Improving short-term sea ice concentration forecasts using deep learning

Autor: C. Palerme, T. Lavergne, J. Rusin, A. Melsom, J. Brajard, A. F. Kvanum, A. Macdonald Sørensen, L. Bertino, M. Müller
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: The Cryosphere, Vol 18, Pp 2161-2176 (2024)
Druh dokumentu: article
ISSN: 1994-0416
1994-0424
DOI: 10.5194/tc-18-2161-2024
Popis: Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 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.
Databáze: Directory of Open Access Journals
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