Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging

TL;DR

This paper presents a hybrid weather prediction system combining physics-based and AI models, leveraging spectral nudging to improve large-scale forecasts and cyclone trajectory accuracy, while maintaining physical consistency.

Contribution

It introduces a novel hybrid NWP-AI approach that enhances large-scale prediction skill by spectral nudging of AI outputs into physics-based models.

Findings

GraphCast outperforms GEM at large scales for longer lead times.

The hybrid system improves tropical cyclone trajectory predictions.

Fine-scale details are preserved while large scales are enhanced.

Abstract

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.