•  
  •  
 

Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

ocean data assimilation, ocean data reanalysis, ocean observation, ocean state estimation, ocean numerical simulation

Document Type

Precision Support Technology for Marine Environment

Abstract

Ocean data reanalysis (ODR) is a technique that combines ocean observational data with ocean circulation models to produce gridded data with higher accuracy and greater spatiotemporal coverage. The generated reanalysis data serve as a foundational dataset for conducting oceanographic research and developing artificial intelligence-based large ocean models. This study reviews the technical approaches of ODR, the current status of global and regional ocean data reanalysis systems developed in the world, and puts forward recommendations for the development of China's ocean reanalysis system.

First page

120

Last Page

129

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1. Stammer D, Balmaseda M, Heimbach P, et al. Ocean data assimilation in support of climate applications: Status and perspectives. Annual Review of Marine Science, 2016, 8: 491-518.

2. Jean-Michel L, Eric G, Romain B B, et al. The Copernicus global 1/12° oceanic and sea ice GLORYS12 reanalysis. Frontiers in Earth Science, 2021, 9: 698876.

3. Fox D N, Barron C N, Carnes M R, et al. The modular ocean data assimilation system. Oceanography, 2002, 15(1): 22-28.

4. Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999-2049.

5. Kalman R E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 1960, 82(1): 35-45.

6. Stammer D, Wunsch C, Fukumori I, et al. State estimation improves prospects for ocean research. Eos, Transactions American Geophysical Union, 2002, 83(27): 289-295.

7. Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 1994, 99(C5): 10143-10162.

8. Köhl A, Willebrand J. An adjoint method for the assimilation of statistical characteristics into eddy-resolving ocean models. Tellus A: Dynamic Meteorology and Oceanography, 2002, 54(4): 406-425.

9. Verdy A, Cornuelle B, Mazloff M R, et al. Estimation of the tropical Pacific Ocean state 2010-13. Journal of Atmospheric and Oceanic Technology, 2017, 34(7): 1501-1517.

10. Zaba K D, Rudnick D L, Cornuelle B D, et al. Annual and interannual variability in the California current system: Comparison of an ocean state estimate with a network of underwater gliders. Journal of Physical Oceanography, 2018, 48(12): 2965-2988.

11. Wang X W, Liu C Y, Köhl A, et al. The adjoint-based two oceans one sea state estimate (TOOSSE). Journal of Oceanology and Limnology, 2022, 40(1): 1-21.

12. He Z W, Yang D Z, Wang Y G, et al. Impact of 4D-Var data assimilation on modelling of the East China Sea dynamics. Ocean Modelling, 2022, 176: 102044.

13. Zeng X Z, Peng S Q, Li Z J, et al. A reanalysis dataset of the South China Sea. Scientific Data, 2014, 1: 140052.

14. Lin P F, Yu Z P, Liu H L, et al. LICOM model datasets for the CMIP6 ocean model intercomparison project. Advances in Atmospheric Sciences, 2020, 37(3): 239-249.

15. Liu H L, Lin P F, Zheng W P, et al. A global eddy-resolving ocean forecast system in China—LICOM Forecast System (LFS). Journal of Operational Oceanography, 2023, 16(1): 15-27.

16. Yan C X, Zhu J. Evaluation of an ocean reanalysis system in the Indian and Pacific Oceans. Atmosphere, 2023, 14(2): 220.

17. Shi J Q, Yin X Q, Shu Q, et al. Evaluation on data assimilation of a global high resolution wave-tide-circulation coupled model using the tropical Pacific TAO buoy observations. Acta Oceanologica Sinica, 2018, 37(3): 8-20.

18. Chao G F, Wu X R, Zhang L X, et al. China ocean reanalysis (CORA) version 1.0 products and validation for 2009-18. Atmospheric and Oceanic Science Letters, 2021, 14(5): 39-43.

19. Li Z J, Wang Z Y, Li Y, et al. Evaluation of global high-resolution reanalysis products based on the Chinese Global Oceanography Forecasting System. Atmospheric and Oceanic Science Letters, 2021, 14(5): 44-50.

20. 李志锦, 费建芳, 张人禾, 等. 西太平洋区域海洋中尺度数值预报及卫星高度计资料同化方法. 中国科学:地球科学, 2025, 55(3): 936-954. Li Z J, Fei J F, Zhang R H, et al. Numerical prediction of oceanic mesoscale circulation and satellite altimetry data assimilation in the Western Pacific. Scientia Sinica (Terrae), 2025, 55(3): 936-954. (in Chinese)

21. Chen Y, Bao S L, Cao Y, et al. The Yin-He global ocean data assimilation and forecast system. Ocean-Land-Atmosphere Research, 2025, 4: 0121.

22. Lyu G K, Koehl A, Serra N, et al. Arctic ocean-sea ice reanalysis for the period 2007-2016 using the adjoint method. Quarterly Journal of the Royal Meteorological Society, 2021, 147(736): 1908-1929.

23. Lyu G K, Mu L J, Koehl A, et al. Adjoint-based simultaneous state and parameter estimation in an Arctic Sea Ice-ocean Model using MITgcm (c63m). Geoscientific Model Development, 2025, 18(23): 9451-9468.

24. Arcucci R, Zhu J C, Hu S, et al. Deep data assimilation: Integrating deep learning with data assimilation. Applied Sciences, 2021, 11(3): 1114.

25. Beauchamp M, Febvre Q, Georgenthum H, et al. 4DVarNet-SSH: End-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry. Geoscientific Model Development, 2023, 16(8): 2119-2147.

26. Martin S A, Manucharyan G E, Klein P. Generative data assimilation for surface ocean state estimation from multi-modal satellite observations. Journal of Advances in Modeling Earth Systems, 2025, 17(8): e2025MS005063.

27. Wang R X, Shen Z Q. A deep neural network-ensemble adjustment Kalman filter and its application on strongly coupled data assimilation. Journal of Marine Science and Engineering, 2024, 12(1): 108.

28. Mu M, Duan W S, Wang B. Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 2003, 10(6): 493-501.

29. Yang W S, Liu C Y, Köhl A, et al. The adjoint-based favorable winds for the generation of the central Pacific El Niño. Journal of Climate, 2023, 36(16): 5491-5510.

30. Rao W X, Tang Y M, Wu Y L, et al. A new ensemble-based targeted observational method and its application in the TPOS 2020. National Science Review, 2023, 10(11): nwad231.

31. Liu C Y, Köhl A, Stammer D. Adjoint-based estimation of eddy-induced tracer mixing parameters in the global ocean. Journal of Physical Oceanography, 2012, 42(7): 1186-1206.

32. Fenty I, Heimbach P. Coupled sea ice-ocean-state estimation in the Labrador Sea and Baffin Bay. Journal of Physical Oceanography, 2013, 43(5): 884-904.

33. Laloyaux P, Balmaseda M, Dee D, et al. A coupled data assimilation system for climate reanalysis. Quarterly Journal of the Royal Meteorological Society, 2016, 142(694): 65-78.

Download
Request a Copy

Share

COinS