Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation
"> Figure 1
<p>Schematic diagram of vertical total electron content (vTEC) modelling by thin plate splines (TPS).</p> "> Figure 2
<p>Variations of ΣKp and Dst indices during March 2015. The vertical red lines highlight the seven test days (14–20 March 2015).</p> "> Figure 3
<p>Scheme of the L4 data fitting into Global Ionosphere Maps’ (GIM) slant TEC (sTEC) (sTEC calibration).</p> "> Figure 4
<p>Locations of EUREF Permanent network (EPN) station used in the statistical analysis.</p> "> Figure 5
<p>Example TEC maps derived from the UWM-rt1 model on a quiet day before the storm (<b>A</b>), the stormy day (<b>B</b>) and one day after the storm (<b>C</b>) all at 11:00 GPST. Dark lines represent tracks of the GPS PRN30 satellite observed by POTS station.</p> "> Figure 6
<p>sTEC for selected PRN arcs from calibrated geometry-free L4 (L4_sTEC—red) and selected GIMs (GIM_sTEC—blue) on the quiet day before the storm (<b>A</b>), the stormy day (<b>B</b>) and one day after the storm (<b>C</b>).</p> "> Figure 7
<p>Daily RMS distribution for all analyzed TEC maps (TECU).</p> "> Figure 8
<p>Average RMS based on all days and satellite arcs for selected test stations.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Carrier Phase Bias Estimation
2.2. TEC Calculation Procedure
2.3. TEC Modelling by TPS
3. Numerical Results and Discussion
3.1. Self-Consistency Analysis
- Firstly, a geometry-free linear combination (L4) of carrier phase observations is formed for each continuous data arc (red line, Figure 3 left panel);
- next, sTEC for the same satellite arc extracted from a tested model (GIM_sTEC) is calculated (blue line, Figure 3 left panel);
- then, carrier phase bias is estimated by fitting carrier phase data (L4) into GIM_sTEC, resulting in calibrated sTEC (L4_sTEC) (red line, Figure 3 right panel).
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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DOY | UWM-rt1 | IGS | UQRG | JPL | CODG | ESA |
---|---|---|---|---|---|---|
73 | 0.44 | 0.81 | 0.94 | 0.91 | 0.61 | 0.95 |
74 | 0.36 | 0.85 | 0.95 | 0.94 | 0.63 | 1.00 |
75 | 0.38 | 0.83 | 1.00 | 0.95 | 0.62 | 0.84 |
76 | 0.61 | 1.67 | 1.32 | 1.81 | 1.14 | 2.36 |
77 | 0.22 | 0.71 | 0.54 | 0.78 | 0.52 | 0.82 |
78 | 0.24 | 0.84 | 0.61 | 0.91 | 0.56 | 1.20 |
79 | 0.15 | 0.76 | 0.55 | 0.86 | 0.42 | 0.79 |
UWM-rt1 | IGS | UQRG | JPL | CODG | ESA |
---|---|---|---|---|---|
0.34 | 0.92 | 0.84 | 1.02 | 0.64 | 1.14 |
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Krypiak-Gregorczyk, A.; Wielgosz, P.; Borkowski, A. Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation. Remote Sens. 2017, 9, 1221. https://doi.org/10.3390/rs9121221
Krypiak-Gregorczyk A, Wielgosz P, Borkowski A. Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation. Remote Sensing. 2017; 9(12):1221. https://doi.org/10.3390/rs9121221
Chicago/Turabian StyleKrypiak-Gregorczyk, Anna, Pawel Wielgosz, and Andrzej Borkowski. 2017. "Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation" Remote Sensing 9, no. 12: 1221. https://doi.org/10.3390/rs9121221