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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2024
Model description paper |
| 17 Jun 2024
| 17 Jun 2024
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
Slovenian Environment Agency, Ljubljana, Slovenia
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Antonio Ricchi
Department of Physical and Chemical Sciences (DSFC), University of L'Aquila, L'Aquila, Italy
Center of Excellence in Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Sandro Carniel
Institute of Polar Sciences of the National Research Council (CNR-ISP), Venice, Italy
currently at: NATO STO Centre for Maritime Research and Experimentation, La Spezia, Italy
Davide Bonaldo
Institute of Marine Sciences of the National Research Council (CNR-ISMAR), Venice, Italy
Slovenian Environment Agency, Ljubljana, Slovenia
National Institute of Biology, Marine Biology Station, Piran, Slovenia
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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.
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which...
