An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps

High-precision ionospheric prediction is essential for real-time applications of the Global Navigation Satellite System (GNSS), especially for single-frequency receivers. Various machine learning (ML) algorithms have been utilized for ionospheric forecasting and shown great potential. However, previous studies have primarily relied on IGS global ionospheric maps (GIMs) as training data to develop models for global vertical total electron content (VTEC) forecasting. The forecasting accuracy is thereby limited by the input IGS GIMs due to their low spatio-temporal resolution.

Our previous work proposed a neural network-based (NN-based) global ionospheric model. GIMs generated with this approach showcased enhanced accuracy compared with conventional IGS GIMs as we can finely resolve VTEC irregularities. In this study, we benefit from these ML-based GIMs by employing the transfer learning principle to improve the quality of GIM forecasts. The ML-based model for 1-day ahead global VTEC forecasting is first trained based on a series of IGS GIMs from 2004 to 2022. Then, it is fine-tuned using the recent NN-based GIMs from 2020 to 2022. In this context, the model can gain good generalizability from the large dataset of IGS GIMs while having comparable accuracy with NN-based GIMs. Different machine learning approaches, including convolution long short-term memory (ConvLSTM) network and transformer, are implemented and compared. To validate their performance, we perform hindcast studies to compare the 1-day ahead forecasts of our model with satellite altimetry VTEC and conducted single-frequency precise point positioning tests based on the forecast maps.