Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Foundation Models
<|reference_start|>Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Foundation Models: Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive. Recent machine learning approaches have shown promising results in downscaling ESM simulations, outperforming state-of-the-art statistical approaches. However, existing methods require computationally costly retraining for each ESM and extrapolate poorly to climates unseen during training. We address these shortcomings by learning a consistency model (CM) that efficiently and accurately downscales arbitrary ESM simulations without retraining in a zero-shot manner. Our foundation model approach yields probabilistic downscaled fields at resolution only limited by the observational reference data. We show that the CM outperforms state-of-the-art diffusion models at a fraction of computational cost while maintaining high controllability on the downscaling task. Further, our method generalizes to climate states unseen during training without explicitly formulated physical constraints.<|reference_end|>
@article{hess2024fast,,
    title={Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System
  Model Fields with Generative Foundation Models},
    author={Philipp Hess, Michael Aich, Baoxiang Pan, and Niklas Boers},
    journal={arXiv preprint arXiv:2403.02774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02774},
    primaryClass={physics.ao-ph cs.CV cs.LG physics.geo-ph}
}