Evaluation of Arctic broadband surface radiation measurements
Bibliographic record
Abstract
Abstract. The Arctic is a challenging environment for making in-situ surface radiation measurements. A standard suite of radiation sensors is typically designed to measure incoming and outgoing shortwave (SW) and thermal infrared, or longwave (LW), radiation. Enhancements may include various sensors for measuring irradiance in narrower bandwidths. Many solar radiation/thermal infrared flux sensors utilize protective glass domes and some are mounted on complex mechanical platforms (solar trackers) that keep sensors and shading devices trained on the sun along its diurnal path. High quality measurements require striking a balance between locating stations in a pristine undisturbed setting free of artificial blockage (such as from buildings and towers) and providing accessibility to allow operators to clean and maintain the instruments. Three significant sources of erroneous data in the Arctic include solar tracker malfunctions, rime/frost/snow deposition on the protective glass domes of the radiometers and operational problems due to limited operator access in extreme weather conditions. In this study, comparisons are made between the global and component sum (direct [vertical component] + diffuse) SW measurements. The difference between these two quantities (that theoretically should be zero) is used to illustrate the magnitude and seasonality of arctic radiation flux measurement problems. The problem of rime/frost/snow deposition is investigated in more detail for one case study utilizing both SW and LW measurements. Solutions to these operational problems that utilize measurement redundancy, more sophisticated heating and ventilation strategies and a more systematic program of operational support and subsequent data quality protocols are proposed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".