TGNet: Intelligent Identification of Thunderstorm Wind Gusts Using Multimodal Fusion

Thunderstorm wind gusts are small in scale, typically occurring within a range of a few kilometers. It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations. Therefore, it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations. This paper introduces a new algorithm, called thunderstorm wind gust identification network (TGNet). It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events. The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations, which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons. Then, the encoder, structured upon the U-shaped network (U-Net) and incorporating recurrent residual convolutional blocks (R2U-Net), is employed to extract the corresponding spatial convective characteristics of satellite, radar, and lightning observations. Finally, by using the multimodal deep fusion module based on multi-head cross-attention, the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts. TGNet products have high accuracy, with a critical success index reaching 0.77. Compared with those of U-Net and R2U-Net, the false alarm rate of TGNet products decreases by 31.28% and 24.15%, respectively. The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°, updated every 10 minutes. The results are finer and more accurate, thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.