Satellite altimeter remote sensing of ice caps
Bibliographic record
Abstract
This thesis investigates the use of satellite altimetry techniques for measuring surface elevation changes of ice caps. Two satellite altimeters, Radar Altimeter 2 (RA-2) and Geoscience Laser Altimeter System (GLAS) are used to assess the surface elevation changes of three Arctic ice caps. This is the first time the RA-2 has been used to assess the elevation changes of ice caps - targets much smaller than the ice sheets which are the instrument’s primary land ice targets. Algorithms for the retrieval of elevation change rates over ice caps using data acquired by RA-2 and GLAS are presented. These algorithms form a part of a European Space Agency (ESA) glacier monitoring system GlobGlacier. A comparison of GLAS elevation data to those acquired by the RA-2 shows agreement between the two instruments. Surface elevation change rate estimates based on RA-2 are given for three ice caps: Devon Ice Cap in Arctic Canada (−0.09 ± 0.29 m/a), Flade Isblink in Greenland (0.03 ± 0.03 m/a) and Austfonna on Svalbard (0.33 ± 0.08 m/a). Based on RA-2 and GLAS measurements it is shown that the areas of Flade Isblink below the late summer snow line have been thinning whereas the areas above the late summer snow line have been thickening. Also GLAS observed dynamic thickening rates of more than 3 m/a are presented. On Flade Isblink and Austfonna RA-2 measurements are compared to surface mass balance (SMB) estimates from a regional atmospheric climate model RACMO2. The comparison shows that SMB is the driver of interannual surface elevation changes at Austfonna. In contrast the comparison reveals areas on Flade Isblink where ice dynamics have an important effect on the surface elevation. Furthermore, RACMO2 estimates of surface mass budget at Austfonna before the satellite altimeter era are presented. This thesis shows that both traditional radar and laser satellite altimetry can be used to quantify the response of ice caps to the changing climate. Direct altimeter measurements of surface elevation and, in consequence volume change of ice caps, can be used to improve their mass budget estimates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".