Articles | Volume 17, issue 4
https://doi.org/10.5194/tc-17-1735-2023
https://doi.org/10.5194/tc-17-1735-2023
Research article
 | 
25 Apr 2023
Research article |  | 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

Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones

Related authors

Marine data assimilation in the UK: the past, the present, and the vision for the future
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025,https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
OceanVar2.0: an open-source variational ocean data assimilation scheme. Sensitivity to altimetry sea level anomaly assimilation
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
EGUsphere, https://doi.org/10.5194/egusphere-2025-1553,https://doi.org/10.5194/egusphere-2025-1553, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Data assimilation schemes for ocean forecasting: state of the art
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025,https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Numerical models for monitoring and forecasting sea ice: a short description of present status
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025,https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Hybrid machine learning data assimilation for marine biogeochemistry
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
EGUsphere, https://doi.org/10.48550/arXiv.2504.05218,https://doi.org/10.48550/arXiv.2504.05218, 2025
Short summary

Cited articles

Alam, J. M. and Lin, J. C.: Toward a Fully Lagrangian Atmospheric Modeling System, Mon. Weather Rev., 136, 4653–4667, https://doi.org/10.1175/2008MWR2515.1, 2008. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280, https://doi.org/10.1016/j.asr.2017.12.030, 2018. a
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Anderson, J. L.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, 2007. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999. a
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Share