Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain
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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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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) | 0.000 | 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) | 0.001 | 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 it