MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Prediction

Andrii Shchur, Inna Skarga-Bandurova

Keywords: AI, WEATHER, DEEP LEARNING, GRAPH

TL;DR: A lightweight, reproducible graph-attention pipeline for fast, accurate regional weather forecasting on multi-scale meshes.

Abstract: Weather forecasting offers an ideal testbed for artificial in-telligence to learn complex, multiscale physical systems. Traditional numerical weather prediction remains compu-tationally costly for frequent regional updates, as high-resolution nests require intensive boundary coupling. We introduce Multi-Resolution Graph Neural Forecasting (MR-GNF), a lightweight, physics-aware model that per-forms short-term regional forecasts directly on an ellipsoi-dal, multi-scale graph of the Earth. The framework cou-ples a 0.25° region of interest with a 0.5° context belt and 1.0° outer domain, enabling continuous cross-scale mes-sage passing without explicit nested boundaries. Its axial graph-attention network alternates vertical self-attention across pressure levels with horizontal graph attention across surface nodes, capturing implicit 3-D structure in just 1.6 M parameters. Trained on 40 years of ERA5 rea-nalysis (1980–2024), MR-GNF delivers stable +6 h to +24 h forecasts for near-surface temperature, wind, and precip-itation over the UK-Ireland sector. Despite a total com-pute cost below 80 GPU-hours on a single RTX 6000 Ada, the model matches or exceeds heavier regional AI systems while preserving physical consistency across scales. These results demonstrate that graph-based neural operators can achieve trustworthy, high-resolution weather prediction at a fraction of NWP cost opening a practical path toward AI-driven early-warning and renewable-energy forecasting systems.

Submission Number: 12