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投稿时间：2010-04-22 修订日期：2010-08-03
投稿时间：2010-04-22 修订日期：2010-08-03
中文摘要: 高光谱分辨率大气红外探测器AIRS(Atmospheric Infrared Sounder)作为第一个超高光谱大气红外探测仪，开辟了卫星大气探测的新时代。以无线电探空值与SARTA(Stand Alone Radiative Transfer Algorithm辐射传输模式)v1.05版的前向模式模拟出的AIRS辐射亮温值组成样本对，利用神经网络法反演大气湿度廓线。将反演所得的结果与特征向量统计法的反演结果进行比较，结果表明，与特征向量统计法相比，神经网络法反演精度高，所获得的水汽廓线更加贴近真实廓线。AIRS因其高光谱分辨率（即高垂直分辨率）显示了精细的大气结构。在基于高光谱资料反演大气湿度廓线技术上，神经网络显示出了较强的非线性处理能力。
Abstract:AIRS(Atmospheric Infrared Sounder), the first high spectral atmospheric infrared detector, started a new era of satellite sounding atmosphere. The samples, composed of the radiosonde observations and the AIRS brightness temperature value simulated by the SARTA (Stand Alone Radiative Transfer Algorithm)v1.05 forward mode, were inversed to the atmospheric humidity profiles using neural network. The results show that, compared with the result of eigenvector statistics, the neural network inversion is of higher precision, and the humidity profiles obtained are closer to the true profiles. AIRS displays the fine structure of the atmosphere because of its high spectral resolution (high vertical resolution).Neural network has a strong nonliner processing capability in the issue that the inversion of the atmospheric humidity profiles is based on high spectral data.
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基金项目:公益性行业科研专项(GYHY200806014)资助
引用文本：
刘旸,官莉,2011.人工神经网络法反演晴空大气湿度廓线的研究[J].气象,37(3):318-324.
LIU Yang,GUAN Li,2011.Study on the Inversion of Clear Sky Atmospheric Humidity Profiles with Artificial Neural Network[J].Meteor Mon,37(3):318-324.
刘旸,官莉,2011.人工神经网络法反演晴空大气湿度廓线的研究[J].气象,37(3):318-324.
LIU Yang,GUAN Li,2011.Study on the Inversion of Clear Sky Atmospheric Humidity Profiles with Artificial Neural Network[J].Meteor Mon,37(3):318-324.