the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved BEC SMOS Arctic Sea Surface Salinity product v3.1
Justino Martínez
Antonio Turiel
Verónica González-Gambau
Marta Umbert
Nina Hoareau
Cristina González-Haro
Estrella Olmedo
Manuel Arias
Rafael Catany
Laurent Bertino
Roshin P. Raj
Jiping Xie
Roberto Sabia
Diego Fernández
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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.
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