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Assessment Study: Contribution of SGG Measurements to Gravimetric Satellite Missions

This study at ESTEC reviews the numerical assessment of the contribution of SGG measurements to future gravimetric satellite missions. Results indicate noise reduction benefits in different scenarios and emphasize the importance of reducing noise amplitude for data accuracy and isotropy improvement. The study underlines the significance of reducing temporal aliasing errors and spatial aliasing caused by coverage gaps. Summary highlights the added value of SGG data in improving estimation periods and identifying spatial and temporal aliasing errors. Additional issues like Polar gaps and SST errors are also discussed.

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Assessment Study: Contribution of SGG Measurements to Gravimetric Satellite Missions

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  1. NGGM ASSESSMENT STUDYMission Architecture ReviewESTEC, Noordwijk, 2 September 2010

  2. NGGM Numerical study Contribution of SGG measurements to future gravimetric satellite missions

  3. Assumptions • 32-days simulation, 1s sampling period (3/1/96-3/2/96) • SH degrees 2 to 80 • Vyy (cross-track) and Vzz (radial) components (Satellite Body Frame) • One gradiometer per satellite (i.e. 2 datasets considered) • “Shaped noise”, (from E2E simulator) • Attitude error negligible • Temporal aliasing assumed to be HIS-0.1AO MT3 models • Frequency-dependent data weighting applied to SGG data inversion

  4. First results: inline-polar scenario

  5. Isotropy of SGG observations Increasing contribution of SGG data

  6. 32-days simulations • Single-pair scenarios: •  inline-polar (in-90) • pendulum (pend-90) • inline-SSO (in-SSO) • cartwheel (CW-90) • Multi-pair scenarios: •  inline-63 + inline-polar (in-63+in-90) • inline-63 + pendulum (in-63+pend-90) •  inline-63 + inline-SSO (in-63+in-SSO) • inline-polar + pendulum (in-90+pend-90) • inline-63 + inline-polar + pendulum (in-63+in-90+pend-90)

  7. Overview of SGG contribution Original noise magnitude

  8. Overview of SGG contribution SGG noise 10 times lower

  9. Single-pair scenarios SGG noise 10 times lower

  10. Multi-pair scenarios SGG noise 10 times lower

  11. Overview of SGG contribution SGG noise 100 times lower

  12. Single-pair scenarios SGG noise 100 times lower

  13. Multi-pair scenarios SGG noise 100 times lower

  14. Single-pair scenarios Isotropy improvement

  15. Multi-pair scenarios Isotropy improvement

  16. 4 and 7-day simulations • Single-pair: •  inline-polar (in-90) • pendulum (pend-90) • Multi-pair: •  inline-63 + inline-polar (in-63+in-90) • inline-63 + pendulum (in-63+pend-90)

  17. 7-day simulations Nominal SGG noise

  18. 4-day simulations

  19. 4-day simulations Nominal SGG noise

  20. 4-day simulations Isotropy improvement

  21. Conclusions • For any scenario and nominal noise amplitude, SGG data is too noisy to make any contribution at all. • For a visible imprevement, SGG noise amplitude (i.e. accelerometer noise) needs to decreased: • 10-fold for single-pair inline scenarios; • 100-fold for dual-pair inline scenarios. • Scenarios (single or multi-pair) considering the Pendulum/Carthweel formation have no gain regarding estimated gravity field error amplitude, only a marginal isotropy improvement at the 100-fold SGG noise down-scaling. • However…

  22. Comments • Short-period estimations important for understanding rapid-changing mass-transport processes, so that de-aliasing models are accurate and temporal aliasing errors minimized. • Minimization of temporal aliasing errors is critical for improvement of the accuracy and resolution of mass transport models at all time scales. • Thanks to the mitigation of spatial aliasing, 4-day estimation periods for single-pair scenarios are only accurate with gradiometric data. • Shorter estimations periods (1-day or less) might only be possible with SGG data, even for multi-pair scenarios (numerical verification needed).

  23. Summary • The added value of SGG data, relative to the original SST scenario, is dependent on: • anisotropy (i.e. inline formations); • temporal aliasing caused by low temporal resolution of the same geographical location (i.e. single-pair scenarios); • spatial aliasing caused coverage gaps (i.e. short estimation periods) • => SGG more significant to inline-polar scenario at 4-days estimation periods (or shorter).

  24. Additional issues: polar gaps

  25. Polar gaps • SST-only • 32-day simulations • Eq. Water H. [m] • -0.5 to 0.5m color scale • Latitude > 65 deg

  26. Polar gaps • SST-only • 32-day simulations • Eq. Water H. [m] • -0.5 to 0.5m color scale • Latitude < -65 deg

  27. Additional issues: SST errors

  28. SST errors: motivation • The PSD of GRACE’s relative acceleration residuals can be split into two regions: • Temporal aliasing (low-frequency, < 30mHz); • KBR noise (high-frequency, > 30mHz). • First item is a hypothesis under research! Replacing GRACE’s KBR with laser ranging has no (significant) influence on the accuracy of the residuals below 10mHz, given current processing methods.

  29. SST errors: mitigation of temporal aliasing 12 monthly solutions (2008) compared to a long time mean (C) T. Mayer-Gürr • Recent improvements in data processing, taking into account GRACE data-based short-period snapshot (i.e. more accurate handling of mass transport signal), show ~ 3-fold increase in the accuracy of the models (above deg 30).

  30. SST errors: conclusions • Temporal aliasing is a dominant source of error in GRACE. • If temporal aliasing is simulated realistically, the estimated gravity field model errors should be comparable to GRACE-based model errors. • Geoid height error @ deg 80: • Inline-polar SST-only simulations: 0.4mm; • Traditional GRACE-based models: 3mm; • Recent GRACE-based models: 1mm. • Noise ratio between SGG and SST data probably ~3-10 times too large, since SGG data is much less sensitive to temporal aliasing

  31. SST errors: updated nominal SGG/SST error ratio SGG noise 10 times lower

  32. 4-day simulations Isotropy improvement

  33. SST errors: updated nominal SGG/SST error ratio SGG noise 10 times lower

  34. 7-day simulations Isotropy improvement

  35. SST errors: updated nominal SGG/SST error ratio SGG noise 10 times lower

  36. 32-day simulations Isotropy improvement

  37. SST errors: practical added value of SGG data Inline-polar + SGG vs. inline-polar + inline 63o (no SGG): Factor of 2 lower accuracy in inline-polar + SGG; Comparable level of anisotropic error pattern.

  38. Additional issues: attitude of the baseline vector as function of latitude

  39. Attitude of the baseline vector as function of latitude: pendulum high error at low degrees Pendulum-polar

  40. Attitude of the baseline vector as function of latitude: pendulum

  41. Auxiliary slides

  42. Attitude of the baseline vector as function of latitude: inline-polar

  43. Attitude of the baseline vector as function of latitude: inline-SSO

  44. Attitude of the baseline vector as function of latitude: bender

  45. Attitude of the baseline vector as function of latitude: cartwheel

  46. Optimal data weighting Inline-polar

  47. Optimal data weighting Inline-SSO

  48. Optimal data weighting Pendulum-polar

  49. Optimal data weighting Cartwheel

  50. Optimal data weighting Inline-polar + inline 63o

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