River ice phenology and thickness from satellite altimetry: potential for ice bridge road operation and climate studies
Bibliographic record
Abstract
Abstract. River ice is a key component of the cryosphere. Satellite monitoring of river ice is a rapidly developing area of scientific enquiry, which has wide-ranging implications for climate, environmental and socioeconomic applications. Spaceborne radar altimetry is widely used for monitoring river water regimes; however, its potential for the observation of river ice processes and properties has not been demonstrated yet. Using Ku-band backscatter measurements from the Jason-2 and Jason-3 satellite missions (2008–2019), we demonstrate the potential of radar altimetry for the retrieval of river ice phenology dates and ice thickness for the first time. The altimetric measurements were determined to be sensitive enough to detect the first appearance of ice and the beginning of thermal breakup on the lower Ob River (Western Siberia). The uncertainties in the retrieval of ice event timing were within the 10 d repeat cycle of Jason-2 and Jason-3 in 88 %–90 % of the cases analysed. The uncertainties in the river ice thickness retrievals made via empirical relations between the satellite backscatter measurements and in situ observations, expressed as the root mean square error (RMSE), were of 0.07–0.18 m. A novel application of radar altimetry is the prediction of ice bridge road operations, which is demonstrated herein. We established that the dates of ferry closing and ice road opening and closing in the city of Salekhard can be predicted with an accuracy (expressed as RMSE) of 3–5 d.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".