Bulk or modal parameterizations for below‐cloud scavenging of fine, coarse, and giant particles by both rain and snow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bulk or modal parameterizations for below‐cloud mass and number scavenging coefficients Λ m (s −1 ) and Λ n (s −1 ) of three aerosol modes—fine (PM 2.5 ), coarse (PM 2.5–10 ), and giant (PM 10+ )—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 PM 2.5 , PM 2.5–10 , and PM 10+ . The resulting modal‐mean formulas for Λ m and Λ n 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 Λ m due to the size distribution or size range assumed for each aerosol mode are generally smaller than 30% for PM 2.5–10 and PM 10+ but could be on the order of factor of 2 for PM 2.5 . 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it