En klimatologisk studie av Clear Air Turbulence över Nordatlanten
Bibliographic record
Abstract
Clear Air Turbulence (CAT) is the turbulence experienced at high altitude on board an aircraft. The main mechanisms for its generation are often said to be Kelvin-Helmholtz instability and mountain waves. CAT is an issue to the aviation industry in the sense that it is hard to predict its magnitude and exact location. Mostly, it is just a nuisance for the crew and passengers, but occasionally it causes serious injuries and aircraft damage. It also prevents air-to-air refuelling to be conducted in a safe manner. The micro scale nature of CAT makes it necessary to describe it with turbulence indices. The first part of this study presents a verification of the two commonly used turbulence indices, TI1 and TI2, developed by Ellrod and Knapp in 1992. The verification is done with AMDAR (Aircraft Meteorological Data Relay) reports and computed indices from ERA-Interim data. The second part presents a 33-year climatology of the indices for describing CAT. Results show that the index TI1 is generally the better of the two indices based on hit rate, but TI2 performs better based on false alarm rate. The climatology suggests that CAT is more frequent at the northern east coast of the U.S., over the island of Newfoundland and east of Greenland. In the vertical, CAT seems to occur most frequently at the 225 hPa level but also occur frequently at the 300 hPa level at the aforementioned areas. Based on AMDAR reports from 2011, only 0.014% of the reports were positive turbulence observations. The low amount of reports suggests that CAT can be avoided effectively with current CAT predicting skills and flight planning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".