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

Wahle, Kathrin; Stanev, Emil V.; Staneva, Joanna

doi:10.5194/nhess-23-415-2023

Articles | Volume 23, issue 2

https://doi.org/10.5194/nhess-23-415-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Coastal hazards and hydro-meteorological extremes

https://doi.org/10.5194/nhess-23-415-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 2

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

Extreme wave events and forecast reliability in the Black Sea in 2025

Marcel Ricker, Arno Behrens, Johannes Schulz-Stellenfleth, Carolina Barnez Gramcianinov, and Joanna Staneva

State Planet Discuss., https://doi.org/10.5194/sp-2026-28,https://doi.org/10.5194/sp-2026-28, 2026

Preprint under review for SP

Short summary

Assessing the 2025 warming of the European North West Shelf and the potential of combined methods for monitoring temperature and its trend

Peter Grabbert, Corinna Jensen, Wei Chen, Irene Pérez González, Tim Kruschke, Dagmar Kieke, Frank Janssen, Joanna Staneva, and Thorger Brüning

State Planet Discuss., https://doi.org/10.5194/sp-2026-20,https://doi.org/10.5194/sp-2026-20, 2026

Preprint under review for SP

Short summary

Evaluation of coupled and uncoupled ocean–ice–atmosphere simulations using icon-2024.07 and NEMOv4.2.0 for the EURO-CORDEX domain

Vera Maurer, Wibke Düsterhöft-Wriggers, Rebekka Beddig, Janna Meyer, Claudia Hinrichs, Ha Thi Minh Ho-Hagemann, Joanna Staneva, Birte-Marie Ehlers, and Frank Janssen

Geosci. Model Dev., 19, 543–578, https://doi.org/10.5194/gmd-19-543-2026,https://doi.org/10.5194/gmd-19-543-2026, 2026

Short summary

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

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.