风云三号D星微波温度计条带噪声抑制研究

利用主成分分析法(Principal Components Analysis, PCA)分析了风云三号D星微波温度计(Microwave Temperature Sounder, MWTS)观测数据中条带噪声的分布特征，描述了存在条带噪声的主要模态。基于集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)方法对存在条带噪声的模态所对应的时间系数进行了分解并滤波。通过重构观测亮温实现了对条带噪声的抑制。在定性验证条带噪声抑制效果的基础上，通过将观测亮温与模态亮温进行比较来定量评估条带噪声抑制后的数据精度。统计结果表明，经过条带噪声抑制后，观测亮温与模态亮温的标准差减小约0.018 K，进一步提升了MWTS的数据精度。

Research on Stripe Noise Suppression of Microwave Temperature Sounder on FY-3D Satellite

Principal components analysis (PCA) is used to analyze the distribution characteristics of stripe noise in the observational data of the microwave temperature sounder (MWTS) on FY-3D satellite, and the modes in which stripe noise exists primarily are described. Based on the ensemble empirical mode decomposition (EEMD) method, the time coefficients corresponding to the mode in which the stripe noise exists are decomposed and filtered, and the stripe noise is suppressed by reconstructing the observed brightness temperatures. On the basis of qualitative verification of the stripe noise suppression effect, a method of comparing the observed brightness temperatures with the mode brightness temperatures is proposed to evaluate the data accuracy after stripe noise suppression quantitatively. The statistical results show that the standard deviation of observed brightness temperatures and mode brightness temperatures decreases by about 0.018 K after stripe noise suppression, which further improves the data accuracy of the MWTS .