Comparing Precipitation Particle Sizes and Phases from the Surface to Aloft during the In-Cloud Icing and Large-Drop Experiment (ICICLE)
Bibliographic record
Abstract
Abstract Precipitation sizes and types can vary significantly throughout an airport’s terminal airspace and pose a significant threat to aircraft safety. When an aircraft encounters supercooled drops, ice can accrete on the critical surfaces of the plane, resulting in decreased performance. This can be particularly problematic on takeoff and landing when it can limit a pilot’s options for escaping the hazard and/or removing ice buildup. By establishing relationships between hydrometeor sizes with height above ground, it may be possible to improve the diagnosis and forecasting of icing conditions within the terminal area knowing the ground-based observations of particle sizes. In this study, in situ and ground-based measurements of particle phase and size are compared to explore their horizontal and vertical variations within the terminal area. In situ microphysical data from five flights conducted during the In-Cloud Icing and Large-Drop Experiment (ICICLE) were used for this study. Ground-based in situ measurements of hydrometeor size and phase were also collected at stations collocated with airports in the region. In the stratiform cloud cases analyzed, the ground observations of precipitation aligned extremely well with the trends of the particle sizes observed aloft. In the convective cases, however, particle size at the surface differed by as much as 1.5 mm when compared to trends of particle sizes measured aloft. The type of cloud was found to be relevant to the spatial variations in particle size and phase. The challenges associated with using ground-based measurements to discern possible aircraft icing aloft are discussed.
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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 | 0.000 |
| 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".