Boundary Layer Updrafts Driven by Airflow over Heated Terrain
Bibliographic record
Abstract
Abstract This study presents linear and nonlinear scalings for boundary layer ascent forced by airflow over heated terrain and compares them to results from corresponding high-resolution numerical simulations. Close agreement between theory and simulation is found over most of the parameter space considered, including variations in background winds, boundary layer stability, mountain height, and diabatic heating rate. As expected, the linear and nonlinear scalings perform best for linear and nonlinear flows, respectively. For a convective boundary layer, the scalings accurately predict vertical motion for all flows considered, including those that extend well into the nonlinear regime. Thus, these scalings may ultimately help to improve the parameterization of subgrid orographic ascent in large-scale models. The vertical velocity scalings are less accurate for mechanically blocked flows in stable boundary layers, for which a simple vertical displacement scaling is superior. Although the scalings do not treat interactions between mechanical and thermal flow responses, these interactions are generally weak except in blocked flows with strong surface heating. Numerical simulations of such cases suggest that a hydrostatically induced pressure decrease in the lee associated with the diabatic surface heating drives stronger flow reversal within the wake and leeside convergence downwind of it, both of which produce strong surface-based updrafts. Thus, nonlinear interactions between mechanical and thermal flow responses may significantly enhance the likelihood of convection initiation over heated mountains.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".