42 citations as recorded by crossref.

1. Multiple-scale error growth in a convection-resolving model F. Uboldi & A. Trevisan
2. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al.
3. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error C. Grudzien et al.
4. Structural origins and real-time predictors of intermittency A. Barone et al.
5. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi
6. Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution K. Gurumoorthy et al.
7. Mathematical foundations of hybrid data assimilation from a synchronization perspective S. Penny
8. 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.
9. Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model S. Penny et al.
10. Asymptotic properties of linear filter for deterministic processes A. Reddy et al.
11. Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation T. Yoshida & E. Kalnay
12. Impacts of the Lagrangian Data Assimilation of Surface Drifters on Estimating Ocean Circulation during the Gulf of Mexico Grand Lagrangian Deployment L. Sun et al.
13. The onset of chaos in nonautonomous dissipative dynamical systems: a low-order ocean-model case study S. Pierini et al.
14. Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error C. Grudzien et al.
15. Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems C. Quinn et al.
16. New Trends in Ensemble Forecast Strategy: Uncertainty Quantification for Coarse-Grid Computational Fluid Dynamics V. Resseguier et al.
17. Recurrent neural network spectral method and its application in stable filtering problems Z. Sun et al.
18. Predictability of Extreme Waves in the Lorenz-96 Model Near Intermittency and Quasi-Periodicity A. Sterk & D. van Kekem
19. An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling B. Shi & G. Sun
20. Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter L. Palatella & A. Trevisan
21. Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation S. Penny et al.
22. Symmetries in the Lorenz-96 Model D. van Kekem & A. Sterk
23. Detectability Conditions and State Estimation for Linear Time-Varying and Nonlinear Systems M. Tranninger et al.
24. Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators K. Law et al.
25. Particle filtering for chaotic dynamical systems using future right-singular vectors R. Beeson & N. Sri Namachchivaya
26. Detectability Analysis and Observer Design for Linear Time Varying Systems M. Tranninger et al.
27. Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model S. Wu & R. Wang
28. Predictability of extreme values in geophysical models A. Sterk et al.
29. Finite Cascades of Pitchfork Bifurcations and Multistability in Generalized Lorenz-96 Models A. Pelzer & A. Sterk
30. Nonlinear stability of traffic models and the use of Lyapunov vectors for estimating the traffic state L. Palatella et al.
31. The EKF-AUS-NL algorithm implemented without the linear tangent model and in presence of parametric model error L. Palatella & F. Grasso
32. Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck
33. The Hybrid Local Ensemble Transform Kalman Filter S. Penny
34. Travelling waves and their bifurcations in the Lorenz-96 model D. van Kekem & A. Sterk
35. Ensemble Riemannian data assimilation: towards large-scale dynamical systems S. Tamang et al.
36. Simulation of Complex Systems Using the Observed Data Based on Recurrent Artificial Neural Networks A. Seleznev et al.
37. Latent space data assimilation by using deep learning M. Peyron et al.
38. Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace M. Bocquet et al.
39. Wave propagation in the Lorenz-96 model D. van Kekem & A. Sterk
40. The role of model dynamics in ensemble Kalman filter performance for chaotic systems G. Ng et al.
41. Lyapunov vectors and assimilation in the unstable subspace: theory and applications L. Palatella et al.
42. CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS A. TREVISAN & L. PALATELLA