53 citations as recorded by crossref.

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24. Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter Y. Ham et al. 10.1007/s00382-009-0582-z
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27. The Application of Nonlinear Local Lyapunov Vectors to Ensemble Predictions in Lorenz Systems J. Feng et al. 10.1175/JAS-D-13-0270.1
28. Tailored Ensemble Prediction Systems: Application of Seamless Scale Bred Vectors A. HERMOSO et al. 10.2151/jmsj.2020-053
29. Empirical singular vector method for ensemble El Niño–Southern Oscillation prediction with a coupled general circulation model J. Kug et al. 10.1029/2010JC006851
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31. Maximizing the Statistical Diversity of an Ensemble of Bred Vectors by Using the Geometric Norm D. Pazó et al. 10.1175/2011JAS3729.1
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33. Flow-dependent empirical singular vector with an ensemble Kalman filter data assimilation for El Nino prediction Y. Ham & M. Rienecker 10.1007/s00382-012-1302-7
34. Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors J. Kang et al. 10.3390/atmos13122070
35. The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii J. Feng et al. 10.1007/s00376-018-8123-5
36. Proper orthogonal decomposition reduced-order model of the global oceans V. Kitsios et al. 10.1007/s00162-024-00719-9
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42. Stochastically perturbed bred vectors in multi‐scale systems B. Giggins & G. Gottwald 10.1002/qj.3457
43. Investigating atmospheric predictability on Mars using breeding vectors in a general‐circulation model C. Newman et al. 10.1256/qj.03.209
44. Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000 Y. Tang & Z. Deng 10.1175/2010JCLI3491.1
45. Stochastically perturbed bred vectors in single‐scale systems B. Giggins & G. Gottwald 10.1002/qj.3888
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47. Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model M. Ouyang et al. 10.5194/npg-30-183-2023
48. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics R. Bannister 10.1002/qj.340
49. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
50. Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems S. Kim & H. Kim 10.1175/JTECH-D-18-0240.1
51. Logarithmic bred vectors. A new ensemble method with adjustable spread and calibration time C. Primo et al. 10.1029/2007JD008998
52. Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System S. Lim et al. 10.3390/app10249010
53. Low-dimensional nonlinearity of ENSO and its impact on predictability Y. Tang & Z. Deng 10.1016/j.physd.2009.11.006