Global Ionospheric Modeling Using Multi-GNSS: A Machine Learning Approach

The ionosphere is a significant error source in space-geodetic techniques, such as the Global Navigation Satellite System (GNSS) and satellite radar altimetry. This is especially pronounced for single-frequency receivers, as ionospheric delays cannot be mitigated by ionosphere-free combinations. Therefore, for high-precision space applications, we need an accurate ionospheric model to provide ionospheric corrections at desired times and locations, such as global ionospheric maps (GIMs) that depict the global distribution of vertical total electron content (VTEC). In this study, we propose a neural network (NN)-based global ionospheric model to predict global VTEC with higher accuracy compared with conventional GIMs. We first determined VTEC based on the carrier-to-code leveling method using multi-GNSS observations from global IGS stations. The derived VTEC time series for all training station-satellite pairs were then used to train the NN-based model. During our experiment in April 2022, a period of high solar activity, the average mean absolute error of VTEC predictions at 47 global test stations was 1.7 TECU. The performance of the NN-based models were also evaluated by single-frequency precise point positioning and compared with GIMs provided by the Chinese Academy of Sciences since the same differential code bias products were used. The NN-based models demonstrated a noteworthy enhancement in the positioning precision at 47 test stations, achieving improvements of 12%, 20%, and 8% for the east, north, and up components, respectively.