Assessment of Finescale Local Wind Forecasts Using Small Unmanned Aircraft Systems

Control of small unmanned aircraft systems (sUAS) is influenced by local wind field characteristics. Small UAS missions contained within subgrid regions of current numerical weather prediction (NWP) model outputs cannot benefit from state-of-the-art forecasting tools. The National Center for Atmospheric Research is developing a real-time meso-to-microscale coupled weather research and forecasting-large-eddy simulation (WRF-LES) capability for supporting sUAS missions. The present work compares sUAS measurements of horizontal wind speed and vertical component of the wind velocity to predictions from a real-time implementation of WRF-LES. Results show that the WRF-LES scale of predictability of the horizontal wind speed changes during the development of the boundary layer, yielding better predictability before boundary-layer deepening. The WRF-LES predicts the vertical component of the wind velocity within measurement limits of accuracy before and after boundary-layer deepening and accurately predicts the increase in variance during deepening. The reduced predictability after deepening is related to offsets in timing and location of localized areas of enhanced wind speeds. These offsets (on the order of 2 km and 20 min) indicate the need to increase the size of the sampling space and time windows of the forecast data to fully capture the range of measured wind conditions.