17 citations as recorded by crossref.

1. A new ensemble-based targeted observational method and its application in the TPOS 2020 W. Rao et al. https://doi.org/10.1093/nsr/nwad231
2. The evolving ENSO observing system D. Chen et al. https://doi.org/10.1093/nsr/nwy137
3. A Nonlinear Theory and Technology for Reducing the Uncertainty of High-Impact Ocean–Atmosphere Event Prediction M. Mu & W. Duan https://doi.org/10.1007/s00376-025-4467-9
4. On the sensitive areas for targeted observations in ENSO forecasting J. Zhang et al. https://doi.org/10.1016/j.aosl.2021.100054
5. Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG W. Duan & R. Feng https://doi.org/10.1007/s13351-016-6099-6
6. Toward an optimal observational array for improving two flavors of El Niño predictions in the whole Pacific M. Hou et al. https://doi.org/10.1007/s00382-022-06342-w
7. Evaluation of the sensitivity on mesoscale eddy associated with the sea surface height anomaly forecasting in the Kuroshio Extension L. Jiang et al. https://doi.org/10.3389/fmars.2023.1097209
8. Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting M. Mu et al. https://doi.org/10.1093/nsr/nwv021
9. Evaluating the Joint Effect of Tropical and Extratropical Pacific Initial Errors on Two Types of El Niño Prediction Using Particle Filter Approach M. Hou et al. https://doi.org/10.3390/jmse11071292
10. The role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole events R. Feng & W. Duan https://doi.org/10.1007/s11430-018-9296-2
11. Toward targeted observations of the meteorological initial state for improving the PM2.5 forecast of a heavy haze event that occurred in the Beijing–Tianjin–Hebei region L. Yang et al. https://doi.org/10.5194/acp-22-11429-2022
12. Using an ensemble nonlinear forcing singular vector data assimilation approach to address the ENSO forecast uncertainties caused by the “spring predictability barrier” and El Niño diversity Y. Zheng et al. https://doi.org/10.1007/s00382-023-06834-3
13. IOD-related optimal initial errors and optimal precursors for IOD predictions from reanalysis data R. Feng & W. Duan https://doi.org/10.1007/s11430-016-0103-9
14. To improve the prediction skills of typhoon intensity by identifying target observation using particle filter assimilation method J. Zhang et al. https://doi.org/10.1016/j.atmosres.2024.107326
15. Increasingly important role of numerical modeling in oceanic observation design strategy: A review K. Zhang et al. https://doi.org/10.1007/s11430-020-9674-6
16. Impacts of two types of errors on the predictability of terrestrial carbon cycle G. Sun & M. Mu https://doi.org/10.1002/ecs2.3315
17. Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting T. Lingjiang & D. Wansuo https://doi.org/10.1175/WAF-D-19-0050.1