Analysis of Turbulence Kinetic Energy Dynamics in Complex Terrain
Bibliographic record
Abstract
Boundary layer processes and turbulence in a complex terrain are influenced by thermally driven flows, as well as dynamical or non-local winds. We investigate the variability in turbulence kinetic energy (TKE) with elevation, and topography in a shallow high mountain valley in the Canadian Rockies. The Fortress Mountain Research Basin in the Kananaskis Valley, Alberta, was chosen for this study. Data from three high-frequency eddy-covariance systems at a north-west-facing slope location, and at two ridgetops at the south and north valley side walls were used for the analysis, and combined with large-eddy simulations (LES). The observed data and simulations focused on a sunny summer day when turbulence was well-developed, and cross-ridge flows interacted with thermally driven circulations. The observed TKE time series compared reasonably well with simulations at the north-west-facing slope and southern ridgetop. The model was then used to evaluate the vertical and horizontal TKE budget equation. Analysis of the TKE budget showed that horizontal shear driven by interactions of cross-ridge flows with the up-valley flow could be an important source of TKE production on the north-west-facing slope station in the Fortress Valley. At the northern ridgetop, both model and observations showed no contributions from the vertical production terms, while model showed a significant contribution from the horizontal shear production to TKE at this location. The correlation between the TKE at the valley station and the wind speed at a different location above the valley suggests that both horizontal and vertical exchange processes are an important part of TKE production mechanisms in this high mountain valley.
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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 | 0.001 |
| 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) | 0.000 | 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".