26 citations as recorded by crossref.

1. Experiences in multiyear combined state–parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter E. Simon et al. 10.1016/j.jmarsys.2015.07.004
2. Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach J. Amezcua & P. Van Leeuwen 10.3402/tellusa.v66.23493
3. A comparison of nonlinear extensions to the ensemble Kalman filter I. Grooms 10.1007/s10596-022-10141-x
4. A generic approach to explicit simulation of uncertainty in the NEMO ocean model J. Brankart et al. 10.5194/gmd-8-1285-2015
5. An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments C. Germineaud et al. 10.1175/JTECH-D-19-0002.1
6. Status and future of data assimilation in operational oceanography M. Martin et al. 10.1080/1755876X.2015.1022055
7. Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model D. Ford et al. 10.5194/os-8-751-2012
8. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
9. Foundations for Universal Non‐Gaussian Data Assimilation S. Van Loon & S. Fletcher 10.1029/2023GL105148
10. Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO–PISCES simulator M. Popov et al. 10.5194/os-20-155-2024
11. Generating atmospheric forcing perturbations for an ocean data assimilation ensemble I. Mirouze & A. Storto 10.1080/16000870.2019.1624459
12. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution L. Bessières et al. 10.5194/gmd-10-1091-2017
13. Correcting for Position Errors in Variational Data Assimilation T. Nehrkorn et al. 10.1175/MWR-D-14-00127.1
14. Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model M. Doron et al. 10.1016/j.jmarsys.2013.02.007
15. Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems J. Brankart 10.3389/fams.2019.00058
16. Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation G. Candille et al. 10.5194/os-11-425-2015
17. A framework for high‐resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions A. Devers et al. 10.1002/qj.3663
18. A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity I. Grooms et al. 10.1371/journal.pone.0248266
19. Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
20. Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean Y. Santana-Falcón et al. 10.5194/os-16-1297-2020
21. FYRE Climate: a high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012 A. Devers et al. 10.5194/cp-17-1857-2021
22. A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation S. Metref et al. 10.5194/npg-21-869-2014
23. Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations T. Nguyen et al. 10.1109/TGRS.2023.3338296
24. Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea M. Yaremchuk et al. 10.1029/2018JC014581
25. EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. 10.5194/gmd-17-5619-2024
26. Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data C. Fontana et al. 10.5194/os-9-37-2013