Arctic waters and marginal ice zones: 2. An investigation of arctic atmospheric infrared absorption for advanced very high resolution radiometer sea surface temperature estimates
Bibliographic record
Abstract
The derivation of sea surface temperatures (SST) from satellite radiometric data is well established in temperate latitudes. Water vapor is typically the greatest clear sky absorber of infrared (IR) energy between the emitting surface and spaceborne sensor, necessitating a corrective term for SST calculation. Algorithms developed for advanced very high resolution radiometers (AVHRR) use the difference in brightness temperatures between Channel 4 (10.3 to 11.3 μm) and Channel 5 (11.5 to 12.5 μm), or T45, to estimate the amount of IR absorption in the atmosphere. While relatively accurate in temperate latitudes, this approach is not applicable to Arctic waters, typically overestimating the SST by 2 to 3 K as a result of high T45 values that are not indicative of IR absorption by water vapor. The high T45 values in the Arctic may be attributable to atmospheric ice crystals. The attenuation of IR energy increases sharply across Channel 4 and 5 for ice crystals, the amount of which is a function of crystal size, shape and orientation. In the development of the Composite Arctic Sea Surface Temperature Algorithm in the North Water polynya (NOW), it was demonstrated that when T45 exceeded a threshold of 2 K the surface temperature could not be estimated owing to the presence of a clear sky absorptive feature. Observations from the NOW study led to the assessment that areas where T45 > 2K were covered by ice fog. This is a significant finding since these regions must be identified to achieve an accurate mapping of the surface temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".