16 citations as recorded by crossref.

1. Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet https://doi.org/10.5194/npg-25-765-2018
2. Data assimilation using adaptive, non-conservative, moving mesh models A. Aydoğdu et al. https://doi.org/10.5194/npg-26-175-2019
3. A Hybrid Particle–Ensemble Kalman Filter for Lagrangian Data Assimilation L. Slivinski et al. https://doi.org/10.1175/MWR-D-14-00051.1
4. Assimilating en-route Lagrangian observations E. Spiller et al. https://doi.org/10.3402/tellusa.v65i0.20319
5. Efficient quadratures for high-dimensional Bayesian data assimilation M. Cheng et al. https://doi.org/10.1016/j.jcp.2024.112945
6. Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter L. Sun & S. Penny https://doi.org/10.1175/MWR-D-18-0406.1
7. An efficient and statistically accurate Lagrangian data assimilation algorithm with applications to discrete element sea ice models N. Chen et al. https://doi.org/10.1016/j.jcp.2022.111000
8. Uncertainty quantification of nonlinear Lagrangian data assimilation using linear stochastic forecast models N. Chen & S. Fu https://doi.org/10.1016/j.physd.2023.133784
9. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
10. 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. https://doi.org/10.1175/MWR-D-21-0123.1
11. Launching drifter observations in the presence of uncertainty N. Chen et al. https://doi.org/10.1016/j.physd.2024.134086
12. Lagrangian descriptors with uncertainty N. Chen et al. https://doi.org/10.1016/j.physd.2024.134282
13. A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation E. Bollt et al. https://doi.org/10.1016/j.physd.2024.134283
14. A coherent structure approach for parameter estimation in Lagrangian Data Assimilation J. Maclean et al. https://doi.org/10.1016/j.physd.2017.08.007
15. Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations H. Ruzayqat et al. https://doi.org/10.1002/qj.4716
16. Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting H. Holm et al. https://doi.org/10.1016/j.jcpx.2020.100053