53 citations as recorded by crossref.

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7. Coupled bred vectors in the tropical Pacific and their application to ENSO prediction Y. Ham et al. 10.1016/j.pocean.2012.04.005
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9. Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model S. Yang et al. 10.1175/2008MWR2396.1
10. Bred Vectors and Tropical Pacific Forecast Errors in the NASA Coupled General Circulation Model S. Yang et al. 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. 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. 10.2478/s11600-006-0035-6
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15. Coupled initialization in an ocean–atmosphere tropical cyclone prediction system P. Sandery & T. O'Kane 10.1002/qj.2117
16. Predicting air quality: Improvements through advanced methods to integrate models and measurements G. Carmichael et al. 10.1016/j.jcp.2007.02.024
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18. Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model S. Shin et al. 10.1002/qj.3429
19. Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model S. Yang et al. 10.1175/MWR-D-11-00313.1
20. Bred vectors of the Lorenz63 system Y. Zhang et al. 10.1007/s00376-015-4275-8
21. Identifying Martian atmospheric instabilities and their physical origins using bred vectors S. Greybush et al. 10.1002/qj.1990
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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
25. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter H. Li et al. 10.1002/qj.371
26. Predicting the East Australian Current T. O’Kane et al. 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. 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
30. Ensemble kalman variational objective: a variational inference framework for sequential variational auto-encoders T. Ishizone et al. 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. 10.1175/2011JAS3729.1
32. A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation P. Tandeo et al. 10.1175/MWR-D-19-0240.1
33. Model error and sequential data assimilation: A deterministic formulation A. Carrassi et al. 10.1002/qj.284
34. 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
35. Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors J. Kang et al. 10.3390/atmos13122070
36. 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
37. The temporal cascade structure of reanalyses and global circulation models J. Stolle et al. 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. 10.1260/1748-3018.5.4.667
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41. Comparison of Nonlinear Local Lyapunov Vectors and Bred Vectors in Estimating the Spatial Distribution of Error Growth J. Feng et al. 10.1175/JAS-D-17-0266.1
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
46. A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al. 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. 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