Droplet Growth and Its Impact on Visibility During Freezing Fog Events from CFACT
Bibliographic record
Abstract
Freezing fog is a type of cold fog that forms when the air temperature (Ta) is below 0℃. Although Ta is below 0℃, the water droplets can remain in a liquid state rather than freezing. Freezing-fog conditions can pose a significant hazard to aviation and marine operations because it can reduce visibility severely, and ice accumulates rapidly on the surfaces such as aircraft, ship, and roads. Observations collected during the CFACT (Cold Fog Amongst Complex Terrain) Project from 7 January – 24 February, representing cold-fog events over Heber Valley of Utah, are used in the analysis. The objectives of this study are to characterize freezing fog microstructure in detail with respect to droplet size distribution, critical diameter related to activation, and visibility. In the analysis, freezing fog (FZFG) and droplet size spectra will be examined theoretically and experimentally. The droplet activation and critical diameter forming in frozen-fog droplets will be revealed using the Köhler curve. The effect of the droplet-growth process on visibility changes for two cold-fog cases is examined and results are discussed. Preliminary analysis suggests that freezing-fog droplet growth strongly depends on environmental conditions, including Ta, relative humidity (RH), and liquid water content (LWC) as well as droplet number concentration (Nd). It is concluded that microphysical parameterizations should investigate freezing-fog droplet formation and growth in more detail because presently it is lacking in NWP predictions.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".