Abstract: This study explores the direct assimilation of Level-1 observations from the Hyperspectral Infrared Atmospheric Sounder (HIRAS-Ⅱ) aboard the Fengyun-3E (FY-3E) satellite into a global real-time atmospheric analysis system. A dedicated operational processing chain for HIRAS-Ⅱ data has been developed within the Gridpoint Statistical Interpolation (GSI) assimilation framework, incorporating data preprocessing, quality control, bias correction, cloud detection and variational assimilation. By combining the CrIS-FSR channel selection method with Jacobian sensitivity analysis of HIRAS-Ⅱ, a subset of 28 temperature-sensitive channels is identified from the long-wave infrared band. Assimilation experiments demonstrate that this channel subset significantly improves temperature assimilation and effectively suppresses errors in humidity. This approach contributes to better temperature analyses in the mid-to-upper troposphere while preserving positive effects in the lower troposphere. A maximum reduction of 2% in the Root Mean Square Error (RMSE) of temperature analyses is observed over the northern Hemisphere. This study marks the first successful operational assimilation of FY-3E/HIRAS-Ⅱ data into the global real-time atmospheric analysis system, providing reliable technical support for the operational assimilation of Fengyun satellite hyperspectral infrared data.