HIDRA2 : deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Rus, Marko (author), Fettich, Anja (author), Kristan, Matej (author), Ličer, Matjaž (author)

We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. Power spectrum analysis indicates that HIDRA2 most accurately represents the energy density peak centered on the ground state sea surface eigenmode (seiche) and comes a close second to SCHISM in the energy band of the first excited eigenmode. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

• Language: English
• Keywords: sea level modelling, deep learning, storm surges
• 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: 2023
• Number of pages: Str. 271-288
• Numbering: Vol. 16, iss. 1
• PID: 20.500.12556/RUL-152345
• UDC: 004.925.8:551.461
• ISSN on article: 1991-959X
• DOI: 10.5194/gmd-16-271-2023
• COBISS.SI-ID: 137128195
• Publication date in RUL: 21.11.2023

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 višine morske gladine, globoko učenje, poplavljanje

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P1-0237
• Name: Raziskave obalnega morja

• Funder: ARRS - Slovenian Research Agency
• Project number: J2-2506
• Name: Adaptivne globoke metode zaznavanja za avtonomna plovila