Research on Aircraft Turbulence Early Warning Technology Driven by Ensemble Kalman Filter and Multiscale Information

Flight turbulence poses a severe threat to aviation safety, and achieving its effective early warning is a critical challenge to be addressed. To overcome the limitations of traditional methods reliant on single information sources or advanced remote sensing equipment, this study constructs a multiscale information fusion framework for aircraft turbulence early warning. The framework initially screens key forecast indices from meteorological reanalysis data. Subsequently, dynamic modes strongly associated with turbulence are extracted via advanced signal decomposition to drive a large‐scale background risk assessment model that integrates Gaussian process classifiers with an entropy‐based weighting method. Concurrently, a small‐scale turbulence life cycle model for vertical wind speed is developed through the analysis of high‐frequency quick access recorder (QAR) data to capture real‐time local variations and critical turbulence precursor characteristics. The core innovation of this framework lies in the utilization of an ensemble Kalman filter (EnKF) for dynamic fusion and state optimization of large‐scale background risk, small‐scale life cycle patterns (particularly precursor features), and high‐frequency vertical wind speed observations. This fusion process exhibits a sensitive, trigger‐like response to identified turbulence precursor signals, generating high‐precision ensemble predictions of vertical wind speed, from which Eddy dissipation rate (EDR) is ultimately estimated for early warning. Experimental results, based on nearly 200 min of real flight data from various flights, demonstrate that the model not only achieves high‐precision prediction of EDR evolutionary trends but also showcases superior warning performance, with an average precision of 95.5% being attained and an average effective early warning lead time of approximately 4 s being provided. This research validates the effectiveness of the proposed multiscale fusion strategy in aircraft turbulence early warning, offering advanced technological support for enhancing aviation safety.