Abstract
This study is an aviation-based application of NOAA’s second-generation medium-range Global Ensemble Forecast System Reforecast (GEFS/R; i.e., hindcast or retrospective forecast) dataset. The study produced a downscaled probabilistic prediction of instrument flight conditions at major U.S. airports using an analog approach. This represents an initial step toward applications of reforecast data to probabilistic aviation decision support services. Results from this study show that even at the very coarse resolution of the GEFS/R dataset, the analog approach yielded skillful probabilistic forecasts of flight conditions (i.e., instrument flight rules vs visual flight rules) at most of the Federal Aviation Administration (FAA)’s Core 30 airports. This was particularly true over the central and eastern United States, including the important Golden Triangle, where aircraft flow affects traffic flow management across the entire national airspace system. Additionally, the results suggest that reforecast systems utilizing better horizontal and vertical resolution, in both the modeling system and the reforecast archive, would be very useful for aviation forecasting applications.
Current affiliation: College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon.
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