Climate Change Research ›› 2020, Vol. 16 ›› Issue (3): 263-275.doi: 10.12006/j.issn.1673-1719.2019.067

• Changes in Climate System •     Next Articles

Prediction of summer precipitation in China based on LSTM network

Hao-Jun SHEN1,Yong LUO1,2(),Zong-Ci ZHAO1,3,Han-Jie WANG1   

  1. 1 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science,Tsinghua University, Beijing 100084, China
    2 Joint Center for Global Change Studies, Beijing 100875, China
    3 National Climate Center, China Meteorological Administration, Beijing 100081, China
  • Received:2019-03-29 Revised:2019-04-29 Online:2020-05-30 Published:2020-06-15
  • Contact: Yong LUO


Based on the historical return data of the BCC-CSM seasonal climate prediction model and the monthly data of the surface precipitation in China provided by the National Meteorological Information Center, the factors affecting the forecast results were compared and discussed in this study by multiple methods. The summer precipitation of China in 2014 and 2015 is predicted by using LSTM network. The results show that the prediction ability of the LSTM network is better than that of the stepwise regression, Back Propagation neural network and BCC models. Parameter optimization has a great influence on the prediction effect of LSTM network. The important parameters include the number of hidden layer nodes, training times and learning rate. Selecting suitable starting months is helpful to improve the accuracy of seasonal forecast, and the forecast effect of summer precipitation is better by using the data reported from April. The sea ice component factors have made a positive contribution to seasonal precipitation forecast. In the summer precipitation return experiment in 2014 and 2015, the LSTM network has the ability to predict the overall precipitation situation. Ps score are 74 and 71, anomaly sign consistency rates are 55.63% and 55.25%. The average Ps score is higher than the national consultation and business model in the same period.

Key words: LSTM network, Machine learning, Flood season precipitation, Seasonal prediction

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