Abstract: Satellite data assimilation is an important method for improving the quality of the initial field in numerical weather prediction. However, due to the generally low observation quality, only a portion of the satellite data typically participates in the assimilation analysis, resulting in a low effective assimilation rate. The variational quality control scheme adjusts the weight of data to ensure that observations of different quality are utilized appropriately, thereby effectively improving the performance of the assimilation analysis. Based on the Huber-VarQC variational quality control scheme, which more appropriately characterizes the non-Gaussian observation errors of the NOAA19/AMUSA and NOAA19/MHS satellite data, the relevant parameters for different satellite channels are optimized according to their specific error characteristics, allowing the assimilation system to adjust the weight of observation in the assimilation analysis based on different observation error features of the NOAA19/AMUSA and NOAA19/MHS at each channel. This enhances the utilization and assimilation efficiency of satellite data and improves the quality of the assimilation analysis. The experiment results indicate that the Huber-VarQC scheme can effectively capture the "fat-tailed" distribution characteristics of observation errors in satellite data across different channels. Statistically analyzing satellite observation errors by channel and optimizing the Huber-VarQC variational quality control scheme can maximize the practical application potential of this approach and enhance the contributions of satellite data to the analysis. The channel separation Huber-VarQC scheme can allocate appropriate weight to the data based on the error characteristics of each channel, and then increase the effective assimilation rate of polar orbit meteorological satellite microwave observation data. This approach not only incorporates beneficial information from the data but also mitigates the negative impact of harmful information on the assimilation analysis. It improves the positive contribution of satellite data to the assimilation analysis field and enhances assimilation efficiency, resulting in a more accurate analysis field.