Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging
This work addresses medium-range weather forecasting challenges for meteorological centers by creating an incremental hybrid system that leverages both physics-based and AI-based models.
This study tackles the limitations of data-driven weather models by proposing a hybrid system that combines the physics-based GEM model with the AI-based GraphCast model, using spectral nudging to improve large-scale predictions while preserving fine-scale details. The hybrid approach enhances prediction skill, generates physically consistent forecasts with a full power spectrum, and improves tropical cyclone trajectory accuracy.
Operational meteorological forecasting has long relied on physics-based numerical weather prediction (NWP) models. Recently, this landscape has faced disruption by the advent of data-driven artificial intelligence (AI)-based weather models, which offer tremendous computational performance and competitive forecasting accuracy. However, data-driven models for medium-range forecasting generally suffer from major limitations, including low effective resolution and a narrow range of predicted variables. This study illustrates the relative strengths and weaknesses of these competing paradigms using the physics-based GEM (Global Environmental Multiscale) and the AI-based GraphCast models. Analyses of their respective global predictions in physical and spectral space reveal that GraphCast-predicted large scales outperform GEM, particularly for longer lead times, even though fine scales predicted by GraphCast suffer from excessive smoothing. Building on this insight, a hybrid NWP-AI system is proposed, wherein temperature and horizontal wind components predicted by GEM are spectrally nudged toward GraphCast predictions at large scales, while GEM itself freely generates the fine-scale details critical for local predictability and weather extremes. This hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model while generating a full suite of physically consistent forecast fields with a full power spectrum. Additionally, trajectories of tropical cyclones are predicted with enhanced accuracy without significant changes in intensity. Work is in progress for operationalization of this hybrid system at the Canadian Meteorological Centre.