Articles | Volume 17, issue 4
https://doi.org/10.5194/tc-17-1735-2023
© Author(s) 2023. 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-17-1735-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Apr 2023
Research article |
| 25 Apr 2023
| 25 Apr 2023
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Navigation College, Dalian Maritime University, Dalian 116026, China
Centre for Ports and Maritime Safety, Dalian Maritime University, Dalian 116026, China
Yumeng Chen
Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6AH, UK
Ali Aydoğdu
Ocean Modelling and Data Assimilation Division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici – CMCC, Bologna, Italy
Laurent Bertino
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Alberto Carrassi
Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6AH, UK
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Bologna, Italy
Pierre Rampal
CNRS, Institut de Géophysique de l’Environnement, Grenoble 38058, France
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Christopher K. R. T. Jones
Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA
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Cited
14 citations as recorded by crossref.
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al.
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al.
- Assimilation of radar freeboard and snow altimetry observations in the Arctic and Antarctic with a coupled ocean/sea ice modelling system A. Chenal et al.
- Optimal localization radius of data assimilation for Arctic sea ice initialization using CICE5/DART J. Kim et al.
- Toward an Arctic Ocean forecast system based on Finite Volume Community Ocean model Y. Zhang et al.
- Regime-dependence when constraining a sea ice model with observations: lessons from a single-column perspective M. Wieringa & C. Bitz
- Evolution of the Floe Size Distribution in Arctic Summer Based on High-Resolution Satellite Imagery Z. Li et al.
- Mechanisms and Predictability of Beaufort Sea Ice Retreat Revealed by Coupled Modeling and Remote Sensing Data H. Nie et al.
- Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses F. Cocetta et al.
- Baltic sea ice thickness estimation based on X-band SAR data and background information J. Karvonen & B. Cheng
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al.
- A data-assimilative approach to air quality analytics for policy and emergency planning E. Poudel et al.
- Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al.
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson
14 citations as recorded by crossref.
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al.
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al.
- Assimilation of radar freeboard and snow altimetry observations in the Arctic and Antarctic with a coupled ocean/sea ice modelling system A. Chenal et al.
- Optimal localization radius of data assimilation for Arctic sea ice initialization using CICE5/DART J. Kim et al.
- Toward an Arctic Ocean forecast system based on Finite Volume Community Ocean model Y. Zhang et al.
- Regime-dependence when constraining a sea ice model with observations: lessons from a single-column perspective M. Wieringa & C. Bitz
- Evolution of the Floe Size Distribution in Arctic Summer Based on High-Resolution Satellite Imagery Z. Li et al.
- Mechanisms and Predictability of Beaufort Sea Ice Retreat Revealed by Coupled Modeling and Remote Sensing Data H. Nie et al.
- Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses F. Cocetta et al.
- Baltic sea ice thickness estimation based on X-band SAR data and background information J. Karvonen & B. Cheng
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al.
- A data-assimilative approach to air quality analytics for policy and emergency planning E. Poudel et al.
- Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al.
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson
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Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data...
