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An Improvement of Brightness Temperature Simulation of FY-3 IRAS Infrared Water Vapor Channel
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摘要: 卫星大气探测仪器的正演模拟是卫星资料同化和定量遥感的基础, 同CO2吸收通道相比, 目前红外水汽探测通道的亮温正演模拟误差较大。利用国际上通用的TIGR (thermodynamic initial guess retrieval database) 43廓线库作为训练样本, NESDIS (national environment satellite, data and information service) 35廓线库作为独立检验样本, 对水汽廓线按照整层大气水汽总量为阈值进行分组训练, 基于RTTOV (radiative transfer for TOVS) 模型训练获得风云三号气象卫星红外分光计的正演回归系数并模拟计算观测亮温。以0.045 kg·m-2作阈值进行分组训练为例, 结果表明:该方法可有效改进水汽通道亮温的正演精度, 特别是对低水汽含量廓线的模拟精度改进比较明显, 最大可达0.17 K。进一步分析表明:分组训练方法改进水汽通道辐射模拟精度的原因是提高了水汽光学厚度的计算精度。Abstract: Forward modeling of satellite atmospheric sounding instruments is the foundation of satellite data assimilation and inversion. Currently, more uncertainties on the accuracy of infrared water vapor channel are still exist compared with temperature detection channel. A method based on the group training of profiles which are classified by their water content of air column is used, to try to improve the forward modeling accuracy in the water vapor channel. Determination of threshold value is based on principles of basic balance profile numbers after group classification. Many threshold values are utilized to classify profiles, only results of threshold for 0.045 kg·m-2 are detailed and analyzed, which lead to quite similar experiment results. TIGR 43 profile library is used as the training sample to get coefficients for fast radiative transfer computation and NESDIS 35 profile library is used to test the precision improvement as the independent sample.Different coefficients got from the group training are used to establish RTTOV forward model of FY-3 IRAS and the channel brightness temperature is calculated using the corresponding profile and coefficients. The forward modeling accuracy of the FY-3 IRAS brightness temperature is get by comparison with line-by-line results, which are considered as accurate and reliable. Research results show accuracy improvement of the brightness temperature after group training. Improvement shows better in the low water content profile case, which up to 0.17 K in the 0.045 kg·m-2 threshold experiment. Further analysis is executed to find the cause for the improvement in the group training experiment. The layer water vapor predictor optical depth of channel 11-13 are calculated and comparison is done between the forward model with and without group training. Results show better consistency of water vapor absorption and channel weighting function distribution, besides, the absorption of water vapor line and continuum is adjusted more reasonable considering the weighting function height. These provide positive influence on the improvement of forward modeling results.It puts forward a method which can improve the fast radiative calculation accuracy of infrared water vapor channel, further work should be done in respect of bias caused by the precision of water vapor molecule absorption line parameters and the input water vapor profile self error. Furthermore, it merely considers the water content of air column, the potential influence caused by the shape of the profile is out of consideration, which may be the cause for the negative effect in channel 13 and research direction of future work.
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图 1 利用生成的快速透过率系数和1976标准大气廓线模拟FY-3 IRAS通道亮温 (θ=0°)
Fig. 1 FY-3 IRAS forward modeling channel brightness temperature result using fast radiative transmittance coefficients and USSA-1976 (θ=0°)
图 2 利用生成的快速透过率系数和1976标准大气廓线模拟FY-3 IRAS通道权重函数 (θ=0°)
(a) 15 μm附近温度探测通道1~7, (b) O3探测通道10及水汽探测通道11~13, (c) 4.5 μm附近温度 探测通道14~18, (d) 13 μm附近地表探测通道8~9和4 μm附近地表探测通道19~20
Fig. 2 FY-3 IRAS forward modeling channel weighting functions using fast radiative transmittance coefficients and USSA-1976 (θ=0°)
(a) near 15 μm temperature detection channel 1-7, (b) ozone detection channel 10 and water vapor detection channel 11-13, (c) near 4.5 μm temperature detection channel 14-18, (d) near 13 μm surface detection channel 8-9 and near 4 μm surface detection channel 19-20
图 3 NESDIS 35廓线库对FY-3 IRAS正演亮温标准差分布
Fig. 3 Forward modeling brightness temperature of FY-3 IRAS using NESDIS 35 profile library
图 4 TIGR 43和NESDIS 35廓线库单位面积大气柱水汽总量分布
(a) TIGR 43廓线库, (b) NESDIS 35廓线库
Fig. 4 Water content of air column per square meter of TIGR 43 and NESDIS 35 profile library
(a) TIGR 43 profile library, (b) NESDIS 35 profile library
图 5 利用TIGR 43廓线库的相关精度检验
(a) 高水汽含量廓线的标准差, (b) 低水汽含量廓线的标准差
Fig. 5 Relative accuracy test using TIGR 43 profile library
(a) standard deviation of brightness temperature of high water content selected, (b) standard deviation of brightness temperature of low water content selected
图 6 利用NESDIS 35廓线库的相关精度检验
(a) 高水汽含量廓线的标准差, (b) 低水汽含量廓线的标准差
Fig. 6 Relative accuracy test using NESDIS 35 profile library
(a) standard deviation of brightness temperature of high water content selected, (b) standard deviation of brightness temperature of low water content selected
图 7 NESDIS 35廓线库高水汽含量廓线在通道11的逐层光学厚度统计
Fig. 7 Layer optical depth of channel 11 using high water content selected from NESDIS 35 profile library
图 8 NESDIS 35廓线库高水汽含量廓线在通道12的逐层光学厚度统计
Fig. 8 Layer optical depth of channel 12 using high water content selected from NESDIS 35 profile library
图 9 NESDIS 35廓线库高水汽含量廓线在通道13的逐层光学厚度统计
Fig. 9 Layer optical depth of channel 13 using high water content selected from NESDIS 35 profile library
表 1 不同阈值的分组与未分组试验标准差对比结果 (正值为改进值)
Table 1 The comparison of standard deviation between experiments with or without group training of different threshold values (positive values for improvement)
阈值/(kg·m-2) 训练前后高水汽含量廓线的标准差差值/K 分组训练前后低水汽含量廓线的标准差差值/K 通道11 通道12 通道13 通道11 通道12 通道13 0.025 0.0387 0.0364 -0.0010 0.0202 0.0395 0.2444 0.045 0.0267 0.0269 0.0012 0.0100 0.0246 0.1772 0.05 0.0195 0.0174 -0.0039 0.0189 0.0297 0.1738 0.1 0.0131 0.0131 -0.0073 0.0189 0.0258 0.0954 
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