数值计算与计算机应用
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数值计算与计算机应用  2018, Vol. 39 Issue (4): 299-309    DOI:
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一种基于CNN和RNN深度神经网络的天气预测模型——以北京地区雷暴的6小时临近预报为例
倪铮1, 文韬2
1. 中国人民解放军96873部队, 宝鸡 721000;
2. 中国人民解放军31008部队, 北京 100000
A WEATHER PREDICTION MODEL BASED ON CNN AND RNN DEEP NEURAL NETWORK——AN EXAMPLE IS THE 6 HOUR FORECAST OF THUNDERSTORM IN BEIJING
Ni Zheng1, Wen Tao2
1. The 96873 of PLA, Baoji 721000, China;
2. The 31008 of PLA, Beijing 100000, China
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摘要 本文提出了一种针对气象数据的CNN-LSTM深度神经网络,先是说明了其理论原理,然后对北京市(站号54511)1984年1月1日到2011年9月14日每日逐6小时实况观测的数据和ECMWF的再分析资料(http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=pl/)建立了北京市未来6小时雷暴预报模型,测试了模型对北京市(站号54511)2011年9月15日到2013年12月30日的雷暴预测准确率.结果表明:模型对北京市未来6小时雷暴预报准确率83.08%,误报率13.02%,能较好的对雷暴的6小时临近预报提供支持,可满足日常业务的需要.
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关键词天气预报   6小时临近预报   雷暴   LSTM神经网络   CNN神经网络   深度神经网络     
Abstract: This paper presents a CNN-LSTM deep neural network for meteorological data. At first, the theory of model is explained. Then, The thunderstorm prediction model of Beijing in the next 6 hours was established with the observation data(every 6 hours) of in Beijing(station no. 54511) and ECMWF reanalysis data (http://apps.ecmwf.int/datasets/data/interim-fulldaily/levtype=pl/) on January 1, 1984 to September 14, 2011. We tested the model for the forecast accuracy of the thunderstorm of the city of Beijing, on September 15, 2013 to December 30, 2013.The results show that the model has an accuracy of 83.08%, the rate of false positives 13.02% for the prediction of thunderstorm in Beijing in the next 6 hours, can provide good support for the 6-hour proximity forecast of thunderstorm.
Key wordsThe weather forecast   Now casting   Thunderstorm   Long-Short Term Memory   Convolution neural network   Deep Neural Networks   
收稿日期: 2018-07-21;
引用本文:   
. 一种基于CNN和RNN深度神经网络的天气预测模型——以北京地区雷暴的6小时临近预报为例[J]. 数值计算与计算机应用, 2018, 39(4): 299-309.
. A WEATHER PREDICTION MODEL BASED ON CNN AND RNN DEEP NEURAL NETWORK——AN EXAMPLE IS THE 6 HOUR FORECAST OF THUNDERSTORM IN BEIJING[J]. Journal on Numerical Methods and Computer Applicat, 2018, 39(4): 299-309.
 
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