Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2161-2024
https://doi.org/10.5194/tc-18-2161-2024
Research article
 | 
30 Apr 2024
Research article |  | 30 Apr 2024

Improving short-term sea ice concentration forecasts using deep learning

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

Related authors

MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic
Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller
EGUsphere, https://doi.org/10.5194/egusphere-2025-2001,https://doi.org/10.5194/egusphere-2025-2001, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Calibration of sea ice drift forecasts using random forest algorithms
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021,https://doi.org/10.5194/tc-15-3989-2021, 2021
Short summary

Cited articles

Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat. Commun., 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021. a, b, c, d, e
Barton, N., Metzger, E. J., Reynolds, C. A., Ruston, B., Rowley, C., Smedstad, O. M., Ridout, J. A., Wallcraft, A., Frolov, S., Hogan, P., Janiga, M. A., Shriver, J. F., McLay, J., Thoppil, P., Huang, A., Crawford, W., Whitcomb, T., Bishop, C. H., Zamudio, L., and Phelps, M.: The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting, Earth Space Sci., 8, e2020EA001199, https://doi.org/10.1029/2020EA001199, 2021. a
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a
Chassignet, E. P., Hurlburt, H. E., Smedstad, O. M., Halliwell, G. R., Hogan, P. J., Wallcraft, A. J., and Bleck, R.: Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM), Springer Netherlands, Dordrecht, https://doi.org/10.1007/1-4020-4028-8_16, pp. 413–426, 2006. a
Director, H. M., Raftery, A. E., and Bitz, C. M.: Probabilistic forecasting of the Arctic sea ice edge with contour modeling, Ann. Appl. Stat., 15, 711–726, https://doi.org/10.1214/20-AOAS1405, 2021. a
Download
Short summary

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.       

Share