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Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting

Created by
  • Haebom

Author

Salva R uhling Cachay, Miika Aittala, Karsten Kreis, Noah Brenowitz, Arash Vahdat, Morteza Mardani, Rose Yu

Outline

In this paper, we present Elucidated Rolling Diffusion Models (ERDM) to solve the Limitations of diffusion models, a powerful tool for probabilistic forecasting in high-dimensional chaotic systems. Existing diffusion models struggle to model complex temporal dependencies by predicting future snapshots one by one and fail to explicitly consider the growth of uncertainty. In this paper, we propose ERDMs that integrate a rolling forecasting architecture with high-performance Elucidated Diffusion Models (EDMs). This is made possible by a novel loss weighting scheme, an efficient initialization strategy using pre-trained EDMs, and a custom hybrid sequence architecture for robust spatiotemporal feature extraction under progressive denoising. We demonstrate that they outperform existing diffusion model-based baseline models on 2D Navier-Stokes simulations and ERA5 global weather forecasts.

Takeaways, Limitations

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Takeaways:
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Improved probabilistic prediction performance in high-dimensional chaotic systems: ERDM provides more accurate predictions in high-dimensional chaotic systems (e.g., 2D Navier-Stokes simulations, ERA5 weather forecasts) than traditional diffusion models.
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Explicitly account for increasing uncertainty: Effectively model increasing uncertainty over time with a rolling forecast structure.
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Integrating the benefits of EDM with the benefits of rolling prediction: Create synergy effects by combining the performance of EDM with the benefits of rolling prediction.
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Provides a flexible framework applicable to a variety of sequence generation problems.
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Limitations:
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Currently, only 2D Navier-Stokes simulations and experimental results for the ERA5 weather forecast dataset are presented, and the generalization performance to other types of high-dimensional chaotic systems requires further study.
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It can be computationally expensive. For high-resolution or long-term predictions, the computation time can increase significantly.
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Hyperparameter optimization is important. Hyperparameters need to be carefully tuned to achieve optimal performance.
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