Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
12 Jan 2024
Technical note |
| 12 Jan 2024
| 12 Jan 2024
Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data
Department of Earth Sciences, Uppsala University, 752 36 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala University, 752 36 Uppsala, Sweden
Morten Andreas Dahl Larsen
Weather Research Department, Danish Meteorological Institute, 2100 Copenhagen, Denmark
Martin Drews
Department of Technology, Management and Economics, Technical University of Denmark, 2800 Lyngby, Denmark
Erik Nilsson
Department of Earth Sciences, Uppsala University, 752 36 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala University, 752 36 Uppsala, Sweden
Anna Rutgersson
Department of Earth Sciences, Uppsala University, 752 36 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala University, 752 36 Uppsala, Sweden
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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.
Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of...
