52 citations as recorded by crossref.

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4. Predictability of the thermally driven laboratory rotating annulus R. Young & P. Read https://doi.org/10.1002/qj.2694
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6. Estimating trajectory uncertainties due to flow dependent errors in the atmospheric analysis A. Engström & L. Magnusson https://doi.org/10.5194/acp-9-8857-2009
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8. Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere S. Kotsuki et al. https://doi.org/10.1002/qj.3060
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10. 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
11. Diagnosing the possible dynamics controlling Sahel precipitation in the short-range ensemble community atmospheric model hindcasts Y. Tseng et al. https://doi.org/10.1007/s00382-016-2995-9
12. Impact of the surface temperature and vertical shear of zonal wind on the dynamics of a simple two-layer model of the atmosphere R. Brojewski et al. https://doi.org/10.2478/s11600-006-0035-6
13. New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector J. Kug et al. https://doi.org/10.1007/s00382-009-0664-y
14. The geometric norm improves ensemble forecasting with the breeding method D. Pazó et al. https://doi.org/10.1002/qj.2115
15. Coupled initialization in an ocean–atmosphere tropical cyclone prediction system P. Sandery & T. O'Kane https://doi.org/10.1002/qj.2117
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19. Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model S. Yang et al. https://doi.org/10.1175/MWR-D-11-00313.1
20. Bred vectors of the Lorenz63 system Y. Zhang et al. https://doi.org/10.1007/s00376-015-4275-8
21. Identifying Martian atmospheric instabilities and their physical origins using bred vectors S. Greybush et al. https://doi.org/10.1002/qj.1990
22. Ensemble Kalman filtering without the intrinsic need for inflation M. Bocquet https://doi.org/10.5194/npg-18-735-2011
23. Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction K. Saito et al. https://doi.org/10.3402/tellusa.v64i0.11594
24. Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter Y. Ham et al. https://doi.org/10.1007/s00382-009-0582-z
25. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter H. Li et al. https://doi.org/10.1002/qj.371
26. Predicting the East Australian Current T. O’Kane et al. https://doi.org/10.1016/j.ocemod.2011.04.003
27. The Application of Nonlinear Local Lyapunov Vectors to Ensemble Predictions in Lorenz Systems J. Feng et al. https://doi.org/10.1175/JAS-D-13-0270.1
28. Tailored Ensemble Prediction Systems: Application of Seamless Scale Bred Vectors A. HERMOSO et al. https://doi.org/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. https://doi.org/10.1029/2010JC006851
30. Ensemble kalman variational objective: a variational inference framework for sequential variational auto-encoders T. Ishizone et al. https://doi.org/10.1587/nolta.14.691
31. Maximizing the Statistical Diversity of an Ensemble of Bred Vectors by Using the Geometric Norm D. Pazó et al. https://doi.org/10.1175/2011JAS3729.1
32. Model error and sequential data assimilation: A deterministic formulation A. Carrassi et al. https://doi.org/10.1002/qj.284
33. 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
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35. The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii J. Feng et al. https://doi.org/10.1007/s00376-018-8123-5
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37. The temporal cascade structure of reanalyses and global circulation models J. Stolle et al. https://doi.org/10.1002/qj.1916
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39. Ensemble Methods for Dynamic Data Assimilation of Chemical Observations in Atmospheric Models A. Sandu et al. https://doi.org/10.1260/1748-3018.5.4.667
40. The 1000-Member Ensemble Kalman Filtering with the JMA Nonhydrostatic Mesoscale Model on the K Computer M. KUNII https://doi.org/10.2151/jmsj.2014-607
41. Comparison of Nonlinear Local Lyapunov Vectors and Bred Vectors in Estimating the Spatial Distribution of Error Growth J. Feng et al. https://doi.org/10.1175/JAS-D-17-0266.1
42. Stochastically perturbed bred vectors in multi‐scale systems B. Giggins & G. Gottwald https://doi.org/10.1002/qj.3457
43. Investigating atmospheric predictability on Mars using breeding vectors in a general‐circulation model C. Newman et al. https://doi.org/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 https://doi.org/10.1175/2010JCLI3491.1
45. Stochastically perturbed bred vectors in single‐scale systems B. Giggins & G. Gottwald https://doi.org/10.1002/qj.3888
46. A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al. https://doi.org/10.5194/gmd-15-8325-2022
47. Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model M. Ouyang et al. https://doi.org/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 https://doi.org/10.1002/qj.340
49. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
50. Logarithmic bred vectors. A new ensemble method with adjustable spread and calibration time C. Primo et al. https://doi.org/10.1029/2007JD008998
51. Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System S. Lim et al. https://doi.org/10.3390/app10249010
52. Low-dimensional nonlinearity of ENSO and its impact on predictability Y. Tang & Z. Deng https://doi.org/10.1016/j.physd.2009.11.006