the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Christopher K. R. T. Jones
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Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
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