Further reading
Data Challenge & PPR Océan & Climat
DC2 GitHub repository (sister challenge evaluating 3-D forecasts)
Ocean reanalysis & emulation — key papers
Lellouche, J.-M. et al. (2021). The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. Frontiers in Earth Science, 9.
El Aouni, A. et al. (2024). GLONET: Mercator’s End-to-End Neural Forecasting System. arXiv preprint arXiv:2412.05454.
Griffies, S. M. et al. (2016). OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project. Geoscientific Model Development, 9.
Bota, P. V. et al. (2023). Learning bias corrections for climate models using deep neural operators. arXiv preprint arXiv:2302.03173.
Wang, X. et al. (2024). XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting. arXiv preprint arXiv:2402.02995.
Fablet, R. et al. (2023). Multimodal learning of ocean dynamics for short-term sea surface height forecasting. Geoscientific Model Development, 16.
Ryan, A. G. et al. (2015). GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison. Journal of Operational Oceanography, 8(sup1).
Observation instruments
Verron, J. et al. (2015). The SARAL/AltiKa Altimetry Satellite Mission. Remote Sensing, 7(1).
Durand, M. et al. (2010). The Surface Water and Ocean Topography Mission: observing terrestrial surface water and oceanic submesoscale eddies. Proceedings of the IEEE, 98(5).
Lambin, J. et al. (2010). The OSTM/Jason-2 Mission. Oceanography, 23(3). (Jason-3 is its direct successor, same orbit geometry.)
Roemmich, D. et al. (2009). The Argo Program: Observing the global ocean with profiling floats. Oceanography, 22(2).
ML for ocean / climate
Irrgang, C. et al. (2021). Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nature Machine Intelligence, 3.
Bi, K. et al. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619. (Pangu-Weather — relevant for the neural-network forecasting approach context.)
Brajard, J. et al. (2021). Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A, 379.