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Arxiv
3d
338
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Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging
- Operational meteorological forecasting traditionally relies on physics-based numerical weather prediction models.
- Data-driven artificial intelligence models are disrupting this landscape, offering improved computational performance and competitive forecasting accuracy.
- However, data-driven models for medium-range forecasting have limitations including low effective resolution and a narrow range of predicted variables.
- A study compares the physics-based GEM model with the AI-based GraphCast model, showcasing their strengths and weaknesses in global predictions.
- GraphCast excels in predicting large scales over longer lead times but suffers from excessive smoothing at fine scales.
- A hybrid NWP-AI system is proposed where GEM's temperature and wind predictions are nudged towards GraphCast predictions for large scales while GEM generates fine-scale details independently.
- This hybrid approach enhances prediction skill by leveraging GraphCast's strengths while maintaining physically consistent forecast fields.
- The system shows improved accuracy in predicting tropical cyclone trajectories without significant intensity changes.
- Efforts are underway to operationalize this hybrid system at the Canadian Meteorological Centre.
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