EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparison of NWP Models Used in Training Surrogate Wave Models

Ajit Pillai1, Ian Ashton1, Jiaxin Chen1, and Edward Steele2
Ajit Pillai et al.
  • 1University of Exeter, Faculty of Environment, Science, and Economy, Renewable Energy Group, TR10 9FE, United Kingdom (
  • 2Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom

Machine learning is increasingly being applied to ocean wave modelling. Surrogate modelling has the potential to reduce or bypass the large computational requirements, creating a low computational-cost model that offers a high level of accuracy. One approach integrates in-situ measurements and historical model runs to achieve the spatial coverage of the model and the accuracy of the in-situ measurements. Once operational, such a system requires very little computational power, meaning that it could be deployed to a mobile phone, operational vessel, or autonomous vessel to give continuous data. As such, it makes a significant change to the availability of met-ocean data with potential to revolutionise data provision and use in marine and coastal settings.

This presentation explores the impact that an underlying physics-based model can have in such a machine learning driven framework; comparing training the system on a bespoke regional SWAN wave model developed for wave energy developments in the South West of the UK against training using the larger North-West European Shelf long term hindcast wave model run by the UK Met Office. The presentation discusses the differences in the underlying NWP models, and the impacts that these have on the surrogate wave models’ accuracy in both nowcasting and forecasting wave conditions at areas of interest for renewable energy developments. The results identify the importance in having a high quality, validated, NWP model for training such a system and the way in which the machine learning methods can propagate and exaggerate the underlying model uncertainties.

How to cite: Pillai, A., Ashton, I., Chen, J., and Steele, E.: Comparison of NWP Models Used in Training Surrogate Wave Models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12355,, 2023.