RainNet

RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling

Xuanhong Chen*, Kairui Feng*, Naiyuan Liu**, Yifan Lu**, Zhengyan Tong, Bingbing Ni, Ziang Liu, Ning Lin

Shanghai Jiao Tong University, Princeton University & University of Technology Sydney

*Equal contribution.
**Equal contribution.

Abstract

Spatial Precipitation Downscaling is one of the most important problems in the geo-science community. However, it still remains an unaddressed issue. Deep learning is a promising potential solution for downscaling. In order to facilitate the research on precipitation downscaling for deep learning, we present the first REAL (non-simulated) Large-Scale Spatial Precipitation Downscaling Dataset, RainNet, which contains 62,424 pairs of low-resolution and high-resolution precipitation maps for 17 years. Contrary to simulated data, this real dataset covers various types of real meteorological phenomena (e.g., Hurricane, Squall, etc.), and shows the physical characters -- Temporal Misalignment, Temporal Sparse and Fluid Properties -- that challenge the downscaling algorithms. In order to fully explore potential downscaling solutions, we propose an implicit physical estimation framework to learn the above characteristics. Eight metrics specifically considering the physical property of the data set are raised, while fourteen models are evaluated on the proposed dataset. Finally, we analyze the effectiveness and feasibility of these models on precipitation downscaling task.

Samples in RainNet

High Resolution Precipitation Maps

Low Resolution Precipitation Maps

High Resolution Precipitation Maps

Low Resolution Precipitation Maps

Downloads

Citation

@misc{chen2020rainnet,
  title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling},
  author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing Ni and Ziang Liu and Ning Lin},
  year={2020},
  eprint={2012.09700},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Contact

Please concat Kairui Feng kairuif@princeton.com, Xuanhong Chen xuanhongchenzju@outlook.com, Naiyuan Liu naiyuan.liu@student.uts.edu.au and Yifan Lu yifan_lu@sjtu.edu.cn for questions about the dataset.