Ice Fog in Arctic During FRAM–Ice Fog Project: Aviation and Nowcasting Applications
Bibliographic record
Abstract
Ice fog and frost occur commonly (at least 26% of the time) in the northern latitudes and Arctic regions during winter at temperatures usually less than about –15°C. Ice fog is strongly related to frost formation—a major aviation hazard in the northern latitudes. In fact, it may be considered a more dangerous event than snow because of the stronger aircraft surface adhesion compared to snow particles. In the winter of 2010/11, the Fog Remote Sensing and Modeling–Ice Fog (FRAM-IF) project was organized near Yellowknife International Airport, Northwest Territories, Canada, with the main goals of advancing understanding of ice fog microphysical and visibility characteristics, and improving its prediction using forecast models and remotesensing retrievals. Approximately 40 different sensors were used to measure visibility, precipitation, ice particle spectra, vertical thermodynamic profiles, and ceiling height. Fog coverage and visibility parameters were estimated using both Geostationary Operational Environmental Satellites (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. During this project, the inversion layer usually was below a height of 1.5 km. High humidity typically was close to the ground, frequently producing ice fog, frost, and light snow precipitation. At low temperatures, snow crystals can be swept away by a very low wind speed (∼1 m s−1). Ice fog during the project was not predicted by any forecast model. These preliminary results in the northern latitudes suggest that ice fog and frost studies, over the Arctic regions, can help us to better understand ice microphysical processes such as ice nucleation, visibility, and parameterizations of ice fog.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".