ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows

@article{Groenke2020ClimAlignUS,
  title={ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows},
  author={Brian Groenke and Luke E. Madaus and Claire Monteleoni},
  journal={Proceedings of the 10th International Conference on Climate Informatics},
  year={2020}
}
Downscaling is a common task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling aims to approximate this task using statistical patterns gleaned from an existing dataset of downscaled values, often obtained from observations or physical models. In this work, we investigate the application of domain alignment to the task of statistical downscaling. We present ClimAlign, a novel method for… 

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