Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-61-2022
© Author(s) 2022. 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-16-61-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2022
Research article |
| 06 Jan 2022
| 06 Jan 2022
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)
Emma K. Fiedler
CORRESPONDING AUTHOR
Met Office, Exeter, EX1 3PB, UK
Matthew J. Martin
Met Office, Exeter, EX1 3PB, UK
Ed Blockley
Met Office, Exeter, EX1 3PB, UK
Davi Mignac
Met Office, Exeter, EX1 3PB, UK
Nicolas Fournier
Met Office, Exeter, EX1 3PB, UK
Andy Ridout
Centre for Polar Observation and Modelling, University College London, London, WC1E 6BT, UK
Andrew Shepherd
Centre for Polar Observation and Modelling, University of Leeds, Leeds, LS2 9JT, UK
Rachel Tilling
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
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Cited
14 citations as recorded by crossref.
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. 10.1007/s10712-023-09781-0
- 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
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness I. Sievers et al. 10.5194/tc-18-5985-2024
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2 M. Gupta et al. 10.5194/tc-19-1241-2025
- Generation of root zone soil moisture from the integration of an all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China Y. Wang et al. 10.1016/j.jhydrol.2024.132098
- The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system N. Williams et al. 10.5194/tc-17-2509-2023
- Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates I. Sievers et al. 10.5194/tc-17-3721-2023
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- 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
- Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. 10.5194/tc-15-5483-2021
13 citations as recorded by crossref.
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. 10.1007/s10712-023-09781-0
- 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
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness I. Sievers et al. 10.5194/tc-18-5985-2024
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2 M. Gupta et al. 10.5194/tc-19-1241-2025
- Generation of root zone soil moisture from the integration of an all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China Y. Wang et al. 10.1016/j.jhydrol.2024.132098
- The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system N. Williams et al. 10.5194/tc-17-2509-2023
- Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates I. Sievers et al. 10.5194/tc-17-3721-2023
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- 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
1 citations as recorded by crossref.
Latest update: 03 May 2025
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been...
