Deep learning for statistical downscaling of sea states

Michel, Marceau; Obakrim, Said; Raillard, Nicolas; Ailliot, Pierre; Monbet, Valérie

doi:https://doi.org/10.5194/ascmo-8-83-2022

Articles | Volume 8, issue 1

https://doi.org/10.5194/ascmo-8-83-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ascmo-8-83-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 8, issue 1

07 Apr 2022

| 07 Apr 2022

Deep learning for statistical downscaling of sea states

Marceau Michel, Said Obakrim, Nicolas Raillard, Pierre Ailliot, and Valérie Monbet

Data sets

HOMERE ACCENSI, Mickael ; MAISONDIEU, Christophe https://doi.org/10.12770/cf47e08d-1455-4254-955e-d66225c9dc90

Model code and software

Deep learning for statistical downscaling of sea states - Data and code MICHEL, Marceau; OBAKRIM, Said; RAILLARD, Nicolas; AILLIOT, Pierre; MONBET, Valérie https://doi.org/10.5281/zenodo.5524370

Short summary

In this study, we introduce a deep learning algorithm to establish the relationship between wind and waves in order to predict the latter. The performance of the proposed method has been evaluated both on the output of numerical wave models and on in situ data and compared to other statistical methods developed by our research team. The results obtained confirm the interest of deep learning methods for forecasting ocean data.