Bridging Physics and Machine Learning to Enhance Weather Forecasting at ECCC

Environment and Climate Change Canada (ECCC) is actively advancing the integration of artificial intelligence (AI) into numerical weather prediction (NWP) through a coordinated research-to-operations strategy that combines state-of-the-art machine learning approaches with established physical modeling frameworks. This presentation summarizes the progress achieved to date.

We first describe the development of GEML (Global Environmental eMuLator), a global AI forecast model, based on Google DeepMind’s GraphCast, trained and fine-tuned in-house using ERA5 reanalysis and ECMWF operational analyses. Building on GEML, ECCC has implemented an experimental hybrid AI–NWP global forecasting system, GDPS-SN, which applies large-scale spectral nudging to improve the operational Global Deterministic Prediction System (GDPS) by leveraging the large-scale accuracy of GEML.

The presentation also introduces a description of PARADIS, a fully Canadian, physically inspired, AI-based weather forecast model, developed by ECCC and its partners. These activities illustrate ECCC’s strategic vision for AI-enabled weather prediction by combining scientific rigor, collaboration and  operational relevance to deliver more accurate forecasting systems.