1. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Satellite Based Augmentation Systems Brazilian Ionosphere Group Training at Stanford University October 27-30, 2003

2. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 MODULE 2: IONOSPHERE ESTIMATION USING GPS Part A: Measurements

3. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 IONOSPHERE ESTIMATION USING GPS, Part A Using GPS signals to measure the ionosphere Understand purpose and operation of SBAS reference stations Understand how ionospheric corrections are formed Forming ionospheric measurements from GPS observables Data quality and editing Calibration of GPS data This module covers: Why?Topics

4. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Introduction Currently the largest error sources in GPS positioning is that of ionospheric refraction causing signal propagation delays  What can be done? If we have a dual-frequency GPS receiver, then the ionospheric effect can be almost totally accounted for What if we have a single-frequency receiver? –We can ignore the effect and live with the consequences  –We can minimize it using various processing techniques –We can model it using empirical ionospheric models such as the GPS single-frequency Broadcast model, IRI2000 model, PIM, etc. –We can measure it using nearby dual-frequency receiver observations (pseudorange only, carrier-phase only, pseudorange/carrier-phase combined) and apply it as a correction to the single-frequency observations. What is the error in positioning accuracy caused by the ionosphere and how can we reduce it?

5. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Illustration for GPS and Ionosphere IRI-95 profile

6. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Broadcast Ionospheric Model

7. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Broadcast Model: Seasonal Variation

8. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Broadcast Model: Solar Cycle Dependence

9. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 International Reference Ionosphere

10. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 IRI Model: Seasonal Dependence

11. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 The Accuracy of Broadcast Model

12. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 GPS Observation Equations GPS pseudorange observation equation: GPS carrier phase observation equation: Range, clock, ambiguity, ionosphere, troposhere, satellite bias, receiver bias, multipath, noise

13. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Generating GPS Ionospheric Observables precise but ambiguous less precise but unambiguous phase-leveled ionospheric observable

14. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 GPS Ionospheric Measurements Code measurement Phase measurement

15. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Leveling the Phase Using Code Measurements The level is computed as: where E is the elevation angle. The uncertainty on the level is computed in a rather rough way using a combination of  th (E) and observed pseudorange scatter : The level is computed by averaging PI-LI using an elevation-dependent weighting. Higher elevation data is weighted more heavily. (The weighting is based on historical Turborogue PI-LI noise/ multipath data giving a historical PI-LI scatter of  th (E) where E is elevation.) The TEC sigma in the JPL Processed Data files are the level uncertainty.

16. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Supertruth Data for Three Threads

17. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 The Impact of Arc Lengths

18. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Major Error Source: The Code Multipath

19. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Global Ionospheric Mapping: GIM is the slant TEC; is the thin shell mapping function for shell 1, etc; is the horizontal basis function (C 2, TRIN, etc); are the basis function coefficients solved for in the filter, indexed by horizontal (i) and vertical (1,2,3 for three shells) indices; are the satellite and receiver instrumental biases. For three shells, our model is where For single shell, our model is

20. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 An Example of the Diurnal Variation of TEC for a Geomagnetically Quiet Day Components in TECU, TECU/hour, TECU/km Example for Single Shell Model Results

21. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 JPL’s GIM Multi-Shell Model

22. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 TOPEX Validation for 12 Oct 2001, Track 10

23. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Recent GIM Validation Using Jason-1

24. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Global Point Plots

25. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Point Plot Differences

26. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 An Example for Repeatibility of Estimated Satellite Biases: Multi-Shell versus Single-Shell Multi-shell significantly improves repeatibility in daily bias estimates –We compare bias averages over 7–10 days –Scatter (std. dev.) over a week improved by factor of 2 to 4 Satellite biases –7-day scatter improved from 2–6 cm to 8–24 mm This may indicate reduction of systematic errors in bias estimation 6 cm 0 cm

27. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 An Example for Repeatibility in Estimated Receiver Biases:Multi-Shell versus Single-Shell 0.6 m 0 m Receiver biases 7-day scatter improved from 8–64 cm to 0.5–19 cm Larger scatter due to stations in low latitude sector Systematic error? Examine long time-series of biases Look for shifts in ionospheric delay level for all biases simultaneously

28. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Comparison of Single and Multi-Shell Results for ENG1 Postfit Residuals Prediction Residuals ENG1 = English Turn, LA Improvement at low elevation angles

29. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 Comparison of Single and Multi-Shell Results for MBWW Postfit Residuals Prediction Residuals MBWW = Medicine Bow, WY Improvement at low elevation angles

30. AXK/JPL SBAS Training at Stanford University, October 27-30, 2003 What You Have Learned 1.Ionosphere is the largest error source in GPS positioning 2.Empirical models can be used to mitigate effects 3.Dual-frequency GPS data can be used to solve for the ionospheric effect 4.Error sources affecting GPS-based ionospheric estimation: arc length,leveling, biases, multipath, noise, etc. 5.Global Ionospheric Mapping techniques: single vs multi- shell approaches: ionospheric delay and biases estimation 6.Validation of maps, point plots, movies, etc.