Airborne Wind Energy Systems (AWESs) are an emerging alternative to conventional wind turbines that harvest wind energy via tethered aircraft at altitudes unreachable to current wind energy technologies. Long-term, high resolution wind data is necessary to optimize the flight path and power production, design and size the aircraft, and ultimately estimate levelized cost of energy. Measurements at these altitudes are expensive, time-consuming and their data availability is constraint by measuring technique. In my research I combine LiDAR measurements, mesoscale simulations (WRF) and high resolution LES (PALM) to produce a high altitude inflow model covering a wide range of the wind spectrum. Initial and boundary conditions of the mesoscale model are nudged by LiDAR to increase precision. Results of this simulation drive the LES which inform on high frequency turbulent fluctuations and cross/autocorrelations. Wind velocity profiles and turbulence intensity significantly vary with time of day. Therefore, AWES will benefit from dynamically adapting their flight pattern and operational height to the prevailing wind situations to reduce systemic losses such as tether drag, tether weight and misalignment with the wind direction. Furthermore, atmospheric stability significantly affects wind conditions which cause a wide range of wind speeds and can result in a multimodal probability distribution at higher altitudes, which cannot be represented by a simple Weibull distribution fit. A better representation of the wind statistics improves load and power estimation.