Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5497-2024
© 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/gmd-17-5497-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Jul 2024
Development and technical paper |
| 23 Jul 2024
| 23 Jul 2024
Development of a novel storm surge inundation model framework for efficient prediction
Xuanxuan Gao
Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Shuiqing Li
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
Laboratory for Ocean and Climate Dynamics, Qingdao Marine Science and Technology Center, Qingdao, 266237, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China
Dongxue Mo
Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
Laboratory for Ocean and Climate Dynamics, Qingdao Marine Science and Technology Center, Qingdao, 266237, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China
Yahao Liu
Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
Laboratory for Ocean and Climate Dynamics, Qingdao Marine Science and Technology Center, Qingdao, 266237, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China
Po Hu
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
Laboratory for Ocean and Climate Dynamics, Qingdao Marine Science and Technology Center, Qingdao, 266237, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China
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Zongyu Li, Shuiqing Li, Jinrui Chen, Yuan Kong, Yong Fang, Jiageng Han, Pei Zhu, and Po Hu
EGUsphere, https://doi.org/10.5194/egusphere-2026-895, https://doi.org/10.5194/egusphere-2026-895, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurate prediction of extreme ocean waves is crucial for coastal safety, yet numerical models often struggle during severe storms. We developed an automated method to adjust key model settings together, rather than one by one, using observations from typhoon events. Tests show that this approach clearly improves wave height predictions and reduces systematic errors. The method is transparent, efficient, and can be applied to many Earth system models to better simulate hazardous conditions.
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Short summary
Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Storm surges generate coastal inundation and expose populations and properties to danger. We...
