Adaptive Flight Planning Using Turbulence and Weather Forecasts
Bibliographic record
Abstract
The North Atlantic airspace, one of the busiest corridors for global aviation, faces challenges due to evolving upper-level jet stream dynamics influenced by climate change. Over the past four decades, vertical wind shear in this region has increased by 15%, contributing to a projected doubling or tripling of clear-air turbulence by 2050–2080. This adversely affects flight safety, efficiency, and passenger comfort. Traditional flight planning methods, such as the North Atlantic Tracks (NATs), are static and fail to account for the increasingly dynamic atmospheric conditions. In this work, we introduce an adaptive flight planning approach that integrates the A* pathfinding algorithm with forecasted atmospheric data to optimize transatlantic flight paths. This atmospheric data is sourced from remote sensing technologies, including geostationary satellites that monitor jet stream patterns and wind dynamics, and radar systems that detect wind shear and turbulence precursors in real time. By leveraging these high-resolution inputs, the proposed system dynamically recalculates routes to leverage favorable wind conditions and avoid regions of high wind shear, reducing flight time, fuel consumption, and turbulence exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".