Robust Precipitation Bias Correction Through an Ordinal Distribution Autoencoder

Numerical precipitation prediction plays a crucial role in weather forecasting and has broad applications in public services including aviation management and urban disaster early-warning systems. However, numerical weather prediction (NWP) models are often constrained by a systematic bias due to coarse spatial resolution, lack of parameterizations, and limitations of observation and conventional meteorological models, including constrained sample size and long-tail distribution. To address these issues, we present a data-driven deep learning model, named the ordinal distribution autoencoder (ODA), which principally includes a precipitation confidence network and a combinatorial network that contains two blocks, i.e., a denoising autoencoder block and an ordinal distribution regression block. As an expert-free model for bias correction of precipitation, it can effectively correct numerical precipitation prediction based on meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and SMS-WARMS, an NWP model used in East China. Experiments in the two NWP models demonstrate that, compared with several classical machine-learning algorithms and deep learning models, our proposed ODA generally performs better in bias correction.

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Work Title Robust Precipitation Bias Correction Through an Ordinal Distribution Autoencoder
Open Access
  1. Youcheng Luo
  2. Xiaoyang Xu
  3. Yiqun Liu
  4. Hanqing Chao
  5. Hai Chu
  6. Lei Chen
  7. Junping Zhang
  8. Leiming Ma
  9. James Z. Wang
License In Copyright (Rights Reserved)
Work Type Article
  1. Institute of Electrical and Electronics Engineers (IEEE)
Publication Date January 1, 2022
Publisher Identifier (DOI)
  1. 10.1109/mis.2021.3088543
  1. IEEE Intelligent Systems
Deposited June 17, 2022




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Work History

Version 1

  • Created
  • Added luo-1.pdf
  • Added Creator Youcheng Luo
  • Added Creator Xiaoyang Xu
  • Added Creator Yiqun Liu
  • Added Creator Hanqing Chao
  • Added Creator Hai Chu
  • Added Creator Lei Chen
  • Added Creator Junping Zhang
  • Added Creator Leiming Ma
  • Added Creator James Z. Wang
  • Published
  • Updated Creator James Z. Wang
  • Updated