Multivariable Characterization of Atmospheric Environment with Data Collected in Flight
Bibliographic record
Abstract
The In-Cloud Icing and Large-drop Experiment (ICICLE) flight campaign, led by the United States Federal Aviation Administration, was conducted in the geographical region over US Midwest and Western Great Lakes, between January and March 2019, with the aim to collect atmospheric data and study the aircraft icing hazard. Measurements were taken onboard the National Research Council of Canada (NRC) Convair-580 aircraft, which was equipped with more than 40 in situ probes, sensors, and remote sensing instruments in collaboration with Environment and Climate Change Canada (ECCC). In each flight, aerosol, cloud microphysics, atmospheric and aircraft state data were collected. Atmospheric environment characterization is critical both for cloud studies and for operational decision making in flight. In this study, we use the advantage of multiple input parameters collected in-flight together with machine learning and clustering techniques to characterize the flight environment. Eleven parameters were evaluated for the classification of the sampled environment along the flight path. Namely, aerosol concentration, temperature, hydrometeor concentration, hydrometeor size, liquid water content, total water content, ice accretion rate, and radar parameters in the vicinity of the aircraft. In the analysis of selected flights, we were able to identify periods of supercooled liquid clouds, glaciated clouds, two types of mixed-phase clouds, and clear air conditions. This approach offers an alternative characterization of cloud boundaries and a complementary identification of flight periods with hazardous icing conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".