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

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. Elevation. Roughness. Introduction:. ICESat:

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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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