# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 5 Observation Noises  
As mentioned above, FY-3E MWHS-2 has noises in both the along-track direction and the along-scan direction, namely, the striping noise and the high-frequency oscillation noise (hereafter mentioned as the along-scan noise). Additionally, the observation noise will have adverse effects on data application, such as increasing the inter-channel correlations between the channels in data assimilation [34, 35]; thus, noise filtering is very significant. Striping noise is common in the new generation of high-resolution microwave sounders onboard polar orbiting satellites. Although the cause of striping noise generation is still controversial, many mitigation methods have been developed by our predecessors [34, 36, 37]. To clarify the characteristics of striping noise, this study adopts the method developed by [PERSON] et al. [34] to extract the striping noise of the instrument. The main principle of this method is to decompose the observed brightness temperature using the Principal Component Analysis method and then to smooth the PC coefficient. The striping noise is mainly contained in the first three PC modes according to its characteristic of fixing along the scanning direction. Meanwhile, according to the randomness of the weather systems, the first three modes mainly represent the \"climate state\" of the bright temperature with gentle spatial variation. In general, the striping noise can be removed without losing the weather signal by filtering the small-scale disturbance from the first PC coefficient and then combining it with the first mode to reconstruct the brightness temperature. Due to the randomness of the striping noise, the Ensemble Empirical Mode Decomposition (EEMD) [38] is employed to smooth the PC coefficients. Since the EEMD method can be used to decompose a signal based on the scale characteristics of itself without setting basis functions with fixed frequency beforehand, this method is ideal for filtering striping noise with no fixed frequency. Detailed discussions of the EEMD methods can be found in [PERSON] et al. [38]. Figure 11a,b show the striping noise isolated from the observations in Figure 2a and the new \"O\(-\)B\" distribution after the striping noise mitigation. The striping noise for Channel 3 is at approximately 0.2 K with a relatively uniform global distribution, indicating that noise extraction is not affected by the original brightness temperature distribution. Compared with Figure 2c, the striping noise in the \"O\(-\)B\" distribution in Figure 11b is significantly weakened, while the other features are successfully retained. According to Figure 6a,b, obvious jitters with scanning angles are found in MWHS-II, which is also found in the microwave humidity sounders carried by the early Fengyun series satellites. Thus, [PERSON] et al. [35, 39] and [PERSON] et al. (2021) developed a noise filtering method to compare the characteristics of the along-scan noises in different Fengyun series microwave humidity sounders and found that the along-scan noise is significantly weakened with the instrument upgrade. The method proposed by [PERSON] et al. [39] was also used to extract the along-scan noise to further clarify its influence. [PERSON] et al. [39] found that the along-scan noise has a similar characteristic to the striping noise; in addition, the striping noise is relatively fixed along the scanline, while the along-scan noise is relatively fixed along the track. Therefore, by decomposing the brightness temperature and simply applying the five-point smoothing to the first mode from Principal Component Analysis, the along-scan noises can be well removed from the observations. Figure 12 shows the noise distribution after subtracting the reconstructed brightness temperature from the observation for the 600 scanlines in Channel 3. The noise value is approximately 0.3 K, and the noise of the second and 97 th FOV is significantly larger, which may result from the large errors of the two-edge FOVs (i.e., the first and 98 th FOV).