Advancing Clear-Air Turbulence Detection with Hybrid Predictive Models for a Regional Aviation Corridor in Southeast Brazil

Severe clear-air turbulence (CAT) remains a relevant hazard to aviation safety, often occurring without visible atmospheric indicators. This study presents a hybrid forecasting framework that integrates Global Forecast System outputs with machine-learning algorithms to predict severe CAT events over Southeast Brazil. To enhance predictive performance and reduce model complexity, a statistically grounded dimensionality reduction approach based on p-value filtering and false discovery rate control was applied, resulting in a compact set of physically interpretable predictors. Several machine-learning classifiers were then evaluated using receiver operating characteristic analysis to assess their predictive skill. The results show that relatively simple models can achieve strong discrimination when combined with rigorous feature selection, outperforming baseline turbulence diagnostics. These findings highlight the value of combining physically consistent diagnostics with data-driven approaches for regional severe CAT forecasting. Overall, the proposed framework provides an efficient and adaptable strategy that can support improved turbulence awareness and contribute to enhanced aviation safety.