Generating physically-consistent high-resolution climate data with hard-constrained neural networks

@article{Harder2022GeneratingPH,
  title={Generating physically-consistent high-resolution climate data with hard-constrained neural networks},
  author={Paula Harder and Qidong Yang and Venkatesh Ramesh and Prasanna Sattigeri and Alex Hern{\'a}ndez-Garc{\'i}a and Campbell D. Watson and Daniel Szwarcman and David Rolnick},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.05424}
}
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid re- sponses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from… 

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