Ionformer: A Data-Driven Deep Learning Baseline for Global Ionospheric TEC Forecasting

Abstract

This work proposes a novel design of a Transformer architecture model for ionospheric total electron content (TEC) forecasting called Ionformer. This model is conceptually derived from the Informer model and incorporates patching and learnable position encoding to enhance the focus on local semantic information in the embedding of ionospheric TEC data and enables it to effectively capture complex patterns in it. In our experiments, using ionospheric data from the Crustal Dynamics Data Information System (CDDIS) of NASA and seven data analysis centers of IGS, we processed it into a $15\times 18$ grid of global ionospheric TEC data and forecast a high-solar activity year (2014) and a low-solar activity year (2017). We also compare the performance of Ionformer and other models in different experimental environments, including ionospheric forecasts in different years, locations, and periods of the solar cycle. The results and discussion show that the predictions of our model substantially outperform the other models and are well adapted to both ionospheric magnetic storm periods and quiet periods. An additional experiment shows that the model also outperforms other models for long-term TEC forecasts. In the above experiments, compared with the widely used LSTM-based models, our proposed model significantly improves the prediction performance of ionospheric TEC and can accurately capture the complex patterns of electron density distribution in the ionosphere, ensuring the reliable propagation of Global Navigation Satellite System (GNSS) signals and providing more reliable support for the stable operation of global navigation and communication systems.