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A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network

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Abstract

The total electron content (TEC) is an important parameter for characterizing the morphology of the ionosphere. Modeling the ionospheric TEC accurately during the storm time could contribute to the operation of global navigation satellite systems (GNSS), satellite communications, and other applications. This study uses an image-based convolutional long short-term memory (ConvLSTM) network with multichannel features to forecast ionospheric TEC during the quiet periods and storm periods. The sunspot number (SSN), solar wind velocity (Vsw), Dst, and Kp geomagnetic indices are firstly fed into the model as the channel features to improve generalization performance. Based on the variation of the Dst index, we have collected gridded TEC maps from 2011 to 2018 with a 1-h interval from the global ionospheric maps (GIM) as the data set including quiet periods and storm periods of ionospheric TEC. The performance of the ConvLSTM model in forecasting TEC is also compared with other deep learning models such as LSTM, gated recurrent unit (GRU), and LSTM-CNN. Furthermore, the accuracy consistency of the ConvLSTM model during the different phases of the storm period is also evaluated for the different output steps of predicted TEC maps. The optimal combination of input features for the model is also investigated during the storm period. Testing results show that the ConvLSTM network with multichannel features has good prediction performance for quiet periods and storm periods by incorporating both solar and geomagnetic activity indices. The statistical indicators show that the ConvLSTM model performs well with lower mean absolute error (MAE), root mean square error (RMSE), and larger correlation coefficient (R) compared with other methods. We have demonstrated that the model with a larger prediction step has worse prediction performance at the low-latitude area, especially during the storm period. In our future work, the larger TEC data set and more solar and geomagnetic indices will be investigated.

Highlights

  • An image-based convolutional long short-term memory (ConvLSTM) network with multichannel features for forecasting ionospheric TEC.

  • Solar and geomagnetic indices as the input features for improving the performance of the model.

  • The ConvLSTM model has good prediction performance for quiet and storm periods by incorporating both solar and geomagnetic activity indices.

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Data availability

The GIM TEC maps are available from the CODE analysis center (http://ftp.siub.unibe.ch/CODE/). The solar wind velocity, sunspot number, Dst and Kp indices are available from the GSFC/SPDF OMNIWeb (https://omniweb.gsfc.nasa.gov/form/dx1.html).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant nos. 41721003, 41874033, 42142037). The authors thank the CODE analysis center for providing the gridded TEC data and the NASA Goddard Space Flight Center (GSFC) for providing solar and geomagnetic indices.

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Xin Gao & Yibin Yao

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  1. Xin Gao
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  2. Yibin Yao
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XG and YBY provided the initial idea and designed the research; XG processed data and wrote the manuscript. All authors provided critical feedback and helped shape the analysis and manuscript.

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Correspondence to Yibin Yao.

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Gao, X., Yao, Y. A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network. J Geod 97, 9 (2023). https://doi.org/10.1007/s00190-022-01696-9

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