Mesoscale (10-2000 km) meteorological data assimilation and prediction are challenging due to the sparseness of observations, especially in the upper atmosphere. A new source of sensor data, called TAMDAR (Tropospheric Airborne Meteorological Data Reporting), has been introduced, that can potentially fill the data gaps. TAMDAR sensors, developed by AirDat LLC in collaboration with NASA, FAA and NOAA, are specially designed for smaller commercial aircrafts that fly in the lower troposphere over the CONUS and other parts of the world. These sensors provide a full suite of meteorological measurements, including temperature, winds, humidity, icing, turbulence, etc, with very high space-time density. By 15 July 2005, AirDat had completed sensor installations on 63 Saab 340 aircraft operated by Mesaba Airline which executes ~400 flights a day, providing ~800 soundings. At present, AirDat is working with other airlines to field more TAMDAR sensors, and is targeting to complete the CONUS coverage within the next 1 - 2 years. NCAR has been working with AirDat on evaluation and optimization of the impacts of the existing and the future CONUS-scale TAMDAR data on the NCAR 4DWX real-time, multi-scale, rapid-cycling, four-dimensional data assimilation and forecast (RTFDDA) system. This is being accomplished through real-time modeling and case studies. The 4-D continuous data-assimilation scheme of the model system is capable of weighting each observation according to its observation time and location, and thus it is able to assimilate the aircraft data measured along a flight leg, which can be very irregular in time and space. In this paper, the potential value of the future CONUS-scale TAMDAR observing systems, based on 12 airlines which make 8951 flights a day, is studied using an Observing System Simulation Experiment (OSSE) approach. Two wintertime massive cold-air breakout cases are selected. The nature, control and data impact experiments are conducted using the WRF model with a CONUS domain, and the nature runs are conducted with a 4-km grid that resolves clouds explicitly. Data assimilation experiments with real observations are also conducted with the same model in order to quantify the credibility of the OSSE system. Impacts of TAMDAR data are evaluated, along with those of other existing upper-air platforms, through data assimilation and forecast experiments that use observations extracted from the nature runs and through the use of data withholding. The result shows a very encouraging positive impact of the TAMDAR observing system on mesoscale NWP.