22 citations as recorded by crossref.

1. Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var R. Santana et al. https://doi.org/10.5194/gmd-16-3675-2023
2. On the impact of vertical coordinate choice for innovation when assimilating hydrographic profiles into isopycnal ocean models F. Costa & C. Tanajura https://doi.org/10.1016/j.ocemod.2021.101917
3. Persistency and Surface Convergence Evidenced by Two Maker Buoys in the Great Pacific Garbage Patch B. Sainte-Rose et al. https://doi.org/10.3390/jmse11010068
4. Model sensitivity experiments on data assimilation, downscaling and tides for the representation of the Cape São Tomé Eddies R. Santana et al. https://doi.org/10.1007/s10236-019-01307-w
5. Assessing the extended-range predictability of the ocean model HYCOM with the REMO ocean data assimilation system (RODAS) in the South Atlantic J. Carvalho et al. https://doi.org/10.1080/1755876X.2019.1606880
6. Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model Y. Wang et al. https://doi.org/10.3389/fclim.2022.918572
7. Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5 J. Lee & Y. Ham https://doi.org/10.3389/fclim.2022.797733
8. Assimilation of Argo Temperature and Salinity Profiles Using a Bias-Aware EnOI Scheme for the Labrador Sea K. Scott et al. https://doi.org/10.1175/JTECH-D-17-0222.1
9. A New Approach for Estimating Salinity in the Southwest Atlantic and Its Application in a Data Assimilation Evaluation Experiment G. Dorfschäfer et al. https://doi.org/10.1029/2020JC016428
10. Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham https://doi.org/10.1038/s41612-023-00402-6
11. Statistical Analysis of Physical Characteristics Calculated by NEMO Model After Data Assimilation K. Belyaev et al. https://doi.org/10.3390/math13060948
12. Impact of assimilating buoy directional spectra on tropical cyclone wind-wave modeling: a case study of Isaias (2020) J. Liu et al. https://doi.org/10.1007/s13131-025-2537-6
13. Analog ensemble data assimilation in a quasigeostrophic coupled model I. Grooms et al. https://doi.org/10.1002/qj.4446
14. Data Assimilation of Satellite-Derived Arctic Sea-Ice Thickness During Boreal Summer J. Lee et al. https://doi.org/10.1109/JSTARS.2025.3561257
15. A Frequency-Dependent Assimilation Algorithm: Ensemble Optimal Smoothing Z. He et al. https://doi.org/10.3390/jmse10091324
16. Assessment of static ensembles on a global ocean assimilation system using EAKF L. Liu & X. Chen https://doi.org/10.1007/s10236-024-01620-z
17. Impact of assimilating CFOSAT SWIM omnidirectional spectra on tropical cyclone wind–wave modeling J. Liu et al. https://doi.org/10.1016/j.ocemod.2026.102743
18. Study on the tropical cyclone-generated waves after ensemble-based assimilation of multi-mission satellite altimeters J. Liu et al. https://doi.org/10.1016/j.oceaneng.2024.117361
19. Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders I. Grooms https://doi.org/10.1002/qj.3910
20. Coastal Ocean Observing and Modeling Systems in Brazil: Initiatives and Future Perspectives G. Franz et al. https://doi.org/10.3389/fmars.2021.681619
21. Ensemble-based assimilation of wave model predictions: Contrasting the impact of assimilation in nearshore and offshore forecasting at different distances from assimilated data J. Liu et al. https://doi.org/10.1016/j.apor.2023.103726
22. Observing system experiments over the Atlantic Ocean with the REMO ocean data assimilation system (RODAS) into HYCOM C. Tanajura et al. https://doi.org/10.1007/s10236-019-01309-8