Random Forest Approach for Improving Nonconvective High Wind Forecasting across Southeast Wyoming

Abstract

Abstract High winds are one of the key forecast challenges across southeast Wyoming. The complex mountainous terrain across the region frequently results in strong gap winds in localized areas, as well as more widespread bora and chinook winds in the winter season (October–March). The predictors and general weather patterns that result in strong winds across the region are well understood by local forecasters. However, no single predictor provides notable skill by itself in separating warning-level events from others. Random forest (RF) classifier models were developed to improve upon high wind prediction using a training dataset constructed of archived observations and model parameters from the North American Regional Reanalysis (NARR). Three locations were selected for initial RF model development, including the city of Cheyenne, Wyoming, and two gap regions along Interstate 80 (Arlington) and Interstate 25 (Bordeaux). Verification scores over two winters suggested the RF models were beneficial relative to current operational tools when predicting warning-criteria high wind events. Three case studies of high wind events provide examples of the RF models’ effectiveness to forecast operations over current forecast tools. The first case explores a classic, widespread high wind scenario, which was well anticipated by local forecasters. A more marginal scenario is explored in the second case, which presented greater forecast challenges relating to timing and intensity of the strongest winds. The final case study carefully uses Global Forecast System (GFS) data as input into the RF models, further supporting real-time implementation into forecast operations.