Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
<|reference_start|>Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks: In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling. We propose a probabilistic weather forecasting model called Graph-EFM, combining a flexible latent-variable formulation with the successful graph-based forecasting framework. The use of a hierarchical graph construction allows for efficient sampling of spatially coherent forecasts. Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models, with the added benefit of accurately capturing forecast uncertainty.<|reference_end|>
@article{oskarsson2024probabilistic,
    title={Probabilistic Weather Forecasting with Hierarchical Graph Neural
  Networks},
    author={Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik
  Lindsten},
    journal={arXiv preprint arXiv:2406.04759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04759},
    primaryClass={cs.LG stat.ML}
}