The geomagnetic storm, which is an abnormal space weather phenomenon, can sometimes severely affect GPS signal propagation, thereby impacting the performance of GPS precise point positioning (PPP). However, the investigation of GPS PPP accuracy over the global scale under different geomagnetic storm conditions is very limited. This paper for the first time presents the performance of GPS dual-frequency (DF) and single-frequency (SF) PPP under moderate, intense, and super storms conditions during solar cycle 24 using a large data set collected from about 500 international GNSS services (IGS) stations. The global root mean square (RMS) maps of GPS PPP results show that stations with degraded performance are mainly distributed at high-latitude, and the degradation level generally depends on the storm intensity. The three-dimensional (3D) RMS of GPS DF PPP for high-latitude during moderate, intense, and super storms are 0.393 m, 0.680 m and 1.051 m, respectively, with respect to only 0.163 m on quiet day. RMS errors of mid- and low-latitudes show less dependence on the storm intensities, with values less than 0.320 m, compared to 0.153 m on quiet day. Compared with DF PPP, the performance of GPS SF PPP is inferior regardless of quiet or disturbed conditions. The degraded performance of GPS positioning during geomagnetic storms is attributed to the increased ionospheric disturbances, which have been confirmed by our global rate of TEC index (ROTI) maps. Ionospheric disturbances not only lead to the deteriorated ionospheric correction but also to the frequent cycle-slip occurrence. Statistical results show that, compared with that on quiet day, the increased cycle-slip occurrence are 13.04%, 56.52%, and 69.57% under moderate, intense, and super storms conditions, respectively.

GNSS ionospheric tomography technique is capable to reconstruct the high-quality 3D ionospheric electron density (IED) images with a relatively low cost. We present a new parameterized approach for refining the voxel-based ionospheric tomography modeling. This approach is different to most other voxel-based techniques as they assume a homogeneous IED distribution in each voxel that is unreasonable for the tomography modeling. In this method, IED of any point within a voxel is determined via vertically exponential interpolation and horizontally inverse distance weighted interpolation from the IED values at the eight corners of that voxel. The parameterized tomography is tested with real data collected over the period of June 1–30, 2015 from 45 GPS stations in south China. The superiority of the new parameterized method is verified by comparison with the traditional nonparametric method. The new parameterized method outperforms the traditional method by 12%, 10%, 5% and 2% for vertical resolutions of 25 km, 50 km, 75 km and 100 km, respectively, in the self-consistency validation by GPS data. Such improvements are 20%, 24%, 22% and 16%, respectively, when assessed by the Swarm in situ IEDs. In terms of the vertical layer discretization, configurations using the resolution of 25 km generally performs better than the other three vertical resolutions. Overall, the parameterized method using a vertical resolution of 25 km achieves the best performance from the comprehensive comparisons with ionospheric data derived by GPS, ionosonde and Swarm satellites.

Geomagnetic storm can affect the performance of Global Navigation Satellite System (GNSS) precise positioning services. To mitigate the adverse effects of strong geomagnetic storms, we propose to establish the geometry‐free (GF) cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning (PPP) during strong storm periods, thus improving the accuracy and reliability of GNSS PPP. The performance of our proposed model is validated by using 171 International GNSS Service (IGS) tracking stations data on 8 September 2017. The analysis indicates that compared with conventional PPP scheme, the proposed model can improve the positioning accuracy by approximately 14.0% (36.8%) and 23.1% (51.5%) in the horizonal and vertical components for global (high latitudes) stations. Furthermore, the availability of our proposed model is also validated by PPP experiments using 379 IGS tracking stations data during another strong storm occurred on 26 August 2018.

For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio () based SIGMA‐ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ model.