Forecasting of Local Lightning Using Spatial–Channel-Enhanced Recurrent Convolutional Neural Network

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models at capturing the spatiotemporal predictors of lightning events. However, these methods struggle to integrate predictors from diverse data sources, which leads to lower accuracy and interpretability. To address these challenges, the Multi-Scale Spatial–Channel-Enhanced Recurrent Convolutional Neural Network (SCE-RCNN) is proposed to improve forecasting accuracy and timeliness by utilizing multi-source data and enhanced attention mechanisms. The proposed model incorporates a multi-scale spatial–channel attention module and a cross-scale fusion module, which facilitates the integration of data from diverse sources. The multi-scale spatial–channel attention module utilizes a multi-scale convolutional network to extract spatial features at different spatial scales and employs a spatial–channel attention mechanism to focus on the most relevant regions for lightning prediction. Experimental results show that the SCE-RCNN model achieved a critical success index (CSI) of 0.83, a probability of detection (POD) of 0.991, and a false alarm rate (FAR) reduced to 0.351, outperforming conventional deep learning models across multiple prediction metrics. This research provides reliable lightning forecasts to support real-time decision-making, making significant contributions to aviation safety, outdoor event planning, and disaster risk management. The model’s high accuracy and low false alarm rate highlight its value in both academic research and practical applications.