# Further reading

## Data Challenge & PPR Océan & Climat

- [PPR Océan & Climat project website](https://www.ocean-climat.fr/)
- [DC1 GitHub repository](https://github.com/ppr-ocean-ia/dc1-emulating-global-ocean)
- [DC2 GitHub repository](https://github.com/ppr-ocean-ia/dc2-forecasting-global-ocean-dynamics)
  *(sister challenge evaluating 3-D forecasts)*
- [dc-tools evaluation library](https://github.com/ocean-ai-data-challenges/dc-tools)

## Ocean reanalysis & emulation — key papers

- Lellouche, J.-M. et al. (2021). [The Copernicus Global 1/12° Oceanic and Sea Ice
  GLORYS12 Reanalysis](https://doi.org/10.3389/feart.2021.698876). *Frontiers in Earth Science*, 9.

- El Aouni, A. et al. (2024). [GLONET: Mercator's End-to-End Neural Forecasting
  System](https://doi.org/10.48550/arXiv.2412.05454). *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](https://doi.org/10.5194/gmd-9-3231-2016). *Geoscientific Model Development*, 9.

- Bota, P. V. et al. (2023). [Learning bias corrections for climate models using deep neural
  operators](https://doi.org/10.48550/arXiv.2302.03173). *arXiv preprint arXiv:2302.03173*.

- Wang, X. et al. (2024). [XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving
  Forecasting](https://doi.org/10.48550/arXiv.2402.02995). *arXiv preprint arXiv:2402.02995*.

- Fablet, R. et al. (2023). [Multimodal learning of ocean dynamics for short-term sea
  surface height forecasting](https://doi.org/10.5194/gmd-16-4671-2023).
  *Geoscientific Model Development*, 16.

- Ryan, A. G. et al. (2015). [GODAE OceanView Class 4 forecast verification framework:
  global ocean inter-comparison](https://doi.org/10.1080/1755876X.2015.1022330).
  *Journal of Operational Oceanography*, 8(sup1).

## Observation instruments

- Verron, J. et al. (2015). [The SARAL/AltiKa Altimetry Satellite Mission](https://doi.org/10.3390/rs7010039).
  *Remote Sensing*, 7(1).

- Durand, M. et al. (2010). [The Surface Water and Ocean Topography Mission: observing
  terrestrial surface water and oceanic submesoscale eddies](https://doi.org/10.1109/JPROC.2009.2030054).
  *Proceedings of the IEEE*, 98(5).

- Lambin, J. et al. (2010). [The OSTM/Jason-2 Mission](https://doi.org/10.5670/oceanog.2010.41).
  *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](https://doi.org/10.5670/oceanog.2009.36). *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](https://doi.org/10.1038/s42256-021-00374-3).
  *Nature Machine Intelligence*, 3.

- Bi, K. et al. (2023). [Accurate medium-range global weather forecasting with 3D neural
  networks](https://doi.org/10.1038/s41586-023-06185-3). *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](https://doi.org/10.1098/rsta.2020.0047).
  *Philosophical Transactions of the Royal Society A*, 379.