Arctic sea ice data assimilation combining an ensemble  Kalman filter with a novel Lagrangian sea ice model  for the winter 2019–2020

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

doi:https://doi.org/10.5194/tc-17-1735-2023

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.

https://doi.org/10.5194/tc-17-1735-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

ECMWF Forecast User Guide (https://apps.ecmwf.int/archive-catalogue/?type=fc&class=od&stream=oper&expver=1) R. G. Owens and T. Hewson https://doi.org/10.21957/m1cs7h

Global Sea Ice Concentration (SSMIS) OSI-SAF https://osi-saf.eumetsat.int/products/osi-401-b

Global Low Resolution Sea Ice Drift OSI-SAF https://osi-saf.eumetsat.int/products/osi-405-c

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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.