Application of machine learning to combine global ionospheric maps from IGS analysis centers

The International GNSS Service (IGS) global ionospheric maps (GIMs) are one of the primary sources of information on the ionospheric state. They are used in many research and GNSS positioning applications. IGS GIMs are created using the weighted average of the products derived from the selected IAAC. This method allows for efficient mapping of the state of the ionosphere, especially on days without major disruptions. However, ionospheric disturbances could be more problematic to map correctly. To improve GIMs quality, we used a machine learning (ML) approach to combine individual IAAC GIMs into one product. We used total electron content (TEC) data from Jason altimetric satellite with a 5-minute interval as reference. To improve the modeling, we used auxiliary parameters such as solar and geomagnetic indices, e.g., F10.7 index. The training process was performed on the 2005-2020 dataset. 

This study presents some preliminary results of VTEC modeling using the ML approach. We show inter-validation and inter-comparison with IGS GIMs, and Jason-derived VTEC. We also used pseudorange code and carrier phase single-frequency GNSS observations to show positioning accuracy improvement achieved using ML-based GIMs. For this purpose, we used 34 evenly distributed IGS stations for the selected calm period and strong geomagnetic storms. The results showed that for both calm and stormy days, the differences between the coordinates obtained from our model and those using the IGS product were up to a few centimeters for most stations for the northern and eastern components of the topocentric coordinates. Additionally, for altitude, we noticed accuracy improvement for most stations during the storm periods relative to results obtained using the final IGS product.