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Cited by 78GNSS positioning and interferenceIonosphere and magnetosphere dynamicsStatistical and numerical algorithms

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Assessing the Performance of GPS Precise Point Positioning Under Different Geomagnetic Storm Conditions during Solar Cycle 24

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Luo, Xiaomin, Gu, Shengfeng, Lou, Yidong, Xiong, Chao, Chen, Biyan, Jin, Xueyuan·2018·Sensors
DOI 10.3390/s18061784PMC PMC6022218
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Abstract

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.

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Figure 1 The variations of solar flux index F10.7, sunspot number and the geomagnetic index Dst in the solar cycle 24. The red lines represent the corresponding smoothed data and the red stars represent the geomagnetic storms.
Figure 1 The variations of solar flux index F10.7, sunspot number and the geomagnetic index Dst in the solar cycle 24. The red lines represent the corresponding smoothed data and the red stars represent the geomagnetic storms.CC BY · PubMed Central
Figure 2 Geographical distribution of the 492 IGS stations.
Figure 2 Geographical distribution of the 492 IGS stations.CC BY · PubMed Central
Figure 3 The variations of solar wind speed Vsw, IMF BZ, Kp index and geomagnetic index Dst during the quiet, moderate, intense, and super storm periods.
Figure 3 The variations of solar wind speed Vsw, IMF BZ, Kp index and geomagnetic index Dst during the quiet, moderate, intense, and super storm periods.CC BY · PubMed Central
Figure 4 The temporal TEC variations from 90° S to 90° N MLAT on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.
Figure 4 The temporal TEC variations from 90° S to 90° N MLAT on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.CC BY · PubMed Central
Figure 5 Time series of positioning errors of DF and SF PPP, ROTI, and Dst index for the KIRU station during the quiet, moderate, intense, and super storms period.
Figure 5 Time series of positioning errors of DF and SF PPP, ROTI, and Dst index for the KIRU station during the quiet, moderate, intense, and super storms period.CC BY · PubMed Central
Figure 6 RMS statistics of DF PPP for worldwide GPS stations during the quiet, moderate, intense, and super storm period.
Figure 6 RMS statistics of DF PPP for worldwide GPS stations during the quiet, moderate, intense, and super storm period.CC BY · PubMed Central
Figure 7 The 90% confidence level of time series of DF PPP results for all stations located at high-, mid-, and low-latitude regions on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively. The background gray scatters are the absolute values of positioning results for each IGS station.
Figure 7 The 90% confidence level of time series of DF PPP results for all stations located at high-, mid-, and low-latitude regions on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively. The background gray scatters are the absolute values of positioning results for each IGS station.CC BY · PubMed Central
Figure 8 RMS statistics of SF PPP for worldwide GPS stations during the quiet, moderate, intense, and super storm period.
Figure 8 RMS statistics of SF PPP for worldwide GPS stations during the quiet, moderate, intense, and super storm period.CC BY · PubMed Central
Figure 9 The 90% confidence level of time series of SF PPP results for all stations located at high-, mid-, and low-latitude regions on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively. The background gray scatters are the absolute values of positioning results for each IGS station.
Figure 9 The 90% confidence level of time series of SF PPP results for all stations located at high-, mid-, and low-latitude regions on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively. The background gray scatters are the absolute values of positioning results for each IGS station.CC BY · PubMed Central
Figure 10 Global ROTI maps of the three perturbation times (i.e., the initial phase, storm development, and deep main phase) on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.
Figure 10 Global ROTI maps of the three perturbation times (i.e., the initial phase, storm development, and deep main phase) on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.CC BY · PubMed Central
Figure 11 Distribution of cycle-slip occurrence rate against geomagnetic latitude for all IGS stations statistics on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.
Figure 11 Distribution of cycle-slip occurrence rate against geomagnetic latitude for all IGS stations statistics on: (a) 17 March 2017 as the quiet day reference; (b) 27 March 2017 as the moderate storm; (c) 20 December 2015 as the intense storm; and (d) 17 March 2015 as the super storm, respectively.CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Luo, X., Gu, S., Lou, Y., Xiong, C., Chen, B., & Jin, X.
Affiliations
  • Wuhan University (CN)
  • GFZ Helmholtz Centre for Geosciences (DE)
  • Central South University (CN)
Corresponding author
Gu, Shengfeng
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2018-06-01
Volume
18
Issue
6
Pages
1784
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s18061784
PMC
PMC6022218
OpenAlex
W2805795343
Cited by
78
References
1
Citations by year
2026: 8 · 2025: 17 · 2024: 12 · 2023: 7 · 2022: 13 · 2021: 10 · 2020: 6 · 2019: 5
FWCI
4.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Publication Database GFZ (GFZ German Research Centre for Geosciences) (submittedVersion)
  • Publication Database GFZ (GFZ German Research Centre for Geosciences) (submittedVersion, other-oa)
  • Publication Database GFZ (GFZ German Research Centre for Geosciences) (submittedVersion, cc-by)
Funders
National Aeronautics and Space Administration
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › GNSS positioning and interference
  • Physical Sciences › Earth and Planetary Sciences › Space and Planetary Science › Ionosphere and magnetosphere dynamics
  • Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
Keywords
precise point positioning, geomagnetic storms, solar cycle 24, ionospheric disturbances, total electron content, cycle slips, single-frequency PPP, positioning accuracy, space weather

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Luo, X., Gu, S., Lou, Y., Xiong, C., Chen, B., & Jin, X. (2018). Assessing the Performance of GPS Precise Point Positioning Under Different Geomagnetic Storm Conditions during Solar Cycle 24. Sensors. https://doi.org/10.3390/s18061784