A thermodynamic model for estimating sea and lake ice thickness with optical satellite data
Bibliographic record
Abstract
Sea ice is a very important indicator and an effective modulator of regional and global climate change. Current remote sensing techniques provide an unprecedented opportunity to monitor the cryosphere routinely with relatively high spatial and temporal resolutions. In this paper, we introduce a thermodynamic model to estimate sea and lake ice thickness with optical (visible, near‐infrared, and infrared) satellite data. Comparisons of nighttime ice thickness retrievals to ice thickness measurements from upward looking submarine sonar show that this thermodynamic model is capable of retrieving ice thickness up to 2.8 m. The mean absolute error is 0.18 m for samples with a mean ice thickness of 1.62 m, i.e., an 11% mean absolute error. Comparisons with in situ Canadian stations and moored upward looking sonar measurements show similar results. Sensitivity studies indicate that the largest errors come from uncertainties in surface albedo and downward solar radiation flux estimates from satellite data, followed by uncertainties in snow depth and cloud fractional coverage. Due to the relatively large uncertainties in current satellite retrievals of surface albedo and surface downward shortwave radiation flux, the current model is not recommended for use with daytime data. For nighttime data, the model is capable of resolving regional and seasonal variations in ice thickness and is useful for climatological analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".