Temporal Variability of the North Atlantic Eddy Field From Two Kilometric-Resolution Ocean Models [J].
Ajayi, Adekunle ;
Le Sommer, Julien ;
Chassignet, Eric ;
Molines, Jean-Marc ;
Xu, Xiaobiao ;
Albert, Aurelie ;
Cosme, Emmanuel .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2020, 125 (05)
[2]   Combining networks of drifting profiling floats and gliders for adaptive sampling of the Ocean [J].
Alvarez, Alberto ;
Garau, Bartolome ;
Caiti, Andrea .
PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10, 2007, :157-162
[3]   Up to What Extent Can We Characterize Ocean Eddies Using Present-Day Gridded Altimetric Products? [J].
Amores, Angel ;
Jorda, Gabriel ;
Arsouze, Thomas ;
Le Sommer, Julien .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2018, 123 (10) :7220-7236
[4]   Deep Data Assimilation: Integrating Deep Learning with Data Assimilation [J].
Arcucci, Rossella ;
Zhu, Jiangcheng ;
Hu, Shuang ;
Guo, Yi-Ke .
APPLIED SCIENCES-BASEL, 2021, 11 (03) :1-21
[5]  
ARNOLD CP, 1986, B AM METEOROL SOC, V67, P687, DOI 10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO
[6]  
2
[7]   On the resolutions of ocean altimetry maps [J].
Ballarotta, Maxime ;
Ubelmann, Clement ;
Pujol, Marie-Isabelle ;
Taburet, Guillaume ;
Fournier, Florent ;
Legeais, Jean-Francois ;
Faugere, Yannice ;
Delepoulle, Antoine ;
Chelton, Dudley ;
Dibarboure, Gerald ;
Picot, Nicolas .
OCEAN SCIENCE, 2019, 15 (04) :1091-1109
[8]   DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations [J].
Barth, Alexander ;
Alvera-Azcarate, Aida ;
Licer, Matjaz ;
Beckers, Jean-Marie .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2020, 13 (03) :1609-1622
[9]   Data assimilation in the geosciences: An overview of methods, issues, and perspectives [J].
Carrassi, Alberto ;
Bocquet, Marc ;
Bertino, Laurent ;
Evensen, Geir .
WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE, 2018, 9 (05)
[10]   Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM). [J].
Charantonis, Anastase Alexandre ;
Testor, Pierre ;
Mortier, Laurent ;
D'Ortenzio, Fabrizio ;
Thiria, Sylvie .
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE, 2015, 51 :2198-2206