Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data
Bibliographic record
Abstract
The retrieval of Arctic sea surface temperatures (SSTs) using satellite radiometric imagery has not been well documented owing to the paucity of match‐ups with in situ data. SST algorithms developed in temperate regions lead to positive biases in high latitudes due to an overestimation of atmospheric IR absorption. The composite arctic sea surface temperature algorithm (CASSTA) presented in this paper was developed from concurrent satellite and shipborne radiometric data collected in the North Water Polynya between April and July 1998. This algorithm considers three temperature regimes: seawater above freezing, the transition zones of water and ice, and primarily ice. These regimes, which are determined by advanced very high resolution radiometer (AVHRR) calibrated brightness temperatures, require different calculations for temperature estimates. For seawater above freezing, a specific Arctic SST algorithm was produced through a linear regression of AVHRR against in situ data. Areas consisting mainly of ice use an established ice surface temperature (IST) algorithm. The transition zone uses a combination of the Arctic SST and IST algorithms. CASSTA determines the Channel 4 brightness temperature for each pixel in a calibrated AVHRR image and then applies the appropriate algorithm to create a thermal image. The mean deviation of CASSTA compared to in situ data was 0.17 K with a standard deviation of 0.21 K. This represents a significant improvement over SST values using McClain coefficients for temperate waters, which overestimate the same data set by an average of 2.40 K. Application of CASSTA to the North Water imagery gives superior results compared to existing SST or IST algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".