Ice Fog: The Current State of Knowledge and Future Challenges
Bibliographic record
Abstract
Abstract Ice fog is a natural, outdoor cloud laboratory that provides an excellent opportunity to study ice microphysical processes. Ice crystals in fog are formed through similar pathways as those in elevated clouds; that is, cloud condensation or ice nuclei are activated in an atmosphere supersaturated with respect to liquid water or ice. The primary differences between surface and elevated ice clouds are related to the sources of water vapor, the cooling mechanisms and dynamical processes leading to supersaturation, and the microphysical characteristics of the nuclei that affect ice fog crystal physical properties. As with any fog, its presence can be a hazard for ground or airborne traffic because of poor visibility and icing. In addition, ice fog plays a role in climate change by modulating the heat and moisture budgets. Ice fog wintertime occurrence in many parts of the world can have a significant impact on the environment. Global climate models need to accurately account for the temporal and spatial microphysical and optical properties of ice fog, as do weather forecast models. The primary handicap is the lack of adequate information on nucleation processes and microphysical algorithms that accurately represent glaciation of supercooled water fog. This chapter summarizes the current understanding of ice fog formation and evolution; discusses operating principles, limitations, and uncertainties associated with the instruments used to measure ice fog microphysical properties; describes the prediction of ice fog by the numerical forecast models and physical parameterizations used in climate models; identifies the outstanding questions to be resolved; and lists recommended actions to address and solve these questions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".