Spatiotemporal weather forecasting via multi-scale graph neural networks and latent diffusion models

Area of Science:

• Atmospheric science
• Artificial intelligence
• Data science

Background:

• Accurate weather prediction is vital for agriculture, disaster prevention, and public safety.
• Traditional numerical weather models face challenges with high computational costs, atmospheric nonlinearity, and chaos.
• Existing deep learning approaches struggle with spatial heterogeneity and non-Euclidean weather data.

Purpose of the Study:

• To introduce an advanced deep learning framework for enhanced weather forecasting.
• To address the limitations of current numerical and deep learning weather prediction models.
• To improve the accuracy and efficiency of predicting key meteorological variables.

Main Methods:

• The STGLDWeather method combines multi-scale spatiotemporal graph neural networks (MS-ST-GNN) with latent diffusion models (LDM).
• MS-ST-GNN captures complex multi-scale spatiotemporal dependencies within weather data.
• LDM models the temporal evolution of weather conditions in a compressed latent space.

Main Results:

• STGLDWeather demonstrates significant improvements in prediction accuracy compared to state-of-the-art baselines.
• The method achieves superior computational efficiency in weather forecasting tasks.
• Experimental results show particular excellence in forecasting temperature, geopotential height, and wind speed.

Conclusions:

• STGLDWeather offers a powerful new approach to weather prediction, overcoming limitations of existing methods.
• The hybrid deep learning architecture effectively handles complex spatiotemporal weather dynamics.
• This advancement has significant implications for improving weather forecasting accuracy and efficiency across various applications.