113 citations as recorded by crossref.

1. Improving ecological modeling: Integrating CNOP-P and adjoint assimilation in a coupled ecological model Y. Liu et al. https://doi.org/10.1016/j.ocemod.2024.102462
2. Large Ensemble Simulations of Climate Models for Climate Change Research: A Review P. Lin et al. https://doi.org/10.1007/s00376-024-4012-2
3. Identifying Sensitive Model Parameter Combinations for Uncertainties in Land Surface Process Simulations over the Tibetan Plateau F. Peng & G. Sun https://doi.org/10.3390/w11081724
4. Conditional Nonlinear Optimal Perturbation of a Coupled Lorenz Model L. Yang https://doi.org/10.1155/2013/846973
5. Extended range (10–30 days) heavy rain forecasting study based on a nonlinear cross-prediction error model Z. Xia et al. https://doi.org/10.1007/s00376-015-4252-2
6. An Efficient Approach Based on the Gradient Definition for Solving Conditional Nonlinear Optimal Perturbation B. Mu et al. https://doi.org/10.1155/2017/3208431
7. Recent Progress in Applications of the Conditional Nonlinear Optimal Perturbation Approach to Atmosphere-Ocean Sciences M. Mu et al. https://doi.org/10.1007/s11401-022-0376-8
8. Research and Operational Development of Numerical Weather Prediction in China X. Shen et al. https://doi.org/10.1007/s13351-020-9847-6
9. Evaluation of sensitive physical parameter combinations for determining the uncertainty of fire simulations and predictions in China G. Sun https://doi.org/10.1002/asl.1085
10. Fine-tuning Atmospheric Parameters for Improving ENSO Simulation in the Zebiak–Cane Model X. Wei et al. https://doi.org/10.1007/s00376-025-4423-8
11. Variations in soil moisture over the ‘Huang-Huai-Hai Plain’ in China due to temperature change using the CNOP-P method and outputs from CMIP5 G. Sun et al. https://doi.org/10.1007/s11430-016-9061-3
12. Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau F. Peng et al. https://doi.org/10.1080/16000870.2019.1704963
13. Adjoint-free calculation method for conditional nonlinear optimal perturbations M. Cui https://doi.org/10.1007/s11425-014-4939-3
14. Application of targeted observation in a model's physical parameters for the simulation and forecast of heat flux with a land surface model G. Sun et al. https://doi.org/10.1002/met.1883
15. The Application of CNOP in the Management of the Desert Ecosystem B. Wang et al. https://doi.org/10.4028/www.scientific.net/AMR.599.851
16. Methods, current status, and prospect of targeted observation M. Mu https://doi.org/10.1007/s11430-013-4727-x
17. Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander Q. Wang et al. https://doi.org/10.1007/s00376-011-0199-0
18. Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model L. Yu et al. https://doi.org/10.1007/s00376-013-3058-3
19. An improved method for nonlinear parameter estimation: a case study of the Rössler model W. He et al. https://doi.org/10.1007/s00704-015-1528-5
20. Uncertainty assessment and sensitivity analysis of soil moisture based on model parameter errors – Results from four regions in China G. Sun et al. https://doi.org/10.1016/j.jhydrol.2017.09.059
21. 海气耦合资料同化对北大西洋涛动中期预报的改进 斌. 穆 et al. https://doi.org/10.1360/N072025-0494
22. Conditional Nonlinear Optimal Perturbations of Moisture Triggering Primary MJO Initiation Y. Wei et al. https://doi.org/10.1029/2018GL081755
23. Role of hydrological parameters in the uncertainty in modeled soil organic carbon using a coupled water-carbon cycle model G. Sun & M. Mu https://doi.org/10.1016/j.ecocom.2022.100986
24. A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty Q. Wang & M. Mu https://doi.org/10.1002/2015JC011095
25. Model errors of an intermediate model and their effects on realistic predictions of El Niño diversity L. Tao et al. https://doi.org/10.1002/joc.7656
26. Numerical Analysis of the Mixed 4th-Order Runge-Kutta Scheme of Conditional Nonlinear Optimal Perturbation Approach for the EI Niño-Southern Oscillation Model X. Zhao et al. https://doi.org/10.4208/aamm.2014.m786
27. Targeted Observations on Arctic Sea Ice Concentration for Improving Extended‐Range Prediction of Ural Blocking Y. Gao et al. https://doi.org/10.1029/2023JD039630
28. Nonlinear Ensemble Parameter Perturbation for Climate Models X. Yin et al. https://doi.org/10.1175/JCLI-D-14-00244.1
29. 70 Years of Development in China’s Operational Numerical Weather Prediction W. Zhang et al. https://doi.org/10.1007/s13351-025-4917-4
30. Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau G. Sun et al. https://doi.org/10.1029/2020JD033128
31. Inducing unstable grassland equilibrium states due to nonlinear optimal patterns of initial and parameter perturbations: Theoretical models G. Sun & M. Mu https://doi.org/10.1007/s00376-011-0226-1
32. Responses of soil carbon variation to climate variability in China using the LPJ model G. Sun & M. Mu https://doi.org/10.1007/s00704-012-0619-9
33. 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
34. Multi-model seasonal prediction of global surface temperature based on partial regression correction method Y. Yang et al. https://doi.org/10.3389/fenvs.2022.1036006
35. Recent Advances in China on the Predictability of Weather and Climate W. Duan et al. https://doi.org/10.1007/s00376-023-2334-0
36. Predictable component analysis of a system based on nonlinear error information entropy A. Li et al. https://doi.org/10.1007/s11430-016-5127-8
37. 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
38. The Research on the Lake Eutrophication with CNOP Method B. Wang et al. https://doi.org/10.4028/www.scientific.net/AMR.599.705
39. Using the Newtonian relaxation technique in numerical sensitivity studies A. Shao et al. https://doi.org/10.1007/s11430-016-0033-3
40. An Optimization Strategy for Identifying Parameter Sensitivity in Atmospheric and Oceanic Models Q. Wang et al. https://doi.org/10.1175/MWR-D-16-0393.1
41. A new climate scenario for assessing the climate change impacts on soil moisture over the Huang–Huai–Hai Plain region of China F. PENG & G. SUN https://doi.org/10.1080/16742834.2017.1255536
42. Application of the CNOP‐P Ensemble Prediction (CNOP‐PEP) Method in Evapotranspiration Forecasting Over the Tibetan Plateau to Model Parameter Uncertainties G. Sun et al. https://doi.org/10.1029/2022MS003110
43. Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P L. Wang et al. https://doi.org/10.1007/s00376-020-9262-z
44. Uncertainty range of projected soil carbon responses to climate warming in China G. Sun & M. Mu https://doi.org/10.1002/met.1993
45. A study of parameter uncertainties causing uncertainties in modeling a grassland ecosystem using the conditional nonlinear optimal perturbation method G. Sun & D. Xie https://doi.org/10.1007/s11430-016-9065-9
46. Projections of soil carbon using the combination of the CNOP-P method and GCMs from CMIP5 under RCP4.5 in north-south transect of eastern China G. Sun & M. Mu https://doi.org/10.1007/s11104-016-3098-4
47. Application of the Observation‐Oriented CNOP‐P Sensitivity Analysis Method in Evapotranspiration Simulation and Prediction Over the Tibetan Plateau G. Sun et al. https://doi.org/10.1029/2022WR033216
48. Nonlinear optimal moisture perturbations as excitation of primary MJO events in a hybrid coupled climate model Y. Wei et al. https://doi.org/10.1007/s00382-019-05021-7
49. A nonlinear least‐squares‐based ensemble method with a penalty strategy for computing the conditional nonlinear optimal perturbations X. Tian & X. Feng https://doi.org/10.1002/qj.2946
50. Assessing the characteristics of net primary production due to future climate change and CO 2 under RCP4.5 in China G. Sun & M. Mu https://doi.org/10.1016/j.ecocom.2018.04.001
51. An optimization approach for analysing nonlinear stability with transition to turbulence in fluids as an exemplar R. Kerswell et al. https://doi.org/10.1088/0034-4885/77/8/085901
52. A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application Q. Ren et al. https://doi.org/10.1007/s00376-022-1445-3
53. A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example G. Sun & M. Mu https://doi.org/10.1007/s00704-015-1690-9
54. Attractor radius and global attractor radius and their application to the quantification of predictability limits J. Li et al. https://doi.org/10.1007/s00382-017-4017-y
55. CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm S. Yuan et al. https://doi.org/10.1007/s00343-019-7266-2
56. Information theory in nonlinear error growth dynamics and its application to predictability: Taking the Lorenz system as an example A. Li et al. https://doi.org/10.1007/s11430-012-4506-0
57. Evaluation of conditional non-linear optimal perturbation obtained by an ensemble-based approach using the Lorenz-63 model X. Yin et al. https://doi.org/10.3402/tellusa.v66.22773
58. Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem G. Sun & M. Mu https://doi.org/10.5194/npg-18-883-2011
59. Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO C. Gao et al. https://doi.org/10.1007/s00376-017-7109-z
60. Determinants of Physical Processes and Their Contributions for Uncertainties in Simulated Evapotranspiration Over the Tibetan Plateau G. Sun et al. https://doi.org/10.1029/2021JD035756
61. Progress in the study of nonlinear atmospheric dynamics and predictability of weather and climate in China (2007–2011) F. Zhou et al. https://doi.org/10.1007/s00376-012-1204-y
62. Responses of soil moisture to climate change based on projections by the end of the 21st century under the high emission scenario in the ‘Huang–Huai–Hai Plain’ region of China F. Peng et al. https://doi.org/10.1016/j.jher.2016.10.003
63. Impact of Uncertainties in Land Surface Processes on Subseasonal Predictability of Heat Waves Onset Over the Yangtze River Valley Q. Zhang et al. https://doi.org/10.1029/2023JD038674
64. Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events G. Dai & M. Mu https://doi.org/10.1007/s00376-019-9222-7
65. Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter X. Ma et al. https://doi.org/10.1029/2021JD036282
66. Fused Spatial–Temporal Graph Convolutional Networks for Ocean Currents Forecasting Using Underwater Glider Measurements P. Yang et al. https://doi.org/10.1109/JOE.2025.3573004
67. A new approach for parameter optimization in land surface model H. Li et al. https://doi.org/10.1007/s00376-010-0050-z
68. Nonlinear Nonmodal Stability Theory R. Kerswell https://doi.org/10.1146/annurev-fluid-122316-045042
69. Evaluation of future runoff variations in the north–south transect of eastern China: effects of CMIP5 models outputs uncertainty G. Sun & F. Peng https://doi.org/10.2166/wcc.2019.261
70. Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model G. Sun & M. Mu https://doi.org/10.1007/s10584-013-0833-1
71. The Sensitivity Analysis of a Lake Ecosystem with the Conditional Nonlinear Optimal Perturbation Method B. Wang et al. https://doi.org/10.1155/2012/562081
72. Advances in Predictability and Numerical Prediction of 10~30 Days Extended-Range Forecast 启. 王 https://doi.org/10.12677/AG.2019.96052
73. Applications of CNOP-P Method to Predictability Studies of Terrestrial Ecosystems G. Sun & M. Mu https://doi.org/10.3390/atmos14040617
74. Predictability-based extended-range ensemble prediction method and numerical experiments . Zheng Zhi-Hai et al. https://doi.org/10.7498/aps.61.199203
75. Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions L. Tao https://doi.org/10.3390/jmse12040601
76. Convex Optimization of Initial Perturbations toward Quantitative Weather Control T. Ohtsuka et al. https://doi.org/10.2151/sola.2025-020
77. 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
78. A Biological‐Parameter‐Optimized Modeling Study of Physical Drivers Controlling Seasonal Chlorophyll Blooms off the Southern Coast of Java Island G. Gao et al. https://doi.org/10.1029/2022JC018835
79. Global optimization for data assimilation in landslide tsunami models A. Ferreiro-Ferreiro et al. https://doi.org/10.1016/j.jcp.2019.109069
80. Numerical model error estiamtion by derivative-free optimization method . Huang Qi-Can et al. https://doi.org/10.7498/aps.63.149203
81. Impacts of two types of errors on the predictability of terrestrial carbon cycle G. Sun & M. Mu https://doi.org/10.1002/ecs2.3315
82. Detecting the sensitive area of evaporation duct and evaluating its impact on forecasts of electromagnetic propagation over the South China Sea X. Zhang et al. https://doi.org/10.3389/fmars.2026.1829139
83. An application of the A-4DEnVar to coupled parameter optimization Y. Gong et al. https://doi.org/10.1007/s13131-022-1997-1
84. Is model parameter error related to a significant spring predictability barrier for El Niño events? Results from a theoretical model W. Duan & R. Zhang https://doi.org/10.1007/s00376-009-9166-4
85. An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model R. Zhang et al. https://doi.org/10.1007/s00382-017-3919-z
86. Evolutionary modeling-based approach for model errors correction S. Wan et al. https://doi.org/10.5194/npg-19-439-2012
87. A flexible method to determine the sensitive physical parameter combination for soil carbon under five plant types G. Sun & M. Mu https://doi.org/10.1002/ecs2.1920
88. An extension to ensemble forecast of conditional nonlinear optimal perturbation considering nonlinear interaction between initial and model parametric uncertainties B. Mu et al. https://doi.org/10.1016/j.atmosres.2024.107682
89. An Ensemble CNOP Method Based on a Pre-Screening Mechanism for Targeted Observations in the South China Sea R. Wang et al. https://doi.org/10.3390/jmse12010135
90. Calibration and Assessment of Burned Area Simulation Capability of the LPJ-WHyMe Model in Northeast China D. Yue et al. https://doi.org/10.3390/f10110992
91. Using the Lund-Potsdam-Jena model to understand the different responses of three woody plants to land use in China G. Sun & M. Mu https://doi.org/10.1007/s00376-012-2011-1
92. The analyses of the net primary production due to regional and seasonal temperature differences in eastern China using the LPJ model G. Sun & M. Mu https://doi.org/10.1016/j.ecolmodel.2014.06.021
93. The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events G. Dai et al. https://doi.org/10.1007/s00376-023-2371-8
94. Application of the Conditional Nonlinear Optimal Perturbations Method in the Shallow Lake Ecological Degradation and Restoration B. Wang & Q. Qi https://doi.org/10.1155/2015/215367
95. Sensitivity of El Niño diversity prediction to parameters in an intermediate coupled model H. Chen et al. https://doi.org/10.1007/s00382-023-06695-w
96. Response of a grassland ecosystem to climate change in a theoretical model G. Sun & M. Mu https://doi.org/10.1007/s00376-011-0169-6
97. A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation Q. Wang et al. https://doi.org/10.1093/nsr/nwz039
98. On the application of a genetic algorithm to the predictability problems involving “on-off” switches Q. Zheng et al. https://doi.org/10.1007/s00376-011-1054-z
99. A New Scheme for Capturing Global Conditional Nonlinear Optimal Perturbation S. Liu et al. https://doi.org/10.3390/jmse10030340
100. An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei region L. Yang et al. https://doi.org/10.5194/gmd-16-3827-2023
101. The predictability of atmospheric and oceanic motions: Retrospect and prospects M. Mu et al. https://doi.org/10.1007/s11430-016-9101-x
102. CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms B. Mu et al. https://doi.org/10.1155/2020/6070789
103. A robust, discrete-gradient descent procedure for optimisation with time-dependent PDE and norm constraints P. Mannix et al. https://doi.org/10.5802/smai-jcm.104
104. Estimation of atmospheric predictability for multivariable system using information theory in nonlinear error growth dynamics A. Li et al. https://doi.org/10.1007/s11430-014-4823-6
105. Model parameter-related optimal perturbations and their contributions to El Niño prediction errors L. Tao et al. https://doi.org/10.1007/s00382-018-4202-7
106. Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach Y. Gao et al. https://doi.org/10.1007/s00343-020-0020-y
107. Sources of Forecast Errors for Rainstorms in the South China Monsoon Region L. Lin et al. https://doi.org/10.1155/2022/3298501
108. Attractor radius for fractional Lorenz systems and their application to the quantification of predictability limits Y. Wang et al. https://doi.org/10.1063/5.0113709
109. Inter-seasonal variability in predictability of South China seasonal precipitation S. Zhen et al. https://doi.org/10.1088/1748-9326/adc021
110. Does Model Parameter Error Cause a Significant “Spring Predictability Barrier” for El Niño Events in the Zebiak–Cane Model? Y. Yu et al. https://doi.org/10.1175/2011JCLI4022.1
111. Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties Q. Zhang et al. https://doi.org/10.1038/s41612-024-00876-y
112. Coupled atmosphere-ocean data assimilation for enhanced medium-range prediction of the North Atlantic Oscillation B. Mu et al. https://doi.org/10.1007/s11430-025-1943-7
113. Extended application of the conditional nonlinear optimal parameter perturbation method in the common land model B. Wang & Z. Huo https://doi.org/10.1007/s00376-012-2025-8