Clouds at Arctic Atmospheric Observatories. Part I: Occurrence and Macrophysical Properties
Bibliographic record
Abstract
Abstract Cloud observations over the past decade from six Arctic atmospheric observatories are investigated to derive estimates of cloud occurrence fraction, vertical distribution, persistence in time, diurnal cycle, and boundary statistics. Each observatory has some combination of cloud lidar, radar, ceilometer, and/or interferometer for identifying and characterizing clouds. By optimally combining measurements from these instruments, it is found that annual cloud occurrence fractions are 58%–83% at the Arctic observatories. There is a clear annual cycle wherein clouds are least frequent in the winter and most frequent in the late summer and autumn. Only in Eureka, Nunavut, Canada, is the annual cycle shifted such that the annual minimum is in the spring with the maximum in the winter. Intersite monthly variability is typically within 10%–15% of the all-site average. Interannual variability at specific sites is less than 13% for any given month and, typically, is less than 3% for annual total cloud fractions. Low-level clouds are most persistent at the observatories. The median cloud persistence for all observatories is 3–5 h; however, 5% of cloud systems at far western Arctic sites are observed to occur for longer than 100 consecutive hours. Weak diurnal variability in cloudiness is observed at some sites, with a daily minimum in cloud occurrence near solar noon for those seasons for which the sun is above the horizon for at least part of the day.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".