On June 4 and 17, 201 I, two separate oil spill accidents occurred at platforms B and C of the Penglai 19- 3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first a...On June 4 and 17, 201 I, two separate oil spill accidents occurred at platforms B and C of the Penglai 19- 3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first available Synthetic Aperture Radar （SAR） image acquired on June 11, 2011, we performed a numerical experiment to simulate the potential oil spill beaching area with the General NOAA Operational Modeling Environment （GNOME） model. The model was driven by ocean surface currents from an operational ocean model （Navy Coastal Ocean Model） and surface winds from operational scatterometer measurements （the Advanced Scatterom- eter）. Under the forcing of wind and ocean currents, some of the oil spills reached land along the coast of Qinhuangdao within 12 days. The results also demonstrate that the ocean currents are likely to carry the remaining oil spills along the Bohai coast towards the northeast. The predicted oil spill beaching area was verified by reported in-situ measurements and former studies based on MODIS observations.展开更多
The Geostationary Orbiting Satellite(GOS)offers extensive opportunities for the study of oceanic internal waves(IWs)through high-frequency observations.In this study,the spatial and temporal distributions of sunglint ...The Geostationary Orbiting Satellite(GOS)offers extensive opportunities for the study of oceanic internal waves(IWs)through high-frequency observations.In this study,the spatial and temporal distributions of sunglint from 3 GOSs(Himawari-8,FY-4A,and GK-2A)were calculated,and the observation times of IWs in various seas were also recorded.The GOS can continuously observe IWs at a frequency of 10 min for 2 to 3 h.As demonstrated by the application to IWs in the Andaman Sea,the GOS effectively captures the surface features of IWs,including soliton number,the length and wavelength of the leading wave,and the speed and direction of propagation.Furthermore,the GOS can be used to track the dynamic processes of IWs within a short duration and provide more accurate“instantaneous”phase speeds.In the case of the Indonesian Seas,the average error of the GOS-derived phase speeds is 0.13 m/s compared to the Korteweg–de Vries phase speeds.Additionally,a 7-day observation from FY-4A suggests the possibility of diurnal IWs in the Sulu Sea.The advent of high-temporal-resolution GOS provides an enriched dataset for oceanic IW studies,which will contribute greatly to a more comprehensive understanding of IW mechanisms.展开更多
Ocean internal waves(IWs)are widespread submesoscale dynamical phenomena in oceans,and they have important impacts on energy transfer,nutrient transport,and human activities.In this study,Sentinel-1 synthetic aperture...Ocean internal waves(IWs)are widespread submesoscale dynamical phenomena in oceans,and they have important impacts on energy transfer,nutrient transport,and human activities.In this study,Sentinel-1 synthetic aperture radar(SAR)images from 2014 to 2023 were collected to construct a global IW dataset(S1-IW-2023)through a series of optimized data processing.S1-IW-2023 was constructed to address the issue of insufficient data and lack of variation in the deep learning IW datasets;it can be used in IW studies using deep learning methods to enhance model generalizability and robustness.Moreover,considering the limitations of existing convolutional neural network(CNN)-based IW detection models in handling complex interferences in SAR images,resulting in frequent false positives,false negatives,or inaccurate bounding box positioning,we employed transfer learning to train a Transformer-based hierarchical IW detector,IWD-Net,that extracts features via Swin Transformer and fuses the visual,semantic,and contextual features of IWs via a multiscale feature fusion network.Experimental results demonstrated the effectiveness of applying Transformer concepts to IW detection for addressing complex interferences.This study provides an efficient and stable new method for extracting IW features from massive SAR data and lays the foundation for applying Transformer concepts to detect IWs in SAR images.展开更多
基金Helpful discussion with Dr. Xiaofeng Li from NOAA is appreciated. This work was supported in part by the National Natural Science Foundation of China （Grant No. 41306194）, the Open Fund of Shandong Provincial Key Laboratory of Marine Ecology and Environment ＆ Disaster Prevention and Mitigation （No. 2011001）, the Shanghai Municipal Science and Technology Commission （No. 13dz12044000）, and the outstanding innovative talent program of Hohai University.
文摘On June 4 and 17, 201 I, two separate oil spill accidents occurred at platforms B and C of the Penglai 19- 3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first available Synthetic Aperture Radar （SAR） image acquired on June 11, 2011, we performed a numerical experiment to simulate the potential oil spill beaching area with the General NOAA Operational Modeling Environment （GNOME） model. The model was driven by ocean surface currents from an operational ocean model （Navy Coastal Ocean Model） and surface winds from operational scatterometer measurements （the Advanced Scatterom- eter）. Under the forcing of wind and ocean currents, some of the oil spills reached land along the coast of Qinhuangdao within 12 days. The results also demonstrate that the ocean currents are likely to carry the remaining oil spills along the Bohai coast towards the northeast. The predicted oil spill beaching area was verified by reported in-situ measurements and former studies based on MODIS observations.
基金supported by the National Natural Science Foundation of China(42227901)the Zhejiang Provincial Natural Science Foundation of China(LR21D060002 and LGF21D060002)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021004)。
文摘The Geostationary Orbiting Satellite(GOS)offers extensive opportunities for the study of oceanic internal waves(IWs)through high-frequency observations.In this study,the spatial and temporal distributions of sunglint from 3 GOSs(Himawari-8,FY-4A,and GK-2A)were calculated,and the observation times of IWs in various seas were also recorded.The GOS can continuously observe IWs at a frequency of 10 min for 2 to 3 h.As demonstrated by the application to IWs in the Andaman Sea,the GOS effectively captures the surface features of IWs,including soliton number,the length and wavelength of the leading wave,and the speed and direction of propagation.Furthermore,the GOS can be used to track the dynamic processes of IWs within a short duration and provide more accurate“instantaneous”phase speeds.In the case of the Indonesian Seas,the average error of the GOS-derived phase speeds is 0.13 m/s compared to the Korteweg–de Vries phase speeds.Additionally,a 7-day observation from FY-4A suggests the possibility of diurnal IWs in the Sulu Sea.The advent of high-temporal-resolution GOS provides an enriched dataset for oceanic IW studies,which will contribute greatly to a more comprehensive understanding of IW mechanisms.
基金supported by the National Key Research and Development Program of China(grant no.2022YFC3103101)National Natural Science Foundation of China(42306200 and 42306216)Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021004).
文摘Ocean internal waves(IWs)are widespread submesoscale dynamical phenomena in oceans,and they have important impacts on energy transfer,nutrient transport,and human activities.In this study,Sentinel-1 synthetic aperture radar(SAR)images from 2014 to 2023 were collected to construct a global IW dataset(S1-IW-2023)through a series of optimized data processing.S1-IW-2023 was constructed to address the issue of insufficient data and lack of variation in the deep learning IW datasets;it can be used in IW studies using deep learning methods to enhance model generalizability and robustness.Moreover,considering the limitations of existing convolutional neural network(CNN)-based IW detection models in handling complex interferences in SAR images,resulting in frequent false positives,false negatives,or inaccurate bounding box positioning,we employed transfer learning to train a Transformer-based hierarchical IW detector,IWD-Net,that extracts features via Swin Transformer and fuses the visual,semantic,and contextual features of IWs via a multiscale feature fusion network.Experimental results demonstrated the effectiveness of applying Transformer concepts to IW detection for addressing complex interferences.This study provides an efficient and stable new method for extracting IW features from massive SAR data and lays the foundation for applying Transformer concepts to detect IWs in SAR images.
