Doppler Lidar Measurements of a Severe Thunderstorm Outflow Over Downtown Area
Bibliographic record
Abstract
This study investigates the urban boundary layer prior to and during a thunderstorm event that occurred in, Montréal on 13 July 2023, between approximately 1600 and 1800 local time. High-resolution data on back-scattered light intensity and radial Doppler velocity were collected using a lidar wind profiler at 25-second intervals, covering altitudes from approximately 100 to 1200 m above ground level. The lidar was a HALO Photonics lidar located in downtown Montréal, Quebec, Canada, with a wavelength of 1.56 μm. The analysis focuses on examining vertical profiles of mean wind velocity and turbulence characteristics before and during the thunderstorm. The urban boundary layer height during the day varied between 750 and 1050 m above ground level and the evolution of the mixing layer was clearly observed in the turbulence kinetic energy dissipation rate. The mixing layer was governed by cloud-driven and convective mixing turbulence. A high-resolution weather forecasting model is used to assess the accuracy of its UBL height and wind speed predictions during the storm. The dynamics of thunderstorm outflow were also analyzed. The maximum wind speed in the outflow exceeded 40 m/s slightly below 600 m above ground level. The primary vortex of thunderstorm outflow was clearly identified from velocity time series measured at different heights in the urban boundary layer. The study also reports the nose-shaped vertical profile of wind speed with the height, as well as the so-called double nose-shaped profile, which in the first evidence of this thunderstorm feature observed in a highly urbanized environment.
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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 | 0.000 | 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".