一种电离层TEC格点预测模型

殷梦婷; 邹自明; 钟佳

doi:10.11728/cjss2021.04.568

一种电离层TEC格点预测模型

doi: 10.11728/cjss2021.04.568

殷梦婷^1,2,
邹自明¹,
钟佳^1,2

1. 中国科学院国家空间科学中心北京 100190;
2. 中国科学院大学北京 100049

基金项目:

中国科学院“十三五”信息化建设专项（XXH13505-04）和北京市科技计划空间科学大数据管理与应用服务平台建设项目（Z181100002918002）共同资助

详细信息

作者简介:
殷梦婷,E-mail:2694897602@qq.com

中图分类号: P352
计量
- 文章访问数: 426
- HTML全文浏览量: 56
- PDF下载量: 41
- 被引次数: 0
出版历程
- 收稿日期: 2020-02-17
- 修回日期: 2020-12-24
- 刊出日期: 2021-07-15

A Prediction Model of the Grid Point Ionospheric TEC

1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;
2. University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 基于分析时间序列数据的门限控制单元（GRU）神经网络模型，利用电离层TEC网格点历史数据、太阳活动指数、地磁活动指数作为预测因子，提出一种高精度电离层TEC格点预测模型.对全球60个网格点的数据进行了模型预测和对比实验，得到北半球平均相对精度的均值为83.96%，高于南半球的73.60%，表明预测模型在北半球的适应性更好，且中低纬地区的适应性优于高纬地区；预测模型在磁扰动期的平均相对精度的均值比磁平静期平均相对精度的均值高，约1.95%；与基于递归神经网络（RNN）、长短时记忆网络（LSTM）和双向长短时记忆网络（Bi-LSTM）的电离层TEC单站预测模型相比，本文预测模型的均方根误差（RMSE）平均为原来的80.8%.
- 电离层 /
- TEC格点预测模型 /
- GRU模型
Abstract: A high-precision ionospheric TEC grid point prediction model is established using Gate Recurrent Unit (GRU) neural network model suitable for analyzing time series data. Ionospheric TEC grid point historical data, solar activity index, and geomagnetic activity index are used as inputs of our model. After our in-depth research and analysis, the data of 60 grid points were employed to predict model and do comparative experiments, and the results show that the mean value of average relative accuracy of the northern hemisphere is 83.96%, higher than 73.60% of the southern hemisphere. It presents that the adaptability of the prediction model is better in the northern hemisphere, and especially in the middle and low latitudes rather than in the high latitudes. The second result is that the mean value of the average relative accuracy of the prediction model in magnetic disturbance period is about higher 1.95% higher than that in magnetic quiet period. Finally, we compared the prediction results of several representative models. Compared with the single station prediction model based on RNN, LSTM and Bi-LSTM, the RMSE of this prediction model is reduced to 80.8% on average.
- Ionosphere /
- Grid point prediction model /
- Gate Recurrent Unit model

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参考文献(23)

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