The Two-Parts Step-by-Step Ionospheric Assimilation Based on Ground-Based/Spaceborne Observations and Its Verification

Abstract

This study introduced a Kalman filtering assimilation model that considers the DCB errors of GPS/LEO satellites and GNSS stations. The assimilation results and reliability were verified by various types of data, such as ionMap, ionosonde, ISR, and the EDP of ionPrf from COSMIC. The following analyses were carried out. Assimilating the measured ground-based/spaceborne ionospheric observation data from DOY 010, 2008 and DOY 089, 2012 revealed that the introduction of GPS/LEO satellite and GPS station DCB errors can effectively suppress the STEC observation errors caused by the single-layer hypothesis. Furthermore, the top of the ionosphere contributes 2.8 TECU (approximately 10–20% of the STEC) of electrons during the ionospheric quiet period, greatly influencing the ionospheric assimilation at altitudes of 100–800 km. The assimilation results also show that, after subtracting the influence of the top of the ionosphere, the ionospheric deviation during the quiet period improved from 1.645 TECU to 1.464 TECU; when the ionosphere was active, the standard deviation was improved from 4.408 TECU to 3.536 TECU. The IRI-Imp model introduced by Wu et al. and the IRI (2007) model were used as background fields to compare the effects of COSMIC occultation observation data on the ionospheric assimilation process. Upon comparison, the occultation data introduced by the improved model showed the greatest improvement in the vertical structure of the ionosphere; additionally, the assimilation process reused the horizontal structure information of the occultation data, and the assimilation result (IRI-Imp-Assi) was the most ideal. Due to the lack of an occultation data correction, the IRI2007 model was relatively more prone to errors. With the strategy of the IRI-Imp-Assi model, the introduction of occultation data caused a more significant reduction in the error between the assimilation model with the IRI model as the background field and the ionMap.