the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records
Atle Macdonald Sørensen
Stefan Kern
Rasmus Tonboe
Dirk Notz
Signe Aaboe
Louisa Bell
Gorm Dybkjær
Steinar Eastwood
Carolina Gabarro
Georg Heygster
Mari Anne Killie
Matilde Brandt Kreiner
John Lavelle
Roberto Saldo
Stein Sandven
Leif Toudal Pedersen
Related authors
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
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