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

Mlakar, Peter (author), Ricchi, Antonio (author), Carniel, Sandro (author), Bonaldo, Davide (author), Ličer, Matjaž (author)

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.

• Language: English
• Keywords: surrogate modelling, deep learning, DEep Learning WAVe Emulating model, DELWAVE, Simulating WAves Nearshore, SWAN
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: Str. 4705-4725
• Numbering: Vol. 17, iss. 12
• PID: 20.500.12556/RUL-166688
• UDC: 004.9
• ISSN on article: 1991-959X
• DOI: 10.5194/gmd-17-4705-2024
• COBISS.SI-ID: 203614211
• Publication date in RUL: 21.01.2025

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

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.

Secondary language

• Language: Slovenian
• Keywords: modeliranje podatkov, računalništvo

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: PON Ricerca e Innovazione
• Project number: DM 1062

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P1-0237
• Name: Raziskave obalnega morja