Review and uncertainty assessment of size-resolved scavenging coefficient formulations for below-cloud snow scavenging of atmospheric aerosols
Bibliographic record
Abstract
Abstract. Theoretical parameterizations for the size-resolved scavenging coefficient for atmospheric aerosol particles scavenged by snow (Λsnow) need assumptions regarding (i) snow particle–aerosol particle collection efficiency E, (ii) snow-particle size distribution N(Dp), (iii) snow-particle terminal velocity VD, and (iv) snow-particle cross-sectional area A. Existing formulas for these parameters are reviewed in the present study, and uncertainties in Λsnow caused by various combinations of these parameters are assessed. Different formulations of E can cause uncertainties in Λsnow of more than one order of magnitude for all aerosol sizes for typical snowfall intensities. E is the largest source of uncertainty among all the input parameters, similar to rain scavenging of atmospheric aerosols (Λrain) as was found in a previous study by Wang et al. (2010). However, other parameters can also cause significant uncertainties in Λsnow, and the uncertainties from these parameters are much larger than for Λrain. Specifically, different N(Dp) formulations can cause one-order-of-magnitude uncertainties in Λsnow for all aerosol sizes, as is also the case for a combination of uncertainties from both VD and A. Assumptions about dominant snow-particle shape (and thus different VD and A) will cause an uncertainty of up to one order of magnitude in the calculated scavenging coefficient. In comparison, uncertainties in Λrain from N(Dp) are smaller than a factor of 5, and those from VD are smaller than a factor of 2. As expected, Λsnow estimated from empirical formulas generated from field measurements falls in the upper range of, or is higher than, the theoretically estimated values, which can be explained by additional processes/mechanisms that influence field-derived Λsnow but that are not considered in the theoretical Λsnow formulas. Predicted aerosol concentrations obtained by using upper range vs. lower range of Λsnow values (a difference of around two orders of magnitude in Λsnow) can differ by a factor of 2 for just a one-centimetre snowfall (liquid water equivalent of approximately 1 mm). Based on the median and upper range of theoretically generated Λsnow and Λsnow values, it is likely that, for typical rain and snow events, the removal of atmospheric aerosol particles by snow is more effective than removal by rain for equivalent precipitation amounts, although a firm conclusion requires much more evidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".