Ionospheric data fusion with GNSS, GNSS-RO and satellite altimetry based on machine learning

Global ionospheric maps (GIMs) are widely used ionospheric products, especially in Global Navigation Satellite System (GNSS) applications. They allow for instance to correct for the ionospheric delays in single-frequency applications. The input data for generating GIMs is typically obtained from dual-frequency observations at globally distributed GNSS stations. However, the distribution of these stations is in-homogeneous and predominantly concentrated in continental regions, resulting in large data gaps over the oceanic regions. The absence of data reduces the accuracy of GIMs in these regions. There exist other space techniques, such as satellite altimetry and GNSS radio occultation (GNSS-RO) that can retrieve the state of the ionosphere over ocean areas and have the potential to address such data gaps. However, the vertical total electron content (VTEC) observations from GNSS, satellite altimetry, and GNSS-RO differ due to variations in orbital altitudes and instrumental biases. Another challenge is the sparsity of observations from satellite altimetry and GNSS-RO, particularly when only considering data from a single day.

In this study, we developed a framework that integrates GNSS, Jason-3 satellite altimetry, and COSMIC-2 GNSS-RO observations in a GIM, based on a neural network (NN). First, we calibrated the satellite altimetry and GNSS-RO VTEC to be more consistent with GNSS VTEC. To address data sparsity, we used VTEC observations from satellite altimetry and GNSS-RO for the entire year 2023 to build a background ionospheric model with XGBoost. This background model captures the general climatological characteristics of the ionosphere over the oceans. We then utilized the background model to generate VTEC samples for training the NN-based GIM in regions lacking GNSS station observations. For our three test regions (Hawaii, Southern Atlantic, Antarctic), we find relative improvements in MAE of 37%, 11%, and 37% over the year 2023 compared to GNSS-only GIMs

The results demonstrate that the proposed data fusion method can effectively improve the modeling accuracy in regions with missing data.