Joint Hail Detection from Satellite and Radar Observations with Spatially Adaptive Alignment and Wavelet-Gated Refinement

Detecting hail from remote sensing observations remains challenging because hail develops rapidly and its signatures may appear at different levels within a storm. Ground-based radar and geostationary meteorological satellites are the two primary observing systems for this task, yet their observations are often spatially misaligned. Satellite measurements mainly characterize the thermal structure near the cloud top, whereas radar observations capture the lower-level precipitation core. This mismatch is further exacerbated by satellite parallax, namely the apparent horizontal shift of high cloud tops caused by the oblique viewing geometry of a geostationary satellite, together with the vertical tilt of convective storms. Existing joint methods generally combine satellite cloud-top information with radar precipitation information directly, without explicitly correcting the spatial displacement, which limits detection accuracy. To address this issue, we propose HailDeformer, a deep learning framework that first aligns satellite and radar features through a bidirectional deformable cross-attention module equipped with a position-wise confidence gate and optimized with smoothness, contrastive alignment, and observation-structure consistency losses, and then refines the fused representation using an inter-scale attention module and a wavelet-guided refinement module. Experiments on a four-region dataset from China show that HailDeformer consistently outperforms Direct Fusion, Manual Weighting, Cross-Attention Fusion, and Optical Flow Alignment, achieving a mean Average Precision at IoU 0.5 ([email protected]) of 0.916, an F1 score of 0.864, a Critical Success Index (CSI) of 0.760, and the lowest False Alarm Ratio (FAR) of 0.149. Ablation studies further confirm that all proposed modules and associated constraints contribute to the overall performance, with the alignment module providing the largest improvement. Additional evaluations demonstrate that HailDeformer remains effective throughout storm evolution and under challenging observational conditions.