Characterizing and Predicting Marine Fog Offshore Newfoundland and Labrador
Bibliographic record
Abstract
Abstract As several review papers have concluded, marine fog is imperfectly characterized, and quantitative visibility forecasts are difficult to produce accurately. Some unique measurements have been made offshore Newfoundland and Labrador of the climatology of occurrence and the microphysical characteristics of marine, or open-ocean, fog. Based on measurements made at an offshore installation over 21 years, the percent of time with visibilities less than 0.5 n mi or approximately 1 km (1 n mi ≈ 1.85 km) reaches 45% in July, with a low of about 5% during the winter. The occurrence of fog is mainly due to warm air advection, with the highest frequency occurring with wind directions from over the warm Gulf Stream, and with air temperatures about 2°C warmer than the sea surface temperature. There is no diurnal variation in the frequency of occurrence of fog. The microphysical properties of the fog have been documented in the summer time frame, with over 550 h of in situ measurements made offshore with fog liquid water content greater than 0.005 g m−3. The fog droplet number concentration spectra peaks near 6 μm, with a secondary peak near 25–40 μm, which typically contains most of the liquid water content. The median droplet concentration is approximately 70–100 cm−3. The microphysical spectra have been used to develop a new NWP visibility parameterization scheme, and this scheme is compared with other parameterizations currently in use.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".