Spatiotemporal weather forecasting via multi-scale graph neural networks and latent diffusion models - Zhipeng Wu (PLoS ONE, 2026)
Accurate weather prediction is crucial in agriculture, disaster prevention, and public safety. Challenge: Traditional numerical models have high computational costs and struggle with atmospheric nonlinearity and chaos, while existing deep learning methods face limitations in handling spatial heterogeneity and non-Euclidean data. Solution: This paper introduces the STGLDWeather method. It combines multi-scale spatiotemporal graph neural networks (MS-ST-GNN) and latent diffusion models (LDM) to capture multi-scale spatiotemporal dependencies in weather data and model the temporal evolution of weather conditions in latent space. Conclusion: Experiments on real weather datasets show that STGLDWeather significantly outperforms existing state-of-the-art baselines in prediction accuracy and computational efficiency, particularly excelling in temperature, geopotential height, and wind speed forecasts.