Improved processing and calibration of the interferometric mode ofthe CryoSat radar altimeter allows height measurements ofsupraglacial lakes in west Greenland
Bibliographic record
Abstract
Abstract. We compare geocoded heights derived from the interferometric mode (SARIn) of CryoSat to surface heights from calibration-validation sites on Devon Ice Cap and West Greenland. Comparisons are included for both the heights derived from the first return (the "point-of-closest-approach" or POCA) as well as heights derived from delayed waveform returns ("swath" processing). While swath processed heights are normally less accurate than edited POCA heights, of order 1–5 m instead of order 1–2 m, the increased coverage possible with swath data complements the POCA data and provides useful information for both system calibration and improving digital elevation models (DEMs). We show that the pre-launch interferometric baseline coupled with an additional roll correction (~ 0.0075°), or equivalent phase correction (~ 0.0435 radians), provides an improved calibration of the interferometric SARIn mode. We extend the potential use of SARIn data by showing the influence of surface conditions, especially melt, on the return waveforms, and that it is possible to detect and measure the height of summer supraglacial lakes in West Greenland. A supraglacial lake can provide a strong radar target in the waveform, stronger than the initial POCA return, if viewed at near normal incidence. This provides an ideal situation for swath processing and we demonstrate height accuracies of ~ 0.5 m for two lake sites, one in the accumulation zone and one in the ablation zone, which were measured every year from 2010 or 2011 to 2016. Each year the lake in the ablation zone was viewed in June by ascending passes and then 5.5 days later by descending passes which allows an approximate estimate of the filling rate. The results suggest that CryoSat waveform data and measurements of supraglacial lake height change could complement the use of optical satellite and be helpful as proxy indicators for surface melt around Greenland.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".