DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
Abstract cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. 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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin ; volume:17 ; number:12 ; year:2024 ; pages:4705-4725 ; extent:21
Geoscientific model development ; 17, Heft 12 (2024), 4705-4725 (gesamt 21)
- Urheber
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Mlakar, Peter
Ricchi, Antonio
Carniel, Sandro
Bonaldo, Davide
Ličer, Matjaž
- DOI
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10.5194/gmd-17-4705-2024
- URN
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urn:nbn:de:101:1-2408051445181.323808263627
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:55 MESZ
Datenpartner
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Beteiligte
- Mlakar, Peter
- Ricchi, Antonio
- Carniel, Sandro
- Bonaldo, Davide
- Ličer, Matjaž
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