66 citations as recorded by crossref.

1. Application of Coupled Bred Vectors to Seasonal-to-Interannual Forecasting and Ocean Data Assimilation S. Yang et al. https://doi.org/10.1175/2008JCLI2427.1
2. Stochastically perturbed bred vectors in multi‐scale systems B. Giggins & G. Gottwald https://doi.org/10.1002/qj.3457
3. On transversality and the characterization of finite time hyperbolic subspaces in chaotic attractors T. O'Kane & C. Quinn https://doi.org/10.5194/npg-33-51-2026
4. A 24-variable low-order coupled ocean–atmosphere model: OA-QG-WS v2 S. Vannitsem & L. De Cruz https://doi.org/10.5194/gmd-7-649-2014
5. Climate Model State Estimation Using Variants of EnKF Coupled Data Assimilation P. Sandery et al. https://doi.org/10.1175/MWR-D-18-0443.1
6. Interactions between Physics Diversity and Multiscale Initial Condition Perturbations for Storm-Scale Ensemble Forecasting A. Johnson & X. Wang https://doi.org/10.1175/MWR-D-20-0112.1
7. Identifying Efficient Ensemble Perturbations for Initializing Subseasonal‐To‐Seasonal Prediction J. Demaeyer et al. https://doi.org/10.1029/2021MS002828
8. Dynamic analysis of a slow-fast oscillator based on a coupled Duffing memristive system M. Kountchou Noube et al. https://doi.org/10.1007/s40435-022-01011-6
9. Using a snow ablation optimizer in an autonomous echo state network for the model-free prediction of chaotic systems L. Ding et al. https://doi.org/10.1007/s11071-024-09656-y
10. Use of breeding to detect and explain instabilities in the global ocean M. Hoffman et al. https://doi.org/10.1029/2009GL037729
11. Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control R. Nagai et al. https://doi.org/10.5194/npg-32-281-2025
12. Well-estimated global surface warming in climate projections selected for ENSO phase J. Risbey et al. https://doi.org/10.1038/nclimate2310
13. Coupled Data Assimilation and Ensemble Initialization with Application to Multiyear ENSO Prediction T. O’Kane et al. https://doi.org/10.1175/JCLI-D-18-0189.1
14. The application of nonlinear local Lyapunov vectors to the Zebiak–Cane model and their performance in ensemble prediction Z. Hou et al. https://doi.org/10.1007/s00382-017-3920-6
15. Decadal North Pacific Bred Vectors in a Coupled GCM Y. Vikhliaev et al. https://doi.org/10.1175/2007JCLI1620.1
16. Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations A. Carrassi et al. https://doi.org/10.5194/npg-21-521-2014
17. Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors J. Kang et al. https://doi.org/10.3390/atmos13122070
18. Mixing and ventilation in a living laboratory due to fast and slow response modes C. Rodda et al. https://doi.org/10.1017/flo.2026.10045
19. A general theory to estimate Information transfer in nonlinear systems C. Pires et al. https://doi.org/10.1016/j.physd.2023.133988
20. Ensemble singular vectors and their use as additive inflation in EnKF S. Yang et al. https://doi.org/10.3402/tellusa.v67.26536
21. Common Issues in Verification of Climate Forecasts and Projections J. Risbey et al. https://doi.org/10.3390/cli10060083
22. Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics S. Soldatenko et al. https://doi.org/10.3390/math9222920
23. Chaotic climate system forecasting using an improved echo state network with sparse observations L. Ding et al. https://doi.org/10.1007/s11430-024-1593-9
24. Causality Analysis: Identifying the Leading Element in a Coupled Dynamical System A. BozorgMagham et al. https://doi.org/10.1371/journal.pone.0131226
25. Fixed points, stable manifolds, weather regimes, and their predictability B. Deremble et al. https://doi.org/10.1063/1.3230497
26. Stochastically perturbed bred vectors in single‐scale systems B. Giggins & G. Gottwald https://doi.org/10.1002/qj.3888
27. Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems C. Quinn et al. https://doi.org/10.5194/npg-27-51-2020
28. On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean–atmosphere systems S. Vannitsem & W. Duan https://doi.org/10.1007/s00382-020-05313-3
29. A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts A. Johnson et al. https://doi.org/10.1175/MWR-D-14-00345.1
30. Nonlinear dynamics approach to the predictability of the Cane–Zebiak coupled ocean–atmosphere model L. Siqueira & B. Kirtman https://doi.org/10.5194/npg-21-155-2014
31. Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000 Y. Tang & Z. Deng https://doi.org/10.1175/2010JCLI3491.1
32. Standard assessments of climate forecast skill can be misleading J. Risbey et al. https://doi.org/10.1038/s41467-021-23771-z
33. Linking the Anomaly Initialization Approach to the Mapping Paradigm: A Proof-of-Concept Study R. Weber et al. https://doi.org/10.1175/MWR-D-14-00398.1
34. Weather and Climate Manipulation as an Optimal Control for Adaptive Dynamical Systems S. Soldatenko https://doi.org/10.1155/2017/4615072
35. Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model S. Penny et al. https://doi.org/10.1029/2019MS001652
36. Sensitivity of Extreme Rainfall in Taiwan to SST Over the South China Sea Through Modulation of Marine Boundary Layer Jet: A Mei‐Yu Front Event During 1–4 June 2017 K. Lin et al. https://doi.org/10.1029/2023GL104441
37. Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual timescales J. Baehr & R. Piontek https://doi.org/10.5194/gmd-7-453-2014
38. Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning E. Kalnay et al. https://doi.org/10.5194/npg-30-217-2023
39. Some Aspects of Sensitivity Analysis in Variational Data Assimilation for Coupled Dynamical Systems S. Soldatenko et al. https://doi.org/10.1155/2015/753031
40. Flow-dependent empirical singular vector with an ensemble Kalman filter data assimilation for El Nino prediction Y. Ham & M. Rienecker https://doi.org/10.1007/s00382-012-1302-7
41. An Analysis on Perturbation Features of Convection-Allowing Ensemble Prediction Based on the Local Breeding Growth Mode S. Ma et al. https://doi.org/10.1175/WAF-D-18-0111.1
42. Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
43. Nonconventional fluctuation dissipation process in non-Hamiltonian dynamical systems M. Bianucci https://doi.org/10.1142/S0217979215410040
44. 融合稀疏观测与改进回声状态网络的混沌气候系统预测 琳. 丁 et al. https://doi.org/10.1360/N072024-0311
45. Coarse-grained sensitivity for multiscale data assimilation N. Sugiura https://doi.org/10.1103/PhysRevE.93.052212
46. On finite-size Lyapunov exponents in multiscale systems L. Mitchell & G. Gottwald https://doi.org/10.1063/1.4704805
47. Bred Vectors and Tropical Pacific Forecast Errors in the NASA Coupled General Circulation Model S. Yang et al. https://doi.org/10.1175/2007MWR2118.1
48. Exploring the influence of spatio-temporal scale differences in coupled data assimilation L. Garcia-Oliva et al. https://doi.org/10.5194/npg-32-439-2025
49. ENSO Bred Vectors in Coupled Ocean–Atmosphere General Circulation Models S. Yang et al. https://doi.org/10.1175/JCLI3696.1
50. Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities A. Norwood et al. https://doi.org/10.1088/1751-8113/46/25/254021
51. A kernel principal component analysis of coexisting attractors within a generalized Lorenz model J. Cui & B. Shen https://doi.org/10.1016/j.chaos.2021.110865
52. Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability M. Zhang et al. https://doi.org/10.1007/s00376-024-3297-5
53. The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0 L. De Cruz et al. https://doi.org/10.5194/gmd-9-2793-2016
54. Correlation and Spectral Properties of a Coupled Nonlinear Dynamical System in the Context of Numerical Weather Prediction and Climate Modeling S. Soldatenko & D. Chichkine https://doi.org/10.1155/2014/498184
55. Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation T. Yoshida & E. Kalnay https://doi.org/10.1175/MWR-D-17-0365.1
56. Factors Influencing Skill Improvements in the ECMWF Forecasting System L. Magnusson & E. Källén https://doi.org/10.1175/MWR-D-12-00318.1
57. Dynamics of blinking vortices A. Daitche & T. Tél https://doi.org/10.1103/PhysRevE.79.016210
58. Finite size Lyapunov exponent: review on applications M. Cencini & A. Vulpiani https://doi.org/10.1088/1751-8113/46/25/254019
59. The geometric norm improves ensemble forecasting with the breeding method D. Pazó et al. https://doi.org/10.1002/qj.2115
60. Suppression of dynamics and frequency synchronization in coupled slow and fast dynamical systems K. Gupta & G. Ambika https://doi.org/10.1140/epjb/e2016-70068-8
61. A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics J. Platt et al. https://doi.org/10.1016/j.neunet.2022.06.025
62. On the Relation between Energy-Conserving Low-Order Models and a System of Coupled Generalized Volterra Gyrostats with Nonlinear Feedback S. Lakshmivarahan & Y. Wang https://doi.org/10.1007/s00332-007-9006-6
63. Predictability of a low-order interactive ensemble L. Siqueira & B. Kirtman https://doi.org/10.5194/npg-19-273-2012
64. The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Niño Event S. Yang et al. https://doi.org/10.1175/2010JCLI3319.1
65. Coupled initialization in an ocean–atmosphere tropical cyclone prediction system P. Sandery & T. O'Kane https://doi.org/10.1002/qj.2117
66. A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System G. Han et al. https://doi.org/10.1155/2015/530764