27 citations as recorded by crossref.

1. Statistical and dynamical properties of covariant lyapunov vectors in a coupled atmosphere-ocean model—multiscale effects, geometric degeneracy, and error dynamics S. Vannitsem & V. Lucarini 10.1088/1751-8113/49/22/224001
2. Projected Shadowing-Based Data Assimilation B. de Leeuw et al. 10.1137/17M1141163
3. Three‐dimensional spatial interpolation of 2 m temperature over Norway C. Lussana et al. 10.1002/qj.3208
4. Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model A. Albarakati et al. 10.1016/j.camwa.2021.05.026
5. CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS A. TREVISAN & L. PALATELLA 10.1142/S0218127411030635
6. Lyapunov vectors and assimilation in the unstable subspace: theory and applications L. Palatella et al. 10.1088/1751-8113/46/25/254020
7. Multiple-scale error growth in a convection-resolving model F. Uboldi & A. Trevisan 10.5194/npg-22-1-2015
8. On the Kalman Filter error covariance collapse into the unstable subspace A. Trevisan & L. Palatella 10.5194/npg-18-243-2011
9. Four‐dimensional variational assimilation in the unstable subspace and the optimal subspace dimension A. Trevisan et al. 10.1002/qj.571
10. Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution K. Gurumoorthy et al. 10.1137/15M1025839
11. Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
12. Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al. 10.1016/j.physd.2023.133765
13. A detectability criterion and data assimilation for nonlinear differential equations J. Frank & S. Zhuk 10.1088/1361-6544/aaddcb
14. Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China Q. Qiu et al. 10.1007/s11676-017-0534-0
15. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. 10.1063/5.0230837
16. Latent space data assimilation by using deep learning M. Peyron et al. 10.1002/qj.4153
17. Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace M. Bocquet et al. 10.1137/16M1068712
18. Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble C. Chang et al. 10.1175/MWR-D-19-0128.1
19. The onset of chaos in nonautonomous dissipative dynamical systems: a low-order ocean-model case study S. Pierini et al. 10.5194/npg-25-671-2018
20. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
21. Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter L. Palatella & A. Trevisan 10.1103/PhysRevE.91.042905
22. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
23. Asymptotic behavior of the forecast–assimilation process with unstable dynamics D. Crisan & M. Ghil 10.1063/5.0105590
24. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error C. Grudzien et al. 10.5194/npg-25-633-2018
25. Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting Y. Cheng et al. 10.5194/npg-30-289-2023
26. Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models L. De Cruz et al. 10.5194/npg-25-387-2018
27. Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error C. Grudzien et al. 10.1137/17M114073X