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Attila Komjathy, Lawrence Sparks and Anthony J. Mannucci

Attila Komjathy, Lawrence Sparks and Anthony J. Mannucci. Mid-latitude and Equatorial Supertruth Data. Jet Propulsion Laboratory California Institute of Technology M/S 238-600 4800 Oak Grove Drive Pasadena CA 91109 Email: Attila.Komjathy@jpl.nasa.gov. Overview.

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Attila Komjathy, Lawrence Sparks and Anthony J. Mannucci

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  1. Attila Komjathy, Lawrence Sparks and Anthony J. Mannucci Mid-latitude and Equatorial Supertruth Data Jet Propulsion Laboratory California Institute of Technology M/S 238-600 4800 Oak Grove Drive Pasadena CA 91109 Email: Attila.Komjathy@jpl.nasa.gov

  2. Overview • Overview of supertruth data processing strategy • Mid-latitude (CONUS) and low-latitude (Brazil) data processing • Examples of using supertruth to investigate • planar fit residuals for quite and storm days • gradients for CONUS and equatorial region • mapping function error for middle and low latitude regions. • Error sources affecting CONUS and equatorial ionospheric mapping • Summary

  3. Overview of Current Supertruth Data Processing Strategy • Edit 1 sec phase data using GIM software developed at JPL; The data editing we are currently performing is: • Identify cycle slips using GIPSY program SanEdit: L1-L2 criteria of 2 meters • Remove very short arcs (< 5 minute); • Smoothing pseudoranges with a 5-sec smoothing window • Level each WRE continuous phase arc to the pseudorange; use 5 degree elevation cutoff; • We use 36 hour data for leveling; • Elevation weighting, using model of P-code scatter that varies with elevation angle; leveling constant calculated after weighting the data. • Checks for scatter of the data: if the actual P-code scatter computed from the data deviates from the levelling model by a significant amount, then portions of the data arc will be discarded. • Run Kalman filter on the data on a network of 75 stations to estimate satellite and receiver differential biases using a single-shell GIM model; use quiet days for bias estimation;

  4. Supertruth Data Processing Strategy, cont’d • Remove satellite and receiver biases from the data; • Produce truth files for WRE1, WRE2, WRE3; • Apply the truth selection algorithm (voting algorithm) to produce supertruth data. • We use three receiver threads at each reference station to clean data • 36 hours data processing helps leveling process • Smoothing the 1-sec data to 5 sec interval to reduce multipath • Leveling scatter-threshold fine-tuned to keep as much data as possible during the leveling process • Latest GIPSY 4.0 and GIM 4.0 modules implemented and tested throughout the processing

  5. The Multi-Shell Model: Observation Equation Single shell model Multi-shell model where is the slant TEC; is the thin shell mapping function for shell 1, etc; is the horizontal basis function (C2, 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.

  6. Kalman Filter Estimation Prediction states: Gauss-Markov stochastic process: Exponential correlation between states: State vector updates: System noise covariance elements:

  7. Ionospheric Mapping Functions Investigated Clynch mapping function: Broadcast model mapping function: Standard geometric mapping function: “h” shell height could be fixed or varying “E” elevation of the satellite

  8. The Three-Shell Model

  9. Global Distribution of Stations

  10. 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

  11. Repeatibility in Estimated Receiver Biases: Multi-Shell versus Single-Shell 0.6 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 0 m

  12. Leveling the Phase Using Code Measurements 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 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 TEC sigma in the JPL Processed Data files are the level uncertainty.

  13. GPS Ionospheric Measurements Code measurement Phase measurement

  14. Differences between slant TEC measurements using three thresholds for GPS43 s18-s800 s18-s80 s80-s800 Largest slip occurred at the end of arc: 0.5 TECU level

  15. WAAS WRS Locations

  16. Slant Delay and WAAS Planar Fit Residuals Oct 28 delays up to 35 meters Residuals < 2 m RMS of 0.4 meter Oct 29 Oct 30 delays up to 100 meters Residuals < 25 m RMS of 3.5 meters Oct 31

  17. New and Old Supertruth Comparison for WRS ZMA

  18. New and Old Supertruth Comparison for WRS ZJX

  19. New and Old Supertruth Comparison for WRS ZJX

  20. Number of Measurements vs Time

  21. 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;

  22. Description of Dataset

  23. Station Distribution in Brazil for February 19, 2002

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

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

  26. Slant Ionospheric Range Delays Conditions typical for CONUS and Brazil stations for a quiet day: CONUS receiver at PRCO, Purcell OK Brazil receiver at UEPP, Sao Paulo

  27. Elevation Angle Dependence WAAS planar fit residuals for CONUS and Brazilian stations for a quiet day: 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

  28. 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) and storm day ionospheric slant delays (March 31) for CONUS (PRCO) and Brazilian (UEPP) stations Kp index

  29. WAAS Planar Fit Residuals in CONUS Quiet and storm days:

  30. WAAS Planar Fit Residuals in Brazil Quiet and storm days:

  31. 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

  32. 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.

  33. 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

  34. 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)

  35. 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

  36. 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

  37. 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.

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

  39. Summary and Lessons Learned • We have described the algorithm we use to generate high-precision calibrated TEC measurements (“supertruth”) • We showed examples of data processing results for mid-latitude CONUS and equatorial (Brazil) regions • We demonstrated the use of this data for • computing planar fit residuals using CONUS and equatorial data sets • estimating ionospheric gradients effecting the ionospheric parameter estimation • Assessing ionospheric mapping function errors • We discussed the usefulness of using supertruth data sets for ionospheric algorithm development and validation

  40. Conclusions • 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 • Storm had a small 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

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