Enhanced Real-time Global Ionospheric Maps using Machine Learning

Accurate ionospheric models are essential for single-frequency high-precision Global Navigation Satellite Systems (GNSS) applications. Global ionospheric maps (GIMs), which depicts the global distribution of vertical total electron content (VTEC), are a widely used ionospheric product provided by the International GNSS Service (IGS). To meet the increasing need for real-time applications, the IGS real-time service (RTS) has been established and offers real-time (RT) GIMs that can be used for real or near-real time applications. However, the accuracy of present RT GIMs is still significantly lower compared with the final GIMs. IGS RT GIMs show an RMSE of 3.5-5.5 TECU compared to the IGS final GIMs. In this study, we focus on enhancing the accuracy of RT GIMs through machine learning (ML) approaches, specifically a classical Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). The objective is to bridge the gap between the RT GIMs and the final IGS GIMs. This is achieved by using RT GIMs as input and final GIMs as target. The ML approach is applied to the IGS combined RT GIMs and Universitat Politècnica de Catalunya (UPC) RT GIMs. The performance of the improved RT GIMs is evaluated in comparison to the combined IGS final GIM.

We consider over 11'000 pairs of RT GIMs and final GIMs. Over a comprehensive test period spanning 3.5 months, the proposed approach shows promising results with an enhancement of more than 30% in accuracy of RT GIMs. Especially for regions with high VTEC values, which are most critical, the results show a significant improvement. The results demonstrate the model’s great potential in generating more accurate and refined real-time maps.

The integration of ML techniques proves to be a promising avenue for refining and augmenting the precision of real-time ionospheric maps, thereby addressing critical needs in the realm of space weather monitoring and single-frequency applications.