Thermodynamic and microphysical properties of summertime marine fog observed from Sable Island
Bibliographic record
Abstract
Abstract This study focuses on the physical processes in marine fog and its impact on optical attenuation using measurements from Sable Island during the 2022 FATIMA Grand Banks field campaign. The analyses used the water‐droplet size distribution from a fog monitor (FM‐120) and the meteorological optical range (MOR) measured by a present weather sensor (PWD22) as primary data sources, augmented by frequent radiosonde launches collocated with other FATIMA instruments. Analyses of the frequent radiosonde launches revealed the presence of fog associated with frequent frontal passages throughout the intensive measurement period. Based on the 35 days of microphysics measurements, we also identified and analyzed nine reduced visibility events (RVEs) when the mean MOR was less than 1 km. Furthermore, the data were categorized based on the weather code to characterize the observed hydrometeor further into clear, mist, fog, and precipitation categories. This study shows evidence of persistent stable thermal stratification in the fog layer, often accompanied with low‐level jets in or above the fog layer. The droplet spectra in fog indicated a bimodal distribution below the droplet size of 50 m. Our results also show a consistent power‐law relationship between visibility and fog liquid water content for pure marine advection fog events, which are different from coastal fog, only. In particular, a power‐law fit seems to resemble closely the theoretical relationship given by previous studies of the marine fog.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".