Influence of sea surface waves on numerical modeling of an oil spill: Revisit of symphony wheel accident
Journal of Sea Research, ISSN: 1385-1101, Vol: 201, Page: 102529
2024
- 5Captures
- 1Mentions
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Metrics Details
- Captures5
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- Mentions1
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Most Recent News
Findings from Shanghai Ocean University Update Knowledge of Energy (Influence of Sea Surface Waves On Numerical Modeling of an Oil Spill: Revisit of Symphony Wheel Accident)
2024 OCT 08 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Investigators discuss new findings in Energy. According to news
Article Description
The greatest purpose of this study is to analyze the importance of surface waves on the hindcasting of the oil spill through the Symphony wheel accident in the Qingdao coastal waters. During the accident period, a total of four synthetic aperture radar (SAR) images by Gaofen-3 (GF-3) were acquired from 2 to 19 May 2021. The hindcasting of two sea surface dynamics, namely currents and waves, is carried out using a coupled marine numeric model. This model, known as the finite-volume community ocean model-simulating waves nearshore (FVCOM-SWAVE), employs a triangular grid. Simulated significant wave height (SWH) is validated against remotely sensed product by the Haiyang-2B (HY-2B) altimeter on April 2021 yields a root mean square error (RMSE) of 0.38, a correlation coefficient (COR) of 0.78, and a scatter index (SI) of 0.34. Subsequently, Stokes drift estimated by waves are included to hindcasting oil spills using the oil particle-tracing method. The bias of the spatial coverage (SAR minus simulations) of an algorithm called the constant false alarm rate (CFAR) is −73.92 km 2 with Stokes drift, which is significantly less than the 55.45 km 2 coverage without Stokes drift. Moreover, compared with model-simulated oil spills, the bias of the geographic location at the center point with Stokes drift is 8.18 km, which is less than the 12.95 km bias without Stokes drift. These results demonstrate that Stokes drift needs to be included in the prediction of oil spills.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1385110124000625; http://dx.doi.org/10.1016/j.seares.2024.102529; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201091882&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1385110124000625; https://dx.doi.org/10.1016/j.seares.2024.102529
Elsevier BV
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