Low-Level Turbulence Forecasts From Fine-Scale Models

Abstract : Research conducted by the U.S. Army Research Laboratory s (ARL s) Battlefield Environment Division (BED) has been identified as helpful to the Air Force Weather Agency s capability gaps in forecasting boundary-layer and low-level clear-air turbulence (CAT). It was determined that ARL would research, test, and validate low-level turbulence prediction techniques using the Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW). The WRF-ARW is a mesoscale weather prediction system designed to serve both operational and forecasting needs. The initial part of this study included a literature search and scientific coordination with other researchers to understand low-level turbulence forecasting techniques, algorithms, and indices. Five methods were selected to test using output from ARL s 1-km study over the Los Angeles domain and the U.S. Air Force Weather Agency s (AFWA s) 1.67-km domain over several areas with high airport traffic to use as verification. Upon completion of the model runs, basic statistical methods were applied to evaluate the forecast usefulness. A final step was to find the strengths of weaknesses of the techniques selected to see which methods were most useful for small-scale turbulence forecasts, what adjustments might be needed, and the direction of future research in this area.