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Title:DELWAVE 1.0 : deep learning surrogate model of surface wave climate in the Adriatic Basin
Authors:ID Mlakar, Peter (Author)
ID Ricchi, Antonio (Author)
ID Carniel, Sandro (Author)
ID Bonaldo, Davide (Author)
ID Ličer, Matjaž (Author)
Files:URL URL - Source URL, visit https://doi.org/10.5194/gmd-17-4705-2024
 
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MD5: 8D76F57D675F473AE94CF49BC887B45B
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo NIB - National Institute of Biology
Abstract:We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 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.
Keywords:surrogate modelling, deep learning, DEep Learning WAVe Emulating model, DELWAVE, Simulating WAves Nearshore, SWAN
Publication status:Published
Publication version:Version of Record
Publication date:17.06.2024
Year of publishing:2024
Number of pages:str. 4705-4725
Numbering:Vol. 17, iss. 12
PID:20.500.12556/DiRROS-20061 New window
UDC:004.9
ISSN on article:1991-959X
DOI:10.5194/gmd-17-4705-2024 New window
COBISS.SI-ID:203614211 New window
Note:Soavtorji: Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer;
Publication date in DiRROS:05.08.2024
Views:416
Downloads:277
Metadata:XML DC-XML DC-RDF
:
MLAKAR, Peter, RICCHI, Antonio, CARNIEL, Sandro, BONALDO, Davide and LIČER, Matjaž, 2024, DELWAVE 1.0 : deep learning surrogate model of surface wave climate in the Adriatic Basin. Geoscientific model development [online]. 2024. Vol. 17, no. 12, p. 4705–4725. [Accessed 2 April 2025]. DOI 10.5194/gmd-17-4705-2024. Retrieved from: https://dirros.openscience.si/IzpisGradiva.php?lang=eng&id=20061
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Record is a part of a journal

Title:Geoscientific model development
Shortened title:Geosci. model dev.
Publisher:Copernicus Publications
ISSN:1991-959X
COBISS.SI-ID:517533209 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P1-0237-2020
Name:Raziskave obalnega morja
Funder:Other - Other funder or multiple funders
Funding programme:PON Ricerca e Innovazione 2014–2020
Project number:DM 1062

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

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