A new LES-based system for short-range forecasting of near-surface wind gusts at airports

Abstract

<p>Severe wind gusts associated with strong-wind events like winter storms are regarded as the biggest thread to aviation safety during takeoff and landing. In order to get forecasts for gust hazards during strong-wind conditions, airport operators currently still rely on empirical wind gust estimation (WGE) methods that are driven solely by mesoscale and regional numerical weather prediction (NWP) model data. These models currently have grid resolutions on the order of 1 kilometer and do not adequately resolve local topography and vegetation. This is especially problematic, as these local obstacles are a primary source of near surface turbulence. This turbulence is responsible for wind gusts, but it can also no be resolved by mesoscale and regional NWP models and has to be parameterized entirely. Consequently the empirical WGE methods completely neglect the real local obstacles around a specific airport, which leads to significant uncertainties in the wind gust hazard forecasts for airspace that is downwind from those obstacles. In case this airspace is used for takeoff and landing, airport operators have no access to reliable wind gust hazard forecasts. A more adequate and physical approach to resolving local obstacles and the turbulence they induce, is high-resolution large-eddy simulation (LES). We use the high-res LES model PALM to investigate, how wind gusts induced by local obstacles impact airspace used for takeoff and landing at the airport in Frankfurt, Germany. Moreover, we evaluate how accurate these LES results are compared to high-res wind measurements from multiple sites throughout the airport and we quantify the forecast quality of the LES approach vs. the traditional empirical WGE approach. Finally, we outline a path to apply PALM as a magnifying glass nested into mesoscale models with the goal of providing a new and improved forecasting system for gust hazards at airports. This new forecasting system can be used to improve existing empirical WGE approaches and it can be applied in an operational setting to produce new gust forecast products.</p>