Lagrangian Coherent Structure Analysis of Terminal Winds Detected by Lidar. Part I: Turbulence Structures
Bibliographic record
Abstract
Abstract The accurate real-time detection of turbulent airflow patterns near airports is important for safety and comfort in commercial aviation. In this paper, a method is developed to identify Lagrangian coherent structures (LCS) from horizontal lidar scans at Hong Kong International Airport (HKIA) in China. LCS are distinguished frame-independent material structures that create localized attraction, repulsion, or high shear of nearby trajectories in the flow. As such, they are the fundamental structures behind airflow patterns such as updrafts, downdrafts, and wind shear. Based on a recently developed finite-domain–finite-time Lyapunov exponent (FDFTLE) algorithm from Tang et al. and on new Lagrangian diagnostics presented in this paper that are pertinent to the extracted FDFTLE ridges, the authors differentiate LCS extracted from lidar data. It is found that these LCS derived from horizontal lidar scans compare well to convergence and divergence suggested by vertical slice scans. At HKIA, horizontal scans are predominant: they cover much bigger azimuthal ranges as compared with only two azimuthal angles from the vertical scans. LCS extracted from horizontal scans are thus advantageous in providing organizing turbulence structures over the entire observational domain as compared with a single line along the vertical scan direction. In Part II of this study, the authors will analyze the evolution of LCS and their impacts on landing aircraft based on recorded flight data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".