Regional assimilation experiments of clear-sky Atmospheric Infrared Sounder (AIRS) radiances were performed using the gridpoint statistical interpolation three-dimensional variational assimilation system coupled to the weather research and forecasting model. The data assimilation system and forecast model used in this study are separate community models; it cannot be assumed that the coupled systems work optimally. Tuning was performed on the data assimilation system and forecast model. Components tuned included the background error covariance matrix, the satellite radiance bias correction, the quality control procedures for AIRS radiances, the forecast model resolution, and the infrared channel selection. Assimilation metrics and diagnostics from the assimilation system were used to identify problems when combining separate systems. Forecasts initiated from analyses after assimilation were verified with model analyses, rawinsondes, nonassimilated satellite radiances, and 24 h–accumulated precipitation. Assimilation of clear sky AIRS radiances showed the largest improvement in temperature and radiance brightness temperature bias when compared with rawinsondes and satellite observations, respectively. Precipitation skill scores displayed minor changes with AIRS radiance assimilation. The 00 and 12 coordinated universal time (UTC) forecasts were typically of better quality than the 06 and 18 UTC forecasts, possibly due to the amount of AIRS data available for each assimilation cycle.