This study investigated the ef fects of two typhoons(Nari and Wipha) on sea surface temperature(SST) and chlorophyll- a(Chl- a) concentration. Typhoons Nari and Wipha passed through the Yellow Sea on September 13, 200...This study investigated the ef fects of two typhoons(Nari and Wipha) on sea surface temperature(SST) and chlorophyll- a(Chl- a) concentration. Typhoons Nari and Wipha passed through the Yellow Sea on September 13, 2007 and the East China Sea(ECS) on September 16, 2007, respectively. The SST and Chl- a data were obtained from the Aqua/Terra MODIS and NOAA18, respectively, and the temperature and salinity in the southeast of the study area were observed in situ from Argo. The average SST within the study area dropped from 26.33°C on September 10 to a minimum of 22.79°C on September 16. Without the usual phenomenon of ‘right bias', the most striking response of SST was in the middle of the typhoons' tracks, near to coastal waters. Strong cooling of the upper layers of the water column was probably due to increased vertical mixing, discharge from the Changjiang River estuary, and heavy rainfall. During the typhoons, average Chl-a increased by 11.54% within the study area and by 21.69% in the off shore area near to the southeast ECS. From September 1 to 13, average Chl-a was only 0.10 mg/m^3 in the of fshore waters but it reached a peak of >0.17 mg/m^3 on September 18. This large increase in Chl-a concentration in of fshore waters might have been triggered by strong vertical mixing, upwelling induced by strong typhoons, and sedimentation and nutrient infl ux following heavy rainfall.展开更多
The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many ...The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.展开更多
基金Supported by the National Marine Important Charity Special Foundation of China(No.201305019)the National Natural Science Foundation of China(No.41340049)+4 种基金the Natural Foundation of Guangdong(No.2014A030313603)the Science and Technology Planning Project of Guangdong(No.2013B030200002)the Zhejiang’s Post-Doctoral Funding(No.BSH1301015)the Novel Project for Developing University Sponsored by GDOU(No.GDOU2014050226)the Second Institute of Oceanography,State Oceanic Administration Post-Doctoral Starting Fund(No.JG1319)
文摘This study investigated the ef fects of two typhoons(Nari and Wipha) on sea surface temperature(SST) and chlorophyll- a(Chl- a) concentration. Typhoons Nari and Wipha passed through the Yellow Sea on September 13, 2007 and the East China Sea(ECS) on September 16, 2007, respectively. The SST and Chl- a data were obtained from the Aqua/Terra MODIS and NOAA18, respectively, and the temperature and salinity in the southeast of the study area were observed in situ from Argo. The average SST within the study area dropped from 26.33°C on September 10 to a minimum of 22.79°C on September 16. Without the usual phenomenon of ‘right bias', the most striking response of SST was in the middle of the typhoons' tracks, near to coastal waters. Strong cooling of the upper layers of the water column was probably due to increased vertical mixing, discharge from the Changjiang River estuary, and heavy rainfall. During the typhoons, average Chl-a increased by 11.54% within the study area and by 21.69% in the off shore area near to the southeast ECS. From September 1 to 13, average Chl-a was only 0.10 mg/m^3 in the of fshore waters but it reached a peak of >0.17 mg/m^3 on September 18. This large increase in Chl-a concentration in of fshore waters might have been triggered by strong vertical mixing, upwelling induced by strong typhoons, and sedimentation and nutrient infl ux following heavy rainfall.
基金Supported by the National Natural Science Foundation of China(No.41271409)
文摘The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.
