17 citations as recorded by crossref.

1. A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field M. Morzfeld & B. Buffett https://doi.org/10.5194/npg-26-123-2019
2. Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck https://doi.org/10.1002/qj.4001
3. An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo https://doi.org/10.1063/5.0083071
4. A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity J. Lopez-Alvis et al. https://doi.org/10.1016/j.advwatres.2019.103427
5. Calibrate, emulate, sample E. Cleary et al. https://doi.org/10.1016/j.jcp.2020.109716
6. 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. https://doi.org/10.1016/j.camwa.2021.05.026
7. Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems A. Chattopadhyay et al. https://doi.org/10.2139/ssrn.4142015
8. Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering J. Fiddes et al. https://doi.org/10.5194/hess-23-4717-2019
9. Toward Utilizing Similarity in Hydrologic Data Assimilation H. Lee et al. https://doi.org/10.3390/hydrology11110177
10. Efficient Bayesian inference for large chaotic dynamical systems S. Springer et al. https://doi.org/10.5194/gmd-14-4319-2021
11. Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods O. Dunbar et al. https://doi.org/10.1137/21M1410853
12. A testbed for geomagnetic data assimilation K. Gwirtz et al. https://doi.org/10.1093/gji/ggab327
13. Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay et al. https://doi.org/10.1016/j.jcp.2023.111918
14. Bayesian spatiotemporal modeling for inverse problems S. Lan et al. https://doi.org/10.1007/s11222-023-10253-z
15. Rigorous convergence bounds for stochastic differential equations with application to uncertainty quantification L. Blake et al. https://doi.org/10.1016/j.physd.2025.134742
16. Estimating parameters of the nonlinear cloud and rain equation from a large-eddy simulation S. Lunderman et al. https://doi.org/10.1016/j.physd.2020.132500
17. Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods I. Lopez‐Gomez et al. https://doi.org/10.1029/2022MS003105