On the discrepancies between theoretical and measured below-cloud particle scavenging coefficients for rain – a numerical study
Bibliographic record
Abstract
Abstract. Existing theoretical formulations for size-resolved scavenging coefficient Λ (r) for atmospheric aerosol particles scavenged by rain predict values lower by one to two orders of magnitude than those estimated from field measurements of particle-concentration changes for particles smaller than 3 μm in diameter. Vertical turbulence does not influence the theoretical formulation of Λ (r), but contributed to the field-generated Λ (r) due to its influence on the overall concentration changes of aerosol particles in the layers undergoing impaction scavenging. A detailed one-dimensional cloud microphysics model is used to simulate rain production and below-cloud particle scavenging, and to quantify the contribution of turbulent diffusion to the overall Λ (r) generated from concentration changes. The relative contribution of vertical diffusion to below-cloud scavenging is found to be largest for submicron particles under weak precipitation conditions. The discrepancies between theoretical and field Λ (r) values can largely be explained by the contribution of vertical diffusion for all particles larger than 0.01 μm in diameter for which field data were available. The results presented here suggest that the current theoretical framework for Λ (r) can provide a reasonable approximation of below-cloud aerosol particle scavenging by rain in size-resolved aerosol transport models if vertical diffusion is also considered.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".