Multi-source data ingestion for IRI-2020 model: a combination of ground-based and space-borne observations

The International Reference Ionosphere (IRI) model is a widely used empirical model to describe ionospheric climatology. However, IRI represents the monthly averages of the ionospheric parameters, which makes it difficult to capture the local and short-term ionospheric variations. To overcome this limitation, we propose a data ingestion method using a combination of ground-based and space-borne observations. The ionospheric parameters from ground-based Global Navigation Satellite System (GNSS), ionosondes, space-borne GNSS radio occultation and satellite altimetry observations are ingested into the IRI-2020 model to improve its accuracy. The outputs of the ingested IRI (IRI_inge) are assessed by case study and statistical analysis, with reference to independent ionosonde observations and global ionospheric maps. The case study shows that IRI_inge expresses the diurnal and local variations of the ionosphere better than the standard IRI (IRI_stan) in both high and low solar activity periods. The relative error of ionospheric electron density profiles from IRI_inge is generally less than 10%, and the vertical total electron content from IRI_inge has an accuracy improvement of 39.0% compared to that from IRI_stan. The statistical analysis shows that IRI_inge performs more stable than IRI_stan, and its output generally has smaller REs and root-mean-square errors, especially in daytime and storm time. The proposed method significantly improves IRI-2020 on the accuracy of the output parameters and the ability to present the short-term variations of the ionosphere.