Machine learning for global modeling of the ionosphere based on multi-GNSS data

High-precision global ionospheric modeling is important for radio communication, navigation, or studies on space weather. Traditional spatial ionospheric modeling approaches include spherical harmonics and trigonometric B-splines. The Ionospheric Associated Analysis Centers (IAAC) of the International GNSS Service (IGS) use these methods to model vertical total electron content (VTEC) globally, and generate Global Ionospheric Maps (GIMs). Due to the limitations of spatial modeling approaches, conventional GIMs cannot comprehensively describe the spatial feature of the ionosphere. With the capability of capturing complex and non-linear relationships of diverse data, machine learning (ML) has been increasingly applied to ionospheric modeling. Currently, most of the existing ML-related studies focused on temporal prediction of ionospheric states and rarely considered the aspect of the spatial modeling of VTEC. Although some studies predicted global ionosphere maps using machine learning, they used conventional GIMs as inputs, implying that the precision of the ML-based spatial modeling could be limited by traditional methods and quantity of input GNSS observations utilized to generate GIMs.

The goal of this study is the spatial interpolation of VTEC using ML methods for the generation of ML-based GIMs. We first determine VTEC using carrier-to-code levelling through Kalman filter and based on geometry-free multi-GNSS observations from GNSS stations of the IGS network. The derived satellite-specific VTEC time series are then used to train the ML models. Several algorithms, such as extreme gradient boosting and random forest, are applied and their performance is evaluated. Moreover, VTEC from satellite altimetry is used as an additional means to assess the quality of the generated ML models. Finally, we compare the acquired ML-based GIMs with conventional GIMs to investigate the advantage of using the proposed approach for global VTEC modeling.