One of the most powerful tools for observing the atmosphere at fine scales is the Doppler radar. Radar data can be used as a key component of a numerical model initialization, especially for mesoscale and stormscale prediction. Despite the importance of radar data for use in warning and numerical simulations, there remains no effort to include analyzed radar data in the data assimilation cycle of operational weather prediction models in Korea. The first step in bringing Korean radar data into a numerical model for heavy rainfall forecasting has been undertaken within this study. The purpose is to assess the impact of Doppler radar data in the numerical forecast of a heavy rainfall event in Korea. In this study, a comprehensive 3-D non-hydrostatic prediction system, the Advanced Regional Prediction System (ARPS) Version 4.5.1, in combination with NEXRAD Level II data gathered by the US Air Force in Pyungtaek, Korea, is applied to a heavy rainfall event in Korea. One-way nesting is employed with a horizontal resolution of 27-km for the coarse outer grid (99x103x37 points), 9-km for the middle grid (115x139x37 points), and 3-km for the inner fine grid (144x187x37 points). And, the ARPS Data Analysis System (ADAS) incremental analysis updating (IAU) scheme based on the IAU technique by Bloom et al., is employed for the radar data assimilation. Totally seven experiments, one for 27-km, each three for 9-km and 3-km resolution forecasts, have been conducted in this study. Through these experiments, we find a significant improvement for the forecasting in the position of maximum heavy rainfall when radar data assimilation is applied in 3-km resolution. We have also examined x-z cross sections and several important instability indices for each experiment and observation and they also show some positive impact of the radar data assimilation at high spatial resolution (3-km grid spacing). The details of results will be presented.