Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-885-2025
https://doi.org/10.5194/gmd-18-885-2025
Methods for assessment of models
 | 
14 Feb 2025
Methods for assessment of models |  | 14 Feb 2025

Tuning parameters of a sea ice model using machine learning

Anton Korosov, Yue Ying, and Einar Ólason

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Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
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