22 citations as recorded by crossref.

1. The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model W. Zhou et al. https://doi.org/10.1007/s13131-021-1732-3
2. A possible bias of simulating the post-2000 changing ENSO F. Zheng et al. https://doi.org/10.1007/s11434-015-0912-y
3. An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model X. Chen et al. https://doi.org/10.1007/s13131-020-1568-2
4. Impact of satellite and regional in-situ profile data assimilation on a high-resolution ocean prediction system in the Northwest Pacific I. Chang et al. https://doi.org/10.3389/fmars.2023.1085542
5. The ‘Two oceans and one sea’ extended range numerical prediction system with an ultra-high resolution atmosphere-ocean-land regional coupled model S. Zhang et al. https://doi.org/10.1080/16742834.2018.1494498
6. Roles of initial ocean states on predicting the 2002/03 central Pacific El Niño F. Zheng et al. https://doi.org/10.1007/s13131-015-0770-0
7. ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization Q. SUN et al. https://doi.org/10.1080/16742834.2018.1411753
8. Estimating the Role of SST in Atmospheric Surface Wind Variability over the Tropical Atlantic and Pacific I. Richter & T. Doi https://doi.org/10.1175/JCLI-D-18-0468.1
9. A Review of Research on Tropical Air-Sea Interaction, ENSO Dynamics, and ENSO Prediction in China H. Ren et al. https://doi.org/10.1007/s13351-020-9155-1
10. Key Processes on Triggering the Moderate 2020/21 La Niña Event as Depicted by the Clustering Approach T. Cao et al. https://doi.org/10.3389/feart.2022.822854
11. Evaluation of Oceanic Surface Observation for Reproducing the Upper Ocean Structure in ECHAM5/MPI‐OM H. Luo et al. https://doi.org/10.1002/2017JC013413
12. Contrasting the skills and biases of deterministic predictions for the two types of El Niño F. Zheng & J. Yu https://doi.org/10.1007/s00376-017-6324-y
13. The cloud‐radiative effect when simulating strength asymmetry in two types of El Niño events using CMIP5 models X. Fang et al. https://doi.org/10.1002/2014JC010683
14. Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global Ocean Data Assimilation System W. Zhou et al. https://doi.org/10.1007/s00376-015-4282-9
15. Skillful prediction of boreal winter-spring seasonal precipitation in Southern China based on machine learning approach and dynamical ENSO prediction T. Cao et al. https://doi.org/10.1007/s00704-024-05011-8
16. Assessment of an ENSO prediction system based on the community earth system model and ensemble adjustment Kalman filter Y. Chen et al. https://doi.org/10.1007/s13131-025-2509-x
17. A Simple Bias Correction Scheme in Ocean Data Assimilation C. Yan & J. Zhu https://doi.org/10.3390/jmse11010205
18. Analysis of Long-Term Meteorological Observation for Weather and Climate Fundamental Data over the Northern Tibetan Plateau F. Huang & W. Ma https://doi.org/10.1155/2016/4878353
19. Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model F. Zheng & J. Zhu https://doi.org/10.1007/s00382-016-3048-0
20. On the “spring predictability barrier” for strong El Niño events as derived from an intermediate coupled model ensemble prediction system Q. Qi et al. https://doi.org/10.1007/s11430-017-9087-2
21. A Reasonable Mean Dynamic Topography State on Improving the Ability of Assimilating the Altimetry Observations into a Coupled Climate System Model: An Example With CAS‐ESM‐C X. Dong et al. https://doi.org/10.1029/2020JC016760
22. On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model Y. Chen et al. https://doi.org/10.1029/2022MS003106