Effect of different source terms in atmospheric boundary modelling over the complex terrain site of Perdigao
Bibliographic record
Abstract
Abstract. The assessment of wind conditions in complex terrain requires the use of Computational Fluid Dynamics (CFD) simulations incorporating an accurate parameterization of forest canopy effects and variable thermal stability effects. This study aims to investigate how incorporating the presence of trees can improve flow predictions. A three-dimensional steady Reynolds-averaged Navier-Stokes (RANS) equations model is set up using OpenFOAM to simulate the flow over a complex terrain site comprising two parallel ridges located near Perdigão, Portugal. A 7.5 km × 7.5 km terrain of the Perdigao site is constructed from elevation data centered around a 100 m met-mast located on the northeast ridge. A 30-min averaged stationary period corresponding to near-neutral conditions on a single met-mast tower is simulated. The impact of incorporating different source terms is studied such as forest canopy, Coriolis forces as well as also buoyancy forces. The prediction capability of the models is analyzed for different groups of towers on the South-West ridge, inside the valley, and on the North-East ridge based on the flow topology. The inclusion of a canopy model is shown to improve predictions close to the ground for most of the towers, while reducing prediction accuracy on top of the ridges, illustrating the need to represent terrain heterogeneity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".