Advanced weather typing for downscaling of wave climate and storm surge at a UK nuclear power station
Advanced weather typing for downscaling of wave climate and storm surge at a UK nuclear power station
Evaluating risks from external hazards is crucial for the safety of nuclear power stations throughout their lifecycle. In coastal areas, a key threat arises from the risks of coastal flooding and erosion via a combination of simultaneous processes (e.g., tides, waves, and storm surges) acting on varying spatial and temporal scales. Therefore, an accurate characterisation of local sea state conditions is essential for risk assessment and mitigation. In this paper, we use a weather typing method to downscale local wave climate and storm surge conditions at the Hartlepool nuclear power station. Model validation suggests that the use of 36 weather types can effectively downscale multivariate wave variables (wave height, period, and direction) and storm surge with overall good performance, though the accuracy is limited for wave direction and extreme wave height. Comprehensive sensitivity tests are conducted to investigate key factors influencing the downscaling process, including predictor variable, spatial and temporal definitions, predictor resolution, the number of weather types, and the weighting parameter in semi-supervised classification. For example, we find that the model with sea level pressure and sea level pressure gradient as the predictor has better overall performance in downscaling multivariate predictands than the model using either one individually. These results can facilitate the development of weather typing models to enable efficient and reliable estimations of local predictands in wider applications. This approach links atmospheric conditions to potential coastal threats, which offers a valuable tool for proactive hazard preparedness and risk management in nuclear power and oth-er critical infrastructure sectors.
Sensitivity analysis, Statistical downscaling, Storm surge, Wave climate, Weather types
Zhong, Zehua
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Kassem, Hachem
658efa7a-a02c-4b29-9d07-5d57e95a4b51
Haigh, Ivan D.
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Sifnioti, Dafni E.
3d1c3b08-169c-4c3a-ba7e-8fbc47f09091
Gouldby, Ben
2945624f-ae17-478b-be1e-8c522a26f685
Liu, Ye
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Camus, Paula
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28 March 2025
Zhong, Zehua
4b3c0a88-565f-4a4f-87ae-0bb04d87dbd0
Kassem, Hachem
658efa7a-a02c-4b29-9d07-5d57e95a4b51
Haigh, Ivan D.
945ff20a-589c-47b7-b06f-61804367eb2d
Sifnioti, Dafni E.
3d1c3b08-169c-4c3a-ba7e-8fbc47f09091
Gouldby, Ben
2945624f-ae17-478b-be1e-8c522a26f685
Liu, Ye
78dbbc03-1a3e-47a1-9ce5-9dc04360e178
Camus, Paula
66625386-9051-4ea8-a0fa-956751534796
Zhong, Zehua, Kassem, Hachem, Haigh, Ivan D., Sifnioti, Dafni E., Gouldby, Ben, Liu, Ye and Camus, Paula
(2025)
Advanced weather typing for downscaling of wave climate and storm surge at a UK nuclear power station.
Ocean Dynamics, 75 (4), [32].
(doi:10.1007/s10236-025-01682-7).
Abstract
Evaluating risks from external hazards is crucial for the safety of nuclear power stations throughout their lifecycle. In coastal areas, a key threat arises from the risks of coastal flooding and erosion via a combination of simultaneous processes (e.g., tides, waves, and storm surges) acting on varying spatial and temporal scales. Therefore, an accurate characterisation of local sea state conditions is essential for risk assessment and mitigation. In this paper, we use a weather typing method to downscale local wave climate and storm surge conditions at the Hartlepool nuclear power station. Model validation suggests that the use of 36 weather types can effectively downscale multivariate wave variables (wave height, period, and direction) and storm surge with overall good performance, though the accuracy is limited for wave direction and extreme wave height. Comprehensive sensitivity tests are conducted to investigate key factors influencing the downscaling process, including predictor variable, spatial and temporal definitions, predictor resolution, the number of weather types, and the weighting parameter in semi-supervised classification. For example, we find that the model with sea level pressure and sea level pressure gradient as the predictor has better overall performance in downscaling multivariate predictands than the model using either one individually. These results can facilitate the development of weather typing models to enable efficient and reliable estimations of local predictands in wider applications. This approach links atmospheric conditions to potential coastal threats, which offers a valuable tool for proactive hazard preparedness and risk management in nuclear power and oth-er critical infrastructure sectors.
Text
Zhongetal_Downscaling_OD_manuscript_accepted
- Accepted Manuscript
Text
s10236-025-01682-7
- Version of Record
More information
Accepted/In Press date: 17 March 2025
e-pub ahead of print date: 28 March 2025
Published date: 28 March 2025
Keywords:
Sensitivity analysis, Statistical downscaling, Storm surge, Wave climate, Weather types
Identifiers
Local EPrints ID: 500308
URI: http://eprints.soton.ac.uk/id/eprint/500308
ISSN: 1616-7341
PURE UUID: 0c4dfc7d-6ebc-4d2b-aa47-831c3d642268
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Date deposited: 24 Apr 2025 16:41
Last modified: 04 Sep 2025 02:35
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Contributors
Author:
Zehua Zhong
Author:
Dafni E. Sifnioti
Author:
Ben Gouldby
Author:
Ye Liu
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