Frequency-Aware Hierarchical Feature Fusion Network for Tornado Detection Using Dual-Polarization Weather Radar

Tornadoes are extremely hazardous weather phenomena characterized by intense vortex structures, and weather radar is currently one of the most effective means for detecting them. The existing detection algorithms rely on threshold judgments of weather radar variables and have insufficient ability to deeply represent complex weather features, which leads to a low probability of detection (POD) and high false-alarm ratio (FAR). Therefore, a Frequency-Aware Hierarchical Feature Fusion Network (FA-HFFN) is proposed based on dual-polarization weather radar observations. FA-HFFN adopts a heterogeneous dual-path architecture. The main path incorporates the frequency-aware and high-frequency enhancement module, which is designed to extract high-frequency features associated with localized rapid variations in radar variables and to enhance high-frequency abrupt details. The auxiliary path employs the multi-head attention multi-scale module to capture complex background weather information in the weather radar echoes. The dual paths achieve hierarchical adaptive fusion through a stage-wise gating mechanism to jointly represent the frequency response, spatial structure, and rotational characteristics of tornadoes. Experimental results show that FA-HFFN outperforms the comparison models in overall evaluation metrics, with higher detection sensitivity and stronger false-alarm suppression. In tornado case studies, the detection lead time is extended to 54 min, and the three-dimensional evolution trajectory of the tornado is successfully tracked through continuous multi-elevation detection. This study provides a technical approach for automated tornado detection and intelligent warning under complex severe convective weather conditions.