Forecasting Global Ionospheric TEC Using Deep Learning Approach
Abstract Global ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LST...
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| Format: | Article |
| Language: | English |
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Wiley
2020-11-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2020SW002501 |
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| author | Lei Liu Shasha Zou Yibin Yao Zihan Wang |
| author_facet | Lei Liu Shasha Zou Yibin Yao Zihan Wang |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Abstract Global ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). Multiple input data, including historical time series of the SH coefficients, solar extreme ultraviolet (EUV) flux, disturbance storm time (Dst) index, and hour of the day, are used in the developed LSTM NN model. Different combinations of the above parameters have been used in constructing the LSTM NN model, and it is found that the model using all four parameters performs the best. Then the best performing LSTM model is used to forecast the SH coefficients, and the global hourly TEC maps are reproduced using the 256 predicted SH coefficients. A comprehensive evaluation is carried out with respect to the CODE GIM TEC. Results show that the first/second hour TEC root mean square error (RMSE) is 1.27/2.20 TECU during storm time and 0.86/1.51 TECU during quiet time, so the developed model performs well during both quiet and storm times. Moreover, typical ionospheric structures, such as equatorial ionization anomaly (EIA) and storm‐enhanced density (SED), are well reproduced in the predicted TEC maps during storm time. The developed model also shows competitive performance in predicting global TEC when compared to the persistence model and two empirical models (IRI‐2016 and NeQuick‐2). |
| format | Article |
| id | doaj-art-77f48668a5a144fba663431245b2c866 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-77f48668a5a144fba663431245b2c8662025-01-14T16:30:48ZengWileySpace Weather1542-73902020-11-011811n/an/a10.1029/2020SW002501Forecasting Global Ionospheric TEC Using Deep Learning ApproachLei Liu0Shasha Zou1Yibin Yao2Zihan Wang3School of Geodesy and Geomatics Wuhan University Wuhan ChinaDepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USASchool of Geodesy and Geomatics Wuhan University Wuhan ChinaDepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USAAbstract Global ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). Multiple input data, including historical time series of the SH coefficients, solar extreme ultraviolet (EUV) flux, disturbance storm time (Dst) index, and hour of the day, are used in the developed LSTM NN model. Different combinations of the above parameters have been used in constructing the LSTM NN model, and it is found that the model using all four parameters performs the best. Then the best performing LSTM model is used to forecast the SH coefficients, and the global hourly TEC maps are reproduced using the 256 predicted SH coefficients. A comprehensive evaluation is carried out with respect to the CODE GIM TEC. Results show that the first/second hour TEC root mean square error (RMSE) is 1.27/2.20 TECU during storm time and 0.86/1.51 TECU during quiet time, so the developed model performs well during both quiet and storm times. Moreover, typical ionospheric structures, such as equatorial ionization anomaly (EIA) and storm‐enhanced density (SED), are well reproduced in the predicted TEC maps during storm time. The developed model also shows competitive performance in predicting global TEC when compared to the persistence model and two empirical models (IRI‐2016 and NeQuick‐2).https://doi.org/10.1029/2020SW002501 |
| spellingShingle | Lei Liu Shasha Zou Yibin Yao Zihan Wang Forecasting Global Ionospheric TEC Using Deep Learning Approach Space Weather |
| title | Forecasting Global Ionospheric TEC Using Deep Learning Approach |
| title_full | Forecasting Global Ionospheric TEC Using Deep Learning Approach |
| title_fullStr | Forecasting Global Ionospheric TEC Using Deep Learning Approach |
| title_full_unstemmed | Forecasting Global Ionospheric TEC Using Deep Learning Approach |
| title_short | Forecasting Global Ionospheric TEC Using Deep Learning Approach |
| title_sort | forecasting global ionospheric tec using deep learning approach |
| url | https://doi.org/10.1029/2020SW002501 |
| work_keys_str_mv | AT leiliu forecastingglobalionospherictecusingdeeplearningapproach AT shashazou forecastingglobalionospherictecusingdeeplearningapproach AT yibinyao forecastingglobalionospherictecusingdeeplearningapproach AT zihanwang forecastingglobalionospherictecusingdeeplearningapproach |