Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-415-2023
https://doi.org/10.5194/nhess-23-415-2023
Research article
 | 
02 Feb 2023
Research article |  | 02 Feb 2023

Detecting anomalous sea-level states in North Sea tide gauge data using an autoassociative neural network

Kathrin Wahle, Emil V. Stanev, and Joanna Staneva

Related authors

The representation of rivers in operational ocean forecasting systems: a review
Pascal Matte, John Wilkin, and Joanna Staneva
State Planet, 5-opsr, 19, https://doi.org/10.5194/sp-5-opsr-19-2025,https://doi.org/10.5194/sp-5-opsr-19-2025, 2025
Short summary
A description of existing operational ocean forecasting services around the globe
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego A. Narvaez, Heather Regan, Claudia G. Simionato, Gregory C. Smith, Joanna Staneva, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, Jennifer Veitch, and Jorge Zavala Hidalgo
State Planet, 5-opsr, 5, https://doi.org/10.5194/sp-5-opsr-5-2025,https://doi.org/10.5194/sp-5-opsr-5-2025, 2025
Short summary
Solving coastal dynamics: introduction to high-resolution ocean forecasting services
Joanna Staneva, Angelique Melet, Jennifer Veitch, and Pascal Matte
State Planet, 5-opsr, 4, https://doi.org/10.5194/sp-5-opsr-4-2025,https://doi.org/10.5194/sp-5-opsr-4-2025, 2025
Short summary
Sea Level Rise in Europe: Impacts and consequences
Roderik van de Wal, Angélique Melet, Debora Bellafiore, Paula Camus, Christian Ferrarin, Gualbert Oude Essink, Ivan D. Haigh, Piero Lionello, Arjen Luijendijk, Alexandra Toimil, Joanna Staneva, and Michalis Vousdoukas
State Planet, 3-slre1, 5, https://doi.org/10.5194/sp-3-slre1-5-2024,https://doi.org/10.5194/sp-3-slre1-5-2024, 2024
Short summary
Characteristics and trends of marine heatwaves in the northwest European Shelf and the impacts on density stratification
Wei Chen and Joanna Staneva
State Planet, 4-osr8, 7, https://doi.org/10.5194/sp-4-osr8-7-2024,https://doi.org/10.5194/sp-4-osr8-7-2024, 2024
Short summary

Related subject area

Sea, Ocean and Coastal Hazards
Semi-empirical forecast modelling of rip-current and shore-break wave hazards
Bruno Castelle, Jeoffrey Dehez, Jean-Philippe Savy, Sylvain Liquet, and David Carayon
Nat. Hazards Earth Syst. Sci., 25, 2379–2397, https://doi.org/10.5194/nhess-25-2379-2025,https://doi.org/10.5194/nhess-25-2379-2025, 2025
Short summary
A multiscale modelling framework of coastal flooding events for global to local flood hazard assessments
Irene Benito, Jeroen C. J. H. Aerts, Philip J. Ward, Dirk Eilander, and Sanne Muis
Nat. Hazards Earth Syst. Sci., 25, 2287–2315, https://doi.org/10.5194/nhess-25-2287-2025,https://doi.org/10.5194/nhess-25-2287-2025, 2025
Short summary
Super typhoons Mangkhut (2018) and Saola (2023) during landfall: comparison and insights for wind engineering practice
Yujie Liu, Yuncheng He, Pakwai Chan, Aiming Liu, and Qijun Gao
Nat. Hazards Earth Syst. Sci., 25, 2255–2269, https://doi.org/10.5194/nhess-25-2255-2025,https://doi.org/10.5194/nhess-25-2255-2025, 2025
Short summary
Recent Baltic Sea storm surge events from a climate perspective
Nikolaus Groll, Lidia Gaslikova, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 25, 2137–2154, https://doi.org/10.5194/nhess-25-2137-2025,https://doi.org/10.5194/nhess-25-2137-2025, 2025
Short summary
Development of a wind-based storm surge model for the German Bight
Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke
Nat. Hazards Earth Syst. Sci., 25, 2081–2096, https://doi.org/10.5194/nhess-25-2081-2025,https://doi.org/10.5194/nhess-25-2081-2025, 2025
Short summary

Cited articles

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, 2021. 
Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019, 2019.  
Bonaduce, A., Staneva, J., Grayek, S., Bidlot, J. R., and Breivik, Ø.: Sea-state contributions to sea-level variability in the European Seas, Ocean Dynamics, 70, 1547–1569, 2020. 
Bruneau, N., Polton, J., Williams, J., and Holt, J.: Estimation of global coastal sea level extremes using neural networks. Environ. Res. Lett., 15, 074030, https://doi.org/10.1088/1748-9326/ab89d6, 2020. 
Climate Data Store (CDS): https://cds.climate.copernicus.eu/, last access: 20 July 2021. 
Download
Short summary
Knowledge of what causes maximum water levels is often key in coastal management. Processes, such as storm surge and atmospheric forcing, alter the predicted tide. Whilst most of these processes are modeled in present-day ocean forecasting, there is still a need for a better understanding of situations where modeled and observed water levels deviate from each other. Here, we will use machine learning to detect such anomalies within a network of sea-level observations in the North Sea.
Share
Altmetrics
Final-revised paper
Preprint