Robust Precipitation Bias Correction
Through an Ordinal Distribution Autoencoder

Youcheng Luo (1), Xiaoyang Xu (1), Yiqun Liu (1), Hanqing Chao (1), Hai Chu (2), Lei Chen (2),
Junping Zhang (1), Leiming Ma (2), James Z. Wang (3)
(1) Fudan University, China
(2) Shanghai Central Meteorological Observatory, China
(3) The Pennsylvania State University, USA
Abstract:

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 parametrizations, 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.

Full Paper
(high-resolution PDF, 5.9MB)

More information


Citation: Youcheng Luo, Xiaoyang Xu, Yiqun Liu, Hanqing Chao, Hai Chu, Lei Chen, Junping Zhang, Leiming Ma and James Z. Wang, ``Robust Precipitation Bias Correction Through an Ordinal Distribution Autoencoder,'' IEEE Intelligent Systems, vol. 36, no. , pp. -, 8 pages, 2021.

© 2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Last Modified: June 8, 2021
© 2021