Effect of surface roughness on large-scale downburst-like impinging jets
Bibliographic record
Abstract
Downbursts are cold descending winds that develop from thunderstorm clouds and, after impingement on the ground, produce an intense low-level horizontal front characterized by an axisymmetric toroidal vortex structure. Surface roughness is a key factor in the characterization of mean and turbulent wind speed features of synoptic-scale stationary atmospheric boundary layer winds. The goal of the present research is to physically assess whether the same can apply to the surface layer produced during thunderstorms, which are non-stationary, highly time-transient, and spatially limited phenomena. Downburst-like flows were produced through the impinging jet technique at the WindEEE Dome, at Western University in Canada. Three different surfaces were tested, and an equivalent full-scale roughness length (z0,eq) was determined. Experimental records are made publicly available. The large geometric and kinematic scales produced high Reynolds numbers, which enabled us to classify the flow as “fully turbulent” and therefore representative of full-scale downbursts. Results indicate a weak dependency on the Reynolds number, which suggests no relevant flaws in extending the results to the natural environment. The overall wind speed maxima weakly depend on z0, whereas a sharp velocity decrease is observed beyond the radial position of the maxima with increasing z0. Surface roughness enhances the boundary layer separation and consequently elevates the height of maximum wind speed above the surface. Vertical profiles of the horizontal velocity return a quite clear nose shape. Turbulence intensity shows a C-like shape with maxima in the near proximity of the ground that increase with z0.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".