MODULE 4: RECENT WORK ON THE EQUATORIAL IONOSPHERE

1. MODULE 4: RECENT WORK ON THE EQUATORIAL IONOSPHERE

2. This module covers: Why? Topics RECENT WORK ON THE EQUATORIAL IONOSPHERE Recent algorithm development and research needed to deploy a low-latitude SBAS New algorithms are needed for vertical guidance in a low-latitude SBAS New standards (EGOPS) must be proposed Low-latitude data sets New algorithms for estimating user ionospheric corrections Current understanding of plasma depletions and expected impact

3. Introduction • Ionospheric conditions in mid-latitude CONUS is compatible with accurate range corrections for WAAS. • Could the current WAAS ionospheric correction algorithm be used in Brazil? • In Brazil range delays and spatial gradients are among the highest in the world. • Is the ionosphere any different over Europe compared to middle latitude CONUS? Would major storms have the same impact over Europe and CONUS?

4. Outline • Reminder of currently used estimation algorithms for GIM and WAAS • Data set used included 31 quiet and storm days between January 2000 and September 2002. • Ground-truth generated using JPL’s GIM software • Comparison of WAAS ionospheric model residuals for • CONUS , Europe and Brazilian sectors and • Quiet and storm days to provide measure of performance in reproducing slant TEC for the user • Investigating error sources affecting model performance such as • Ionospheric gradients • Thin-shell mapping function • Conclusions

5. Brazilian Processed GPS Data Sets • jpl_processed_000111.dat.Z • jpl_processed_000212.dat.Z • jpl_processed_000405.dat.Z • jpl_processed_000406.dat.Z • jpl_processed_000407.dat.Z • jpl_processed_000525.dat.Z • jpl_processed_000608.dat.Z • jpl_processed_000702.dat.Z • jpl_processed_000715.dat.Z • jpl_processed_000716.dat.Z • jpl_processed_000811.dat.Z • jpl_processed_000812.dat.Z • jpl_processed_010330.dat.Z • jpl_processed_010331.dat.Z • jpl_processed_010401.dat.Z • jpl_processed_020216.dat.Z • jpl_processed_020217.dat.Z • jpl_processed_020218.dat.Z • jpl_processed_020219.dat.Z • jpl_processed_020220.dat.Z • jpl_processed_020221.dat.Z • jpl_processed_020222.dat.Z • jpl_processed_020223.dat.Z • jpl_processed_020224.dat.Z • jpl_processed_020225.dat.Z • jpl_processed_020226.dat.Z • jpl_processed_020227.dat.Z • jpl_processed_020904.dat.Z • jpl_processed_020905.dat.Z • jpl_processed_020908.dat.Z • jpl_processed_020911.dat.Z • 31 days of JPL processed “truth” data has been made available to the community; • Data set included 18 quiet and storm days between January 2000 and September 2002; the 18 days are the same days the CONUS threat model is based on; • The data set covers a variety of (quiet, minor, major and severe) storm conditions; • Truth data provided in “supertruth” data format; • Brazilian data provided by Dr. Eurico Paula at INPE and RBMC;

6. Current Brazilian Data Processing Strategy • Edit 30 sec phase data using GIM software developed at JPL; • Level each continuous phase arc to the pseudorange; • Run Kalman filter on the data from a global network of 98 stations to estimate satellite and receiver differential biases; • Edit Brazil and IGS data using “loose” editing parameters; • Level Brazil and IGS phase data to pseudorange; • Remove satellite and receiver biases from the data; • Produce processed data in supertruth format. • Only editing we are currently performing is in step 4: • Identify cycle slips using GIPSY program Sanedit using L1-L2 criteria of 6 meters • Remove very short arcs (< 1 minute)

7. Description of Dataset

8. Network of CORS, IGS and RBMC Stations Investigated 230 stations used

9. GIM and WAAS Ionospheric Correction Algorithm For single shell, our GIM is For WAAS the ionospheric model is where Pseudo-IGP approach: IPP treated as if it were collocated with IGP is the slant TEC is the thin shell mapping function is the horizontal basis function (C2, TRIN, etc); are the basis function coefficients are the satellite and receiver instrumental biases are the planar fit parameters distances from IGP to IPP

10. Slant Ionospheric Range Delays Conditions typical for CONUS and Brazil stations for a quiet day (March 30, 2001): CONUS receiver at PRCO, Purcell OK Brazil receiver at UEPP, Sao Paulo

11. Elevation Angle Dependence WAAS planar fit residuals for CONUS and Brazilian stations for a quiet day (March 30, 2001): IPP treated as if it were collocated with IGP (so-called “pseudo IGP” approach) WAAS planar fit algorithm applied Computing residuals between between estimated and measured slant values at IGP

12. Vertical TEC Difference Map Difference map between two subsequent days (for UT interval 19:30 to 19:45) using unbiased slant measurements projected into the vertical Differences between subsequent quiet (March 30, 2001) and storm day ionospheric slant delays (March 31, 2001) for CONUS (PRCO) and Brazilian (UEPP) stations Kp index

13. WAAS Planar Fit Residuals in CONUS for Quiet and Storm Days of March 30-31, 2001 Quiet and storm days:

14. WAAS Planar Fit Residuals in Brazil for Quiet and Storm Days of March 30-31, 2001 Quiet and storm days:

15. Brazilian Planar Fit Residuals for Quiet and Storm days of April 5-6, 2000 Brazilian planar fit residuals for quiet and storm days (April 5-6, 2000).

16. Summary of Results for CONUS, Europe and Brazil • It is demonstrated that 14 out 18 quiet and storm days, European RMS of • planar fit residuals are higher than those for WAAS CONUS residuals; • Brazilian planar fit residuals are on average, a factor of 2 to 4 higher than those for • Europe and CONUS

17. Histogram of Slant Residuals for Storm Day Brazil CONUS Neither distribution appears to be Gaussian probably due to highly varying ionospheric conditions that cannot be described by a simple Gaussian distribution

18. Characterizing WAAS Ionospheric Gradients • To investigate gradients, we looked at pairs of GPS receivers observing the same • satellites at nearly identical elevation and azimuth angles. • Vertical delay differences were computed by projecting the differenced • slant ionospheric range delay into the vertical.

19. Time Series of Measured and Estimated WAAS Gradients for CONUS Quiet Day: • Delay differences as high as • 2.5 meters • Diurnal variation of differences is apparent • Larger differences between dawn and dusk hours Storm Day: • Delay differences as high as • 6 meters. • Storm effect is evident starting at • 16 hour UT • Largest difference between measured • and estimated delay differences • at the 5 meter level

20. Distance Dependence of Measured and Estimated WAAS Gradients for CONUS Quiet day: • Gradients found as high as 2.5 meters over 500 km • (0.5 meter over 100 km) Storm day: Gradients found as high as 6 meters over 500 km (1.2 meters over 100 km)

21. Time Series of Measured and Estimated WAAS Gradients for Brazil Quiet day: • Large differences between • measured and estimated values • during pre-sunrise and • post-sunset hours Storm day: • Storm has no major impact • Overall structure of delay differences very similar to that for the quiet day

22. Distance Dependence of Measured and Estimated WAAS Gradients for Brazil Quiet and storm days: • Gradients as high as • 10 meters over 500 km • (2 meters over 100 km) Clusters of points related to uneven distribution of sites in Brazil

23. Mapping Function Error • We only included measurements where IPPs were • nearly co-located but differing elevation angles. • Mapping function errors were computed by taking the difference • between the two slant ionospheric measurements, • each projected to the vertical using the WAAS thin-shell mapping function.

24. Mapping Function Error Elevation Angle Dependence • Mapping function error: • < 2 meters for CONUS • < 8 meters for Brazil Time Dependence

25. WAAS-Type Ionospheric Models Investigated WAASplanar fit ionospheric model is Pseudo-IGP approach: IPP treated as if it were collocated with IGP where are the planar fit parameters, are the distances from the IGP to the IPP in the eastern and northern directions, respectively. WAAS-type quadratic fit ionospheric model is are the additional planar fit parameters describing quadratic and cross terms.

26. Another Low-Latitude Data Set Used for the Study Behavior of Kp indices during the focus period in July 2000 Network of IGS and RBMC stations processed for July 2 to 18, 2000 time interval

27. Comparison of Residuals for Quiet and Storm Days Slant ionospheric delay for the quiet (July 2, 2000) and storm days (July 16, 2000). Planar and quadratic fit residuals in Brazil for the quiet and storm days: Quiet day Storm day

28. Comparison of Planar and Quadratic Fit Performances • Highlights of the Results: • Quadratic fit approach reduced the • residuals over planar fit method • by an average of 20 percent in Brazil • LNAV/VNAV service will still likely be • significantly reduced in Brazil

29. Station Distribution in Brazil for February 19, 2002

30. Global Distribution of Stations

31. Quiet Day of February 19, 2002 BRAZ BRAZ VICO VICO

32. Storm days of 15 July 2000 and 31 March 2001

33. Summary of Results 1. • Slant delays up to 30 meters in CONUS and up to 60 meters in Brazil • It appears that the inherent spatial variability of the ionosphere is driving the residual errors seen at low-latitude • Storms have limited impact on planar fit residuals in Brazil • In Brazil, increased planar fit residuals by a factor of 4, will result GIVEs above 6, transmitted as 15 meters

34. Summary of Results 2. • We have compared the performance of WAAS CONUS and Brazilian planar fit residuals using 18 days of quiet and storm-time data; the same 18 days the CONUS threat model is based on. • We have used GPS data from IGS,CORS and Brazil stations. • We have found that quiet and storm-time behavior of planar fit residuals is very similar in Brazil. • RMS of planar fit residuals are 2 to 4 times higher than those for CONUS and Europe. • Large residuals in Brazil are due to spatial gradients, large absolute TEC and mapping function error. • GIVEs likely above 6 meters significantly reducing LNAV/VNAV availability in Brazil. • Quadratic fit approach reduced the residuals over planar fit method by an average of 20 percent in Brazil