Evaluating Icing Nowcasts using CloudSat
Bibliographic record
Abstract
The Current Icing Product (CIP) is a model/observation fusion tool to diagnose aircraft icing probability and severity. Implemented at the Aviation Weather Center, it is used by forecasters to assess icing conditions and improve aviation support over the contiguous United States and southern Canada. However, given the three dimensional nature of the icing threat, it has been difficult to completely assess CIP effectiveness. CloudSat is a low-earth orbiting satellite containing a 3 mm cloud radar (94 GHz) that gives a two dimensional vertical profile of cloud along the orbital track of the satellite. When combined with temperature profile information, CloudSat can be used to infer information about the location and vertical structure of supercooled liquid water. Therefore, it can be used to compare to CIP. In this study first we compare CIP and CloudSat in several case studies to illustrate CIP‟s strengths and limitations. In the process we illustrate that CloudSat products are powerful observational tools in their own right, allowing unprecedented cross-sectional views of cloud systems which contain aviation hazards. Second, we compare CloudSat and CIP cloud heights statistically. For low cloud systems in particular, CIP tends to analyze cloud tops that are too high, leading to vertical overestimation of the icing hazard.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".