Corpus ID: 207779626

Predicting Weather Uncertainty with Deep Convnets

@article{Grnquist2019PredictingWU,
  title={Predicting Weather Uncertainty with Deep Convnets},
  author={Peter Gr{\"o}nquist and Tal Ben-Nun and Nikoli Dryden and P. Dueben and Luca Lavarini and Shigang Li and Torsten Hoefler},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.00630}
}
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify… Expand
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