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NGGM ASSESSMENT STUDY Mission Architecture Review ESTEC, Noordwijk, 2 September 2010

NGGM ASSESSMENT STUDY Mission Architecture Review ESTEC, Noordwijk, 2 September 2010. NGGM Numerical study. Contribution of SGG measurements to future gravimetric satellite missions. Assumptions. 32-days simulation, 1s sampling period (3/1/96-3/2/96 ) SH degrees 2 to 80

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NGGM ASSESSMENT STUDY Mission Architecture Review ESTEC, Noordwijk, 2 September 2010

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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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