• Corpus ID: 220665816

Statistical Downscaling of Temperature Distributions from the Synoptic Scale to the Mesoscale Using Deep Convolutional Neural Networks

@article{Sekiyama2020StatisticalDO,
  title={Statistical Downscaling of Temperature Distributions from the Synoptic Scale to the Mesoscale Using Deep Convolutional Neural Networks},
  author={Tsuyoshi Thomas Sekiyama},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.10839}
}
  • T. Sekiyama
  • Published 20 July 2020
  • Environmental Science, Physics, Computer Science
  • ArXiv
Deep learning, particularly convolutional neural networks for image recognition, has been recently used in meteorology. One of the promising applications is developing a statistical surrogate model that converts the output images of low-resolution dynamic models to high-resolution images. Our study exhibits a preliminary experiment that evaluates the performance of a model that downscales synoptic temperature fields to mesoscale temperature fields every 6 hours. The deep learning model was… 
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