Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 12
Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 12
Model description paper
 | 
17 Jun 2024
Model description paper |  | 17 Jun 2024

DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin

Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer

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Short summary
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
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