Utilisation of CryoSat-2 SAR altimeter in operational ice charting
Bibliographic record
Abstract
Abstract. We present methods to utilise CryoSat-2 (CS-2) synthetic aperture radar (SAR) mode data in operational ice charting. We compare CS-2 data qualitatively to SAR mosaics over the Barents and Kara seas. Furthermore, we compare the CS-2 to archived operational ice charts. We present distributions of four CS-2 waveform parameters for different ice types as presented in the ice charts. We go on to present an automatic classification method for CS-2 data which, after training with operational ice charts, is capable of determining open ocean from ice with a hit rate of > 90 %. The training data are dynamically updated every 5 days using the most recent 15 days of CS-2 data and operative ice charts. This helps the adaption of the classifier to the evolving ice/snow conditions throughout winter. The classifier is also capable of detecting three different ice classes (thin and thick first-year ice as well as old ice) with success rates good enough for the output to be usable to support operational ice charting. Finally, we present a near-real-time CS-2 product just plotting the waveform characteristics and conclude that even such a simple product is usable for some of the needs of ice charting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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 teacher head, 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".