基于LSTM网络的中国夏季降水预测研究

doi:10.12006/j.issn.1673-1719.2019.067

摘要/Abstract

摘要：

基于BCC-CSM季节气候预测模式系统历史回报数据和国家气象信息中心提供的中国地面降水月值数据,通过多方法对比并讨论了影响预测结果的因素,利用长短期记忆（Long Short-Term Memory,LSTM）网络预测2014年和2015年中国夏季降水。结果表明:LSTM网络的预测效果较逐步回归、BP神经网络及模式输出结果有一定优势。参数调优对于LSTM网络预测效果影响较大,重要参数有隐含层节点数、训练次数和学习率。选择合适的起报月份数据有助于提升季节预测的准确性,利用4月起报的数据预测夏季降水效果较好。海冰分量因子对降水季节预测有正贡献。在2014年、2015年夏季降水回报试验中,LSTM网络对降水整体形势有一定的预测能力,Ps评分分别为74分、71分,距平符号一致率分别为55.63%、55.25%,Ps评分的均值高于同期全国会商及业务模式。

关键词: LSTM网络, 机器学习, 汛期降水, 季节预测

Abstract:

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

沈皓俊, 罗勇, 赵宗慈, 王汉杰. 基于LSTM网络的中国夏季降水预测研究[J]. 气候变化研究进展, 2020, 16(3): 263-275.

Hao-Jun SHEN, Yong LUO, Zong-Ci ZHAO, Han-Jie WANG. Prediction of summer precipitation in China based on LSTM network[J]. Climate Change Research, 2020, 16(3): 263-275.

图/表 11

图1 中国160个站点分布

Fig. 1 The 160 stations over China

表1 降水趋势预报评分分级标准

Table 1 Grading standard for forecast of precipitation trend

表2 60个夏季降水预测因子

Table 2 Sixty summer precipitation prediction factors

表3 典型城市LSTM网络参数

Table 3 LSTM network parameters of typical cities

图2 基于3月起报得到的北京市6月降水预测结果对比

Fig. 2 Comparison of precipitation forecast results in June in Beijing based on forecast data for March

表4 基于3月起报得到的北京市6月降水预测各结果指标对比

Table 4 Comparison of results indexes of Beijing precipitation forecast in June based on forecast data for March

表5 北京市不同起报月份LSTM预测降水与观测的指标对比

Table 5 Comparison of indexes between LSTM forecast precipitation and observed precipitation in different forecasting months in Beijing

图3 北京市月观测降水与不同起报月份LSTM预测降水的对比

Fig. 3 Comparison between monthly observed precipitation in Beijing and LSTM prediction in different forecasting months

图4 典型城市月观测降水和不同起报月份的LSTM预测降水量

Fig. 4 Monthly observation of precipitation in typical cities and LSTM prediction of precipitation in different forecasting months

图5 北京市月观测降水与基于4月起报不同输入因子的LSTM预测降水对比

Fig. 5 Comparison between monthly observed precipitation and LSTM prediction of different input factors in Beijing based on forecast data for April

图6 2014年、2015年中国夏季LSTM网络预测结果与实况降水距平百分率

Fig. 6 Comparison of LSTM network forecast and actual summer precipitation anomaly percentage in China in 2014 and 2015

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