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A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-06-09 , DOI: 10.1007/s00376-020-9249-9 Peipei Yu , Chunxiang Shi , Ling Yang , Shuai Shan
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-06-09 , DOI: 10.1007/s00376-020-9249-9 Peipei Yu , Chunxiang Shi , Ling Yang , Shuai Shan
Hyperspectral data have important research and application value in the fields of meteorology and remote sensing. With the goal of improving retrievals of atmospheric temperature profiles, this paper outlines a novel temperature channel selection method based on singular spectrum analysis (SSA) for the Geostationary Interferometric Infrared Sounder (GIIRS), which is the first infrared sounder operating in geostationary orbit. The method possesses not only the simplicity and rapidity of the principal component analysis method, but also the interpretability of the conventional channel selection method. The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum (700–1130 cm −1 ), and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence. At 700–780 cm −1 , the channels selected using our method perform better than IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) temperature channels when used as inputs to the neural network retrieval model. 高光谱数据在气象及遥感领域具有重要的研究应用价值. 针对搭载于中国最新一代静止气象卫星上的干涉式大气垂直探测仪 (GIIRS), 本文介绍了一种基于奇异频谱分析 (SSA) 的温度通道选择方法以改善晴空大气温度廓线的神经网络反演. SSA 将 GIIRS 所测红外亮温光谱 (700–1130 cm −1 ) 进行分解, 获得经重构的组合成分, 并根据组合成分标准差 (SD) 自适应选择温度通道. 该方法具有简便快速的特点且一定程度上补充了通道选择的可解释性. 在 700–780 cm −1 范围内, GIIRS 温度通道子集用作神经网络输入时, 其反演结果优于 IASI 和 CrIS 温度通道子集.
更新日期：2020-06-09
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