1. An T Nguyen (MIT) Thomas A Herring (MIT)
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. Introduction: ICESat: Elevation Roughness
first laser satellite to study ice sheets
high accuracy (< 20cm)
high spatial resolution (~172m spacing)
Elevation
Roughness

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

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

5. ICESat Data: 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

6. ICESat Data: (cont)
asc
des

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

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

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

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

11. Summary 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

12. BLANK