Robust Precipitation Bias Correction Through an Ordinal Distribution Autoencoder

Youcheng Luo, Xiaoyang Xu, Yiqun Liu, Hanqing Chao, Hai Chu, Lei Chen, Junping Zhang, Leiming Ma, James Z. Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)60-70
Number of pages11
JournalIEEE Intelligent Systems
Issue number1
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence


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