Climatology of Estimated Altimeter Error due to Nonstandard Temperatures
Bibliographic record
Abstract
Abstract General aviation (GA) accidents involving controlled flight into terrain often occur when pilots are unaware that their aircraft’s true altitude is lower than the altitude indicated by the pressure altimeter as a result of colder-than-standard temperatures. However, little guidance is available that quantifies the magnitude of these altimeter errors and their variation with season. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is combined with the pressure–altitude equation to construct a 30-yr monthly climatology, covering much of the United States and Canada, of D value (i.e., true altitude minus pressure altitude) corrected for the standard-atmosphere height separation between the altimeter setting and standard mean sea level pressure. This “corrected” D value therefore provides a useful estimate of the error between true and altimeter-indicated altitude. During winter, the mean corrected D values reach values as low as −350 m (~−1200 ft) in northern, low-terrain regions for flights near a pressure altitude of 3600 m, meaning the aircraft would be nearly 350 m lower than the altimeter indicates. Furthermore, the minimum (i.e., maximum negative) corrected D values are nearly double their mean values for the same time period. In addition, the reanalysis-based corrected D values are compared with estimated values calculated using a simple rule of thumb that is based solely on the air temperature at altitude and the surface elevation. The rule of thumb tends to underpredict the magnitude of the estimated error, in some cases by 70 m (~200 ft), and therefore gives a lower margin of safety.
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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 | 0.002 |
| 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 | 0.000 |
| 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".