A dual-weighted loss function for lightning nowcastingOA

Lightning associated with severe convective weather poses a significant threat to public safety and infrastructure.While traditional numerical weather prediction and statistical methods have limitations in computational efficiency and accuracy, deep learning (DL) approaches often yield poor predictive performance for rare but critical lightning events due to the severe class imbalance inherent in the data. To address this challenge, this study introduces a dual-weighted cross-entropy loss function (DWCELoss). This novel function combines a static, global class weight with a dynamic, sample-specific grid weight to enhance the model''s sensitivity in lightning-prone regions. Experiments on the test set demonstrate that, compared to using class-weight alone, the DWCELosstrained model (at a 0.9 probability threshold) increases the probability of detection by 78.74% (from 0.334 to 0.597), the critical success index by 36.80% (from 0.299 to 0.409), and the F1-score by 26.30% (from 0.460 to 0.581). A case study further validates the method''s ability to capture localized lightning activity, with the CSI score increasing from 0.29 to 0.43 for the event. This work provides a novel and effective pathway for DL-based lightning nowcasting, with significant implications for disaster prevention and public safety.