Assessment of Aircraft Icing Potential and Maximum Icing Altitude from Geostationary Meteorological Satellite Data
Bibliographic record
Abstract
Abstract A satellite product that displays regions of aircraft icing potential, along with corresponding cloud-top heights, has been developed using data from the Geostationary Operational Environmental Satellite (GOES) imager and sounder. The icing product, referred to as the Icing Enhanced Cloud-top Altitude Product (ICECAP), is created hourly for the continental United States and southern Canada, and is color coded to show cloud-top altitudes in 1.9-km (6000 ft) intervals. Experimental ICECAP images became routinely available on the Internet during the spring of 2004. Verification of separate ICECAP components (imager icing potential and sounder cloud-top heights) using aircraft pilot reports (PIREPs) indicates that the product provides useful guidance on the spatial coverage and maximum altitude of current icing conditions, but not icing intensity, stratification, or minimum altitude. The imager icing potential component of ICECAP was compared with the operational 40-km resolution National Weather Service (NWS) current icing potential and NWS Airman’s Meteorological Advisories via the NOAA Real-Time Verification System, while GOES cloud-top heights were compared with altitudes of moderate or greater icing from PIREPs. Benefits and deficiencies of the GOES icing product 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".