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A multiplicative model with frequency-domain features superimposed on time-domain mutations for predicting ionospheric TEC methods[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2024-0123
Citation: A multiplicative model with frequency-domain features superimposed on time-domain mutations for predicting ionospheric TEC methods[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2024-0123

A multiplicative model with frequency-domain features superimposed on time-domain mutations for predicting ionospheric TEC methods

doi: 10.11728/cjss2024-0123
  • Received Date: 2024-09-30
  • Accepted Date: 2024-12-13
  • Rev Recd Date: 2024-11-19
  • Available Online: 2025-04-29
  • Total Electronic Content (TEC) is an important characteristic parameter of the ionosphere, which has a great influence on the navigation error correction and other applications, but the current ionospheric TEC prediction accuracy cannot fully meet the demand, and there are deficiencies in the accuracy and lead time. The paper focuses on the needs of regional ionospheric TEC forecasting, comprehensively considers the characteristics of ionospheric TEC in both frequency and time domains, analyzes the ionospheric TEC changes in multiple cycle lengths in the frequency domain according to the characteristics of trend, periodicity, and suddenness of the changes in the ionospheric TEC affected by solar activities, considers the suddenness of the geomagnetic storms and other factors on the ionospheric TEC in the time domain, and considers the Dst index and latitude/longitude as the input parameters for forecasting. forecast input parameters, and train the specificity of the magnetosphere-ionosphere coupling in each region. The experimental results show that in the geomagnetically quiet period, the RMSE of the 7-day forecast is better than 1.262 TECU, and the RMSE of the 1-day forecast is better than 1.094 TECU; in the geomagnetically active period, the RMSE of the 7-day forecast is better than 4.186 TECU, and the RMSE of the 1-day forecast is better than 4.115 TECU. model, and the method performs well in terms of forecasting accuracy and timing.

     

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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