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LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
- LaDCast is a new global latent-diffusion framework introduced for medium-range ensemble weather forecasting.
- It generates hourly ensemble forecasts in a learned latent space by compressing high-dimensional ERA5 reanalysis fields into a compact representation using an autoencoder.
- A transformer-based diffusion model is employed to produce sequential latent updates with arbitrary hour initialization.
- LaDCast incorporates Geometric Rotary Position Embedding (GeoRoPE) to consider the Earth's spherical geometry, a dual-stream attention mechanism for efficient conditioning, and sinusoidal temporal embeddings for capturing seasonal patterns.
- The model achieves deterministic and probabilistic forecasting skill comparable to the European Centre for Medium-Range Forecast IFS-ENS, without explicit perturbations.
- LaDCast excels in tracking rare extreme events like cyclones, providing more accurate trajectory predictions compared to established models.
- By operating in latent space, LaDCast significantly reduces storage and computational requirements, offering a practical approach to real-time kilometer-scale resolution forecasting.
- The code and models for LaDCast are open-source, and training and evaluation pipelines are available at https://github.com/tonyzyl/ladcast.
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