Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain
Bibliographic record
Abstract
Abstract. Current theoretical and empirical size-resolved parameterizations of the scavenging coefficient (Λ), a parameter commonly used in aerosol transport models to describe below-cloud particle scavenging by rain, have been reviewed in detail and compared with available field and laboratory measurements. Use of different formulations for raindrop-particle collection efficiency can cause uncertainties in size-resolved Λ values of one to two orders of magnitude for particles in the 0.01–3 μm diameter range. Use of different formulations of raindrop number size distribution can cause Λ values to vary by a factor of 3 to 5 for all particle sizes. The uncertainty in Λ caused by the use of different droplet terminal velocity formulations is generally small than a factor of 2. The combined uncertainty due to the use of different formulations of raindrop-particle collection efficiency, raindrop size spectrum, and raindrop terminal velocity in the current theoretical framework is not sufficient to explain the one to two order of magnitude under-prediction of Λ for the theoretical calculations relative to the majority of field measurements. These large discrepancies are likely caused by additional known physical processes (i.e, turbulent transport and mixing, cloud and aerosol microphysics) that influence field data but that are not considered in current theoretical Λ parameterizations. The predicted size-resolved particle concentrations using different theoretical Λ parameterization can differ by up to a factor of 2 for particles smaller than 0.01 μm and by a factor of >10 for particles larger than 3 μm after 2–5 mm of rain. The predicted bulk mass and number concentrations (integrated over the particle size distribution) can differ by a factor of 2 between theoretical and empirical Λ parameterizations after 2–5 mm of moderate intensity rainfall.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".