Articles | Volume 21, issue 4
https://doi.org/10.5194/os-21-1315-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-1315-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2025
Research article |
| 14 Jul 2025
| 14 Jul 2025
Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea
Amirhossein Barzandeh
CORRESPONDING AUTHOR
Department of Marine Systems, Tallinn University of Technology, Tallinn, Estonia
Matjaž Ličer
Slovenian Environment Agency, Ljubljana, Slovenia
National Institute of Biology, Marine Biology Station, Piran, Slovenia
Marko Rus
Slovenian Environment Agency, Ljubljana, Slovenia
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Matej Kristan
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Ilja Maljutenko
Department of Marine Systems, Tallinn University of Technology, Tallinn, Estonia
Jüri Elken
Department of Marine Systems, Tallinn University of Technology, Tallinn, Estonia
Priidik Lagemaa
Department of Marine Systems, Tallinn University of Technology, Tallinn, Estonia
Rivo Uiboupin
Department of Marine Systems, Tallinn University of Technology, Tallinn, Estonia
Related authors
Urmas Raudsepp, Ilja Maljutenko, Amirhossein Barzandeh, Rivo Uiboupin, and Priidik Lagemaa
State Planet, 1-osr7, 7, https://doi.org/10.5194/sp-1-osr7-7-2023, https://doi.org/10.5194/sp-1-osr7-7-2023, 2023
Short summary
Short summary
The freshwater content in the Baltic Sea has wide sub-regional variability characterized by the local climate dynamics. The total freshwater content trend is negative due to the recent increased inflows of saltwater, but there are also regions where the increase in runoff and decrease in ice content have led to an increase in the freshwater content.
Marko Rus, Matjaž Ličer, and Matej Kristan
EGUsphere, https://doi.org/10.5194/egusphere-2025-3187, https://doi.org/10.5194/egusphere-2025-3187, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper introduces HIDRA-D, a novel deep-learning model for dense, gridded sea level forecasting from sparse satellite altimetry and tide gauge data. By forecasting low-frequency spatial components, HIDRA-D offers a faster alternative to traditional numerical models. Evaluated in the Adriatic Sea, it outperforms the NEMO general circulation model, reducing the mean absolute error by 28.0 %. The model is robust but shows limitations in complex coastal areas.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Jüri Elken, Ilja Maljutenko, Priidik Lagemaa, Rivo Uiboupin, and Urmas Raudsepp
State Planet, 4-osr8, 9, https://doi.org/10.5194/sp-4-osr8-9-2024, https://doi.org/10.5194/sp-4-osr8-9-2024, 2024
Short summary
Short summary
Baltic deep water is generally warmer than surface water during winter when district heating is required. Depending on the location, depth, and oceanographic situation, bottom water of Tallinn Bay can be used as an energy source for seawater heat pumps until the end of February, covering the major time interval when heating is needed. Episodically, there are colder-water events when seawater heat extraction has to be complemented by other sources of heating energy.
Anja Lindenthal, Claudia Hinrichs, Simon Jandt-Scheelke, Tim Kruschke, Priidik Lagemaa, Eefke M. van der Lee, Ilja Maljutenko, Helen E. Morrison, Tabea R. Panteleit, and Urmas Raudsepp
State Planet, 4-osr8, 16, https://doi.org/10.5194/sp-4-osr8-16-2024, https://doi.org/10.5194/sp-4-osr8-16-2024, 2024
Short summary
Short summary
In 2022, large parts of the Baltic Sea experienced the third-warmest to warmest summer and autumn temperatures since 1997 and several marine heatwaves (MHWs). Using remote sensing, reanalysis, and in situ data, this study characterizes regional differences in MHW properties in the Baltic Sea in 2022. Furthermore, it presents an analysis of long-term trends and the relationship between atmospheric warming and MHW occurrences, including their propagation into deeper layers.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Urmas Raudsepp, Ilja Maljutenko, Priidik Lagemaa, and Karina von Schuckmann
State Planet Discuss., https://doi.org/10.5194/sp-2024-19, https://doi.org/10.5194/sp-2024-19, 2024
Preprint under review for SP
Short summary
Short summary
Over the last three decades, the Baltic Sea has experienced rising temperature and salinity, reflecting broader atmospheric warming. Heat content fluctuations are driven by subsurface temperature changes in the upper 100 meters, including the thermocline and halocline, influenced by air temperature, evaporation, and wind stress. Freshwater content changes mainly result from salinity shifts in the halocline, with saline water inflow, precipitation, and wind stress as key factors.
Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin
EGUsphere, https://doi.org/10.5194/egusphere-2024-1701, https://doi.org/10.5194/egusphere-2024-1701, 2024
Short summary
Short summary
The sea ice statistics study highlights the bias in model estimations compared to satellite data and provides a simple approach to minimise that. During the study period, the model estimates sea ice forming slightly earlier but aligns well with the satellite data for ice season's end. Rapid decrease in the sea ice parameters is observed across the Baltic Sea, especially the ice thickness in the Bothnian Bay sub-basin. These statistics could be crucial for regional adaptation strategies.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
Short summary
Short summary
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. 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.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
Short summary
Short summary
The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Urmas Raudsepp, Ilja Maljutenko, Amirhossein Barzandeh, Rivo Uiboupin, and Priidik Lagemaa
State Planet, 1-osr7, 7, https://doi.org/10.5194/sp-1-osr7-7-2023, https://doi.org/10.5194/sp-1-osr7-7-2023, 2023
Short summary
Short summary
The freshwater content in the Baltic Sea has wide sub-regional variability characterized by the local climate dynamics. The total freshwater content trend is negative due to the recent increased inflows of saltwater, but there are also regions where the increase in runoff and decrease in ice content have led to an increase in the freshwater content.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
Short summary
Short summary
We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Nydia Catalina Reyes Suárez, Valentina Tirelli, Laura Ursella, Matjaž Ličer, Massimo Celio, and Vanessa Cardin
Ocean Sci., 18, 1321–1337, https://doi.org/10.5194/os-18-1321-2022, https://doi.org/10.5194/os-18-1321-2022, 2022
Short summary
Short summary
Explaining the dynamics of jellyfish blooms is a challenge for scientists. Biological and meteo-oceanographic data were combined on different timescales to explain the exceptional bloom of the jellyfish Rhizostoma pulmo in the Gulf of Trieste (Adriatic Sea) in April 2021. The bloom was associated with anomalously warm seasonal sea conditions. Then, a strong bora wind event enhanced upwelling and mixing of the water column, causing jellyfish to rise to the surface and accumulate along the coast.
Begoña Pérez Gómez, Ivica Vilibić, Jadranka Šepić, Iva Međugorac, Matjaž Ličer, Laurent Testut, Claire Fraboul, Marta Marcos, Hassen Abdellaoui, Enrique Álvarez Fanjul, Darko Barbalić, Benjamín Casas, Antonio Castaño-Tierno, Srđan Čupić, Aldo Drago, María Angeles Fraile, Daniele A. Galliano, Adam Gauci, Branislav Gloginja, Víctor Martín Guijarro, Maja Jeromel, Marcos Larrad Revuelto, Ayah Lazar, Ibrahim Haktan Keskin, Igor Medvedev, Abdelkader Menassri, Mohamed Aïssa Meslem, Hrvoje Mihanović, Sara Morucci, Dragos Niculescu, José Manuel Quijano de Benito, Josep Pascual, Atanas Palazov, Marco Picone, Fabio Raicich, Mohamed Said, Jordi Salat, Erdinc Sezen, Mehmet Simav, Georgios Sylaios, Elena Tel, Joaquín Tintoré, Klodian Zaimi, and George Zodiatis
Ocean Sci., 18, 997–1053, https://doi.org/10.5194/os-18-997-2022, https://doi.org/10.5194/os-18-997-2022, 2022
Short summary
Short summary
This description and mapping of coastal sea level monitoring networks in the Mediterranean and Black seas reveals the existence of 240 presently operational tide gauges. Information is provided about the type of sensor, time sampling, data availability, and ancillary measurements. An assessment of the fit-for-purpose status of the network is also included, along with recommendations to mitigate existing bottlenecks and improve the network, in a context of sea level rise and increasing extremes.
Emma Reyes, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Vanessa Cardin, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Maria J. Fernandes, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Pablo Lorente, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Adèle Révelard, Catalina Reyes-Suárez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Alejandro Orfila
Ocean Sci., 18, 797–837, https://doi.org/10.5194/os-18-797-2022, https://doi.org/10.5194/os-18-797-2022, 2022
Short summary
Short summary
This work reviews the existing advanced and emerging scientific and societal applications using HFR data, developed to address the major challenges identified in Mediterranean coastal waters organized around three main topics: maritime safety, extreme hazards and environmental transport processes. It also includes a discussion and preliminary assessment of the capabilities of existing HFR applications, finally providing a set of recommendations towards setting out future prospects.
Pablo Lorente, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Alejandro Orfila, Adèle Révelard, Emma Reyes, Jorge Sánchez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Laura Ursella, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Vanessa Cardin
Ocean Sci., 18, 761–795, https://doi.org/10.5194/os-18-761-2022, https://doi.org/10.5194/os-18-761-2022, 2022
Short summary
Short summary
High-frequency radar (HFR) is a land-based remote sensing technology that can provide maps of the surface circulation over broad coastal areas, along with wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network as well as present and future applications of this sensor for societal benefit such as search and rescue operations, safe vessel navigation, tracking of marine pollutants, and the monitoring of extreme events.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
Short summary
Short summary
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
Short summary
Short summary
We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
Jukka-Pekka Jalkanen, Lasse Johansson, Magda Wilewska-Bien, Lena Granhag, Erik Ytreberg, K. Martin Eriksson, Daniel Yngsell, Ida-Maja Hassellöv, Kerstin Magnusson, Urmas Raudsepp, Ilja Maljutenko, Hulda Winnes, and Jana Moldanova
Ocean Sci., 17, 699–728, https://doi.org/10.5194/os-17-699-2021, https://doi.org/10.5194/os-17-699-2021, 2021
Short summary
Short summary
This modelling study describes a methodology for describing pollutant discharges from ships to the sea. The pilot area used is the Baltic Sea area and discharges of bilge, ballast, sewage, wash water as well as stern tube oil are reported for the year 2012. This work also reports the release of SOx scrubber effluents. This technique may be used by ships to comply with tight sulfur limits inside Emission Control Areas, but it also introduces a new pollutant stream from ships to the sea.
Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, https://doi.org/10.5194/gmd-14-2057-2021, 2021
Short summary
Short summary
Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Mihhail Zujev, Jüri Elken, and Priidik Lagemaa
Ocean Sci., 17, 91–109, https://doi.org/10.5194/os-17-91-2021, https://doi.org/10.5194/os-17-91-2021, 2021
Short summary
Short summary
The proposed method of data assimilation is capable of effectively correcting basin-scale mismatch of oceanographic models when the domain is under nearly coherent external forcing. The method uses basin-scale EOF modes, calculated from the long-term model statistics. These modes are used to reconstruct gridded fields from point observations, which are further fed to the model using relaxation. Tests with sea surface temperature and salinity in the NE Baltic Sea were successful.
Lasse Johansson, Erik Ytreberg, Jukka-Pekka Jalkanen, Erik Fridell, K. Martin Eriksson, Maria Lagerström, Ilja Maljutenko, Urmas Raudsepp, Vivian Fischer, and Eva Roth
Ocean Sci., 16, 1143–1163, https://doi.org/10.5194/os-16-1143-2020, https://doi.org/10.5194/os-16-1143-2020, 2020
Short summary
Short summary
Very little is currently known about the activities and emissions of private leisure boats. To change this, a new model was created (BEAM). The model was used for the Baltic Sea to estimate leisure boat emissions, also considering antifouling paint leach. When compared to commercial shipping, the modeled leisure boat emissions were seen to be surprisingly large for some pollutant species, and these emissions were heavily concentrated on coastal inhabited areas during summer and early autumn.
Matjaž Ličer, Solène Estival, Catalina Reyes-Suarez, Davide Deponte, and Anja Fettich
Nat. Hazards Earth Syst. Sci., 20, 2335–2349, https://doi.org/10.5194/nhess-20-2335-2020, https://doi.org/10.5194/nhess-20-2335-2020, 2020
Short summary
Short summary
In 2018 windsurfer’s mast broke about 1 km offshore during a scirocco storm in the northern Adriatic. He was drifting in severe conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory. We simulate his trajectory in several ways and estimate the optimal search-and-rescue area for a civil rescue response. Properly calibrated virtual drifter properties are key to reliable rescue area forecasting.
L. Sipelgas, A. Aavaste, and R. Uiboupin
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 627–632, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-627-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-627-2020, 2020
Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers
Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, https://doi.org/10.5194/gmd-13-1609-2020, 2020
Short summary
Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.
Christian Ferrarin, Andrea Valentini, Martin Vodopivec, Dijana Klaric, Giovanni Massaro, Marco Bajo, Francesca De Pascalis, Amedeo Fadini, Michol Ghezzo, Stefano Menegon, Lidia Bressan, Silvia Unguendoli, Anja Fettich, Jure Jerman, Matjaz̆ Ličer, Lidija Fustar, Alvise Papa, and Enrico Carraro
Nat. Hazards Earth Syst. Sci., 20, 73–93, https://doi.org/10.5194/nhess-20-73-2020, https://doi.org/10.5194/nhess-20-73-2020, 2020
Short summary
Short summary
Here we present a shared and interoperable system to allow a better exchange of and elaboration on information related to sea storms among countries. The proposed integrated web system (IWS) is a combination of a common data system for sharing ocean observations and forecasts, a multi-model ensemble system, a geoportal, and interactive geo-visualization tools. This study describes the application of the developed system to the exceptional storm event of 29 October 2018.
Edith Soosaar, Ilja Maljutenko, Rivo Uiboupin, Maris Skudra, and Urmas Raudsepp
Ocean Sci., 12, 417–432, https://doi.org/10.5194/os-12-417-2016, https://doi.org/10.5194/os-12-417-2016, 2016
Short summary
Short summary
Remote sensing imagery and numerical model study of river bulge evolution and dynamics in a non-tidal sea showed an anti-cyclonically rotating bulge during the studied low wind period in the Gulf of Riga. In about 7–8 days the bulge grew up to 20 km in diameter, before being diluted. Both model and satellite images showed river water mainly contained in the bulge. The study shows significant effects of the wind in the evolution of the river bulge, even if the wind speed was moderate (3–4 m s−1).
M. Ličer, P. Smerkol, A. Fettich, M. Ravdas, A. Papapostolou, A. Mantziafou, B. Strajnar, J. Cedilnik, M. Jeromel, J. Jerman, S. Petan, V. Malačič, and S. Sofianos
Ocean Sci., 12, 71–86, https://doi.org/10.5194/os-12-71-2016, https://doi.org/10.5194/os-12-71-2016, 2016
Short summary
Short summary
We compare the northern Adriatic response to an extreme bora event, as simulated by one-way and two-way (i.e. with ocean feedback to the atmosphere) atmosphere-ocean coupling. We show that two-way coupling yields significantly better estimates of heat fluxes, most notably sensible heat flux, across the air-sea interface. When compared to observations in the northern Adriatic, two-way coupled system consequently leads to a better representation of ocean temperatures throughout the event.
T. Liblik, J. Laanemets, U. Raudsepp, J. Elken, and I. Suhhova
Ocean Sci., 9, 917–930, https://doi.org/10.5194/os-9-917-2013, https://doi.org/10.5194/os-9-917-2013, 2013
Related subject area
Approach: Numerical Models | Properties and processes: Sea level, tides, tsunamis and surges
Constraining local ocean dynamic sea-level projections using observations
Investigation of the impact of complex coastline geometry on the evolution of storm surges along the eastern coast of India: a sensitivity study using a numerical model
Indications of improved seasonal sea level forecasts for the United States Gulf and East Coasts using ocean-dynamic persistence
Assessing storm surge model performance: what error indicators can measure the model's skill?
The characteristics of tides and their effects on the general circulation of the Mediterranean Sea
Effect of nonlinear tide-surge interaction in the Pearl River Estuary during Typhoon Nida (2016)
Effects of sea level rise and tidal flat growth on tidal dynamics and geometry of the Elbe estuary
Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data
Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea
Observations and modeling of tidally generated high-frequency velocity fluctuations downstream of a channel constriction
Dewi Le Bars, Iris Keizer, and Sybren Drijfhout
Ocean Sci., 21, 1303–1314, https://doi.org/10.5194/os-21-1303-2025, https://doi.org/10.5194/os-21-1303-2025, 2025
Short summary
Short summary
While preparing a new set of sea level scenarios for the Netherlands, we found out that many climate models overestimate the changes in ocean circulation for the last 30 years. To quantify this effect, we defined three methods that rely on diverse and independent observations: tide gauges, satellite altimetry, temperature and salinity in the ocean, land ice melt, etc. Based on these observations, we define a few methods to select models and discuss their advantages and disadvantages.
Pawan Tiwari, Ambarukhana D. Rao, Smita Pandey, and Vimlesh Pant
Ocean Sci., 21, 381–399, https://doi.org/10.5194/os-21-381-2025, https://doi.org/10.5194/os-21-381-2025, 2025
Short summary
Short summary
Concave coasts act as funnels, concentrating storm waters and leading to higher storm surges (SSs); convex coasts redistribute waters, reducing surges. We use the ADCIRC model to simulate peak surges (PSs) for different cyclone tracks, showing how coastline geometry, landfall location, and cyclone angle influence PSs. Cyclones passing near concave coasts without landfall can still cause high SSs, highlighting vulnerability in these regions. This insight aids in assessing coastal flood risks.
Xue Feng, Matthew J. Widlansky, Tong Lee, Ou Wang, Magdalena A. Balmaseda, Hao Zuo, Gregory Dusek, William Sweet, and Malte F. Stuecker
EGUsphere, https://doi.org/10.5194/egusphere-2025-98, https://doi.org/10.5194/egusphere-2025-98, 2025
Short summary
Short summary
Forecasting sea level changes months in advance along the Gulf and East Coasts of the United States is challenging. Here, we present a method that uses past ocean states to forecast future sea levels, while assuming no knowledge of how the atmosphere will evolve other than its typical annual cycle near the ocean’s surface. Our findings indicate that this method improves sea level outlooks for many locations along the Gulf and East Coasts, especially south of Cape Hatteras.
Rodrigo Campos-Caba, Jacopo Alessandri, Paula Camus, Andrea Mazzino, Francesco Ferrari, Ivan Federico, Michalis Vousdoukas, Massimo Tondello, and Lorenzo Mentaschi
Ocean Sci., 20, 1513–1526, https://doi.org/10.5194/os-20-1513-2024, https://doi.org/10.5194/os-20-1513-2024, 2024
Short summary
Short summary
Here we show the development of high-resolution simulations of storm surge in the northern Adriatic Sea employing different atmospheric forcing data and physical configurations. Traditional metrics favor a simulation forced by a coarser database and employing a less sophisticated setup. Closer examination allows us to identify a baroclinic model forced by a high-resolution dataset as being better able to capture the variability and peak values of the storm surge.
Bethany McDonagh, Emanuela Clementi, Anna Chiara Goglio, and Nadia Pinardi
Ocean Sci., 20, 1051–1066, https://doi.org/10.5194/os-20-1051-2024, https://doi.org/10.5194/os-20-1051-2024, 2024
Short summary
Short summary
Tides in the Mediterranean Sea are typically of low amplitude, but twin experiments with and without tides demonstrate that tides affect the circulation directly at scales away from those of the tides. Analysis of the energy changes due to tides shows that they enhance existing oscillations, and internal tides interact with other internal waves. Tides also increase the mixed layer depth and enhance deep water formation in key regions. Internal tides are widespread in the Mediterranean Sea.
Linxu Huang, Tianyu Zhang, Shouwen Zhang, and Hui Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1940, https://doi.org/10.5194/egusphere-2024-1940, 2024
Short summary
Short summary
This study utilized a hydrodynamic model to explore the complex dynamics between storm surges and tides, the result shows that the nonlinear effect is mainly generated by local acceleration and convection while it is predominantly governed by wind stress and bottom friction in shallow water regions. By adjusting typhoon landfall times, we demonstrated that the contribution ratio of each nonlinear term changes little, their magnitudes fluctuate depending on the timing of landfall.
Tara F. Mahavadi, Rita Seiffert, Jessica Kelln, and Peter Fröhle
Ocean Sci., 20, 369–388, https://doi.org/10.5194/os-20-369-2024, https://doi.org/10.5194/os-20-369-2024, 2024
Short summary
Short summary
To analyse the influence of potential future mean sea level rise (SLR) and tidal flat elevation scenarios on the tidal dynamics in the Elbe estuary, we used a highly resolved hydrodynamic numerical model. The results show increasing tidal range in the Elbe estuary due to SLR alone. In combination with different tidal flat growth scenarios, they reveal strongly varying changes in tidal range. We discuss how changes in estuarine geometry can provide an explanation for the changes in tidal range.
Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson
Ocean Sci., 20, 21–30, https://doi.org/10.5194/os-20-21-2024, https://doi.org/10.5194/os-20-21-2024, 2024
Short summary
Short summary
Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.
Marvin Lorenz and Ulf Gräwe
Ocean Sci., 19, 1753–1771, https://doi.org/10.5194/os-19-1753-2023, https://doi.org/10.5194/os-19-1753-2023, 2023
Short summary
Short summary
We study the variability of extreme sea levels in a 13 member hindcast ensemble for the Baltic Sea. The ensemble mean shows good agreement with observations regarding return levels and trends. However, we find great variability and uncertainty within the ensemble. We argue that the variability of storms in the atmospheric data directly translates into the variability of the return levels. These results highlight the need for large regional ensembles to minimise uncertainties.
Håvard Espenes, Pål Erik Isachsen, and Ole Anders Nøst
Ocean Sci., 19, 1633–1648, https://doi.org/10.5194/os-19-1633-2023, https://doi.org/10.5194/os-19-1633-2023, 2023
Short summary
Short summary
We show that tidally generated eddies generated near the constriction of a channel can drive a strong and fluctuating flow field far downstream of the channel constriction itself. The velocity signal has been observed in other studies, but this is the first study linking it to a physical process. Eddies such as those we found are generated because of complex coastal geometry, suggesting that, for example, land-reclamation projects in channels may enhance current shear over a large area.
Cited articles
Ahmad, H.: Machine learning applications in oceanography, Aquatic Research, 2, 161–169, https://doi.org/10.3153/AR19014, 2019. a
Andersson, H.: Influence of long-term regional and large-scale atmospheric circulation on the Baltic sea level, Tellus A, 54, 76–88, https://doi.org/10.3402/tellusa.v54i1.12125, 2002. a
Bahari, N. A. A. B. S., Ahmed, A. N., Chong, K. L., Lai, V., Huang, Y. F., Koo, C. H., Ng, J. L., and El-Shafie, A.: Predicting sea level rise using artificial intelligence: a review, Arch. Comput. Method. E., 30, 4045–4062, https://doi.org/10.1007/s11831-023-09934-9, 2023. a
Bajo, M., Medugorac, I., Umgiesser, G., and Orlić, M.: Storm surge and seiche modelling in the Adriatic Sea and the impact of data assimilation, Q. J. Roy. Meteor. Soc., 145, 2070–2084, https://doi.org/10.1002/qj.3544, 2019. a
Bajo, M., Ferrarin, C., Umgiesser, G., Bonometto, A., and Coraci, E.: Modelling the barotropic sea level in the Mediterranean Sea using data assimilation, Ocean Sci., 19, 559–579, https://doi.org/10.5194/os-19-559-2023, 2023. a
Balogun, A.-L. and Adebisi, N.: Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models' accuracy, Geomat. Nat. Haz. Risk, 12, 653–674, https://doi.org/10.1080/19475705.2021.1887372, 2021. a
BALTICSEA_ANALYSIS_FORECAST_PHY_003_006: E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS), Copernicus Marine Service [code], https://doi.org/10.48670/moi-00010, 2024. a
Barron, C., Birol Kara, A., Hurlburt, H., Rowley, C., and Smedstad, L.: Sea surface height predictions from the global Navy Coastal Ocean Model during 1998–2001, J. Atmos. Ocean. Tech., 21, 1876–1893, https://doi.org/10.1175/JTECH-1680.1, 2004. a
Bindoff, N., Willebrand, J., Artale, V., Cazenave, A., Gregory, J., Gulev, S., Hanawa, K., Le Quere, C., Levitus, S., and Nojiri, Y.: Observations: oceanic climate change and sea level, https://nora.nerc.ac.uk/id/eprint/15400 (last access: 13 July 2025), 2007. a
Byrne, D., Horsburgh, K., and Williams, J.: Variational data assimilation of sea surface height into a regional storm surge model: benefits and limitations, J. Oper. Oceanogr., 16, 1–14, https://doi.org/10.1080/1755876X.2021.1884405, 2023. a
Campos-Caba, R., Alessandri, J., Camus, P., Mazzino, A., Ferrari, F., Federico, I., Vousdoukas, M., Tondello, M., and Mentaschi, L.: Assessing storm surge model performance: what error indicators can measure the model's skill?, Ocean Sci., 20, 1513–1526, https://doi.org/10.5194/os-20-1513-2024, 2024. a, b
Cannaby, H., Palmer, M. D., Howard, T., Bricheno, L., Calvert, D., Krijnen, J., Wood, R., Tinker, J., Bunney, C., Harle, J., Saulter, A., O'Neill, C., Bellingham, C., and Lowe, J.: Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore, Ocean Sci., 12, 613–632, https://doi.org/10.5194/os-12-613-2016, 2016. a
Carson, M., Lyu, K., Richter, K., Becker, M., Domingues, C. M., Han, W., and Zanna, L.: Climate model uncertainty and trend detection in regional sea level projections: a review, Surv. Geophys., 40, 1631–1653, https://doi.org/10.1007/s10712-019-09559-3, 2019. a
Cazenave, A., Dieng, H.-B., Meyssignac, B., Von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Chang., 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014. a
Church, J., Gregory, J., White, N., Platten, S., and Mitrovica, J.: Understanding and projecting sea level change, Oceanography, 24, 130–143, https://doi.org/10.5670/oceanog.2011.33, 2011. a
CMEMS – EU Copernicus Marine Service Information: Baltic Sea Physics Analysis and Forecast, Marine Data Store (MDS), CMEMS [data set], https://doi.org/10.48670/moi-00010, 2024. a
Conte, D. and Lionello, P.: Characteristics of large positive and negative surges in the Mediterranean Sea and their attenuation in future climate scenarios, Global Planet. Change, 111, 159–173, https://doi.org/10.1016/j.gloplacha.2013.09.006, 2013. a
Dong, Z., Hu, H., Liu, H., Baiyin, B., Mu, X., Wen, J., Liu, D., Chen, L., Ming, G., Chen, X., and Li, X.: Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream, J. Hydrol., 633, 130941, https://doi.org/10.1016/j.jhydrol.2024.130941, 2024. a
ECMWF: Ensemble Prediction System (EPS) – 50-member global ensemble forecasts, retrieved via MARS (Meteorological Archival and Retrieval System), https://www.ecmwf.int/en/forecasts/datasets (last access: 24 April 2024), 2024. a
Ekman, M.: The changing level of the Baltic Sea during 300 years: a clue to understanding the Earth, https://www.historicalgeophysics.ax/downloads/the-changing-level-of-the-baltic-sea.pdf (last access: 13 July 2025), 2009. a
Elken, J., Maljutenko, I., Lagemaa, P., and Verjovkina, S.: Mere operatiivmudelisüsteemi NEMO kasutuselevðtt ja töölerakendamine mereala operatiivprognooside parandamiseks, Tech. Rep. 82, TTÜ Meresüsteemide Inst., https://keskkonnaportaal.ee/sites/default/files/andmekataloog/lisakirjeldus/NEMO_II_aruanne2021_v2.pdf (last access: 10 July 2025), 2021. a
Elken, J., Barzandeh, A., Maljutenko, I., and Rikka, S.: Reconstruction of Baltic gridded sea levels from tide gauge and altimetry observations using spatiotemporal statistics from reanalysis, Remote Sens.-Basel, 16, 2702, https://doi.org/10.3390/rs16152702, 2024. a
Estonian Environment Agency: Time series of hydrological monitoring data in JSON format, Estonian Environment Agency [data set], https://keskkonnaportaal.ee/et/avaandmed/hudroloogilise-seire-andmestik (last access: 13 July 2025), 2024. a
Ganaie, M., Hu, M., Malik, A., Tanveer, M., and Suganthan, P.: Ensemble deep learning: a review, Eng. Appl. Artif. Intel., 115, 105151, https://doi.org/10.1016/j.engappai.2022.105151, 2022. a
Gröger, M., Arneborg, L., Dieterich, C., Höglund, A., and Meier, H. E. M.: Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model, Clim. Dynam., 53, 5945–5966, https://doi.org/10.1007/s00382-019-04908-9, 2019. a
Hamlington, B. D., Gardner, A. S., Ivins, E., Lenaerts, J. T., Reager, J., Trossman, D. S., Zaron, E. D., Adhikari, S., Arendt, A., Aschwanden, A., Beckley, B. D., Bekaert, D. P. S., Blewitt, G., Caron, L., Chambers, D. P., Chandanpurkar, H. A., Christianson, K., Csatho, B., Cullather, R. I., DeConto, R. M., Fasullo, J. T., Frederikse, T., Freymueller, J. T., Gilford, D. M., Girotto, M., Hammond, W. C., Hock, R., Holschuh, N., Kopp, R. E., Landerer, F., Larour, E., Menemenlis, D., Merrifield, M., Mitrovica, J. X., Nerem, R. S., Nias, I. J., Nieves, V., Nowicki, S., Pangaluru, K., Piecuch, C. G., Ray, R. D., Rounce, D. R., Schlegel, N.-J., Seroussi, H., Shirzaei, M., Sweet, W. V., Velicogna, I., Vinogradova, N., Wahl, T., Wiese, D. N., and Willis, M. J.: Understanding of contemporary regional sea-level change and the implications for the future, Rev. Geophys., 58, e2019RG000672, https://doi.org/10.1029/2019RG000672, 2020. a
Hieronymus, M. and Hieronymus, F.: A novel machine learning based bias correction method and its application to sea level in an ensemble of downscaled climate projections, Tellus A, 75, 129–144, https://doi.org/10.16993/tellusa.3216, 2023. a
Hünicke, B. and Zorita, E.: Statistical analysis of the acceleration of Baltic mean sea-level rise, 1900–2012, Front. Mar. Sci., 3, 125, https://doi.org/10.3389/fmars.2016.00125, 2016. a
Hünicke, B., Zorita, E., Soomere, T., Madsen, K., Johansson, M., and Suursaar, U.: Recent change – sea level and wind waves, in: Second Assess. Clim. Chang. Balt. Sea Basin, Springer, https://doi.org/10.1007/978-3-319-16006-1_9, 155–185, 2015. a
Ishida, K., Tsujimoto, G., Ercan, A., Tu, T., Kiyama, M., and Amagasaki, M.: Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network, Sci. Total Environ., 720, 137613, https://doi.org/10.1016/j.scitotenv.2020.137613, 2020. a
Jahanmard, V., Hordoir, R., Delpeche-Ellmann, N., and Ellmann, A.: Quantification of hydrodynamic model sea level bias utilizing deep learning and synergistic integration of data sources, Ocean Model., 186, 102286, https://doi.org/10.1016/j.ocemod.2023.102286, 2023. a
Jandt-Scheelke, S., Panteleit, T., Verjovkina, S., Lagemaa, P., Spruch, L., and Morrison, H.: Quality Information Document: Baltic Sea Physical Analysis and Forecasting Product (BALTICSEA_ANALYSIS_FORECAST_PHY_003_006), cMEMS product quality coordination team, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-BAL-QUID-003-006.pdf (last access: 10 July 2025), 2023. a
Jensen, C., Mahavadi, T., Schade, N. H., Hache, I., and Kruschke, T.: Negative storm surges in the Elbe estuary – large-scale meteorological conditions and future climate change, Atmosphere-Basel, 13, 1634, https://doi.org/10.3390/atmos13101634, 2022. a
Jevrejeva, S., Moore, J., and Grinsted, A.: Influence of the Arctic Oscillation and El Niño Southern Oscillation (ENSO) on ice conditions in the Baltic Sea: the wavelet approach, J. Geophys. Res.-Atmos., 108, 4677, https://doi.org/10.1029/2003JD003417, 2003. a
Johansson, M., Kahma, K., Boman, H., and Launiainen, J.: Scenarios for sea level on the Finnish coast, Boreal Environ. Res., 9, 153–166, 2004. a
Jönsson, B., Döös, K., Nycander, J., and Lundberg, P.: Standing waves in the Gulf of Finland and their relationship to the basin-wide Baltic seiches, J. Geophys. Res.-Oceans, 113, C03004, https://doi.org/10.1029/2006JC003862, 2008. a
Kärnä, T., Ljungemyr, P., Falahat, S., Ringgaard, I., Axell, L., Korabel, V., Murawski, J., Maljutenko, I., Lindenthal, A., Jandt-Scheelke, S., Verjovkina, S., Lorkowski, I., Lagemaa, P., She, J., Tuomi, L., Nord, A., and Huess, V.: Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea, Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, 2021. a
Khojasteh, D., Glamore, W., Heimhuber, V., and Felder, S.: Sea level rise impacts on estuarine dynamics: a review, Sci. Total Environ., 780, 146470, https://doi.org/10.1016/j.scitotenv.2021.146470, 2021. a
Kont, A., Jaagus, J., Aunap, R., Ratas, U., and Rivis, R.: Implications of sea-level rise for Estonia, J. Coast. Res., 24, 423–431, https://doi.org/10.2112/07A-0015.1, 2008. a
Kulikov, E. and Medvedev, I.: Variability of the Baltic Sea level and floods in the Gulf of Finland, Oceanology, 53, 145–151, https://doi.org/10.1134/S0001437013020094, 2013. a
Leutbecher, M. and Palmer, T.: Ensemble forecasting, ECMWF, https://doi.org/10.21957/c0hq4yg78, 2007. a
Liu, Y. and Fu, W.: Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea, Ocean Sci., 14, 525–541, https://doi.org/10.5194/os-14-525-2018, 2018. a
Lou, R., Lv, Z., Dang, S., Su, T., and Li, X.: Application of machine learning in ocean data, Multimedia Syst., 29, 1815–1824, https://doi.org/10.1007/s00530-020-00733-x, 2023. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., and Delrosso, D.: NEMO ocean engine, https://epic.awi.de/id/eprint/39698/1/NEMO_book_v6039.pdf (last access: 10 July 2025), 2017. a
Maljutenko, I., Elken, J., Lagemaa, P., and Verjovkina, S.: Mere operatiivmudelisüsteemi NEMO kasutuselevõtt ja töölerakendamine mereala operatiivprognooside parandamiseks, Tech. rep., TTÜ Meresüsteemide Inst., https://keskkonnaportaal.ee/sites/default/files/andmekataloog/lisakirjeldus/NEMO aruanne 2022_III etapp_puhas.pdf (last access: 13 July 2025), 2022. a
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Problems in RMSE-based wave model validations, Ocean Model., 72, 53–58, https://doi.org/10.1016/j.ocemod.2013.08.003, 2013. a
Mentaschi, L., Vousdoukas, M. I., García-Sánchez, G., Fernández-Montblanc, T., Roland, A., Voukouvalas, E., Federico, I., Abdolali, A., Zhang, Y. J., and Feyen, L.: A global unstructured, coupled, high-resolution hindcast of waves and storm surge, Front. Mar. Sci., 10, 1233679, https://doi.org/10.3389/fmars.2023.1233679, 2023. a
Metzner, M., Gade, M., Hennings, I., and Rabinovich, A. B.: The observation of seiches in the Baltic Sea using a multi data set of water levels, J. Marine Syst., 24, 67–84, https://doi.org/10.1016/S0924-7963(99)00079-2, 2000. a
Miles, E., Spillman, C., Church, J., and McIntosh, P.: Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model, Clim. Dynam., 43, 2131–2145, https://doi.org/10.1007/s00382-013-2039-7, 2014. a
Owens, R. and Hewson, T.: ECMWF Forecast User Guide, Tech. rep., Reading, https://doi.org/10.21957/m1cs7h, 2018. a
Pärt, S., Björkqvist, J.-V., Alari, V., Maljutenko, I., and Uiboupin, R.: An ocean–wave–trajectory forecasting system for the eastern Baltic Sea: validation against drifting buoys and implementation for oil spill modeling, Mar. Pollut. Bull., 195, 115497, https://doi.org/10.1016/j.marpolbul.2023.115497, 2023. a
Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., Mitchum, G., Van De Wal, R., Woodworth, P. L., Ablain, M., Ardhuin, F., Ballu, V., Becker, M., Benveniste, J., Birol, F., Bradshaw, E., Cazenave, A., De Mey-Frémaux, P., Durand, F., Ezer, T., Fu, L.-L., Fukumori, I., Gordon, K., Gravelle, M., Griffies, S. M., Han, W., Hibbert, A., Hughes, C. W., Idier, D., Kourafalou, V. H., Little, C. M., Matthews, A., Melet, A., Merrifield, M., Meyssignac, B., Minobe, S., Penduff, T., Picot, N., Piecuch, C., Ray, R. D., Rickards, L., Santamaría-Gómez, A., Stammer, D., Staneva, J., Testut, L., Thompson, K., Thompson, P., Vignudelli, S., Williams, J., Williams, S. D. P., Wöppelmann, G., Zanna, L., and Zhang, X.: Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level, Frontiers in Marine Science, 6, 437, https://doi.org/10.3389/fmars.2019.00437, 2019. a
Raissi, M., Perdikaris, P., and Karniadakis, G.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045, 2019. a
Rajabi-Kiasari, S., Delpeche-Ellmann, N., and Ellmann, A.: Forecasting of absolute dynamic topography using deep learning algorithm with application to the Baltic Sea, Comput. Geosci., 178, 105406, https://doi.org/10.1016/j.cageo.2023.105406, 2023. a
Rus, M., Fettich, A., Kristan, M., and Ličer, M.: HIDRA2 – Deep-learning ensemble sea level and storm tide forecasting in the presence of seiches, Zenodo [code], https://doi.org/10.5281/zenodo.7307365, 2022. a
Rus, M., Mihanović, H., Ličer, M., and Kristan, M.: HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures, Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, 2025. a
Rutgersson, A., Kjellström, E., Haapala, J., Stendel, M., Danilovich, I., Drews, M., Jylhä, K., Kujala, P., Larsén, X. G., Halsnæs, K., Lehtonen, I., Luomaranta, A., Nilsson, E., Olsson, T., Särkkä, J., Tuomi, L., and Wasmund, N.: Natural hazards and extreme events in the Baltic Sea region, Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, 2022. a
Samuelsson, M. and Stigebrandt, A.: Main characteristics of the long-term sea level variability in the Baltic sea, Tellus A, 48, 672–683, https://doi.org/10.1034/j.1600-0870.1996.t01-4-00006.x, 1996. a
Scotto, M., Barbosa, S. M., and Alonso, A. M.: Model-based clustering of Baltic sea-level, Appl. Ocean Res., 31, 4–11, https://doi.org/10.1016/j.apor.2009.03.001, 2009. a
Shahabi, A. and Tahvildari, N.: A deep-learning model for rapid spatiotemporal prediction of coastal water levels, Coast. Eng., 190, 104504, https://doi.org/10.1016/j.coastaleng.2024.104504, 2024. a
Sonnewald, M., Lguensat, R., Jones, D. C., Dueben, P. D., Brajard, J., and Balaji, V.: Bridging observations, theory and numerical simulation of the ocean using machine learning, Environ. Res. Lett., 16, 073008, https://doi.org/10.1088/1748-9326/ac0eb0, 2021. a
Suursaar, Ü., Kullas, T., and Otsmann, M.: A model study of the sea level variations in the Gulf of Riga and the Väinameri Sea, Cont. Shelf Res., 22, 2001–2019, https://doi.org/10.1016/S0278-4343(02)00046-8, 2002. a
Tanajura, C. A. S., Lima, L. N., and Belyaev, K. P.: Assimilation of satellite surface-height anomalies data into a Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean, Oceanology, 55, 667–678, https://doi.org/10.1134/S0001437015050161, 2015. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Weisse, R., Dailidienė, I., Hünicke, B., Kahma, K., Madsen, K., Omstedt, A., Parnell, K., Schöne, T., Soomere, T., Zhang, W., and Zorita, E.: Sea level dynamics and coastal erosion in the Baltic Sea region, Earth Syst. Dynam., 12, 871–898, https://doi.org/10.5194/esd-12-871-2021, 2021. a
Wolski, T. and Wiśniewski, B.: Characteristics and long-term variability of occurrences of Storm surges in the Baltic Sea, Atmosphere-Basel, 12, 1679, https://doi.org/10.3390/atmos12121679, 2021. a
Wolski, T., Wiśniewski, B., Giza, A., Kowalewska-Kalkowska, H., Boman, H., Grabbi-Kaiv, S., Hammarklint, T., Holfort, J., and Žydrune Lydeikaitė: Extreme sea levels at selected stations on the Baltic Sea coast, Oceanologia, 56, 259–290, https://doi.org/10.5697/oc.56-2.259, 2014. a
Zhang, Y., Liu, J., and Shen, W.: A review of ensemble learning algorithms used in remote sensing applications, Appl. Sci.-Basel, 12, 8654, https://doi.org/10.3390/app12178654, 2022. a
Zhu, Z., Wang, Z., Dong, C., Yu, M., Xie, H., Cao, X., Han, L., and Qi, J.: Physics informed neural network modelling for storm surge forecasting – A case study in the Bohai Sea, China, Coast. Eng., 197, 104686, https://doi.org/10.1016/j.coastaleng.2024.104686, 2025. a
Short summary
We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast and compared it to traditional numerical models. HIDRA2 performed better overall, offering faster forecasts and valuable uncertainty estimates using ensemble predictions.
We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast...
