Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method
In recent years, deep learning has been extensively used for ionospheric total electron content (TEC) prediction, and many models can yield promising prediction results, particularly under quiet conditions. Owing to the ionosphere's intricate and dramatic changes during geomagnetic storms, the...
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Veröffentlicht in: | Space Weather 2023-03, Vol.21 (3), p.n/a |
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Zusammenfassung: | In recent years, deep learning has been extensively used for ionospheric total electron content (TEC) prediction, and many models can yield promising prediction results, particularly under quiet conditions. Owing to the ionosphere's intricate and dramatic changes during geomagnetic storms, the high‐reliable prediction of the storm‐time ionospheric TEC remains a challenging problem. In this study, we developed a new deep learning‐based multi‐model ensemble method (DLMEM) to forecast geomagnetic storm‐time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. Seven features in 170 geomagnetic storm events, including the three components Bx, By and Bz of interplanetary magnetic field (IMF), the Kp and Dst indices of geomagnetic activity data, the F10.7 index of solar activity data and global TEC data, were used for modeling. The test set results showed that the DLMEM model can reduce the root mean square errors (RMSE) by an average of 43.6% in comparison to our previously presented model Ion‐LSTM, especially during the recovery period of geomagnetic storms. Furthermore, compared to Ion‐LSTM, the RMSE values of the low‐, middle‐ and high‐latitude single‐station forecast TEC can be greatly decreased by 33%, 53% and 59%, respectively. It was shown that the new model allows for more precise short‐term global ionospheric forecasting during geomagnetic storms, enabling real‐time monitoring of ionospheric changes.
Plain Language Summary
The short‐term ionospheric TEC prediction is currently a research focus regarding ionospheric physics and the associated space weather effects. Owing the complex temporal‐spatial variation characteristics of the ionosphere, it brings great difficulties and challenges to ionospheric prediction. Though the rapidly developing machine learning method can discover intricate structures in large data sets, it remains a great problem in ionospheric TEC prediction, especially during geomagnetic storms. This study proposed a deep learning model for storm‐time ionospheric prediction. For the new model, the input data cover more than one solar cycle. Specifically, the geomagnetic indices, interplanetary magnetic field, and global ionospheric maps during storms were selected as data sets. Additionally, the model's structure was redesigned to fit the characteristics of geomagnetic storms, and the model ensembling techni |
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ISSN: | 1542-7390 1539-4964 1542-7390 |
DOI: | 10.1029/2022SW003231 |