Atmospheric Turbulence Forecasts for Air Force and Missile Defense Applications

We have developed a hybrid deterministic/probabilistic methodology that uses a Bayesian Hierarchical Modeling (BHM) framework to forecast atmospheric turbulence in the upper-troposphere and lower-stratosphere for Air Force and Missile Defense applications. The approach incorporates three different modeling methods: 1) the Weather Research and Forecasting mesoscale model (WRF) provides numerical weather prediction (NWP) forecasts of atmospheric conditions, 2) Fourier-ray (FR) and Fourier-Laplace (FL) models simulate the propagation and breakdown of internal gravity waves (GWs) triggered by topography and predicted strong-convective events, and 3) compiled statistics from high-resolution direct-numerical simulations (DNS) of gravity-wave breaking and Kelvin-Helmholtz (KH) turbulence guide the FR and FL wave-saturation models and estimate the resulting optical and mechanical turbulence associated with the WRF, FR, and FL output.