Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain
Bibliographic record
Abstract
Abstract. A detailed review has been conducted of current size-resolved parameterizations of below-cloud scavenging by rain, including their formulation in terms of scavenging coefficient (Λ), their associated input parameters and comparisons with size-resolved Λ values obtained from field measurements. The three dominant factors in the theoretical formulations of Λ – raindrop-particle collection efficiency, raindrop number size distribution and raindrop terminal fall velocity – are investigated through numerical sensitivity tests. It is found that the use of different formulations for raindrop-particle collection efficiency can cause uncertainties in the Λ values of nearly one order of magnitude for particles smaller than 3 μm. The use of different formulations of raindrop number size distribution can cause the 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 smaller than a factor of 2. All of the current theoretical Λ parameterizations, however, underpredict the Λ values by one to two orders of magnitude for particles smaller than 3 μm, compared with most available field measurements or with empirical formulas generated from field observations. The combined uncertainties from known sources are, thus, not enough to explain the large discrepancies between the theoretical and experimental studies, suggesting a need for further investigations of the collection mechanisms through field, laboratory and numerical studies. The differences in the predicted particle concentrations, due to the use of different Λ parameterizations, can be larger than a factor of 10 for ultrafine and coarse particles even after a small amount of rain (e.g., 2–5 mm). The differences for submicron-sized particles can also be larger than a factor of 2 if sufficient rainfall occurs. Lastly, predicted bulk concentrations (integrated over the particle size distribution) from using different theoretical and empirical Λ parameterizations can differ by up to 50% for particle number and by up to 25% for particle mass after just 2–5 mm of rain.
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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 | 0.001 | 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) | 0.002 | 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 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".