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Aerosol Property Retrieval Algorithm over Northeast Asia from TANSO-CAI Measurements Onboard GOSAT

机译:基于GOSAT上TANSO-CAI测量的东北亚气溶胶特性检索算法

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The presence of aerosol has resulted in serious limitations in the data coverage and large uncertainties in retrieving carbon dioxide (CO 2 ) amounts from satellite measurements. For this reason, an aerosol retrieval algorithm was developed for the Thermal and Near-infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) launched in January 2009 on board the Greenhouse Gases Observing Satellite (GOSAT). The algorithm retrieves aerosol optical depth (AOD), aerosol size information, and aerosol type in 0.1° grid resolution by look-up tables constructed using inversion products from Aerosol Robotic NETwork (AERONET) sun-photometer observation over Northeast Asia as a priori information. To improve the accuracy of the TANSO-CAI aerosol algorithm, we consider both seasonal and annual estimated radiometric degradation factors of TANSO-CAI in this study. Surface reflectance is determined by the same 23-path composite method of Rayleigh and gas corrected reflectance to avoid the stripes of each band. To distinguish aerosol absorptivity, reflectance difference test between ultraviolet (band 1) and visible (band 2) wavelengths depending on AODs was used. To remove clouds in aerosol retrieval, the normalized difference vegetation index and ratio of reflectance between band 2 (0.674 μm) and band 3 (0.870 μm) threshold tests have been applied. To mask turbid water over ocean, a threshold test for the estimated surface reflectance at band 2 was also introduced. The TANSO-CAI aerosol algorithm provides aerosol properties such as AOD, size information and aerosol types from June 2009 to December 2013 in this study. Here, we focused on the algorithm improvement for AOD retrievals and their validation in this study. The retrieved AODs were compared with those from AERONET and the Aqua/MODerate resolution Imaging Sensor (MODIS) Collection 6 Level 2 dataset over land and ocean. Comparisons of AODs between AERONET and TANSO-CAI over Northeast Asia showed good agreement with correlation coefficient (R) 0.739 ± 0.046, root mean square error (RMSE) 0.232 ± 0.047, and linear regression line slope 0.960 ± 0.083 for the entire period. Over ocean, the comparisons between Aqua/MODIS and TANSO-CAI for the same period over Northeast Asia showed improved consistency, with correlation coefficient 0.830 ± 0.047, RMSE 0.140 ± 0.019, and linear regression line slope 1.226 ± 0.063 for the entire period. Over land, however, the comparisons between Aqua/MODIS and TANSO-CAI show relatively lower correlation (approximate R = 0.67, RMSE = 0.40, slope = 0.77) than those over ocean. In order to improve accuracy in retrieving CO 2 amounts, the retrieved aerosol properties in this study have been provided as input for CO 2 retrieval with GOSAT TANSO-Fourier Transform Spectrometer measurements.
机译:气溶胶的存在已导致数据覆盖范围的严重限制,以及从卫星测量中获取二氧化碳(CO 2)量的巨大不确定性。因此,针对温室气体观测卫星(GOSAT)于2009年1月启动的碳观测云和气溶胶成像仪(TANSO-CAI)的热和近红外传感器开发了一种气溶胶检索算法。该算法通过使用东北亚Aerosol Robotic NETwork(AERONET)太阳光度计观测的反演产品构建的查找表,以0.1°网格分辨率检索气溶胶光学深度(AOD),气溶胶尺寸信息和气溶胶类型,作为先验信息。为了提高TANSO-CAI气溶胶算法的准确性,我们在本研究中考虑了TANSO-CAI的季节和年度估计辐射降解因子。表面反射率是通过相同的瑞利(Rayleigh)23路径合成方法和气体校正的反射率确定的,以避免每个波段的条纹。为了区分气溶胶的吸收率,使用了取决于AOD的紫外(波段1)和可见(波段2)波长之间的反射率差异测试。为了去除气溶胶回收中的云,已应用归一化差异植被指数以及波段2(0.674μm)和波段3(0.870μm)阈值测试之间的反射率。为了掩盖海洋上的混浊水,还引入了频带2的估计表面反射率的阈值测试。在这项研究中,TANSO-CAI气溶胶算法提供了2009年6月至2013年12月的气溶胶特性,例如AOD,尺寸信息和气溶胶类型。在这里,我们专注于本研究中AOD检索的算法改进及其验证。将检索到的AOD与来自AERONET和Aqua / MODerate分辨率成像传感器(MODIS)集合6 2级数据集在陆地和海洋上的AOD进行比较。东北亚地区AERONET和TANSO-CAI的AOD的比较显示,在整个时期内相关系数(R)为0.739±0.046,均方根误差(RMSE)为0.232±0.047,线性回归线斜率为0.960±0.083,具有很好的一致性。在海洋上,东北亚地区同期Aqua / MODIS与TANSO-CAI的比较显示出改善的一致性,整个期间的相关系数为0.830±0.047,RMSE 0.140±0.019,线性回归线斜率为1.226±0.063。然而,在陆地上,Aqua / MODIS与TANSO-CAI的比较显示出与海洋相比相对较低的相关性(大约R = 0.67,RMSE = 0.40,斜率= 0.77)。为了提高获取CO 2量的准确性,本研究中获取的气溶胶特性已作为GOSAT TANSO-Fourier变换光谱仪测量中CO 2的获取输入。

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