In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers investigated a fine-scale lightning forecasting approach based on weather foundation models (WFMs) and proposed a dual-source data-driven forecasting framework that integrates the strengths of both WFMs and recent lightning observations to enhance predictive performance. Furthermore, a gated spatiotemporal fusion network (gSTFNet) is designed to address the challenges of cross-temporal and cross-modal fusion inherent in dual-source data integration. Experimental results demonstrate that the dual-source framework significantly improves forecasting performance compared to models trained solely on WFMs and outperforms both the ECMWF HRES lightning product and other deep-learning spatiotemporal forecasting models.

A team of scientists from Southern University of Science and Technology has published a paper highlighting the importance of human exploration in the three deeps – deep space, deep sea, and deep Earth. Their paper is published in the journal Ocean-Land-Atmosphere Research on September 11, 2025.