基于星载红外辐射计的北极海表温度数据对比分析
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摘要
本文基于2016年MODIS(ModerateResolutionImagingSpectroradiometer)-Aqua、MODIS-Terra和VIIRS(Visible Infrared Imager Radiometer Suite)三种红外辐射计的海表面温度(Sea Surface Temperature,SST)数据,统计了北极地区红外SST1月和7月的覆盖率及有效覆盖天数,并与Argo(Arrayfor Real-timeGeostrophicOceanography)浮标数据进行了匹配验证,直观获取北极SST误差分布情况并研究SST遥感观测能力,为更好地了解北极地区从而应对气候变化提供一定的资料基础。结果表明,北极地区红外辐射计SST数据7月的覆盖率和有效观测天数均高于1月,1月三种数据相差不大,7月VIIRS的覆盖率和有效观测天数均优于MODIS-Aqua和MODIS-Terra,联合三种红外辐射计的覆盖率和有效观测天数相较于单星有所增加, 1月覆盖率最高为8%, 7月最高接近70%,表明多星联合探测是提高北极地区SST数据覆盖率和观测天数的有效方法;北极地区SST数据的误差普遍高于全球总体水平, VIIRS白天、夜间的均方根误差(E_(rms))均低于MODIS-Aqua和MODIS-Terra, MODIS-Aqua白天SST的E_(rms)高于MODIS-Terra,夜间则低于MODIS-Terra。综合来看, VIIRS在北极的覆盖率、有效观测天数及与浮标的匹配结果在三种红外辐射计中为最优。
In this study, the infrared sea surface temperature(SST) data of MODIS-Aqua, MODIS-Terra, and VIIRS in 2016 are used to investigate the SST remote sensing observation capability in the Arctic, including the statistics of infrared SST coverage and effective cover days in January and July, and analyze the accuracy of SST data with Argo. Therefore, the distribution of the Arctic SST error can be obtained intuitively and the SST remote sensing observation capability can be investigated to provide a basis for better understanding of the Arctic region, thus coping with climate change. The results show that the coverage and effective observation days of infrared radiometer SST data in July are higher than those in January, the three sets of infrared SST data did not exhibit a significant difference in January, and the coverage and effective observation days of VIIRS in July are better than those of MODIS-Aqua and MODIS-Terra. The coverage and effective observation days detected by three infrared radiometers are higher than those by a single star. The coverage rate is up to 8% in January and close to 70% in July, indicating that combining different star detection schemes is an efficient method to improve the coverage and effective cover days of SST data in the Arctic. The error of SST data in the Arctic region is generally higher than the global overall level. The E_(rms) value of VIIRS is lower than those of MODIS-Aqua and MODIS-Terra during the daytime and nighttime. On the contrary, the E_(rms) value of MODIS-Aqua during the daytime is higher than that of MODIS-Terra and the E_(rms) value of MODIS-Aqua during the nighttime is lower than that of MODIS Terra. Taken together, the coverage, effective observation days, and matching results with Argo of VIIRS in the Arctic are the optimal among the three infrared radiometers.
In this study, the infrared sea surface temperature(SST) data of MODIS-Aqua, MODIS-Terra, and VIIRS in 2016 are used to investigate the SST remote sensing observation capability in the Arctic, including the statistics of infrared SST coverage and effective cover days in January and July, and analyze the accuracy of SST data with Argo. Therefore, the distribution of the Arctic SST error can be obtained intuitively and the SST remote sensing observation capability can be investigated to provide a basis for better understanding of the Arctic region, thus coping with climate change. The results show that the coverage and effective observation days of infrared radiometer SST data in July are higher than those in January, the three sets of infrared SST data did not exhibit a significant difference in January, and the coverage and effective observation days of VIIRS in July are better than those of MODIS-Aqua and MODIS-Terra. The coverage and effective observation days detected by three infrared radiometers are higher than those by a single star. The coverage rate is up to 8% in January and close to 70% in July, indicating that combining different star detection schemes is an efficient method to improve the coverage and effective cover days of SST data in the Arctic. The error of SST data in the Arctic region is generally higher than the global overall level. The E_(rms) value of VIIRS is lower than those of MODIS-Aqua and MODIS-Terra during the daytime and nighttime. On the contrary, the E_(rms) value of MODIS-Aqua during the daytime is higher than that of MODIS-Terra and the E_(rms) value of MODIS-Aqua during the nighttime is lower than that of MODIS Terra. Taken together, the coverage, effective observation days, and matching results with Argo of VIIRS in the Arctic are the optimal among the three infrared radiometers.
引文
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[17]Kilpatrick K.A.Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database[J].Journal of Geophysical Research,2001,106(C5):9179-9197.
[2]Reynolds R W,Smith T M.Improved global sea surface temperature analyses using optimum interpolation[J].Journal of Climate,1994,7(6):929-948.
[3]雷国良,朱芸,李志忠,等.北极地区20世纪温度变化趋势的不确定性[J].亚热带资源与环境学报,2012,7(1):34-39.Lei Guoliang,Zhu Yun,Li Zhizhong,et al.Uncertainty of temperature variability in the Arctic of the 20th Century[J].Journal of Subtropical Resources and Environment,2012,7(1):34-39.
[4]李友训,关翔宇,高焱,等.北极地区深海微生物研究进展及对策[J].海洋科学,2016,40(12):138-145.Li Youxun,Guan Xiangyu,Gao Yan,et al.Progress on microbial research in the deep Arctic[J].Marine Sciences,2016,40(12):138-145.
[5]陈立奇.南极和北极地区在全球变化中的作用研究[J].地学前缘,2002,9(2):245-253.Chen Liqi.Study on the role of the Arctic and Antarctic regions in global change[J].Earth Science Frontiers,2002,9(2):245-253.
[6]杨孟倩,葛珊珊,张韧.气候变化与北极响应--机遇、挑战与风险[J].中国软科学,2016,6:17-25.Yang Mengqian,Ge Shanshan,Zhang Ren.Climate change and Arctic response:opportunities,challenges and risks[J].China Soft Science Magazine,2016,6:17-25.
[7]Barton I,Pearce A.Validation of GLI and other satellitederived sea surface temperatures using data from the Rottnest Island ferry,Western Australia[J].Journal of Oceanography,2006,62(3):303-310.
[8]奚萌,宋清涛,林明森,等.西北太平洋红外辐射计海表温度数据交叉比对分析[J].海洋与湖沼,2017,48(3):436-453.Xi Meng,Song Qingtao,Lin Mingsen,et al.Comparison in multi-infrared products of sea surface temperature in Northwest Pacific[J].Oceanologia et Limnologia Sinica,2017,48(3):436-453.
[9]孙凤琴,张彩云,商少平,等.西北太平洋部分海域AVHRR、TMI与MODIS遥感海表层温度的初步验证[J].厦门大学学报(自然版),2007,46(s1):1-5.Sun Fengqin,Zhang Caiyun,Shang Shaoping,et al.Primary validation of AVHRR/MODIS/TMI SST for part of the Northwest Pacific[J].Journal of Xiamen University(Natural Science).2007,46(s1):1-5.
[10]Ninove F,Traon P Y L,Remy E,et al.Spatial scales of temperature and salinity variability estimated from Argo observations[J].Ocean Science Discussions,2016,12(4):1793-1814.
[11]李军,朱建华,韩冰,等.VIIRS在中国渤海的遥感反射率产品验证[J].海洋技术学报,2016,35(2):27-33.Li Jun,Zhu Jianhua,Han Bing,et al.Validation of the VIIRS radiometric products with MODIS and in-situ data in the Bohai Sea[J].Journal of Ocean Technology,2016,35(2):27-33.
[12]Hosoda K,Murakami H,Sakaida F,et al.Algorithm and validation of sea surface temperature observation using MODIS sensors aboard terra and aqua in the western North Pacific[J].Journal of Oceanography,2007,63(2):267-280.
[13]RoemmichD,Owensw B.The ARGO Project:global ocean observations for understanding and prediction of climate variability[J].Oceanography,2000,13(2):45-50.
[14]Roemmich D,Johnson G C,Riser S,et al.The Argo Program:observing the global ocean with profiling floats[J].Oceanography,2009,22(2):34-43.
[15]奚萌,宋清涛,林明森,等.西北太平洋多源微波辐射计海表温度数据交叉比对分析[J].海洋学报,2016,38(7):32-47.Xi Meng,Song Qingtao,Lin Mingsen,et al.Intercomparison analysis of multi-microwave radiometer sea surface temperature data for the Northwest Pacific[J].Haiyang Xuebao,2016,38(7):32-47.
[16]卢少磊,许建平,刘增宏.南半球微波遥感SST与Argo浮标NST的异同分析[J].海洋预报,2014,31(1):1-8.Lu Shaolei,Xu Jianping,Liu Zenghong.Analysis of the differences between microwave remote sensing SSTand Argo NST in the Southern Hemisphere[J].Marine Forecasts,2014,31(1):1-8.
[17]Kilpatrick K.A.Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database[J].Journal of Geophysical Research,2001,106(C5):9179-9197.