A comparison of near-surface potential temperature variance budgets for unstable atmospheric flows with contrasting vegetation cover flat surfaces and a gentle slope
Bibliographic record
Abstract
Over the past decades, researchers have made significant progress toward a fundamental understanding of the budgets of turbulence variables over flat and homogeneous terrain, and only more recently over complex terrain. However, temperature variance budgets, which are parameterized in most meteorological models, are still poorly understood even under relatively idealized conditions. The objective of this study is to analyze the near-surface potential temperature variance budget over contrasting surfaces. To do this, we rely on profiles of near-surface turbulence variables collected as part of the Mountain Terrain Atmospheric Modeling and Observations program. Daytime observations collected in May 2013 in western Utah at three field sites subjected to similar large-scale forcing are analyzed: a desert playa (i.e., dry lakebed), characterized by a flat surface devoid of vegetation; a vegetated site, characterized by a flat valley floor covered with greasewood vegetation, and a slope site with a local slope angle of 2°–4° and covered by 1-m tall sparse desert steppe vegetation. The observations indicate a persistent 5-m surface layer across all three sites, where the flow is equilibrium due to the balance between dominant production and dissipation terms in the potential temperature variance equation. The temperature variances in this layer are well predicted by Monin–Obukhov similarity theory. During convective periods at the Playa and Slope sites, $$\approx 60\%$$ of the data show a ratio of turbulent transport to production greater than 0.1. Within the surface layer, turbulent transport of potential temperature variance acts as a sink term at all three sites. Neither the ratio of turbulent transport to production nor the ratio of production to dissipation show a dependence on atmospheric stability during the unstable periods studied. A short-period comparison of dissipation rates calculated using dissipation-scale resolving cold-wire anemometry and several common indirect methods using sonic anemometry is presented for data acquired at Playa site. The results indicate that the dissipation rates from all methods follow similar trends, however the values can differ by a factor of 2–3.
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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 | 0.001 | 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".