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
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Over the past 30 years, the Baltic Sea’s surface mixed layer has changed unevenly: it is shoaling in the southwest due to warming and increased stratification, while deepening in the northeast as ice loss allows more winter mixing. These shifts affect marine ecosystems—shallower mixing in the south limits oxygen supply to deep waters, worsening hypoxia, while deeper mixing in the north may enhance spring algal blooms. Climate impacts in the Baltic are regionally diverse.
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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...
