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Using stratified Bayesian model averaging in probabilistic forecasts of precipitation over the middle and lower Yangtze River region

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Abstract

To study the impact of training sample heterogeneity on the performance of Bayesian model averaging (BMA), two BMA experiments were performed on probabilistic quantitative precipitation forecasts for the middle and lower reaches of the Yangtze River in summer 2016 using TIGGE (THORPEX Interactive Grand Global Ensemble) multi-model and multi-member precipitation forecast data. The first experiment, conventional Bayesian model averaging (CBMA), pools the subsamples together in training and forecasting without categorisation. The second experiment, stratified sampling BMA (SBMA), divides all available training samples into three subsets according to their ensemble spread. The results show that spread is a good indicator to divide the ensemble precipitation samples into subsets, especially for heavy rainfall. Moreover, the optimal training period for BMA is about 50 days. The CBMA often filters out most information on the high spread cases and produces ill-fitted parameters for them. Additionally, CBMA deterministic forecast and percentile forecast are often underestimated. In contrast to CBMA, parameters estimated using SBMA with stratified samples of different properties are much more reasonable. The mean absolute error, continuous ranked probability scores and Brier scores of the two BMA experiments were examined for all available forecast cases. Although CBMA was slightly superior to the raw ensemble prediction system (REPS) forecasts in low-threshold forecasts, SBMA outperformed REPS, probability-matching ensemble mean, and CBMA in all evaluations.

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Acknowledgements

The authors would like to thank Dr. Zhu Jiangshan, Dr. Peng Ting and Dr. Ji Luying for providing valuable suggestions. The authors are also grateful for support from the National Key Research and Development Program of China (2018YFC1507505, 2018YFC1508002, 2018YFC1507200), and the Research and Operation Project of the Institute of Heavy Rain (IHRKYYW201807).

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Authors and Affiliations

  1. Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan, China

    Haixia Qi, Tao Peng, YongQing Bai & Chunze Lin

  2. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, No. 219, Ningliu Road, Nanjing, Jiangsu Province, 210044, China

    Xiefei Zhi

  3. Department of atmospheric and oceanic science, University of Maryland, College Park, Maryland, USA

    Wen Chen

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  1. Haixia Qi
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  2. Xiefei Zhi
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  3. Tao Peng
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  4. YongQing Bai
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  5. Chunze Lin
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  6. Wen Chen
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Correspondence to Xiefei Zhi.

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Communicated by Clemens Simmer.

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Qi, H., Zhi, X., Peng, T. et al. Using stratified Bayesian model averaging in probabilistic forecasts of precipitation over the middle and lower Yangtze River region. Meteorol Atmos Phys 133, 961–972 (2021). https://doi.org/10.1007/s00703-021-00799-w

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  • DOI: https://doi.org/10.1007/s00703-021-00799-w

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