MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data

Lightning phenomena can instigate a cascade of calamities, encompassing fires, electrical infrastructure damage, and risks to human safety. Deep-learning-based lightning nowcasting models have demonstrated significant effectiveness in disaster prevention and mitigation. However, existing studies often neglect the impacts of surface features on lightning activities, and conventional lightning prediction techniques based on convolutional and recurrent networks face challenges such as the loss of feature information. Addressing these issues, this paper presents a novel model for lightning nowcasting, the Multimodal ConvLSTM-GAN for Lightning Nowcasting (MCGLN). This model integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory network (ConvLSTM), utilizing multi-source data as inputs. It incorporates a spatiotemporal encoder-forecaster framework within the Generator to improve the capture of multidimensional spatiotemporal feature information, thus boosting predictive accuracy. MCGLN offers probabilistic prediction results, allowing users to customize warning thresholds following their specific tolerance for false and missed alarms. The performance of the MCGLN model is evaluated through empirical analysis, utilizing real lightning datasets sourced from Zhejiang and surrounding areas. Experimental results demonstrate that: (a) The MCGLN model outperforms existing methods in terms of detection capability and overall performance, showing significant improvements in the modeling process. (b) Increasing the number of data sources improves detection capabilities, reduces the probability of false alarms, and boosts the model performance. (c) The use of radar data enhances the recognition of high-probability lightning occurrences, and the inclusion of surface feature data increases the capture of terrestrial lightning genesis.