多传感器卫星海表温度数据的印证与交叉比较
摘要
遥感技术具有覆盖范围广、可对同一区域重复观测等特点,为全球海洋的观测研究提供了便利。卫星遥感对海洋表面温度( Sea Surface Temperature,SST)的观测精度是关系到全球气候预测的关键因素之一,不同的星载传感器具有不同的测量精度,显而易见,卫星SST数据的印证和不同SST数据集的之间的交叉印证工作是必不可少的,一方面可以提高不同传感器卫星SST反演产品的精度,促进数据产品的业务化应用,另一方面为SST数据融合、气候分析等工作提供一定的科学依据。本文工作主要对卫星SST数据在全球不同区域的相关印证分析研究进行了调研、综述,并以西北太平洋为研究的目标区域,使用2005年的多传感器卫星SST产品及相关现场实测数据,经过数据的预处理、质量控制、数据匹配等处理后,采用统计方法,比较分析了卫星数据集与现场实测的浮标数据的一致性、差值特征、不同数据集之间的差值特征和误差来源。
首先对多传感器卫星海表温度产品(Satellite SSTs)与现场实测浮标数据(BuoySST)进行了印证分析。中国海洋大学卫星地面站接收的NOAA/AVHRR卫星遥感数据的两种SST反演算法产品MCSST和NLSST分别在西北太平洋印证的结果如下:MCSST和NLSST都要比现场实测数据温度低,MCSST的年平均偏差(BuoySST-MCSST)白天和晚上分别是0.23°C、0.36°C,年标准偏差白天和晚上分别为0.88°C、0.93°C; NLSST的精度相比于MCSST有所提高,年平均偏差(BuoySST-NLSST)白天和晚上分别为0.04°C、0.25°C,年标准偏差变化不大,白天和晚上分别为0.87°C、0.93°C;对卫星天顶角小于45°的观测数据反演的NLSST的印证结果是年平均偏差白天和晚上分别为-0.16°C、0.13°C,年标准偏差也有所提高,白天和晚上分别为0.73°C、0.80°C,因此在SST的交叉印证中采用此数据集进行比较。对NOAA/NASA AVHRR Pathfinder SST(PFSST)、Aqua & Terra/MODIS SSTs(AquaSST、TerraSST)及Aqua/AMSR-E SST这四种数据集分别与BuoySST印证的结果是年平均偏差(BuoySST-Satellite SSTs)分别为:PFSST的是-0.08°C/-0.08°C(白天/晚上,下同)、AquaSST的是-0.12°C/0.34°C、TerraSST的是0.00°C/0.11°C、AMSR-E SST的是-0.20°C/-0.18°C;年标准偏差分别为: PFSST的是0.60°C/0.68°C、AquaSST的是0.67°C/0.76°C、TerraSST的是0.69°C/0.74°C、AMSR-E SST的是0.64°C/0.67°C。结果表明红外数据集除MCSST、NLSST和AquaSST在晚上的年平均偏差明显较大外,其余数据集的年平均偏差都较小;AMSR-E SST总体比实测数据温度高。
其次对不同卫星SST数据集进行了交叉印证,分析了不同数据集之间的差值特征。总体上,卫星天顶角小于45°的观测数据反演的NLSST数据在晚上都要比其他数据集温度低,在白天比其他红外数据集温度高,AquaSST与NLSST的年平均偏差较小,AMSR-E SST与NLSST的年平均偏差最大;AMSR-E SST要比红外数据集温度高。从两两数据集差值的空间分布图中也可以看出PFSST与MODIS SSTs的两个数据集的一致性最好,除AquaSST晚上的数据外,标准偏差和平均偏差都比较低,没有明显的高正值和低负值的分布,且有一定的区域特征,季节性变化明显。不同的测量原理、不同的反演算法、不同的时空分辨率、不同大气状况的影响等都是误差的主要来源。
各传感器的算法反演的温度产品具有较好的一致性。在实际使用的过程中,有必要对AVHRR LAC SST数据进行进一步的云检测算法的改进和反演系数的订正工作;在多传感器海表温度的高精度应用中,如气候变化研究、海表温度数据融合,需要考虑不同传感器获取的海表温度的订正,以提供高精度的产品。
Remote Sensing techniques have the advantage of synoptic and frequent view of the large geographic areas, which is useful and convenient for the studies of the global oceans. The accuracy of the Sea Surface Temperature (SST) derived from satellite measurements is one of the key factors of climate prediction. Obviously, it is necessary to validate the satellite SSTs derived from multi-sensors and compare different SST datasets. The accuracy of SST datasets will be improved, which will promote the operational applications of SST datasets; on the other hand, the results will be very important for data merging and climate analysis. The studies on satellite SST validation and intercomparison published in recent years is investigated. Validation and intercomparison of multi-sensor SST datasets in the Northwest Pacific during the period of 2005 are carried out. After the preprocessing of data, quality control and generation of mach-up database, the consistency and characteristics of the differences between the satellite SST datasets and in situ buoy SST data are compared and analyzed. The characteristics of the differences among different SST datasets and error sources are also analyzed.
Firstly, multi-sensor Satellite SST datasets in the Northwest Pacific are compared with in situ buoy SST data respectively. MCSST and NLSST are two products with different retrieval algorithms, which are received and processed by the Satellite Ground Station of Ocean University of China. The results of preliminary validation show that MCSST and NLSST data are lower than in situ buoy SST data. The annual mean bias of the difference between in situ buoy SST data and MCSST data in day and night time is 0.23°C and 0.36°C respectively, and the annual standard deviation of it in day and night time is 0.88°C and 0.93°C respectively; The mean bias of the difference between in situ buoy SST data and NLSST data in day and night is 0.04°C and 0.25°C respectively. It has a small change in the annual standard deviation in day and night which is 0.87°C and 0.93°C respectively. Matchup data with satellite zenith angle greater than 45°are excluded,the mean bias of the difference between in situ buoy SST data and NLSST in day and night is -0.16°C and 0.13°C respectively; It has a good change in the annual standard deviation in day and night which is 0.73°C and 0.80°C respectively. The results indicate that NLSST data is better than MCSST data,so it is used in intercomparisons of the multi-sensor satellite SST datasets。. The four satellite SST datasets of NOAA/NASA AVHRR Pathfinder (PFSST), Aqua/MODIS SST, Terra/MODIS SST, and AMSR-E SST are compared with in situ buoy SST data too. The annual mean bias of the difference between in situ buoy SST data and these datasets is -0.08°C/-0.08°C (day/night, as follows), -0.12°C/0.34°C, 0.00°C/0.11°C, and -0.20°C/-0.18°C respectively. The annual standard deviation of them is 0.60°C/0.68°C, 0.67°C/0.76°, 0.69°C/0.74°C, and 0.64°C/0.67°C respectively. The statistics shows that the annual mean bias is small except that of MCSST and that of AquaSST in night time. Generally, AMSR-E SST is higher than in situ buoy SST.
Secondly, the multi-sensor satellite SST datasets are intercompared and the characteristics of the differences between them are analyzed. The intercomparisons of multi-sensor SST data with NLSST data show that NLSST is lower than other dataset in night,and it is higher than the other infrared dataset. The annual mean bias between AquaSST and NLSST is lower while that between AMSR-E and it is the largest. The results of the intercomparisons also show AMSR-E SST is higher than the infrared SST datasets. The consistency of PFSST and MODIS SSTs is the best except for AquaSST in night and the annual standard deviation and mean bias are low. The statistics of the differences show regional and seasonal variations. The error sources may come from different measuring principles, different retrieval algorithms, different resolutions and different atmospheric conditions.
In summary, the statistics shows that different SST datasets are consistent with each other. Some infrared SST data are contaminated by cloud. Cloud detection and the retrieval coefficients of AVHRR LAC SST are needed to be improved. Correction of multi-sensor satellite SST datasets should be made for the applications required high accuracy SST such as study of climate change and data merging.
首先对多传感器卫星海表温度产品(Satellite SSTs)与现场实测浮标数据(BuoySST)进行了印证分析。中国海洋大学卫星地面站接收的NOAA/AVHRR卫星遥感数据的两种SST反演算法产品MCSST和NLSST分别在西北太平洋印证的结果如下:MCSST和NLSST都要比现场实测数据温度低,MCSST的年平均偏差(BuoySST-MCSST)白天和晚上分别是0.23°C、0.36°C,年标准偏差白天和晚上分别为0.88°C、0.93°C; NLSST的精度相比于MCSST有所提高,年平均偏差(BuoySST-NLSST)白天和晚上分别为0.04°C、0.25°C,年标准偏差变化不大,白天和晚上分别为0.87°C、0.93°C;对卫星天顶角小于45°的观测数据反演的NLSST的印证结果是年平均偏差白天和晚上分别为-0.16°C、0.13°C,年标准偏差也有所提高,白天和晚上分别为0.73°C、0.80°C,因此在SST的交叉印证中采用此数据集进行比较。对NOAA/NASA AVHRR Pathfinder SST(PFSST)、Aqua & Terra/MODIS SSTs(AquaSST、TerraSST)及Aqua/AMSR-E SST这四种数据集分别与BuoySST印证的结果是年平均偏差(BuoySST-Satellite SSTs)分别为:PFSST的是-0.08°C/-0.08°C(白天/晚上,下同)、AquaSST的是-0.12°C/0.34°C、TerraSST的是0.00°C/0.11°C、AMSR-E SST的是-0.20°C/-0.18°C;年标准偏差分别为: PFSST的是0.60°C/0.68°C、AquaSST的是0.67°C/0.76°C、TerraSST的是0.69°C/0.74°C、AMSR-E SST的是0.64°C/0.67°C。结果表明红外数据集除MCSST、NLSST和AquaSST在晚上的年平均偏差明显较大外,其余数据集的年平均偏差都较小;AMSR-E SST总体比实测数据温度高。
其次对不同卫星SST数据集进行了交叉印证,分析了不同数据集之间的差值特征。总体上,卫星天顶角小于45°的观测数据反演的NLSST数据在晚上都要比其他数据集温度低,在白天比其他红外数据集温度高,AquaSST与NLSST的年平均偏差较小,AMSR-E SST与NLSST的年平均偏差最大;AMSR-E SST要比红外数据集温度高。从两两数据集差值的空间分布图中也可以看出PFSST与MODIS SSTs的两个数据集的一致性最好,除AquaSST晚上的数据外,标准偏差和平均偏差都比较低,没有明显的高正值和低负值的分布,且有一定的区域特征,季节性变化明显。不同的测量原理、不同的反演算法、不同的时空分辨率、不同大气状况的影响等都是误差的主要来源。
各传感器的算法反演的温度产品具有较好的一致性。在实际使用的过程中,有必要对AVHRR LAC SST数据进行进一步的云检测算法的改进和反演系数的订正工作;在多传感器海表温度的高精度应用中,如气候变化研究、海表温度数据融合,需要考虑不同传感器获取的海表温度的订正,以提供高精度的产品。
Remote Sensing techniques have the advantage of synoptic and frequent view of the large geographic areas, which is useful and convenient for the studies of the global oceans. The accuracy of the Sea Surface Temperature (SST) derived from satellite measurements is one of the key factors of climate prediction. Obviously, it is necessary to validate the satellite SSTs derived from multi-sensors and compare different SST datasets. The accuracy of SST datasets will be improved, which will promote the operational applications of SST datasets; on the other hand, the results will be very important for data merging and climate analysis. The studies on satellite SST validation and intercomparison published in recent years is investigated. Validation and intercomparison of multi-sensor SST datasets in the Northwest Pacific during the period of 2005 are carried out. After the preprocessing of data, quality control and generation of mach-up database, the consistency and characteristics of the differences between the satellite SST datasets and in situ buoy SST data are compared and analyzed. The characteristics of the differences among different SST datasets and error sources are also analyzed.
Firstly, multi-sensor Satellite SST datasets in the Northwest Pacific are compared with in situ buoy SST data respectively. MCSST and NLSST are two products with different retrieval algorithms, which are received and processed by the Satellite Ground Station of Ocean University of China. The results of preliminary validation show that MCSST and NLSST data are lower than in situ buoy SST data. The annual mean bias of the difference between in situ buoy SST data and MCSST data in day and night time is 0.23°C and 0.36°C respectively, and the annual standard deviation of it in day and night time is 0.88°C and 0.93°C respectively; The mean bias of the difference between in situ buoy SST data and NLSST data in day and night is 0.04°C and 0.25°C respectively. It has a small change in the annual standard deviation in day and night which is 0.87°C and 0.93°C respectively. Matchup data with satellite zenith angle greater than 45°are excluded,the mean bias of the difference between in situ buoy SST data and NLSST in day and night is -0.16°C and 0.13°C respectively; It has a good change in the annual standard deviation in day and night which is 0.73°C and 0.80°C respectively. The results indicate that NLSST data is better than MCSST data,so it is used in intercomparisons of the multi-sensor satellite SST datasets。. The four satellite SST datasets of NOAA/NASA AVHRR Pathfinder (PFSST), Aqua/MODIS SST, Terra/MODIS SST, and AMSR-E SST are compared with in situ buoy SST data too. The annual mean bias of the difference between in situ buoy SST data and these datasets is -0.08°C/-0.08°C (day/night, as follows), -0.12°C/0.34°C, 0.00°C/0.11°C, and -0.20°C/-0.18°C respectively. The annual standard deviation of them is 0.60°C/0.68°C, 0.67°C/0.76°, 0.69°C/0.74°C, and 0.64°C/0.67°C respectively. The statistics shows that the annual mean bias is small except that of MCSST and that of AquaSST in night time. Generally, AMSR-E SST is higher than in situ buoy SST.
Secondly, the multi-sensor satellite SST datasets are intercompared and the characteristics of the differences between them are analyzed. The intercomparisons of multi-sensor SST data with NLSST data show that NLSST is lower than other dataset in night,and it is higher than the other infrared dataset. The annual mean bias between AquaSST and NLSST is lower while that between AMSR-E and it is the largest. The results of the intercomparisons also show AMSR-E SST is higher than the infrared SST datasets. The consistency of PFSST and MODIS SSTs is the best except for AquaSST in night and the annual standard deviation and mean bias are low. The statistics of the differences show regional and seasonal variations. The error sources may come from different measuring principles, different retrieval algorithms, different resolutions and different atmospheric conditions.
In summary, the statistics shows that different SST datasets are consistent with each other. Some infrared SST data are contaminated by cloud. Cloud detection and the retrieval coefficients of AVHRR LAC SST are needed to be improved. Correction of multi-sensor satellite SST datasets should be made for the applications required high accuracy SST such as study of climate change and data merging.
引文
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[2] Donlon, C. J., I. S. Robinson, 1997: Observations of the oceanic thermal skin in the Atlantic Ocean, J. Geophys. Res., 102(C8), 18585-18606, 10.1029/97JC00468
[3]刘玉光等2005:卫星海洋学中国海洋大学
[4] Guan, L. and H. Kawamura, 2003: SST availabilities of satellite infrared and microwave measurements. J. Oceanogr., Vol. 59, No. 2, 201-209.
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