Articles | Volume 12, issue 11
https://doi.org/10.5194/tc-12-3671-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-3671-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Nov 2018
Research article |
| 26 Nov 2018
| 26 Nov 2018
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
François Counillon
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
Bjerknes Center for Climate Research, Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
Bjerknes Center for Climate Research, Bergen, Norway
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37 citations as recorded by crossref.
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37 citations as recorded by crossref.
- The SARAL/AltiKa mission: A step forward to the future of altimetry J. Verron et al. 10.1016/j.asr.2020.01.030
- Copernicus Marine Service Ocean State Report, Issue 5 K. von Schuckmann et al. 10.1080/1755876X.2021.1946240
- Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al. 10.1029/2022MS003176
- Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3 T. Tian et al. 10.5194/gmd-14-4283-2021
- Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model Q. Yang et al. 10.1016/j.atmosres.2019.04.021
- From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
- Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM) E. Fiedler et al. 10.5194/tc-16-61-2022
- Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar J. Karvonen et al. 10.5194/tc-16-1821-2022
- Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020 S. Cheng et al. 10.5194/tc-17-1735-2023
- Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5 J. Lee & Y. Ham 10.3389/fclim.2022.797733
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
- 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
- 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
- Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak 10.1029/2021MS002533
- Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model M. Wieringa et al. 10.5194/tc-18-5365-2024
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Observations of SAR polarimetric parameters of lake and fast sea ice during the early growth phase M. Shokr & M. Dabboor 10.1016/j.rse.2020.111910
- Ensemble-based estimation of sea-ice volume variations in the Baffin Bay C. Min et al. 10.5194/tc-15-169-2021
- 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
- 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
- Ocean Reanalyses: Recent Advances and Unsolved Challenges A. Storto et al. 10.3389/fmars.2019.00418
- 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
- Better synoptic and subseasonal sea ice thickness predictions are urgently required: a lesson learned from the YOPP data validation Q. Yang et al. 10.1088/1748-9326/acdcaa
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. 10.1007/s10712-023-09781-0
- Assimilation of sea surface salinities from SMOS in an Arctic coupled ocean and sea ice reanalysis J. Xie et al. 10.5194/os-19-269-2023
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- Assessment of contemporary satellite sea ice thickness products for Arctic sea ice H. Sallila et al. 10.5194/tc-13-1187-2019
- Year-round impact of winter sea ice thickness observations on seasonal forecasts B. Balan-Sarojini et al. 10.5194/tc-15-325-2021
- Arctic ocean–sea ice reanalysis for the period 2007–2016 using the adjoint method G. Lyu et al. 10.1002/qj.4002
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations C. Yang et al. 10.1029/2019MS001938
- Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al. 10.5194/tc-18-2381-2024
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
Latest update: 14 Dec 2024
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and...
