Investigation of Coastal Winds and Turbulence Characteristics Using Doppler Lidar
Bibliographic record
Abstract
Abstract Sea ports play a major role in the transport of goods worldwide, and knowledge of wind characteristics in these areas is vital to maintaining safety. However, coastal wind flow can be highly complex and turbulent, necessitating additional analysis. A Doppler lidar providing continuous wind profiles is deployed in the Port of Genoa, Italy, to characterize the mean wind velocity and turbulence properties within the coastal surface layer (40–250 m above ground level). Weather conditions, reanalysis data, and transient wind profiles are combined to analyze wind field characteristics on a day which experienced a thunderstorm. We also utilize a method to identify and categorize sources of turbulence through analysis of lidar derived quantities such as wind shear, turbulent kinetic energy, and vertical skewness. Seasonal variations in the wind properties are investigated by selecting data from June (summer) and December (winter). Differences are found in dominant wind direction and the associated frequency of convective mixing, with onshore winds most common in summer and offshore winds in winter. The measured lidar velocity is also compared against Monin–Obukhov similarity theory predictions, showing satisfactory agreement at low heights but struggling to reproduce observations of the stable atmospheric conditions present during winter.
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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 | 0.001 |
| 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".