Freezing Fog Microphysics and Visibility Based on CFACT Feb 19 Case
Bibliographic record
Abstract
The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RHw) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH4NO3). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations.
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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 | 0.001 |
| 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".