Ultra-High Resolution Aircraft Icing Forecasting During the ICICLE Field Project
Bibliographic record
Abstract
The FAA‑funded In-Cloud ICing and Large drop Experiment (ICICLE) presented a watershed moment to test modern era high resolution numerical model forecasts of supercooled liquid water. Furthermore, the field campaign was relatively unique in its opportunity to assess the explicit prediction between small droplet in-cloud icing and freezing drizzle and freezing rain. This talk will show case study results from two flights on 17 Feb 2019 that included multiple hours in freezing drizzle conditions in addition to ice pellets, snow grains, and the more commonly measured in-cloud small droplet icing. Numerical model results using the Weather Research and Forecasting (WRF) model with 600‑meter grid spacing and the Thompson and Eidhammer (2014) aerosol-aware microphysics scheme are compared against aircraft measurements of aerosol concentration, particle size distributions of water and ice, and on-board radar data as well as surface observations and satellite brightness temperatures. This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".