A real-time, four-dimensional data assimilation (RT-FDDA) weather analysis and forecasting system has been developed at the National Center for Atmospheric Research (NCAR). The system is built upon the 5th generation of Pennsylvania State University/NCAR Mesoscale Model (PSU/NCAR MM5). A cycling method is used to continuously update the initial conditions for forecasts by nudging model soutions toward observations. The system currently assimilates surface observations taken from a wide variety of platforms/networks, along with a limited number of upper-air soundings. A plan is underway to incorporate the Aircraft Communication Addressing and Reporting System (ACARS) data into the assimilation scheme to improve the spatial and temporal resolution in order to augment the existing rawinsonde observations.

In this paper, we will look into the impact and the benefits of assimilating ACARS data on the initial condition and the subsequent short term prediction by validating against observed station profiles at various airport sites over the continental US. We will also present the comparison between the parallel runs -- those with ACARS assimilation and those without.