Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4705-2024
https://doi.org/10.5194/gmd-17-4705-2024
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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Cited articles

Antonioli, F., Anzidei, M., Amorosi, A., Lo Presti, V., Mastronuzzi, G., Deiana, G., De Falco, G., Fontana, A., Fontolan, G., Lisco, S., Marsico, A., Moretti, M., Orrù, P., Sannino, G., Serpelloni, E., and Vecchio, A.: Sea-level rise and potential drowning of the Italian coastal plains: Flooding risk scenarios for 2100, Quaternary Sci. Rev., 158, 29–43, https://doi.org/10.1016/j.quascirev.2016.12.021, 2017. a
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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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