利用卫星资料反演臭氧含量以及进行气溶胶分类
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摘要
本文首先利用两个独立的样本资料进行了反演模拟研究。利用SARTA前向模式对10000条独立样本廓线进行AIRS辐射值的模拟，2200条独立样本廓线用于反演结果的验证。通过取不同的特征向量进行模拟，反演结果显示50和80个特征向量模拟的结果差别很小，最终确定用80个特征向量进行AIRS实测资料的反演。接着我们利用与AIRS相匹配的MODIS资料对AIRS的每个视场进行了云检测，随后利用高光谱AIRS资料对中国地区的大气臭氧含量进行了反演，在回归反演过程中是对AIRS的所有像素点进行反演，只有晴空像素点的结果是有效的。将反演结果与AIRS Level2标准产品和ECMWF模式分析场进行了对比验证，表明用回归反演算法能够快速、较准确的反演大气臭氧，而且反演的臭氧结果显示出了比AIRS标准产品更加精细的空间结构，反演的臭氧的总量与AIRS标准产品基本接近，偏差大约5％左右，与ECMWF相比，反演的臭氧廓线的均方根误差在0.3ppmv之内。
最后我们对AIRS标准臭氧产品与中国区域臭氧地面站资料以及其他卫星的臭氧产品进行了对比分析，即根据不同来源的臭氧资料在不同的空间、时间尺度上的比较来评估AIRS臭氧产品的可靠性。结果发现AIRS在极端的气候条件下如两极和撒哈拉沙漠地区反演的臭氧总量是不可靠的，而在其他区域，AIRS的结果与其他卫星资料的结果基本一致，相差不大，在中国区域与地面站资料存在良好的相关性和一致性。
第二部分我们结合MODIS和OMI数据对气溶胶进行了定性分类。利用MODIS-OMI算法给出了四种(海盐、硫酸盐、沙尘、碳类)气溶胶的时空分布，将分类结果与三维气溶胶辐射传输模式SPRINTARS进行了比较，两者的气溶胶类型分布是一致的。
Firstly, the studies of the thesis are based on simulated AtmosphericInfraRed Sounder (AIRS) radiance by Stand-Alone Radiative Transfer Algorithm(SARTA). An ensemble 10000 profiles selected from the whole globe were used astraining data to generate regression coefficients. An independent samplesconsisting of 2200 profiles was used to verify the accuracy achieved by presentalgorithm. To test the effect of different number of eigenvectors on the ozoneretrieval accuracy, 30, 50 and 80 eigenvectors were used for whole AIRSchannels, respectively. There is a little impact in ozone error using differenteigenvectors. Finally, 80 eigenvectors are used to reconstruct most AIRSchannels. The first step is to perform a cloud detection procedure for each AIRSfootprint. The Moderate Resolution Imaging Spectroradiometer (MODIS) pixelswith 1-km spatial resolution are collocated within an AIRS footprint. Only AIRSclear-sky footprints are availability. Whereafter, The retrieval of ozone profilesand ozone total column was performed using the eigenvector regression and AIRSLIB data. The results were compared with the AIRS L2 ozone production andEuropean Center of Medium-range Weather Forecasts (ECMWF) model. Theozone of retrieval can provide better spatial resolution with respect to the AIRS L2ozone production. Compared with ECMWF, the retrieved profilesroot-mean-square error (RMS) is about 0.3ppmv.
Finally, Distribution and characteristics of global total column ozone(60°N-60°S) data retrieved from AIRS, The TIROS Operational VerticalSounder (TOVS) and Total ozone mapping spectrometer (TOMS) during2003～2005 are investigated and compared, showing that the total column ozonein North hemisphere has an evident seasonal variation with a maximum of322.25DU in spring and a minimum of 277.83DU in autumn, decreasing by45DU. Whereas it is unapparent in South Hemisphere. Comparison among threedatasets indicates the global mean total column ozone retrieved from the AIRS islarger by 3-5DU than these from TOVS and TOMS, and significant abnormal inAntarctic continent and deserts. A comparison of satellite ozone data Withground-based data found that TOMS retrieved value is lower than ground-basedmeasurements. Further evidence depicts that AIRS/TOMS retrieved values havethe moderately good agreement and well relationship with the ground-based ozonedata.
In section 2, the temporal and spatial distribution of four major aerosol types(dust, carbonaceous, sea salt and sulfate) retrieved by MODIS-OMI (OzoneMonitoring Instrument) Algorithm over East-China area. Compared to SpectralRadiation-Transport Model for Aerosol Species (SPRINTARS) model, the resultsshow reasonable consistency in the distribution by aerosol type.
最后我们对AIRS标准臭氧产品与中国区域臭氧地面站资料以及其他卫星的臭氧产品进行了对比分析，即根据不同来源的臭氧资料在不同的空间、时间尺度上的比较来评估AIRS臭氧产品的可靠性。结果发现AIRS在极端的气候条件下如两极和撒哈拉沙漠地区反演的臭氧总量是不可靠的，而在其他区域，AIRS的结果与其他卫星资料的结果基本一致，相差不大，在中国区域与地面站资料存在良好的相关性和一致性。
第二部分我们结合MODIS和OMI数据对气溶胶进行了定性分类。利用MODIS-OMI算法给出了四种(海盐、硫酸盐、沙尘、碳类)气溶胶的时空分布，将分类结果与三维气溶胶辐射传输模式SPRINTARS进行了比较，两者的气溶胶类型分布是一致的。
Firstly, the studies of the thesis are based on simulated AtmosphericInfraRed Sounder (AIRS) radiance by Stand-Alone Radiative Transfer Algorithm(SARTA). An ensemble 10000 profiles selected from the whole globe were used astraining data to generate regression coefficients. An independent samplesconsisting of 2200 profiles was used to verify the accuracy achieved by presentalgorithm. To test the effect of different number of eigenvectors on the ozoneretrieval accuracy, 30, 50 and 80 eigenvectors were used for whole AIRSchannels, respectively. There is a little impact in ozone error using differenteigenvectors. Finally, 80 eigenvectors are used to reconstruct most AIRSchannels. The first step is to perform a cloud detection procedure for each AIRSfootprint. The Moderate Resolution Imaging Spectroradiometer (MODIS) pixelswith 1-km spatial resolution are collocated within an AIRS footprint. Only AIRSclear-sky footprints are availability. Whereafter, The retrieval of ozone profilesand ozone total column was performed using the eigenvector regression and AIRSLIB data. The results were compared with the AIRS L2 ozone production andEuropean Center of Medium-range Weather Forecasts (ECMWF) model. Theozone of retrieval can provide better spatial resolution with respect to the AIRS L2ozone production. Compared with ECMWF, the retrieved profilesroot-mean-square error (RMS) is about 0.3ppmv.
Finally, Distribution and characteristics of global total column ozone(60°N-60°S) data retrieved from AIRS, The TIROS Operational VerticalSounder (TOVS) and Total ozone mapping spectrometer (TOMS) during2003～2005 are investigated and compared, showing that the total column ozonein North hemisphere has an evident seasonal variation with a maximum of322.25DU in spring and a minimum of 277.83DU in autumn, decreasing by45DU. Whereas it is unapparent in South Hemisphere. Comparison among threedatasets indicates the global mean total column ozone retrieved from the AIRS islarger by 3-5DU than these from TOVS and TOMS, and significant abnormal inAntarctic continent and deserts. A comparison of satellite ozone data Withground-based data found that TOMS retrieved value is lower than ground-basedmeasurements. Further evidence depicts that AIRS/TOMS retrieved values havethe moderately good agreement and well relationship with the ground-based ozonedata.
In section 2, the temporal and spatial distribution of four major aerosol types(dust, carbonaceous, sea salt and sulfate) retrieved by MODIS-OMI (OzoneMonitoring Instrument) Algorithm over East-China area. Compared to SpectralRadiation-Transport Model for Aerosol Species (SPRINTARS) model, the resultsshow reasonable consistency in the distribution by aerosol type.
引文
[1] 王贵勤等，大气臭氧研究[M]，科学出版社，1985
[2] 李辉，用卫星资料反演臭氧总量的研究，中国气象科学院硕士学位论文[D]，2003．6
[3] 赵九章等，高空大气物理学[M]，北京：科学出版社，1965
[4] 尹宏，大气辐射学[M]，北京：气象出版是，1993
[5] 廖国男，大气辐射学导论(第二版)[M]，气象出版社，2005
[6] 曾庆存，大气红外遥感原理[M]，科学出版社，1974
[7] 曾庆存，大气红外遥感探测的一些理论问题[J]，1976
[8] 马霞麟，张凤英，利用卫星资料反演臭氧总含量的初步试验[J]，大气科学，Vol．10，No．4，1986．12
[9] 胡筱欣，陆龙骅，张凤英等，南极春季臭氧的TOVS反演及其与Brewer观测的比较，应用气象学报[J]，1996，7(4)：437-442．
[10] Yanni Qu, Mitchell D.Goldberg, Murty Divakarla, Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Inflated Sounding Instrument, Advances in Atomspheric Sciences[J], Vol. 18 No.5 2001.9
[1] http://toms.gsfc.nasa.gov/fltmodel/spacecr.html
[2] http://www.class.ncdc.noaa.gov/data_available/tovs_atovs/index.htm
[3] 刘玉洁，杨忠东，MODIS遥感信息处理原理与算法(北京)[M]，科学出版社，2001
[4] 官莉，卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用，南京信息工程大学理学博士论文[D]，2005．5
[5] http://www-airs.jpl.nasa.gov/Technology/
[6] Edward T. Olsen. AIRS Version 3.0 Level 2 Data Release Documentation, 2003
[7] 刘辉，董超华，张文建，国际卫星红外大气探测器发展新特点[J]，气象科技，34(5)，2006．10，600～605
[1] 廖国南，大气辐射导论[M]，北京，气象出版社，1985
[2] 官莉，卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用，南京信息工程大学理学博士论文[D]，2005．5
[3] Yanni Qu, Mitchell D.Goldberg, Myuty Divakarla, Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Infrared Sounding Instrument.Advances in Atomospheric Sciences[J], Vol. 18 No.5,2001.9
[4] Elisabeth Weisz, Hung-lung Huang et al, IMAPP AIRS Applications and Products
[5] 蒋德明，董超华，陆维松，利用AIRS观测资料进行红外高光谱大气探测能力实验的研究，遥感学报[J]，vol 10 No．4，2006．7
[6] Wu Xuebao, Li Jun, Zhang Wenjian et al, Atmospheric Profile Retrieval with AIRS Data and Validation at the ARM CART Site, ADVANCES IN ATMOSPHERIC SCIENCES[J], VOL. 22, NO. 5, 2005, 647-654
[7] 马霞林，张凤英，用卫星资料反演臭氧总量的初步试验[J]，大气科学，vol．10 No．4，1986．12
[8] 官莉，用AIRS资料反演大气廓线-特征向量统计反演法．南京气象学院学报[J]，vol．29 No．6．2006．12
[1] 周秀骥，李维亮，陈隆勋，等．青藏高原地区大气臭氧变化的研究[J]．气象学报，2004，62(5)：513～527．
[2] 邹捍，郜永祺．60～70°S臭氧总量的QBO和ENSO信号[J]．气候与环境研究，1997，2(1)：61～70．
[3] 卞建春，陈洪滨，张中波，等．2002～2003年北半球中纬度地区臭氧总量TOMS与地面观测的非常差异[J]．科学通报，2005，50(5)：495～497．
[4] K. Bramstedt, J. Gleason, D.loyola et al. Comparison of total ozone from the satellite instruments GOME and TOMS with measurements from the Dobson network 1996-2000[J]. Atmos. Chem.. phys. Discuss., 2002, 2: 1131～1157.
[5] 官莉．卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用[D]．南京信息工程大学理学博士学位论文，2005．
[6] http://www-AIRS.jpl.nasa.gov
[7] 王贵勤，等编译．大气臭氧研究[M]．北京：科学出版社，1985
[1] 李成才，毛节太等．利用MODIS遥感大气气溶胶及气溶胶产品的应用[J]．北京大学学报，第39卷，2003．12
[2] 李成才．MODIS遥感气溶胶光学厚度及应用与区域环境大气污染研究[D]，北京大学 博士研究生学位论文，2002．5
[3] 沈志宝，魏丽．我国西北沙尘气溶胶的辐射效应[J]．大气科学，2000，24(4)：541-547．
[4] 刘玉洁，杨忠东等．MODIS遥感信息处理原理与算法(北京)[M]，科学出版社，2001
[5] Kaufman Y.J., O. Boucher, D. Tanre, M. Chin, L.A. Remer, T. Takemura (2005), Aerosol anthropogenic component estimated from satellite data, Geophys. Res. Lett.[J], 32, L17804, doi:10.1029/2005GL023125.
[6] Chu D.A., L.A. Remer, Y.J. Kaufman, B. Schmid, J. Redemann, K. Knobelspiesse, J.-D. Chem, J. Livingston, P. B. Russell, X. Xiong and W. Ridgway (2005), Evaluation of aerosol properties over ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia, J. Geophys. Res.[J], 110, D07308, doi: 10.1029/2004JD005208.
[7] http://cfors.riam.kyushu-u.ac.jp/～toshi/SPRINTARS/
[8] Higurashi A., and Y. Nakajima (2002), Detection of aerosol types over the East China Sea near Japan from four-channel satellite data, Geophys. Res. Lett.[J], 29(17), 1836, doi: 10.1029/2002GL015357.
[9] Jeong, M.-J., and Z. Li (2005), Quality, compatibility, and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and Total Ozone Mapping Spectrometer, J. Geophys. Res.[J], 110, D10S08, doi: 10.1029/2004JD004647.
[10] Jhoon Kim, Jaehwa Leel et al, Consistency of the aerosol type classification from satellite remote sensing during the ABC EAREX campaign. Accepted in Journal of Geophysical Research-Atmosphere[J], February 21, 2007
[11] Jethva H., S.K. Satheesh, and J. Srinivasan (2005), Seasonal variability of aerosols over the Indo-Gangetic basin, J. Geophys. Res. [J], 110, D21204, doi: 10.1029/2005JD005938.
[2] 李辉，用卫星资料反演臭氧总量的研究，中国气象科学院硕士学位论文[D]，2003．6
[3] 赵九章等，高空大气物理学[M]，北京：科学出版社，1965
[4] 尹宏，大气辐射学[M]，北京：气象出版是，1993
[5] 廖国男，大气辐射学导论(第二版)[M]，气象出版社，2005
[6] 曾庆存，大气红外遥感原理[M]，科学出版社，1974
[7] 曾庆存，大气红外遥感探测的一些理论问题[J]，1976
[8] 马霞麟，张凤英，利用卫星资料反演臭氧总含量的初步试验[J]，大气科学，Vol．10，No．4，1986．12
[9] 胡筱欣，陆龙骅，张凤英等，南极春季臭氧的TOVS反演及其与Brewer观测的比较，应用气象学报[J]，1996，7(4)：437-442．
[10] Yanni Qu, Mitchell D.Goldberg, Murty Divakarla, Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Inflated Sounding Instrument, Advances in Atomspheric Sciences[J], Vol. 18 No.5 2001.9
[1] http://toms.gsfc.nasa.gov/fltmodel/spacecr.html
[2] http://www.class.ncdc.noaa.gov/data_available/tovs_atovs/index.htm
[3] 刘玉洁，杨忠东，MODIS遥感信息处理原理与算法(北京)[M]，科学出版社，2001
[4] 官莉，卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用，南京信息工程大学理学博士论文[D]，2005．5
[5] http://www-airs.jpl.nasa.gov/Technology/
[6] Edward T. Olsen. AIRS Version 3.0 Level 2 Data Release Documentation, 2003
[7] 刘辉，董超华，张文建，国际卫星红外大气探测器发展新特点[J]，气象科技，34(5)，2006．10，600～605
[1] 廖国南，大气辐射导论[M]，北京，气象出版社，1985
[2] 官莉，卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用，南京信息工程大学理学博士论文[D]，2005．5
[3] Yanni Qu, Mitchell D.Goldberg, Myuty Divakarla, Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Infrared Sounding Instrument.Advances in Atomospheric Sciences[J], Vol. 18 No.5,2001.9
[4] Elisabeth Weisz, Hung-lung Huang et al, IMAPP AIRS Applications and Products
[5] 蒋德明，董超华，陆维松，利用AIRS观测资料进行红外高光谱大气探测能力实验的研究，遥感学报[J]，vol 10 No．4，2006．7
[6] Wu Xuebao, Li Jun, Zhang Wenjian et al, Atmospheric Profile Retrieval with AIRS Data and Validation at the ARM CART Site, ADVANCES IN ATMOSPHERIC SCIENCES[J], VOL. 22, NO. 5, 2005, 647-654
[7] 马霞林，张凤英，用卫星资料反演臭氧总量的初步试验[J]，大气科学，vol．10 No．4，1986．12
[8] 官莉，用AIRS资料反演大气廓线-特征向量统计反演法．南京气象学院学报[J]，vol．29 No．6．2006．12
[1] 周秀骥，李维亮，陈隆勋，等．青藏高原地区大气臭氧变化的研究[J]．气象学报，2004，62(5)：513～527．
[2] 邹捍，郜永祺．60～70°S臭氧总量的QBO和ENSO信号[J]．气候与环境研究，1997，2(1)：61～70．
[3] 卞建春，陈洪滨，张中波，等．2002～2003年北半球中纬度地区臭氧总量TOMS与地面观测的非常差异[J]．科学通报，2005，50(5)：495～497．
[4] K. Bramstedt, J. Gleason, D.loyola et al. Comparison of total ozone from the satellite instruments GOME and TOMS with measurements from the Dobson network 1996-2000[J]. Atmos. Chem.. phys. Discuss., 2002, 2: 1131～1157.
[5] 官莉．卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用[D]．南京信息工程大学理学博士学位论文，2005．
[6] http://www-AIRS.jpl.nasa.gov
[7] 王贵勤，等编译．大气臭氧研究[M]．北京：科学出版社，1985
[1] 李成才，毛节太等．利用MODIS遥感大气气溶胶及气溶胶产品的应用[J]．北京大学学报，第39卷，2003．12
[2] 李成才．MODIS遥感气溶胶光学厚度及应用与区域环境大气污染研究[D]，北京大学 博士研究生学位论文，2002．5
[3] 沈志宝，魏丽．我国西北沙尘气溶胶的辐射效应[J]．大气科学，2000，24(4)：541-547．
[4] 刘玉洁，杨忠东等．MODIS遥感信息处理原理与算法(北京)[M]，科学出版社，2001
[5] Kaufman Y.J., O. Boucher, D. Tanre, M. Chin, L.A. Remer, T. Takemura (2005), Aerosol anthropogenic component estimated from satellite data, Geophys. Res. Lett.[J], 32, L17804, doi:10.1029/2005GL023125.
[6] Chu D.A., L.A. Remer, Y.J. Kaufman, B. Schmid, J. Redemann, K. Knobelspiesse, J.-D. Chem, J. Livingston, P. B. Russell, X. Xiong and W. Ridgway (2005), Evaluation of aerosol properties over ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia, J. Geophys. Res.[J], 110, D07308, doi: 10.1029/2004JD005208.
[7] http://cfors.riam.kyushu-u.ac.jp/～toshi/SPRINTARS/
[8] Higurashi A., and Y. Nakajima (2002), Detection of aerosol types over the East China Sea near Japan from four-channel satellite data, Geophys. Res. Lett.[J], 29(17), 1836, doi: 10.1029/2002GL015357.
[9] Jeong, M.-J., and Z. Li (2005), Quality, compatibility, and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and Total Ozone Mapping Spectrometer, J. Geophys. Res.[J], 110, D10S08, doi: 10.1029/2004JD004647.
[10] Jhoon Kim, Jaehwa Leel et al, Consistency of the aerosol type classification from satellite remote sensing during the ABC EAREX campaign. Accepted in Journal of Geophysical Research-Atmosphere[J], February 21, 2007
[11] Jethva H., S.K. Satheesh, and J. Srinivasan (2005), Seasonal variability of aerosols over the Indo-Gangetic basin, J. Geophys. Res. [J], 110, D21204, doi: 10.1029/2005JD005938.