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Summer precipitation prediction in eastern China based on machine learning

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Abstract

In recent years, the development of machine learning, especially deep learning, has provided new methods and ideas for current climate research. Stacked auto-encoder based on deep learning are used to perform nonlinear down-scaling to compress the degree of freedom of climate variables in the early stage. And climate predictors features are extracted from the summer precipitation in four regions in eastern China, from which key climate predictors affecting summer precipitation in each region are identified. On this basis, a variety of regression methods including random forest in machine learning method were used to construct prediction models for key climate predictors in each region. The best model parameters were determined by the sensitivity test of model parameters to forecast results. Several years of predictions suggest that the method for the forecast of precipitation in eastern China has a very high skill, especially in southern China. The results show that the anomaly consistency of the proposed model in regional prediction is better than that of the model. Compared with the mainstream model, the prediction results in South China can be improved by more than 10%. The method has a good application prospect for summer precipitation prediction in eastern China.

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Data availability

The Monthly precipitation data set were downloaded from CPC Merged Analysis of Precipitation (https://psl.noaa.gov/data/gridded/data.cmap.html) and the National Meteorological Information Center, China Meteorological Administration (http://data.cma.cn/data/cdcindex/cid/6d1b5efbdcbf9a58.html). The reanalysis data in this paper were downloaded from the National Centers for Environmental Information (https://psl.noaa.gov/data/gridded/data.gpcp.html) and the National Centers for Environment prediction-Department of Energy (NCEP-DOE) Atmospheric Model Inter-comparison Project-II reanalysis dataset (NCEP2) (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html). And the Ocean data were downloaded from the National Centers for Environmental Information (https://www.ncei.noaa.gov). The Monthly mean data of NCEP_CFS2 seasonal model from MODESv2.

Code availability

Python3.7

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Funding

This study acknowledges the support of the State Key Program of National Natural Science Foundation of China (42130610), Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province (Grant No. Be2022612), Scientific research project of Jiangsu Meteorological Bureau (KZ202206).

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Authors and Affiliations

  1. College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

    Peiyi Fan, Naihui Zang, Yingfa Li & Guolin Feng

  2. Laboratory for Climate Studies, National Climate Research Center, China Meteorological Administration, Beijing, China

    Guolin Feng

  3. Jiangsu Climate Center, Jiangsu Meteorological Bureau, Nanjing, China

    Jie Yang

  4. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China

    Guolin Feng

  5. College of Physical Science and Technology, Yangzhou University, Yangzhou, China

    Jie Yang, Zengping Zhang & Guolin Feng

Authors
  1. Peiyi Fan
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  2. Jie Yang
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  3. Zengping Zhang
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  4. Naihui Zang
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  5. Yingfa Li
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  6. Guolin Feng
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Contributions

GF, JY designed research; PF preformed research; PF and JY analyzed data and wrote the paper. PF, JY, SL, YL, GF contributed to reviewing the manuscript. All authors have read and approved the final manuscript.

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Correspondence to Jie Yang or Guolin Feng.

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Fan, P., Yang, J., Zhang, Z. et al. Summer precipitation prediction in eastern China based on machine learning. Clim Dyn 60, 2645–2663 (2023). https://doi.org/10.1007/s00382-022-06464-1

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