Articles | Volume 10, issue 6
https://doi.org/10.5194/tc-10-2745-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-2745-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Nov 2016
Research article |
| 16 Nov 2016
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system
Nansen Environmental and Remote Sensing Center, Bergen, Norway
François Counillon
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Xiangshan Tian-Kunze
Institute of Oceanography, University of Hamburg, Hamburg, Germany
Lars Kaleschke
Institute of Oceanography, University of Hamburg, Hamburg, Germany
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Cited
36 citations as recorded by crossref.
- 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
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- Sea ice assimilation into a coupled ocean–sea ice model using its adjoint N. Koldunov et al. 10.5194/tc-11-2265-2017
- 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
- Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model Y. Wang et al. 10.5194/tc-16-1141-2022
- 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
- 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
- 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
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
- Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic M. Kimmritz et al. 10.1029/2019MS001825
- Surface and bottom marine heatwave characteristics in the Barents Sea: a model study V. Lien et al. 10.5194/sp-4-osr8-8-2024
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- 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
- 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
- Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble Z. Labe et al. 10.1175/JCLI-D-17-0436.1
- Thin Arctic sea ice in L-band observations and an ocean reanalysis S. Tietsche et al. 10.5194/tc-12-2051-2018
- 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
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Copernicus Marine Service Ocean State Report K. von Schuckmann et al. 10.1080/1755876X.2018.1489208
- 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
- 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
- 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
- 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
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- 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
- 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
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
- 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
- Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously L. Mu et al. 10.1002/qj.3225
- 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
- Copernicus Marine Service Ocean State Report, Issue 4 K. von Schuckmann et al. 10.1080/1755876X.2020.1785097
- 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
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
36 citations as recorded by crossref.
- 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
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- Sea ice assimilation into a coupled ocean–sea ice model using its adjoint N. Koldunov et al. 10.5194/tc-11-2265-2017
- 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
- Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model Y. Wang et al. 10.5194/tc-16-1141-2022
- 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
- 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
- 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
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
- Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic M. Kimmritz et al. 10.1029/2019MS001825
- Surface and bottom marine heatwave characteristics in the Barents Sea: a model study V. Lien et al. 10.5194/sp-4-osr8-8-2024
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- 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
- 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
- Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble Z. Labe et al. 10.1175/JCLI-D-17-0436.1
- Thin Arctic sea ice in L-band observations and an ocean reanalysis S. Tietsche et al. 10.5194/tc-12-2051-2018
- 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
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Copernicus Marine Service Ocean State Report K. von Schuckmann et al. 10.1080/1755876X.2018.1489208
- 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
- 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
- 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
- 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
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- 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
- 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
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
- 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
- Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously L. Mu et al. 10.1002/qj.3225
- 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
- Copernicus Marine Service Ocean State Report, Issue 4 K. von Schuckmann et al. 10.1080/1755876X.2020.1785097
- 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
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
Latest update: 01 Jun 2025
Short summary
As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice...
