| 基于FY-3/IRAS利用非线性模式反演OLR |
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| 引用本文： | 吴晓,白文广.基于FY-3/IRAS利用非线性模式反演OLR[J].应用气象学报,2017,28(2):189-199. |
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| 作者姓名： | 吴晓 白文广 |
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| 作者单位： | 1.中国气象局中国遥感卫星辐射测量和定标重点开放实验室, 北京 100081 |
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| 基金项目： | 国家重点研究发展计划（2016YFA0600104） |
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| 摘 要： | FY-3系列卫星星载IRAS仪器设有26个通道，其中20个通道用于探测地球大气在红外波段的热辐射，通道辐射率代表了地球大气系统在大气顶的向外辐射光谱信息，与总波段的射出长波辐射（OLR）通量相关性高。该文基于逐线辐射传输模式计算软件LBLRTM对全球2521条大气廓线的大气顶射出辐射率模拟数据，计算了每条廓线的OLR和FY-3B/IRAS，FY-3C/IRAS通道辐射率，用统计回归方法建立了利用IRAS的多通道辐射率计算OLR的非线性理论回归模式；应用模式和FY-3B/IRAS，FY-3C/IRAS的L1级数据，处理得到2016年4月1-30日的全球日平均、月平均OLR格点产品。与Aqua/CERES，Terra/CERES仪器宽波段观测OLR产品对比表明:对于水平分辨率为1°×1°的全球月平均OLR格点产品，均方根误差为2.22 W·m-2，相关系数为0.9982 W·m-2，平均偏差为-0.2 W·m-2，表明FY-3/IRAS仪器定标及反演模式均达到较高水平。文中还回顾了历史上不同气象卫星的多种OLR反演算法模式，并对不同模式精度进行了比较。
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| 关 键 词： | OLR 统计回归 反演模式 |
| 收稿时间： | 2016/10/29 0:00:00 |
| 修稿时间： | 2017/1/18 0:00:00 |
Development of Nonlinear Regression Model to Estimate OLR Based on FY-3/IRAS |
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| Wu Xiao and Bai Wenguang.Development of Nonlinear Regression Model to Estimate OLR Based on FY-3/IRAS[J].Quarterly Journal of Applied Meteorology,2017,28(2):189-199. |
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| Authors: | Wu Xiao and Bai Wenguang |
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| Institution: | 1.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 1000812.National Satellite Meteorological Center, Beijing 100081 |
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| Abstract: | OLR(outgoing longwave radiation) is the radiative energy flux the Earth and atmosphere emit out into the outspace, which is one of three components of the Earth and atmosphere radiative budget system, reflecting the climate and weather characteristics. Since the invention of meteorological satellites, OLR products have been processed for more than 40 years. Numerous methods have been developed to estimate OLR from satellite observations, including the relationship between the window channel brightness temperature of AVHRR and the flux equivalent brightness temperature proposed by Arnald Gruber in 1977 and George Ohring in 1984, regression models relating OLR with narrow band fluxes of window channel and water vapour channel of geostationary meteorological satellites developed by Liu in 1988, the linear and none-linear models relating OLR with satellite multi-channel radiances developed by Enllingson in 1994 and Lee in 2010. At the same time, broadband instruments such as ERBE and CERES on board of NOAA, Nimbus, Terra, Aqua are designed to directly observe OLR from outspace. Due to the high quality, CERES OLR products become the best available data to validate other retrieved OLR products.The IRAS(infrared atmospheric sounder) on board of FY-3 polar meteorological satellites carry 26 channels, among which 20 channels are used to observe radiances at the top of the Earth atmosphere at the wavenumber between 669 cm-1 and 2666 cm-1.These narrow band radiances have high relations with the full wavenumber radiative flux (OLR) the Earth and atmosphere emit. Therefore, a formula is derived for calculating OLR with multi-channel radiances of IRAS through infrared radiative transfer simulation. Based on radiances at top of atmosphere simulated with LBLRTM (line by line radiative transfer model) software for 2521 atmospheric profiles and statistical regression, a nonlinear model which relates OLR with multi-channel radiances of FY-3/IRAS are developed. By applying the model into FY-3/IRAS L1 data, the global daily mean OLR and monthly mean OLR data in April 2016 are produced. Comparing the IRAS OLR data with the Aqua/CERES and Terra/CERES OLR products, the root mean square error is 7.5 W·m-2, the correlation coefficient is 0.98, the mean bias is -0.2 W·m-2 when comparing the IRAS daily mean OLR with that of CERES. The root mean square error is 2.22 W·m-2, the correlation coefficient is 0.9982,and the mean bias is -0.2 W·m-2 when comparing the IRAS monthly mean OLR with that of CERES. The accuracy indicates that both the calibration quality of FY-3/IRAS instruments and the OLR retrieval model all achieve at a high level. In addition, OLR retrieval models used by various satellites since 1970 are also reviewed in brief. |
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| Keywords: | OLR statistical regression retrieval model |
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