deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Marko Rus (Avtor), Anja Fettich (Avtor), Matej Kristan (Avtor), Matjaž Ličer (Avtor)

Povzetek

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.

Ključne besede

modeliranje višine morske gladine;globoko učenje;poplavljanje;sea level modelling;deep learning;storm surges;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
UDK: 004.925.8:551.461
COBISS: 137128195 Povezava se bo odprla v novem oknu
ISSN: 1991-959X
Št. ogledov: 16
Št. prenosov: 4
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: modeliranje višine morske gladine;globoko učenje;poplavljanje;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 271-288
Letnik: ǂVol. ǂ16
Zvezek: ǂiss. ǂ1
Čas izdaje: Jan. 2023
DOI: 10.5194/gmd-16-271-2023
ID: 21151660
Priporočena dela:
, deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
, master's thesis
, master thesis no.:82/II. VOI-FRM
, graduation thesis
, Bangladeš, Nizozemska in Maldivi