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Analysis of ICESat Data in East Antarctica using Kriging and Kalman Filter

Detect height change and surface characteristics in East Antarctica using ICESat data with Kriging and Kalman filter analysis. Research conducted at MIT focusing on elevation roughness and seasonal cycles. Preliminary results show promising findings in surface feature identification and noise analysis.

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Analysis of ICESat Data in East Antarctica using Kriging and Kalman Filter

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  1. Analysis of ICESat Data in East Antarctica using Kriging and Kalman Filter Motivation: detect height change + surface characteristics An T Nguyen (MIT) Thomas A Herring (MIT) Special thanks to Dr. Zwally and H. Cornejo

  2. Elevation Roughness Introduction: ICESat: first laser satellite to study ice sheets high accuracy (< 20cm) high spatial resolution (~172m spacing)

  3. Objectives: [100km]2 block:  dh/dt ?  seasonal cycle amplitude & phase ?  surface characteristics ? Region of study:

  4. The basic set up: • a-priori 5-km resolution DEM 5-km DEM adjustment • Estimate: • ICESat derived height z(ti) x = [ho, dh/dt , B1, B2 ,hi ] • Kalman filter: • predict at time t2 using Kriging and t2 • use to improve t1 Kriging / Kalman filter:

  5. GLA06: Laser 2a release 24 Laser 3a release 23 Data editing: 1) Saturation correction 2) Within [0.29o, 0.34o] pointing 3) Gain [13,100] 4) Single profile editing ICESat Data:

  6. asc des ICESat Data: (cont)

  7. –5.0 to –9.0 cm/yr • Laser 2a R24 yields more negative dh/dt than R21 Preliminary results:a) dh/dt • Pointing errors: •  gives s ~ 7cm/yr •  dh/dt results inconclusive at this time.

  8. Kriging/Kalman filter Cross-overs Validation of the technique:

  9. ICESat derived heights + DEM residuals Results (cont’d):b) surface features

  10. s32 : instrument noise, surface roughness Model Model [b1, b2]: length scales of features smaller than 5km [s12 ,s22] [b1, b2] wo s32 wo: 5-km DEM related Surface characteristics (cont’d)

  11. Kriging/Kalman filter results: • –5cm/yr to –9cm/yr in East Antarctica • consistent with cross-over analysis • 5-km DEM: removes long wavelength features (> 5km) • Residual analysis: structures at shorter wavelengths • time-correlated noise process [s12 , s22 , s32, b1, b2, wo] • correlation lengths & roughness from b’s • instrument noise level & roughness, s32 • pointing errors still dominate, ~7cm/yr • in progress to model pointing biases Summary

  12. BLANK

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