Published June 30, 2025
| Version 1.0
Project deliverable
Open
D6.2: Processing techniques
Creators
-
Christensen, Kai Håkon
(Work package leader)1
- Jamet, Quentin (Work package leader)2
- Beyaard, Lotta (Project member)3
- Pathak, Devanshi (Project member)3
- Gourves, Denis (Project member)2
- Reynaud, Stephane (Project member)2
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Capet, Arthur
(Project member)4
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Ricour, Florian
(Project member)5
- Yuan, Bing (Project member)6
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Chen, Wei
(Project member)7
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She, Jun
(Project member)8
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Frishfelds, Vilnis
(Project member)8
- Causio, Salvatore (Project member)9
- Verri, Giorgia (Project member)9
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FEDERICO, Ivan
(Project member)9
- Brajard, Julien (Project member)10
- Bernigaud, Antoine (Project member)10
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Staneva, Joanna
(Project leader)7
- Johnson, Kelli (Project manager)6
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Reyes Reyes, Emma
(Project member)11
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Meszaros, Lorinc
(Project member)3
-
1.
Norwegian Meteorological Institute
-
2.
Service Hydrographique et Océanographique de la Marine
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3.
Deltares
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4.
Royal Belgian Institute of Natural Sciences
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5.
Institute of Natural Sciences
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6.
Helmholtz-Zentrum Hereon GmbH
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7.
Helmholtz-Zentrum Hereon
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8.
Danish Meteorological Institute
-
9.
CMCC Foundation - Euro-Mediterranean Center on Climate Change
-
10.
Nansen Environmental and Remote Sensing Center
-
11.
Balearic Islands Coastal Observing and Forecasting System
Description
The FOCCUS project (https://foccus-project.eu/) aims to enhance coastal ocean observation and forecasting for Copernicus users by designing and implementing a range of high-resolution data products. This short report is meant to provide a software repository of algorithms which involve implementing new modeling techniques (AI and probabilistic approaches) and optimizing integrated systems. Brief descriptions of the work are given here to provide context, along with a complete list of the software/data delivered (with links). All software is listed in Table 4.1, and are referred to in the text according to their position in this
table.
Files
D6.2_Software_Repository_of_ML_and_DA_Algorithms.pdf
Files
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Additional details
Related works
- Is supplemented by
- Publication: 10.5281/zenodo.17064061 (DOI)
Funding
References
- Stainforth, D. A., Allen, M. R., Tredger, E. R., & Smith, L. A. (2007). Confidence, uncertainty and decision-support relevance in climate predictions. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857), 2145-2161.
- Brankart, J. M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P. A., ... & Verron, J. (2015). A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geoscientific Model Development, 8(5), 1285-1297.
- Bonaduce, A., & Raj, R. P. (2025). Sea-level and currents (Versión 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14825347
- Vergara, O., & Pujol, M.-I. (2025). Sea Surface Height for coastal applications obtained from Level-3 satellite altimetry (V1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14938132
- Yuan, B., Ricker, M., Chen, W., Jacob, B., Pham, N.T., Staneva, J. (2025): Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset. Ocean Engineering 329, 121100. https://doi.org/10.1016/j.oceaneng.2025.121100
- Yuan, B., Jacob, B., Chen, W,, & Staneva, J. (2024): Downscaling sea surface height and currents in coastal regions using convolutional neural network. Applied Ocean Research, Vol 151, 104153, https://doi.org/10.1016/j.apor.2024.104153