MT-GAN: A Multitask GAN for Severe Convective Weather Nowcasting

With global warming driving more extreme weather events, real-time monitoring and forecasting of severe convective weather are increasingly critical. This letter presents a multitask generative adversarial network (MT-GAN) to improve nowcasting (short-term prediction) of severe weather, specifically precipitation and lightning. MT-GAN forecasts with 6-min temporal resolution and up to 60-min lead times. Its dual-stream architecture handles radar reflectivity and lightning data, using an encoder-decoder framework with SimVP for temporal feature extraction. A two-level feature fusion enhances spatiotemporal predictions, while a Patch-D discriminator improves realism. Tested on a central China dataset, MT-GAN outperforms ConvLSTM and PredRNN++ in image quality and nowcasting accuracy, with a stronger emphasis on convective regions for better lightning initiation detection.