Improving short-term sea ice concentration forecasts using deep learning

Abstract d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Improving short-term sea ice concentration forecasts using deep learning ; volume:18 ; number:4 ; year:2024 ; pages:2161-2176 ; extent:16 ; The Cryosphere ; 18, Heft 4 (2024), 2161-2176 (gesamt 16)

Contributors, Places and Time

• Creator: Palerme, Cyril ; Lavergne, Thomas ; Rusin, Jozef ; Melsom, Arne ; Brajard, Julien ; Kvanum, Are Frode ; Macdonald Sørensen, Atle ; Bertino, Laurent ; Müller, Malte

Further information

• DOI: 10.5194/tc-18-2161-2024
• URN: urn:nbn:de:101:1-2405020415574.978369419499
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 08.09.0004, 7:20 AM CET