A regional-scale Observing System Simulation Experiment was used to examine the impact of water vapor (WV) sensitive infrared brightness temperature observations on the analysis and forecast accuracy during a high impact weather event across the central U.S. Ensemble data assimilation experiments were performed using the ensemble Kalman filter algorithm in the Data Assimilation Research Testbed system. Vertical error profiles at the end of the assimilation period showed that the wind and temperature fields were most accurate when observations sensitive to WV in the upper troposphere were assimilated; however, the largest improvements in the cloud and moisture analyses occurred after assimilating observations sensitive to WV in the lower and middle troposphere. The more accurate analyses at the end of these cases lead to improved short-range precipitation forecasts compared to the Control case in which only conventional observations were assimilated. Equitable threat scores were consistently higher for all precipitation thresholds during the WV band forecasts. These results demonstrate that the ability of WV-sensitive infrared brightness temperatures to improve not only the 3D moisture distribution, but also the temperature, cloud, and wind fields, enhances their utility within a data assimilation system.