Volume 41 Issue 4
Jul.  2021
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Article Navigation >  Chinese Journal of Space Science  > 2021  >  41(4): 568-579
YIN Mengting, ZOU Ziming, ZHONG Jia. A Prediction Model of the Grid Point Ionospheric TEC[J]. Chinese Journal of Space Science, 2021, 41(4): 568-579. doi: 10.11728/cjss2021.04.568
Citation: YIN Mengting, ZOU Ziming, ZHONG Jia. A Prediction Model of the Grid Point Ionospheric TEC[J]. Chinese Journal of Space Science, 2021, 41(4): 568-579. doi: 10.11728/cjss2021.04.568
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A Prediction Model of the Grid Point Ionospheric TEC

doi: 10.11728/cjss2021.04.568 cstr: 32142.14.cjss2021.04.568

  • 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;

  • 2. University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2020-02-17
  • Rev Recd Date: 2020-12-24
  • Publish Date: 2021-07-15
    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.

     

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