| 基于深度学习的全球电离层TEC预测 |
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| 引用本文： | 张富彬,周晨,王成,赵家奇,刘祎,夏国臻,赵正予. 基于深度学习的全球电离层TEC预测[J]. 电波科学学报, 2021, 36(4): 553-561. DOI: 10.13443/j.cjors.2020051101 |
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| 作者姓名： | 张富彬 周晨 王成 赵家奇 刘祎 夏国臻 赵正予 |
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| 作者单位： | 1.武汉大学电离层实验室，武汉 430072 |
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| 摘 要： | 电离层总电子含量(total electron content,TEC)是卫星时代以来最重要的电离层参数,具有重要的理论意义和应用价值.文中提出了一种基于深度学习方法的全球电离层TEC预测模型,采用编码器-解码器结构配合卷积优化的长短时记忆(long short-term memory,LSTM)网络来实现全球TEC的...
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| 关 键 词： | 电离层预测 深度学习 神经网络 长短时记忆(LSTM) 卷积网络 |
| 收稿时间： | 2020-05-11 |
Global ionospheric TEC prediction based on deep learning |
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| ZHANG Fubin,ZHOU Chen,WANG Cheng,ZHAO Jiaqi,LIU Yi,XIA Guozhen,ZHAO Zhengyu. Global ionospheric TEC prediction based on deep learning[J]. Chinese Journal of Radio Science, 2021, 36(4): 553-561. DOI: 10.13443/j.cjors.2020051101 |
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| Authors: | ZHANG Fubin ZHOU Chen WANG Cheng ZHAO Jiaqi LIU Yi XIA Guozhen ZHAO Zhengyu |
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| Affiliation: | 1.Ionospheric Laboratory, Wuhan University, Wuhan 430072, China2.Research Institute for Frontier Science, Beihang University, Beijing 100191, China3.Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen 518055, China |
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| Abstract: | Total electron content (TEC) is the most important ionospheric parameter since the satellite era, and has important theoretical significance and application value. This paper proposes a global ionospheric TEC prediction model based on deep learning methods. We use an encoder-decoder structure to match the convolution-optimized long and short-term memory network (ConvLSTM) to achieve global TEC spatial and temporal prediction. The spatial latitude and longitude resolution of this model is 5°×2.5°, and the time accuracy is one hour. The prediction results when the geomagnetic activity is calm indicate that the model advances the global root mean-square error (RMSE) predicted for one day is less than 1.5 TECU, and the predicted root mean square error within one week in advance is less than 2 TECU. During the period of weak magnetic storm, the prediction error of this model is about 2.5 TECU. By comparing the results of different geomagnetic activity indexes and different latitudes, we found that with the increase of the forecast time and the intensity of geomagnetic activity, the error of this model will gradually increase, and the model has better predictions in the middle and high latitudes. |
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