Using monthly gridded ocean pathfinder Sea Surface Temperature （SST） data with a spatial resolution of 4kin from AVHRR, variations of SST over the Kuroshio region northeast of Taiwan Is. during the past two decades ...Using monthly gridded ocean pathfinder Sea Surface Temperature （SST） data with a spatial resolution of 4kin from AVHRR, variations of SST over the Kuroshio region northeast of Taiwan Is. during the past two decades （1985-2003） are studied. Some interesting findings are as follows. （1） The climatological SST field shows an expected pattern with southwest-northeast orientated isotherms, and this pattern is mainly dominated by solar irradiance and regional circulation. However, the interannual variation of this pattern is very notable, in particular along Kuroshio path. The most dynamic region is located in the east coast of Taiwan, where cold upwelling is very energetic. （2） Seasonal variation of SST over this region is mainly controlled by see-saw variation of solar irradiance between two hemispheres, but the strong interannual fluctuation of SST is found to be locked to boreal winter （January, February, and last December）, and the energetic region is identified along Kuroshio path. This phenomenon seems to closely connect with El Nifio＇s phase locking characteristics. （3） SST anomalies over Kuroshio region have a positive correlation with El Nifio-Southem Oscillation （ENSO）, which is dramatic due to the weak （strong） North Equatorial Current （NEC） during El Nifio （La Nifia） events, and the weak （strong） NEC is supposed to induce a same polarity of SST variation along the Kuroshio path. How the interannual variation and seasonal variation interact each other and what is the mechanism between ENSO and the thermal and thermodynamic processes over this region deserve our further analyses.展开更多
NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998...NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998. The accuracy of SST retrieved from daytime split window NLSST algorithm and nighttime triple window NLSST algorithm for NOAA 14 AVHRR data was investigated in this study. A matchup dataset of drifting buoys and NOAA 14 satellite measurements in the global ocean was generated to validate these operational split window and triple window algorithms. For NOAA 14 in 1998, we had 14095 and 22643 satellite and buoy matchups that matched within 25 km and 4 hours for daytime and nighttime, respectively. The satellite derived SST had a bias of less than 0.1℃ and standard deviation of about 0.5℃. This study also showed that the NLSST algorithm provided the same order of SST accuracy in different time of the year and under a wide range of satellite zenith angle and water vapor represented by the channel 4 and 5 brightness temperature difference. Therefore, NLSST algorithms are usually independent of season, geographic location, or atmospheric moisture content. Comparison between the low resolution AVHRR GAC data accuracy and high resolution Local Area Coverage (LAC) data accuracy is also discussed.展开更多
Based on several images taken by. the NOAA -11 and - 12 advanced very high resolution radiometer (AVHRR/2) during 1989-1993, and combined with a larger-scale of oceanographic investigation during 8-27 March 1992, som...Based on several images taken by. the NOAA -11 and - 12 advanced very high resolution radiometer (AVHRR/2) during 1989-1993, and combined with a larger-scale of oceanographic investigation during 8-27 March 1992, some related questions of the physical oceanography in the South China As have been discussed. The results show that there were a more complementary action for the investigation and study on the physical oceanographic phenomena in the South China As using the satellite imageries and in-situ data, and the distribution and variation of the satellite-derived sea surface tempera- ture field in the northeast South China Sea basically mirrored the results obtained from in-situ investigation. Some of the large-scale and meso-scale marine phenomena varied with the long and medium periods, for instance, an anticyclonic meandering of the Kuroshio path when it flowed through the Bashi Channel, the win water with high temperature along the west coastline of Lain Island, the western and northern warmer water tongues off the southwest of Taiwan Island, China, as well as the thermal front along the continental shelf off Guangdong, were well presented in both the satellite imageries and in-situ data. Then, there the can be revealed some marine phenomena with a short period, or in the further large area using the satellite imageries, which will provide scientific basis for planning and carrying out the effective marine investigation. Finally, a map of surface circulation pattern in the northeast South China Sea in winter is sketched according to the comprehensive analysis of the satellite and in-situ data.展开更多
Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) ...Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data,and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data.展开更多
基金Cosponsored by the NSFC (No. 40545018) and the National Basic Research Program of China (No. 2005CB422308).
文摘Using monthly gridded ocean pathfinder Sea Surface Temperature （SST） data with a spatial resolution of 4kin from AVHRR, variations of SST over the Kuroshio region northeast of Taiwan Is. during the past two decades （1985-2003） are studied. Some interesting findings are as follows. （1） The climatological SST field shows an expected pattern with southwest-northeast orientated isotherms, and this pattern is mainly dominated by solar irradiance and regional circulation. However, the interannual variation of this pattern is very notable, in particular along Kuroshio path. The most dynamic region is located in the east coast of Taiwan, where cold upwelling is very energetic. （2） Seasonal variation of SST over this region is mainly controlled by see-saw variation of solar irradiance between two hemispheres, but the strong interannual fluctuation of SST is found to be locked to boreal winter （January, February, and last December）, and the energetic region is identified along Kuroshio path. This phenomenon seems to closely connect with El Nifio＇s phase locking characteristics. （3） SST anomalies over Kuroshio region have a positive correlation with El Nifio-Southem Oscillation （ENSO）, which is dramatic due to the weak （strong） North Equatorial Current （NEC） during El Nifio （La Nifia） events, and the weak （strong） NEC is supposed to induce a same polarity of SST variation along the Kuroshio path. How the interannual variation and seasonal variation interact each other and what is the mechanism between ENSO and the thermal and thermodynamic processes over this region deserve our further analyses.
文摘NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998. The accuracy of SST retrieved from daytime split window NLSST algorithm and nighttime triple window NLSST algorithm for NOAA 14 AVHRR data was investigated in this study. A matchup dataset of drifting buoys and NOAA 14 satellite measurements in the global ocean was generated to validate these operational split window and triple window algorithms. For NOAA 14 in 1998, we had 14095 and 22643 satellite and buoy matchups that matched within 25 km and 4 hours for daytime and nighttime, respectively. The satellite derived SST had a bias of less than 0.1℃ and standard deviation of about 0.5℃. This study also showed that the NLSST algorithm provided the same order of SST accuracy in different time of the year and under a wide range of satellite zenith angle and water vapor represented by the channel 4 and 5 brightness temperature difference. Therefore, NLSST algorithms are usually independent of season, geographic location, or atmospheric moisture content. Comparison between the low resolution AVHRR GAC data accuracy and high resolution Local Area Coverage (LAC) data accuracy is also discussed.
基金This project was supported by the National Key Programme for Developing Basic Sciences-Research on the China Seashore Circulatio
文摘Based on several images taken by. the NOAA -11 and - 12 advanced very high resolution radiometer (AVHRR/2) during 1989-1993, and combined with a larger-scale of oceanographic investigation during 8-27 March 1992, some related questions of the physical oceanography in the South China As have been discussed. The results show that there were a more complementary action for the investigation and study on the physical oceanographic phenomena in the South China As using the satellite imageries and in-situ data, and the distribution and variation of the satellite-derived sea surface tempera- ture field in the northeast South China Sea basically mirrored the results obtained from in-situ investigation. Some of the large-scale and meso-scale marine phenomena varied with the long and medium periods, for instance, an anticyclonic meandering of the Kuroshio path when it flowed through the Bashi Channel, the win water with high temperature along the west coastline of Lain Island, the western and northern warmer water tongues off the southwest of Taiwan Island, China, as well as the thermal front along the continental shelf off Guangdong, were well presented in both the satellite imageries and in-situ data. Then, there the can be revealed some marine phenomena with a short period, or in the further large area using the satellite imageries, which will provide scientific basis for planning and carrying out the effective marine investigation. Finally, a map of surface circulation pattern in the northeast South China Sea in winter is sketched according to the comprehensive analysis of the satellite and in-situ data.
文摘Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data,and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data.
