中国物理B ›› 2022, Vol. 31 ›› Issue (8): 80701-080701.doi: 10.1088/1674-1056/ac4487

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Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

Tong-Bao Zhang(张同宝)1,2, Hui-Jian Liang(梁慧剑)2,3, Shi-Guang Wang(王时光)1,2,†, and Chen-Guang Ouyang(欧阳晨光)1   

  1. 1 Department of Precision Instrument, Tsinghua University, Beijing 100084, China;
    2 State Key Laboratory of Precision Measurement Technology and Instrument, Tsinghua University, Beijing 100084, China;
    3 Department of Electronic Engineer, Tsinghua University, Beijing 100084, China
  • 收稿日期:2021-09-16 修回日期:2021-12-13 接受日期:2021-12-18 出版日期:2022-07-18 发布日期:2022-07-27
  • 通讯作者: Shi-Guang Wang E-mail:wangsg@tsinghua.edu.cn
  • 基金资助:
    We would like to thank Data Center for Meridian Space Weather Monitoring Project (NSSC, CAS) for providing detected ionospheric vTEC data, and to thank Space Physics Data Facility of Goddard Space Flight Center (NASA) for providing calculated Dst data and calculated ionospheric vTEC data. We would like to thank Zhengbo Wang, Jianwei Zhang, and Yuhang Li for feedback on our manuscript. Project supported by the National Key Research and Development Program of China (Grant No. 2016YFA0302101) and the Initiative Program of State Key Laboratory of Precision Measurement Technology and Instrument.

Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

Tong-Bao Zhang(张同宝)1,2, Hui-Jian Liang(梁慧剑)2,3, Shi-Guang Wang(王时光)1,2,†, and Chen-Guang Ouyang(欧阳晨光)1   

  1. 1 Department of Precision Instrument, Tsinghua University, Beijing 100084, China;
    2 State Key Laboratory of Precision Measurement Technology and Instrument, Tsinghua University, Beijing 100084, China;
    3 Department of Electronic Engineer, Tsinghua University, Beijing 100084, China
  • Received:2021-09-16 Revised:2021-12-13 Accepted:2021-12-18 Online:2022-07-18 Published:2022-07-27
  • Contact: Shi-Guang Wang E-mail:wangsg@tsinghua.edu.cn
  • Supported by:
    We would like to thank Data Center for Meridian Space Weather Monitoring Project (NSSC, CAS) for providing detected ionospheric vTEC data, and to thank Space Physics Data Facility of Goddard Space Flight Center (NASA) for providing calculated Dst data and calculated ionospheric vTEC data. We would like to thank Zhengbo Wang, Jianwei Zhang, and Yuhang Li for feedback on our manuscript. Project supported by the National Key Research and Development Program of China (Grant No. 2016YFA0302101) and the Initiative Program of State Key Laboratory of Precision Measurement Technology and Instrument.

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摘要： Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than 5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

关键词: long-short-term memory neural network, equatorial ionosphere, vertical total electron content, vertical total electron content (vTEC)

Abstract: Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than 5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

Key words: long-short-term memory neural network, equatorial ionosphere, vertical total electron content, vertical total electron content (vTEC)

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

  • 07.05.Mh
84.35.+i (Neural networks) 94.20.dt (Equatorial ionosphere) 96.12.ji (Ionospheres)