Comparison between the 2D wind fields retrieved by a scanning Doppler lidar and anemometric measurements
Bibliographic record
Abstract
In this work, two-dimensional (2D) wind fields retrieved by SingleDop software using scanning Doppler lidar data are compared with anemometric measurements in Genoa (Italy). SingleDop is a software module based on the theoretical work described in Xu et al. (2006), which is intended to retrieve 2D low-level winds from either real or simulated Doppler radar data. The lidar used in this work is a three-dimensional (3D) scanning WindCube 400S lidar, developed by Leosphere (France), which scans the azimuthal range of 100º –250º, up to a maximum distance of 14 km in the radial direction, for 4 elevations corresponding to 2.5º, 5°, 7.5° and 10º from the horizontal. The anemometer used for comparison is located about 1.3 km (horizontally) from the Doppler lidar and provides the wind velocity with a sampling rate of 1 Hz. The dataset analyzed is from November 2019 to June 2020. The total number of available lidar scans per day is ~420 for each elevation (2.5º, 5º, 7.5º and 10º). The 2D wind fields are retrieved by SingleDop for different de-correlated lengths (L= 10, 5 and 1 km). The overall number of measurements available for the comparison is therefore approximately 420 scans per day × 180 days × 4 heights × 3 L, which results in nearly 106 wind velocity values. The wind direction retrieved by SingleDop properly corresponds to the anemometric data with a BIAS ~13º, RMSE ~40º and a circular correlation of 0.8. Concerning the wind intensity, the results obtained for L = 5 km show the best agreement with the anemometric measurements with a BIAS of 0.8 m/s, RMSE around 1.8 m/s and a correlation coefficient higher than 0.9. Both for wind direction and velocity, the BIAS and RMSE slightly increase with the elevation whereas the circular and linear correlations decrease, as expected due to the increasing distance between lidar and anemometric measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".