Density Altitude: Climatology of Daily Maximum Values and Evaluation of Approximations for General Aviation
Bibliographic record
Abstract
Abstract Density altitude (DA) is an aviation parameter that helps determine specific aircraft performance characteristics for the expected atmospheric conditions. However, there are currently no detailed graphical tools for general aviation (GA) pilot education demonstrating the spatial and temporal variation of DA to help improve situational awareness. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is used to construct a 30-yr monthly climatology of DA for the conterminous United States. Several DA characteristics are also investigated, including the effect of humidity on DA, the determination of reasonable worst-case conditions, and the applicability of two DA rules of thumb (ROTs). Maximum values of DA (worst aircraft performance) occur during July, reaching 3600 m over areas with high surface elevations. Humidity, while tertiary to the effects of temperature and pressure, causes the DA to increase from their dry values by more than 140 m as far north as the U.S.-Canada border. The dry DA ROT performs well for all conditions outside of strong tropical cyclones, where GA flights would not be expected. The ROT to correct for the effects of humidity performs well except in high elevations or when the dewpoint temperatures fall outside the applicable range of ≥5°C. When applied outside this range, in some situations, DA errors can be greater than if no humidity correction were applied. Therefore, a new ROT to correct for humidity is introduced here that extends the applicable dewpoint temperature range to ≥−28°C and reduces errors in estimated DA. Significance Statement The impacts of density altitude on aircraft performance have led to numerous general aviation (GA) accidents. This study helps GA pilots better understand the spatial and temporal variability in density altitude, thereby increasing their situational awareness during flight planning. This study also evaluates commonly used approximations to estimate density altitude, so pilots can understand the situations where these approximations are (in)applicable. Results suggest the need for a humidity correction approximation when dewpoint temperatures are <5°C, which is introduced in this study.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".