Noise analysis and mitigation play an important role in meteorological satellite data processing. This study is based on the idea of noise mitigation by using Principal Component Analysis (PCA), Ensemble Empirical Mode Decomposition (EEMD) algorithm, and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The modified method is used to observe data of the microwave humidity sounder (MWHS-2) of the Fengyun-3C and 3D satellites (FY-3C, FY-3D) to analyze the striping noise in its observed brightness temperature. In this study, the effectiveness of this method for MWHS-2 data is confirmed, and a performance analysis of the improved method for data processing and noise mitigation is conducted.The striping noise has a very high correlation with scan line; thus, using PCA can not only effectively isolate the noise-related principal components, but also reduce the dimension of the processed data. When the noise containing principal components is extracted, the empirical mode decomposition method can be used to adaptively separate each component into multiple modes with different frequencies. The noise can be easily separated from the signal by a method that calculates and compares the average period and energy density by using the differences in energy between noise and signal modes. Finally, the remaining modes are combined to reconstruct the principal components, which reconstruct the observed brightness temperature data.When this method is applied to the MWHS-2 data, we used the hourly global reanalysis data of ERA5 with RTTOV model to generate the simulated brightness temperature data and compared with the observed brightness temperature before and after processing. The result shows that the algorithm successfully extracts the striping noise in the signal, and the noise histogram exhibits a Gaussian distribution. The noise mitigation effect between the original EEMD algorithm and various improved mode decomposition methods is compared, and the results show that the use of ICEEMDAN can effectively avoid some problems in EEMD, such as the residue noise, and can reduce reconstruction errors. Numerical analysis results show that compared with the EEMD method, this improved method further reduces the variance by 0.020 K², and the Signal-to-Noise Ratio (SNR) increases by 0.031 dB, which further improves the noise mitigation capability of the algorithm.PCA combined with ensemble empirical mode decomposition can effectively mitigate the striping noise. Although the mode decomposition method is affected by some of its own properties and has certain limitations in accuracy, it has a more convenient operation and higher adaptability. Moreover, the test result shows that this method can achieve satisfactory results. The improvement using ICEEMDAN and calculating the energy density with average period can also be helpful to noise analysis and mitigation and can enhance the reconstruction accuracy. This condition may have certain value for the further improvement of noise reduction algorithm and may improve the accuracy of meteorological data analysis and forecasting.

FY-3;MWHS-2;microwave radiation measurement;data processing;noise mitigation;PCA;empirical mode decomposition;striping noise