Bibliographic record
Abstract
Abstract. The top of the atmospheric boundary layer (ABL) serves as a boundary separating turbulent air in the ABL and free atmosphere in the troposphere. The increase/decrease of the ABL height (ABL-H) can dilute or concentrate chemical species in the ABL. In numerical simulations the ABL-H is involved in computing the vertical diffusivity and the counter-gradient term under the unstable condition in ABL parameterization schemes. Therefore, uncertainties in the ABL-H due to the uncertainties in meteorological fields and different definitions can affect the numerical predictions of the concentration of chemical species. To understand the impacts, a 1-D diffusion model with the K-profile scheme and an 3-D air quality forecast model with the turbulent kinetic energy (TKE)-based scheme are employed to examine the sensitivities of diffusion coefficient,counter-gradient term and tracer's concentration to the ABL-H analytically and numerically. Sensitivity tests with the 1-D model show that the increase/decrease ofthe ABL-H leads to the decrease/increase of concentration of tracers under both stable and unstable conditions due to the increase/decrease of the volume of air within the ABL. Under the unstable condition, the increase/decrease of the ABL-H also enhances/weakens the vertical diffusivity, and leads to the decrease/increase of the concentration of tracers for negative/positive vertical gradient of tracer. The impact of the ABL-H through the counter-gradient term is much smaller than the impact through changing the volume of trace and vertical diffusivity in the ABL. Sensitivity tests with the 3-D numerical model with the TKE scheme show increase/decrease of the ABL-H leads to strong/weak vertical diffusivity, but hasvery small impact of the ABL-H on the concentrations of pollutants over urban centers for the ABL-Hs which is defined based on the Richardson number and temperature.
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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 | 0.022 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.086 | 0.050 |
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".