Special Issue for the 54th International Liège Colloquium on Machine Learning and Data Analysis in Oceanography
Special Issue for the 54th International Liège Colloquium on Machine Learning and Data Analysis in Oceanography
Editor(s): Alexander Barth, Julien Brajard, Matjaz Licer, Xiaofeng Li, Ana Ruescas, and Aida Alvera-Azcárate
The 54th International Liège Colloquium has focused on the topic “machine learning and data analysis in oceanography”. Data-driven approaches to understand and predict the ocean dynamics have gained significant traction in recent years thanks to the increased number, coverage and quality of ocean observations; improved numerical ocean modeling; the availability of massively parallel computing devices (like graphical processing units); and the advancement of optimization schemes and machine learning (ML) models able to constrain high-dimensional and non-linear systems. Many potential applications of such statistical or ML models (sometimes in combination with dynamical modeling derived from first principles) have been developed. These applications include identifying patterns and features as well as regions with common dynamics and deriving related quantities from easily observed ones (such as estimation of subsurface dynamics from partial observations or chlorophyll concentration from radiances), infilling missing data in satellite observations and adaptive sampling methods guided by machine learning. The spatial and temporal scales from different observation systems have become more and more diverse, which requires new techniques and approaches to combine them in an optimal way for estimating the state of the ocean. Statistical or ML models can also be used to identify bad or questionable observations and can help in quality control and quality assessment of observational data products. Data-driven techniques also have a high potential to complement classical numerical modeling as they allow new ways to represent subgrid parameterization, surrogate models, optimal ensemble predictions, and new and efficient approaches for stochastic modeling and data assimilation to be proposed. While many recent advancements are within machine learning applications, progress in more traditional data-driven analysis techniques such as optimal interpolation, variational analysis and Kalman Filtering, among others, is also within the scope of this special issue. We welcome all submissions that fit this description.

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18 Aug 2023
Machine learning methods to predict Sea Surface Temperature and Marine Heatwave occurrence: a case study of the Mediterranean Sea
Giulia Bonino, Giuliano Galimberti, Simona Masina, Ronan McAdam, and Emanuela Clementi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1847,https://doi.org/10.5194/egusphere-2023-1847, 2023
Preprint under review for OS (discussion: open, 1 comment)
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19 Jul 2023
Combining Neural Networks and Data Assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model
Carolina Amadio, Anna Teruzzi, Gloria Pietropolli, Luca Manzoni, Gianluca Coidessa, and Gianpiero Cossarini
EGUsphere, https://doi.org/10.5194/egusphere-2023-1588,https://doi.org/10.5194/egusphere-2023-1588, 2023
Preprint under review for OS (discussion: open, 1 comment)
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18 Jul 2023
MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion
Edwin Goh, Alice R. Yepremyan, Jinbo Wang, and Brian Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1385,https://doi.org/10.5194/egusphere-2023-1385, 2023
Preprint under review for OS (discussion: open, 0 comments)
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12 Jul 2023
Unsupervised classification of the Northwestern European seas based on satellite altimetry data
Lea Poropat, Dan(i) Jones, Simon D. A. Thomas, and Céline Heuzé
EGUsphere, https://doi.org/10.5194/egusphere-2023-1468,https://doi.org/10.5194/egusphere-2023-1468, 2023
Preprint under review for OS (discussion: open, 2 comments)
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05 Jul 2023
Eddy-induced Chlorophyll-a Variations in the Northern Indian Ocean: A Study Using Multi-Source Satellite Data and Deep Learning
Yingjie Liu and Xiaofeng Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-1440,https://doi.org/10.5194/egusphere-2023-1440, 2023
Revised manuscript under review for OS (discussion: final response, 8 comments)
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