Large eddy simulations of contrail development: Sensitivity to initial and ambient conditions over first twenty minutes
Bibliographic record
Abstract
[1] A three-dimensional large-eddy simulation with size-resolved ice microphysics was used to model persistent contrails and compute their optical depth and area coverage. Eleven cases were run with various levels of vertical wind shear, aircraft type, relative humidity, ice nuclei effective emission index, and atmospheric stability and were analyzed with respect to their fluid dynamics and ice bulk properties. The effects of these properties on optical depth and contrail width were also compared between cases. Ice properties, optical depths, and contrail widths were consistent with limited observational field studies. For the conditions considered, contrail peak optical depth after twenty minutes simulation time ranged from 0.15 to 0.87, while contrail width ranged from 450 m to over 3 km. Optical depth and contrail width varied most strongly with vertical shear. For a 4-engine aircraft and 130% ambient relative humidity with respect to ice, a moderate shear of 0.005 s−1 reduced the peak optical depth by 79% and increased the width by 450% after twenty minutes compared to a zero shear case. In cases with no vertical shear, optical depth was also sensitive to aircraft type, humidity, and effective emission index, but variations in width with these parameters were small. In these cases, larger aircraft, higher humidity, and higher emission indices resulted in optical depths ranging from 20% to 50% larger than baseline cases. Atmospheric stability variations qualitatively changed the fluid dynamical development of the contrail, but differences in optical depth and contrail width were small.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".