Comparing in situ flight observations and GOES-16 satellite-derived icing products during the In-Cloud ICing and Large-drop Experiment (ICICLE)
Bibliographic record
Abstract
Aircraft icing continues to be a serious problem resulting in multiple aviation accidents each year. Inflight icing conditions are created when supercooled liquid water (SLW) adheres to the aircraft, increasing its weight and drag while decreasing lift. Many products have been developed over the years to assist pilots in determining the current and forecasted locations of icing conditions on the ground and aloft. The new series of Geostationary Operational Environmental Satellite (GOES) satellites has provided new products to assist in detecting icing conditions in-cloud. To test the efficacy of these icing products, a flight campaign was undertaken to obtain high quality in situ observations of icing conditions aloft. The In-Cloud ICing and Large-drop Experiment (ICICLE) utilized the National Research Council of Canada Convair-580 aircraft to collect data on icing conditions inflight, including atmospheric aerosols, cloud particle size distributions, particle phase, and total water content concentrations over the Midwest United States from late January to early March 2019. GOES satellite observations including daytime cloud phase and nighttime microphysics were obtained for comparison against the aircraft sensor data. Two flights were chosen for analysis due to the variety of different icing environments that occurred. Though the satellite products are accurate in identifying the presence of supercooled liquid at cloud tops, their ability to differentiate between SLW droplet sizes and characterize mixed phase clouds still needs to be improved. As satellite red-green-blue (RGB) products continue to improve, it will further help to increase safety throughout the aviation community.
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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".