Enhancing Clear Air Turbulence Prediction: A Comparative Analysis of Machine Learning Algorithms Using GFS Forecast and ERA-5 Reanalysis Data

Abstract

This study evaluates twelve categorical machine learning algorithms using performance metrics, including Probability of Detection, False Alarm Rate, and F-measure, to classify Clear Air Turbulence patterns in the meteorological Global Forecast System (GFS), from the National Oceanic and Atmospheric Administration (NOAA) and European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis reanalysis (ERA-5) datasets along the Brazilian air route between the cities São Paulo and Porto Alegre(air-route in Brazil). Two training strategies, cross-validation, and random sample splitting, are employed for evaluating the performances of the algorithms. Results indicate that only the Random Forest algorithm meets the best performance with the adopted criterion for CAT detection. Algorithms trained with ERA-5 reanalysis data perform slightly better. The statistical best algorithm performance values with ERA-5 re-analysis data (GFS forecast data in parentheses) are POD=0.94 (0.87), FAR=0.08 (0.16), and F-measure=0.94 (0.87). As a future direction, it is suggested to investigate the use of regional models with enhanced resolutions in three key domains: horizontal, vertical, and atmospheric pressure.