Bulk or modal parameterizations for below‐cloud scavenging of fine, coarse, and giant particles by both rain and snow
Bibliographic record
Abstract
Abstract Bulk or modal parameterizations for below‐cloud mass and number scavenging coefficients Λm (s−1) and Λn (s−1) of three aerosol modes—fine (PM2.5), coarse (PM2.5–10), and giant (PM10+)—for both rain and snow scavenging are developed for use in modal‐approach aerosol transport models. The new bulk parameterizations are based on the size‐resolved Λ(d) parameterization of Wang et al. (2014), using assumed lognormal mass and number size distributions for PM2.5, PM2.5–10, and PM10+. The resulting modal‐mean formulas for Λm and Λn follow power law relationships with precipitation intensity R, consistent with most existing studies. The empirical parameters in the power law relationships obtained in this study are also within the range of parameter values obtained in previous field and theoretical studies. Uncertainties in Λm due to the size distribution or size range assumed for each aerosol mode are generally smaller than 30% for PM2.5–10 and PM10+ but could be on the order of factor of 2 for PM2.5. These uncertainties, however, are much smaller than other known uncertainties in existing Λ formulations, which are typically larger than 1 order of magnitude. Moreover, the new bulk parameterizations are believed to be more representative than most existing schemes because the size‐resolved parameterization of Wang et al. (2014), which they are based on, was developed with consideration of all available theoretical formulations and field‐derived estimates for size‐resolved Λ and their associated uncertainties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".