A Hybrid Deep Learning Architecture for Enhanced Vertical Wind and FBAR Estimation in Airborne Radar Systems

Accurate prediction of the F-factor averaged over one kilometer (FBAR), a critical wind shear metric, is essential for aviation safety. A central F-factor is used to compute FBAR. i.e., compute the value of FBAR at a point using a spatial interval beginning 500 m prior to the point and ending 500 m beyond the point. Traditional FBAR estimation using the Vicroy method suffers from limited vertical wind speed (W (Formula presented.)) accuracy, particularly in complex, non-idealized atmospheric conditions. This foundational study proposes a hybrid CNN-BiLSTM-Attention deep learning architecture that integrates spatial feature extraction, sequential dependency modeling, and attention mechanisms to address this limitation. The model was trained and evaluated on data generated by the industry-standard Airborne Doppler Weather Radar Simulation (ADWRS) system, using the DFW microburst case (C1-11) as a benchmark hazardous scenario. Following safety assurance principles aligned with SAE AS6983, the proposed model achieved a W (Formula presented.) estimation RMSE (root-mean-squared deviation) of (Formula presented.)   (Formula presented.) (vs. Vicroy’s (Formula presented.)   (Formula presented.)) and a correlation of 0.974 on 14,524 test points. This subsequently improved FBAR prediction RMSE by 98.5% (0.0591 vs. 4.0535) and MAE (Mean Absolute Error) by 96.1% (0.0434 vs. 1.1101) compared to Vicroy-derived values. The model demonstrated a 65.3% probability of detection for hazardous downdrafts with a low 1.7% false alarm rate. These results, obtained in a controlled and certifiable simulation environment, highlight deep learning’s potential to enhance the reliability of airborne wind shear detection for civil aircraft, paving the way for next-generation intelligent weather avoidance systems.