36 citations as recorded by crossref.

1. Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period L. Mu et al. 10.1029/2018JC014316
2. Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
3. Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
4. Sea ice assimilation into a coupled ocean–sea ice model using its adjoint N. Koldunov et al. 10.5194/tc-11-2265-2017
5. Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
6. Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model Y. Wang et al. 10.5194/tc-16-1141-2022
7. Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model P. Dai et al. 10.1007/s00382-020-05196-4
8. Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model G. Stonebridge et al. 10.1080/16000870.2018.1445379
9. Environmental Change at Deep-Sea Sponge Habitats Over the Last Half Century: A Model Hindcast Study for the Age of Anthropogenic Climate Change A. Samuelsen et al. 10.3389/fmars.2022.737164
10. Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
11. Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic M. Kimmritz et al. 10.1029/2019MS001825
12. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study V. Lien et al. 10.5194/sp-4-osr8-8-2024
13. Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
14. Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
15. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system Q. Shu et al. 10.1007/s13131-021-1768-4
16. Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble Z. Labe et al. 10.1175/JCLI-D-17-0436.1
17. Thin Arctic sea ice in L-band observations and an ocean reanalysis S. Tietsche et al. 10.5194/tc-12-2051-2018
18. Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models F. Richter et al. 10.5194/tc-12-921-2018
19. Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
20. Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
21. Copernicus Marine Service Ocean State Report K. von Schuckmann et al. 10.1080/1755876X.2018.1489208
22. Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model M. Kimmritz et al. 10.1080/16000870.2018.1435945
23. Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4 Y. Xiu et al. 10.1017/jog.2020.110
24. Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis J. Xie et al. 10.5194/tc-12-3671-2018
25. Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data J. Xie et al. 10.5194/os-15-1191-2019
26. Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
27. Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation S. FRITZNER et al. 10.1017/jog.2018.33
28. Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness E. Blockley & K. Peterson 10.5194/tc-12-3419-2018
29. Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
30. From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
31. Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts duringXuelong's first trans-Arctic Passage in summer 2017 L. Mu et al. 10.1017/jog.2019.55
32. Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously L. Mu et al. 10.1002/qj.3225
33. Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance T. Kaminski et al. 10.5194/tc-12-2569-2018
34. Copernicus Marine Service Ocean State Report, Issue 4 K. von Schuckmann et al. 10.1080/1755876X.2020.1785097
35. Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner et al. 10.5194/tc-13-491-2019
36. Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166