25 citations as recorded by crossref.

1. Ensemble of 4DVARs (En4DVar) data assimilation in a coastal ocean circulation model, Part I: Methodology and ensemble statistics I. Pasmans & A. Kurapov https://doi.org/10.1016/j.ocemod.2019.101493
2. A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation E. Nino-Ruiz et al. https://doi.org/10.3390/atmos11020167
3. An ensemble framework for time delay synchronization F. Pinheiro et al. https://doi.org/10.1002/qj.3204
4. Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations A. Van Wesemael et al. https://doi.org/10.1016/j.advwatres.2019.05.025
5. Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres N. Gustafsson et al. https://doi.org/10.1002/qj.3179
6. Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA) C. Schraff et al. https://doi.org/10.1002/qj.2748
7. On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. https://doi.org/10.1007/s11356-020-08268-4
8. Systematic Comparison of Four-Dimensional Data Assimilation Methods With and Without the Tangent Linear Model Using Hybrid Background Error Covariance: E4DVar versus 4DEnVar J. Poterjoy & F. Zhang https://doi.org/10.1175/MWR-D-14-00224.1
9. The benefits of a hybrid‐3DEnVar data assimilation scheme for the convection‐permitting NWP model AROME–Austria K. Jyoti et al. https://doi.org/10.1002/qj.70207
10. Limits of constant-parameter constitutive models for hydrogels under inertial cavitation T. Chu et al. https://doi.org/10.1039/D6SM00547K
11. Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean V. Shutyaev https://doi.org/10.1134/S0001433819010080
12. Relevance of climatological background error statistics for mesoscale data assimilation J. Bojarova & N. Gustafsson https://doi.org/10.1080/16000870.2019.1615168
13. An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula E. Nino-Ruiz et al. https://doi.org/10.1007/s11071-019-05411-w
14. Current and Emerging Time-Integration Strategies in Global Numerical Weather and Climate Prediction G. Mengaldo et al. https://doi.org/10.1007/s11831-018-9261-8
15. Numerical Simulation of Ocean Circulation with Ultrahigh Spatial Resolution G. Korotaev & V. Shutyaev https://doi.org/10.1134/S000143382003010X
16. Bayesian optimal design accelerates discovery of soft material properties from bubble dynamics T. Chu et al. https://doi.org/10.1007/s00466-025-02606-4
17. A Hybrid Monte‐Carlo sampling smoother for four‐dimensional data assimilation A. Attia et al. https://doi.org/10.1002/fld.4259
18. A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results T. Montmerle et al. https://doi.org/10.1002/qj.3334
19. Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System J. Caron et al. https://doi.org/10.1175/MWR-D-14-00353.1
20. Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results Y. Wang & X. Wang https://doi.org/10.3390/atmos12101286
21. Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts W. Wu et al. https://doi.org/10.1175/WAF-D-16-0045.1
22. High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar Y. Yang & E. Memin https://doi.org/10.1080/16000870.2017.1308772
23. Data assimilation of radar reflectivity volumes in a LETKF scheme T. Gastaldo et al. https://doi.org/10.5194/npg-25-747-2018
24. Towards a Data-Derived Observation Error Covariance Matrix for Satellite Measurements Y. Liu et al. https://doi.org/10.3390/rs11151770
25. Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations J. Spratt et al. https://doi.org/10.1016/j.jmps.2021.104455