Urban Air Mobility (UAM) vehicles, such as air taxis and delivery drones, are expected to fly at low altitudes in cities where wind patterns are complex and unpredictable. To ensure safe operations, we need microscale atmospheric data that can resolve turbulent air motions around buildings. This study presents a UAM-targeted urban atmospheric modeling framework that couples two advanced simulation systems (WRF-LES and PALM) with real urban data from Ulsan, South Korea, including surface information provided by the MUSE model. The framework can reproduce realistic urban wind fields at very fine resolution (5 meters). The results offer valuable insights into how turbulence behaves around buildings and demonstrate the potential of this framework to support safer UAM operations.