290 citations as recorded by crossref.

1. 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
2. Impacts of two types of errors on the predictability of terrestrial carbon cycle G. Sun & M. Mu https://doi.org/10.1002/ecs2.3315
3. The role of nonlinear forcing singular vector tendency error in causing the “spring predictability barrier” for ENSO W. Duan et al. https://doi.org/10.1007/s13351-016-6011-4
4. Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems L. Liu et al. https://doi.org/10.1016/j.atmosres.2024.107596
5. Typhoon airborne observational field campaigns in the Western North Pacific: Successes and future prospects R. Rogers et al. https://doi.org/10.1016/j.tcrr.2025.11.009
6. 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
7. A regional ensemble forecast system for stratiform precipitation events in northern China. Part I: A case study J. Zhu et al. https://doi.org/10.1007/s00376-011-0137-1
8. Application of orthogonal CNOP-I in a convection-allowing ensemble prediction system based on CMA-MESO for improving extreme precipitation skill H. Zhang et al. https://doi.org/10.1016/j.aosl.2025.100754
9. Targeted observation analysis of the tides and currents in a Coastal Marine Proving Ground J. Zhang et al. https://doi.org/10.1007/s10236-020-01398-w
10. 耦合条件非线性最优扰动及其在ENSO集合预报研究中的应用 晚. 段 et al. https://doi.org/10.1360/SSTe-2023-0180
11. The most sensitive initial error of sea surface height anomaly forecasts and its implication for target observations of mesoscale eddies https://doi.org/10.1175/JPO-D-21-0200.1
12. Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model X. Liu et al. https://doi.org/10.1007/s13131-021-1838-7
13. Overview and Prospect of Data Assimilation in Numerical Weather Prediction L. Lei et al. https://doi.org/10.1007/s13351-025-4905-8
14. Comparison of the initial errors most likely to cause a spring predictability barrier for two types of El Niño events B. Tian & W. Duan https://doi.org/10.1007/s00382-015-2870-0
15. A kind of initial errors related to “spring predictability barrier” for El Niño events in Zebiak‐Cane model M. Mu et al. https://doi.org/10.1029/2006GL027412
16. Impacts of Forecast Time and Verification Area Setting on the Targeted Observation of Typhoon J. Kang et al. https://doi.org/10.3390/atmos15111335
17. Conditional nonlinear optimal perturbation and its applications to the studies of weather and climate predictability M. Mu & W. Duan https://doi.org/10.1007/BF03183626
18. Revealing the most disturbing tendency error of Zebiak–Cane model associated with El Niño predictions by nonlinear forcing singular vector approach W. Duan & P. Zhao https://doi.org/10.1007/s00382-014-2369-0
19. Sensitive Areas’ Observation Simulation Experiments of Typhoon “Chaba” Based on Ensemble Transform Sensitivity Method Y. Ao et al. https://doi.org/10.3390/atmos15030269
20. 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
21. Applications of nonlinear optimization method to numerical studies of atmospheric and oceanic sciences D. Wan-suo & M. Mu https://doi.org/10.1007/BF02466338
22. Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model X. Liu et al. https://doi.org/10.1007/s00376-018-8003-z
23. Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model L. Tao et al. https://doi.org/10.1007/s00376-017-6266-4
24. An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling B. Shi & G. Sun https://doi.org/10.5194/npg-30-263-2023
25. 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
26. 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
27. 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
28. Optimal Precursors Triggering the Kuroshio Intrusion Into the South China Sea Obtained by the Conditional Nonlinear Optimal Perturbation Approach P. Liang et al. https://doi.org/10.1029/2018JC014545
29. A new approach for parameter optimization in land surface model H. Li et al. https://doi.org/10.1007/s00376-010-0050-z
30. Improved ENSO Prediction by Singular Vector Analysis in a Hybrid Coupled Model X. Zhou et al. https://doi.org/10.1175/2008JTECHO599.1
31. Application of conditional nonlinear optimal perturbation to targeted observation studies of the atmosphere and ocean M. Mu et al. https://doi.org/10.1007/s13351-014-4057-8
32. Season-dependent predictability and error growth dynamics for La Niña predictions J. Hu et al. https://doi.org/10.1007/s00382-019-04631-5
33. Behaviors of nonlinearities modulating the El Niño events induced by optimal precursory disturbances W. Duan et al. https://doi.org/10.1007/s00382-012-1557-z
34. Study on the Sensitivity of Initial Perturbations to the Development of a Vortex Observed in Southwest China G. Chen et al. https://doi.org/10.1029/2021JD034715
35. 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
36. Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs) J. Feng et al. https://doi.org/10.2139/ssrn.4135177
37. Applications of conditional nonlinear optimal perturbation to the study of the stability and sensitivity of the Jovian atmosphere Z. Jiang https://doi.org/10.1007/s00376-006-0775-x
38. 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
39. 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
40. 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
41. Dynamics of nonlinear error growth and season‐dependent predictability of El Niño events in the Zebiak–Cane model Y. Yu et al. https://doi.org/10.1002/qj.526
42. Optimal precursor of the transition from Kuroshio large meander to straight path Q. Wang et al. https://doi.org/10.1007/s00343-013-2301-1
43. Adaptive observation in the South China Sea using CNOP approach based on a 3‐D ocean circulation model and its adjoint model Y. Li et al. https://doi.org/10.1002/2014JC010220
44. A Rare Tropical Cyclone Necessitating the Issuance of Gale or Storm Wind Warning Signal in Hong Kong in Late Autumn in 2022—Severe Tropical Storm Nalgae P. Chan et al. https://doi.org/10.3390/atmos14010170
45. Parameter Estimation Using Adaptive Observations Toward Maximum Total Variance Reduction With Ensemble Adjustment Kalman Filter Z. Shen et al. https://doi.org/10.3389/fclim.2022.850386
46. The Sensitivity and Stability of the Ocean's Thermohaline Circulation to Finite-Amplitude Perturbations M. Mu et al. https://doi.org/10.1175/1520-0485(2004)034<2305:TSASOT>2.0.CO;2
47. Global Well-Posedness of Stochastic 3D Primitive Equations with Anticipating Initial Data Z. Dong et al. https://doi.org/10.1007/s10884-022-10211-9
48. Effects of Dropsonde Data in Field Campaigns on Forecasts of Tropical Cyclones over the Western North Pacific in 2020 and the Role of CNOP Sensitivity X. Qin et al. https://doi.org/10.1007/s00376-022-2136-9
49. Optimally growing initial error for predicting the sudden shift in the Antarctic Circumpolar Current transport and its application to targeted observation L. Zhou et al. https://doi.org/10.1007/s10236-022-01531-x
50. 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
51. Simple Sensitivity Analysis Using Ensemble Forecasts T. ENOMOTO et al. https://doi.org/10.2151/jmsj.2015-011
52. Advances in Predictability and Numerical Prediction of 10~30 Days Extended-Range Forecast 启. 王 https://doi.org/10.12677/AG.2019.96052
53. Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability W. Duan & M. Mu https://doi.org/10.1007/s11430-009-0090-3
54. The most sensitive initial error modes modulating intensities of CP- and EP- El Niño events Q. Qi et al. https://doi.org/10.1016/j.dynatmoce.2021.101257
55. Impacts of Initial Zonal Current Errors on the Predictions of Two Types of El Niño Events L. Tao et al. https://doi.org/10.1029/2023JC019833
56. A variant constrained genetic algorithm for solving conditional nonlinear optimal perturbations Q. Zheng et al. https://doi.org/10.1007/s00376-013-2253-6
57. Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations Z. Huo et al. https://doi.org/10.1007/s00376-018-8001-1
58. A Review on Development, Challenges, and Future Perspectives of Ensemble Forecast J. Chen et al. https://doi.org/10.1007/s13351-025-4909-4
59. 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
60. Using Conditional Nonlinear Optimal Perturbation to Generate Initial Perturbations in ENSO Ensemble Forecasts Q. Zhou et al. https://doi.org/10.1175/WAF-D-21-0063.1
61. 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
62. Using conditional nonlinear optimal perturbation method in parameter optimization of land surface processes model . Li Hong-Qi et al. https://doi.org/10.7498/aps.60.019201
63. The ‘spring predictability barrier’ for ENSO predictions and its possible mechanism: results from a fully coupled model W. Duan & C. Wei https://doi.org/10.1002/joc.3513
64. On the sensitive areas for targeted observations in ENSO forecasting J. Zhang et al. https://doi.org/10.1016/j.aosl.2021.100054
65. Targeted observation for the climatology temperature in the Kuroshio region based on the CNOP approach J. Liu et al. https://doi.org/10.1088/1742-6596/2718/1/012022
66. 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
67. 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
68. Can adaptive observations improve tropical cyclone intensity forecasts? X. Qin & M. Mu https://doi.org/10.1007/s00376-013-3008-0
69. The unusual 2014–2016 El Niño events: Dynamics, prediction and enlightenments R. Xie & X. Fang https://doi.org/10.1007/s11430-019-9561-2
70. CNOP‐Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction K. Zhang et al. https://doi.org/10.1029/2018JC014490
71. Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea H. Hu et al. https://doi.org/10.1007/s13131-021-1738-x
72. A Normal Mode Perspective of Intrinsic Ocean-Climate Variability H. Dijkstra https://doi.org/10.1146/annurev-fluid-122414-034506
73. Targeted observations for moored arrays: resolving multi-scale thermal variability in the South China Sea Y. Yang et al. https://doi.org/10.1007/s13131-025-2596-8
74. 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
75. A Comparison Study of the Contributions of Additional Observations in the Sensitive Regions Identified by CNOP and FSV to Reducing Forecast Error Variance for the Typhoon Morakot Q. Xiao-Hao https://doi.org/10.1080/16742834.2010.11446879
76. Nonlinear finite-time Lyapunov exponent and predictability R. Ding & J. Li https://doi.org/10.1016/j.physleta.2006.11.094
77. Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport K. Zhang et al. https://doi.org/10.1016/j.dsr.2016.08.008
78. Review of Chinese atmospheric science research over the past 70 years: Atmospheric physics and atmospheric environment T. Wang et al. https://doi.org/10.1007/s11430-019-9536-1
79. Nonlinear Character Analysis for Bistability in virus–immune Dynamics X. Liu & G. Sun https://doi.org/10.2217/fvl-2019-0059
80. The predictability of atmospheric and oceanic motions: Retrospect and prospects M. Mu et al. https://doi.org/10.1007/s11430-016-9101-x
81. 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
82. The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case C. Shao & L. Nerger https://doi.org/10.3390/rs16020430
83. Matrix-Based ACO for Solving Parametric Problems Using Heterogeneous Reconfigurable Computers and SIMD Accelerators V. Sudakov & Y. Titov https://doi.org/10.3390/math13081284
84. 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
85. Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction M. Mu et al. https://doi.org/10.1002/2016JC012527
86. 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
87. 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
88. Conditional Nonlinear Optimal Perturbations of Moisture Triggering Primary MJO Initiation Y. Wei et al. https://doi.org/10.1029/2018GL081755
89. Ensemble forecasting: A foray of dynamics into the realm of statistics J. Feng et al. https://doi.org/10.1002/qj.4745
90. 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
91. Effects of nonlinear physical processes on optimal error growth in predictability experiments of the Kuroshio Large Meander Q. Wang et al. https://doi.org/10.1002/2013JC009276
92. Influence of Arctic Sea-ice Concentration on Extended-range Forecasting of Cold Events in East Asia C. Li et al. https://doi.org/10.1007/s00376-023-3010-0
93. Marine Adaptive Sampling Scheme Design for Mobile Platforms under Different Scenarios Y. Zhao et al. https://doi.org/10.3390/jmse10050664
94. 70 Years of Development in China’s Operational Numerical Weather Prediction W. Zhang et al. https://doi.org/10.1007/s13351-025-4917-4
95. Season-dependent predictability and error growth dynamics of Pacific Decadal Oscillation-related sea surface temperature anomalies W. Duan & Y. Wu https://doi.org/10.1007/s00382-014-2364-5
96. Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño W. Duan et al. https://doi.org/10.1007/s00382-018-4082-x
97. 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
98. Optimal Precursors Identification for North Atlantic Oscillation Using the Parallel Intelligence Algorithm B. Mu et al. https://doi.org/10.1155/2022/3215039
99. A paralleled embedding high-dimensional Bayesian optimization with additive Gaussian kernels for solving CNOP S. Yuan et al. https://doi.org/10.1016/j.ocemod.2023.102213
100. The role of nonlinearities associated with air-sea coupling processes in El Nino’s peak-phase locking W. Duan et al. https://doi.org/10.1007/s11430-013-4629-y
101. Quantitative study of the relative effects of initial condition and model uncertainties on local predictability in a nonlinear dynamical system X. Li et al. https://doi.org/10.1016/j.chaos.2020.110094
102. Extent of the Impact of Arctic Atmospheric Uncertainty on Extended‐Range Forecasting of Cold Events in East Asia Z. Han et al. https://doi.org/10.1029/2022JD037187
103. The Research on the Lake Eutrophication with CNOP Method B. Wang et al. https://doi.org/10.4028/www.scientific.net/AMR.599.705
104. The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model W. Duan & J. Hu https://doi.org/10.1007/s00382-015-2789-5
105. A study on precursors to blocking anomalies in climatological flows by using conditional nonlinear optimal perturbations Z. Jiang & D. Wang https://doi.org/10.1002/qj.630
106. 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
107. Systematic calculation of finite-time mixed singular vectors and characterization of error growth for persistent coherent atmospheric disturbances over Eurasia C. Quinn et al. https://doi.org/10.1063/5.0066150
108. 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
109. Nonlinear Local Lyapunov Exponent and Quantification of Local Predictability D. Rui-Qiang et al. https://doi.org/10.1088/0256-307X/25/5/109
110. Enhancing ocean environment prediction in Yellow Sea through targeted observation using ocean acoustic tomography C. Baolong et al. https://doi.org/10.3389/fmars.2023.1259864
111. Optimally growing initial errors of El Niño events in the CESM H. Xu et al. https://doi.org/10.1007/s00382-021-05668-1
112. Nonlinear Generalization of Singular Vectors: Behavior in a Baroclinic Unstable Flow O. Rivière et al. https://doi.org/10.1175/2007JAS2378.1
113. Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts X. Qin & M. Mu https://doi.org/10.1002/qj.902
114. An Approach to Generating Mutually Independent Initial Perturbations for Ensemble Forecasts: Orthogonal Conditional Nonlinear Optimal Perturbations W. Duan & Z. Huo https://doi.org/10.1175/JAS-D-15-0138.1
115. Development of the analogue-dynamical method for error correction of numerical forecasts H. Yu et al. https://doi.org/10.1007/s13351-014-4077-4
116. 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
117. Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin L. He et al. https://doi.org/10.1007/s00376-024-3114-1
118. Conditional nonlinear optimal perturbations as the optimal precursors for El Nino–Southern Oscillation events W. Duan et al. https://doi.org/10.1029/2004JD004756
119. Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 B. Mu et al. https://doi.org/10.5194/gmd-15-4105-2022
120. Exploring sensitive area in the tropical Indian Ocean for El Niño prediction: implication for targeted observation Q. Zhou et al. https://doi.org/10.1007/s00343-019-9062-4
121. Estimation of initial conditions for surface suspended sediment simulations with the adjoint method: A case study in Hangzhou Bay Y. Du et al. https://doi.org/10.1016/j.csr.2021.104526
122. The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth Q. Wang et al. https://doi.org/10.1007/s00382-019-05097-1
123. Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions L. Xu et al. https://doi.org/10.1016/j.earscirev.2021.103828
124. PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation B. Mu et al. https://doi.org/10.1016/j.cageo.2015.06.016
125. Passive mechanism of decadal variation of thermohaline circulation L. Sun et al. https://doi.org/10.1029/2005JC002897
126. Comparison of constant and time-variant optimal forcing approaches in El Niño simulations by using the Zebiak–Cane model B. Tian & W. Duan https://doi.org/10.1007/s00376-015-5174-8
127. 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
128. Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method B. Mu et al. https://doi.org/10.3390/jmse11050910
129. 提升天气预报能力: 数据同化中初始条件敏感性与误差动态的问题 伟. 符 https://doi.org/10.1360/SSTe-2024-0256
130. Adjoint Sensitivity of the Niño-3 Surface Temperature to Wind Forcing X. Zhang et al. https://doi.org/10.1175/2011JCLI3917.1
131. Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions M. Mu et al. https://doi.org/10.1007/s00704-013-0909-x
132. Progress and future prospects of decadal prediction and data assimilation: A review W. Zhou et al. https://doi.org/10.1016/j.aosl.2023.100441
133. Dynamic channel selection based on vertical sensitivities for the assimilation of FY‐4A geostationary interferometric infrared sounder targeted observations Y. Li et al. https://doi.org/10.1002/qj.4760
134. Quality of the Target Area for Metrics with Different Nonlinearities in a Mesoscale Convective System L. Huang & Z. Meng https://doi.org/10.1175/MWR-D-13-00244.1
135. Feature extraction-based intelligent algorithm framework with neural network for solving conditional nonlinear optimal perturbation S. Yuan et al. https://doi.org/10.1007/s00500-021-06639-8
136. A comparison study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction Z. Jiang & M. Mu https://doi.org/10.1007/s00376-009-0465-6
137. A New Method to Calculate Nonlinear Optimal Perturbations for Ensemble Forecasting J. Ma et al. https://doi.org/10.1007/s00376-024-4069-y
138. A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track J. Pu et al. https://doi.org/10.1038/s41612-025-01009-9
139. What kind of initial errors cause the severest prediction uncertainty of El Niño in Zebiak-Cane model H. Xu & W. Duan https://doi.org/10.1007/s00376-008-0577-4
140. 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
141. Nonlinear Nonmodal Stability Theory R. Kerswell https://doi.org/10.1146/annurev-fluid-122316-045042
142. Applications of nonlinear optimization methods to quantifying the predictability of a numerical model for El Nino-Southern Oscillation D. Wansuo & M. Mu https://doi.org/10.1080/10020070512331343110
143. The BaSIC method: a new approach to quantitatively assessing the local predictability of extreme weather events X. Li et al. https://doi.org/10.1007/s00382-022-06526-4
144. Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model G. Zou et al. https://doi.org/10.1007/s00343-016-4264-5
145. Integrating an CNOP analysis into a deep learning model to identify optimal initial errors for 2020–2022 La Niña prediction L. Tao et al. https://doi.org/10.1007/s00382-025-07986-0
146. 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
147. Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions L. Tao https://doi.org/10.3390/jmse12040601
148. A novel technique for nonlinear sensitivity analysis: application to moist predictability O. Rivière et al. https://doi.org/10.1002/qj.460
149. Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting M. Mu et al. https://doi.org/10.1093/nsr/nwv021
150. Optimal Precursors Triggering Sudden Shifts in the Antarctic Circumpolar Current Transport Through Drake Passage L. Zhou et al. https://doi.org/10.1029/2021JC017899
151. Applications of CNOP-P Method to Predictability Studies of Terrestrial Ecosystems G. Sun & M. Mu https://doi.org/10.3390/atmos14040617
152. Conditional Nonlinear Optimal Perturbations: Adjoint-Free Calculation Method and Preliminary Test B. Wang & X. Tan https://doi.org/10.1175/2009MWR3022.1
153. 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
154. Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events W. Duan et al. https://doi.org/10.1029/2008JC004925
155. Targeted sounding observations from geostationary satellite and impacts on high impact weather forecasts W. Han et al. https://doi.org/10.1007/s11430-024-1489-5
156. Estimating the local predictability of heatwaves in south China using the backward nonlinear local Lyapunov exponent method X. Li et al. https://doi.org/10.1007/s00382-023-06757-z
157. The role of constant optimal forcing in correcting forecast models F. Feng & W. Duan https://doi.org/10.1007/s11430-012-4568-z
158. 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
159. Arctic Influence on the Eastern Asian Cold Surge Forecast: A Case Study of January 2016 G. Dai & M. Mu https://doi.org/10.1029/2020JD033298
160. Algorithm studies on how to obtain a conditional nonlinear optimal perturbation (CNOP) G. Sun et al. https://doi.org/10.1007/s00376-010-9088-1
161. Application of the Most Sensitive Initial Error in Sampling Scheme Design for a Mobile Platform H. Zhao et al. https://doi.org/10.3390/jmse11112080
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258. 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
259. MJO-equatorial Rossby wave interferences in the tropical intraseasonal oscillation Y. Wei et al. https://doi.org/10.1007/s00382-024-07380-2
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263. 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
264. Intrinsic low-frequency variability and predictability of the Kuroshio Current and of its extension S. Pierini et al. https://doi.org/10.1080/19475721.2014.962091
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268. Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs) J. Feng et al. https://doi.org/10.2139/ssrn.4135176
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276. A Study on the Reduction of Forecast Error Variance by Three Adaptive Observation Approaches for Tropical Cyclone Prediction X. Qin & M. Mu https://doi.org/10.1175/2010MWR3327.1
277. Which area should be preferentially observed to improve the vertical thermal structure prediction: A case study focusing on complex topography areas in the South China Sea. J. Liu et al. https://doi.org/10.1088/1742-6596/3007/1/012011
278. 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
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284. Enhancing ENSO Ensemble Forecast Skill by a Coupled Conditional Nonlinear Optimal Perturbation Method L. Hu et al. https://doi.org/10.1002/joc.70360
285. 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
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287. Ground-Space-Sky Observing System Experiment during Tropical Cyclone Mulan in August 2022 P. Chan et al. https://doi.org/10.1007/s00376-022-2267-z
288. Optimal SoC Balancing Control for Lithium-Ion Battery Cells Connected in Series C. Van et al. https://doi.org/10.3390/en14102875
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