Oil spill models can effectively simulate the trajectories and fate of oil slicks, which is an essential element in contingency planning and effective response strategies prepared for oil spill accidents. However, whe...Oil spill models can effectively simulate the trajectories and fate of oil slicks, which is an essential element in contingency planning and effective response strategies prepared for oil spill accidents. However, when applied to offshore areas such as the Bohai Sea, the trajectories and fate of oil slicks would be affected by time-varying factors in a regional scale, which are assumed to be constant in most of the present models. In fact, these factors in offshore regions show much more variation over time than in the deep sea, due to offshore bathymetric and climatic characteristics. In this paper, the challenge of parameterizing these offshore factors is tackled. The remote sensing data of the region are used to analyze the modification of wind-induced drift factors, and a well-suited solution is established in parameter correction mechanism for oil spill models. The novelty of the algorithm is the self-adaptive modification mechanism of the drift factors derived from the remote sensing data for the targeted sea region, in respect to empirical constants in the present models. Considering this situation, a new regional oil spill model(i4Oil Spill) for the Bohai Sea is developed, which can simulate oil transformation and fate processes by Eulerian-Lagrangian methodology. The forecasting accuracy of the proposed model is proven by the validation results in the comparison between model simulation and subsequent satellite observations on the Penglai 19-3 oil spill accident. The performance of the model parameter correction mechanism is evaluated by comparing with the real spilled oil position extracted from ASAR images.展开更多
Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to...Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.展开更多
Because of weak dissipation effects, swells generated by fierce storms can propagate across an entire ocean basin;therefore, observing swell generation and decay and retrieving storm characteristics from a swell by sa...Because of weak dissipation effects, swells generated by fierce storms can propagate across an entire ocean basin;therefore, observing swell generation and decay and retrieving storm characteristics from a swell by satellite remote sensing are possible. In this study, based on the dispersion relation and geometrical optics principle, we used SAR wave mode data from 2003 to 2010 provided by GlobWave to track swells with peak wavelengths of more than 300 m to locate a storm-generated far-traveling swell and present the swell field related to this "static" origin. Through a comparison with ECMWF wind datasets, we conducted validations and explored some conditions that cause misjudgments in swell origins. Finally, we obtained the spatiotemporal distribution characteristics of satellite-observed swell origins(i.e., the fierce wind condition) and their evolution. This work can be used as a reference for wave models, providing early swell warnings, determining air-sea surface interactions, and determining global climate change.展开更多
基金supported by following programs: 1) NSFC-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404)The National High Technology Research and Development Program of China (Grant No. 2014AA09A511)+2 种基金The Scientific and Technological Innovation Project of the Qingdao National Laboratory for Marine Science and Technology (Grant No. 2015ASKJ01)International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No. 61361136001)Open Fund of Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology SOA (Grant No. 201508)
文摘Oil spill models can effectively simulate the trajectories and fate of oil slicks, which is an essential element in contingency planning and effective response strategies prepared for oil spill accidents. However, when applied to offshore areas such as the Bohai Sea, the trajectories and fate of oil slicks would be affected by time-varying factors in a regional scale, which are assumed to be constant in most of the present models. In fact, these factors in offshore regions show much more variation over time than in the deep sea, due to offshore bathymetric and climatic characteristics. In this paper, the challenge of parameterizing these offshore factors is tackled. The remote sensing data of the region are used to analyze the modification of wind-induced drift factors, and a well-suited solution is established in parameter correction mechanism for oil spill models. The novelty of the algorithm is the self-adaptive modification mechanism of the drift factors derived from the remote sensing data for the targeted sea region, in respect to empirical constants in the present models. Considering this situation, a new regional oil spill model(i4Oil Spill) for the Bohai Sea is developed, which can simulate oil transformation and fate processes by Eulerian-Lagrangian methodology. The forecasting accuracy of the proposed model is proven by the validation results in the comparison between model simulation and subsequent satellite observations on the Penglai 19-3 oil spill accident. The performance of the model parameter correction mechanism is evaluated by comparing with the real spilled oil position extracted from ASAR images.
基金funded by the Social Science Foundation of Shandong(No.20CXWJ08).
文摘Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41331172, 61361136001 & U1406404)the Scientific and Technological Innovation Project of the Qingdao National Laboratory for Marine Science and Technology (Grant No. 2015ASKJ01)
文摘Because of weak dissipation effects, swells generated by fierce storms can propagate across an entire ocean basin;therefore, observing swell generation and decay and retrieving storm characteristics from a swell by satellite remote sensing are possible. In this study, based on the dispersion relation and geometrical optics principle, we used SAR wave mode data from 2003 to 2010 provided by GlobWave to track swells with peak wavelengths of more than 300 m to locate a storm-generated far-traveling swell and present the swell field related to this "static" origin. Through a comparison with ECMWF wind datasets, we conducted validations and explored some conditions that cause misjudgments in swell origins. Finally, we obtained the spatiotemporal distribution characteristics of satellite-observed swell origins(i.e., the fierce wind condition) and their evolution. This work can be used as a reference for wave models, providing early swell warnings, determining air-sea surface interactions, and determining global climate change.
