光谱学与光谱分析 |
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Errors Analysis and Correction in Atmospheric Methane Retrieval Based on Greenhouse Gases Observing Satellite Data |
BU Ting-ting, WANG Xian-hua*, YE Han-han, JIANG Xin-hua |
Key Laboratory of Optical Calibration and Characterization,Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Hefei 230031,China |
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Abstract High precision retrieval of atmospheric CH4 is influenced by a variety of factors. The uncertainties of ground properties and atmospheric conditions are important factors, such as surface reflectance, temperature profile, humidity profile and pressure profile. Surface reflectance is affected by many factors so that it is difficult to get the precise value. The uncertainty of surface reflectance will cause large error to retrieval result. The uncertainties of temperature profile, humidity profile and pressure profile are also important sources of retrieval error and they will cause unavoidable systematic error. This error is hard to eliminate only using CH4 band. In this paper, ratio spectrometry method and CO2 band correction method are proposed to reduce the error caused by these factors. Ratio spectrometry method can decrease the effect of surface reflectance in CH4 retrieval by converting absolute radiance spectrometry into ratio spectrometry. CO2 band correction method converts column amounts of CH4 into column averaged mixing ratio by using CO2 1.61 μm band and it can correct the systematic error caused by temperature profile, humidity profile and pressure profile. The combination of these two correction methods will decrease the effect caused by surface reflectance, temperature profile, humidity profile and pressure profile at the same time and reduce the retrieval error. GOSAT data were used to retrieve atmospheric CH4 to test and validate the two correction methods. The results showed that CH4 column averaged mixing ratio retrieved after correction was close to GOSAT Level2 product and the retrieval precision was up to -0.24%. The studies suggest that the error of CH4 retrieval caused by the uncertainties of ground properties and atmospheric conditions can be significantly reduced and the retrieval precision can be highly improved by using ratio spectrometry method and CO2 band correction method.
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Received: 2014-09-05
Accepted: 2014-12-20
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Corresponding Authors:
WANG Xian-hua
E-mail: xhwang@aiofm.ac.cn
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[1] IPCC. Climate Change 2007:The Physical Science Basis. Cambridge:Cambridge University Press,2007. [2] HE Qian,YU Tao,GU Xing-fa,et al(何 茜, 余 涛, 顾行发, 等). Remote Sensing Information(遥感信息),2012,27(4):35. [3] Schneising O,Buchwitz M,Reuter M,et al. Atmospheric Chemistry and Physics,2011,11(6):2863. [4] ZHANG Xing-ying,BAI Wen-guang,ZHANG Peng,et al(张兴赢,白文广,张 鹏,等). Chinese Science Bulletin(科学通报),2011,56(33):2804. [5] RU Fei,LEI Li-ping,HOU Shan-shan,et al(茹 菲,雷莉萍,侯姗姗,等). Remote Sensing Information(遥感信息),2013,28(1):66. [6] O’Brien D,Rayner P J. Journal of Geophysical Research:Atmospheres (1984—2012),2002,107(D18):4354. [7] Wunch D,Wennberg P O,Toon G C,et al. Atmospheric Chemistry and Physics,2011,11(23):12317. [8] Rodgers C D. Inverse Methods for Atmospheric Sounding:Theory and Practice. Singapore:World Scientific,2000. [9] Nicodemus F E,Richmond J C,Hsia J J. US Department of Commerce,National Bureau of Standards,Washington DC,USA,1977. [10] Christi M J,Stephens G L. Journal of Geophysical Research:Atmospheres (1984—2012),2004,109(D4). [11] YE Han-han,WANG Xian-hua,WU Jun,et al(叶函函,王先华,吴 军,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2013,33(8):2184. [12] YE Han-han,WANG Xian-hua,WU Jun,et al(叶函函,王先华,吴 军,等). High Power Laser and Paricle Beams(强激光与粒子束),2013,25(11):2841. [13] Miller C E,Crisp D,DeCola P L,et al. Journal of Geophysical Research:Atmospheres (1984—2012),2007,112(D10). |
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