The Cryosphere
The Cryosphere
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Articles | Volume 17, issue 10
Articles | Volume 17, issue 10
https://doi.org/10.5194/tc-17-4487-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-4487-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 10
Research article
 | 
26 Oct 2023
Research article |  | 26 Oct 2023

Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Keguang Wang, Alfatih Ali, and Caixin Wang

Data sets

SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC K. Wang, A. Ali, and C. Wang https://doi.org/10.5281/zenodo.7533372

SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC (Version V2) K. Wang, A. Ali, and C. Wang https://doi.org/10.5281/zenodo.10025338

Model code and software

keguangw/hycom-cice_coin: LAON assimilation of SIC K. Wang and A. Ali https://doi.org/10.5281/zenodo.7572286

Global Ocean Physics Analysis and Forecast Copernicus Marine Service https://doi.org/10.48670/moi-00016

Arctic Ocean Physics Analysis and Forecast Copernicus Marine Service https://doi.org/10.48670/moi-00001

Arctic Ocean Sea Ice Analysis and Forecast Copernicus Marine Service https://doi.org/10.48670/moi-00004

Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland Copernicus Marine Service https://doi.org/10.48670/moi-00128

Global Ocean-Real time in-situ observations objective analysis Copernicus Marine Service https://doi.org/10.48670/moi-00037

Global Sea Ice Concentration (SSMIS) OSI SAF https://doi.org/10.15770/EUM_SAF_OSI_NRT_2004

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Short summary
A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.
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