Articles | Volume 17, issue 4
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

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Cited articles

Alam, J. M. and Lin, J. C.: Toward a Fully Lagrangian Atmospheric Modeling System, Mon. Weather Rev., 136, 4653–4667,, 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,, 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
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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.