Solutions to the 3D Transport Equation and 1D Diffusion Equation for Passive Tracers in the Atmospheric Boundary Layer and Their Applications
Bibliographic record
Abstract
Abstract A solution to the 3D transport equation for passive tracers in the atmospheric boundary layer (ABL), formulated in terms of Green’s function (GF), is derived to show the connection between the concentration and surface fluxes of passive tracers through GF. Analytical solutions to the 1D vertical diffusion equation are derived to reveal the nonlinear dependence of the concentration and flux on the diffusivity, time, and height, and are employed to examine the impact of the diffusivity on the diurnal variations of CO2 in the ABL. The properties of transport operator H and their implications in inverse modeling are discussed. It is found that H has a significant contribution to the rectifier effect in the diurnal variations of CO2. Since H is the integral of GF in time, the narrow distribution of GF in time justifies the reduction of the size of H in inverse modeling. The exponential decay of GF with height suggests that the estimated surface fluxes in inverse modeling are more sensitive to the observations in the lower ABL. The solutions and first mean value theorem are employed to discuss the uncertainties associated with the concentration–mean surface flux equation used to link the concentrations and mean surface flux. Both analytical and numerical results show that the equation can introduce big errors, particularly when surface flux is sign indefinite. Numerical results show that the conclusions about the evolution properties of passive tracers based on the analytical solutions also hold in the cases with a more complicated diffusion coefficient and time-varying ABL height.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".