A parameterization of sub-grid particle formation in sulfur-rich plumes for global- and regional-scale models
Bibliographic record
Abstract
Abstract. New-particle formation in the plumes of coal-fired power plants and other anthropogenic sulfur sources may be an important source of particles in the atmosphere. It remains unclear, however, how best to reproduce this formation in global and regional aerosol models with grid-box lengths that are tens of kilometres and larger. Based on the results of the System for Atmospheric Modelling (SAM), a large-eddy simulation/cloud-resolving model (LES/CRM) with online two-moment aerosol sectional (TOMAS) microphysics, we have developed a computationally efficient, but physically based, parameterization that predicts the characteristics of aerosol formed within sulfur-rich plumes based on parameters commonly available in global- and regional-scale models. Given large-scale mean meteorological parameters ((1) wind speed, (2) boundary-layer height and (3) downward shortwave radiative flux), (4) emissions of SO2 and (5) NOx from the source, (6) mean background condensation sink, (7) background SO2 and (8) NOx concentrations, and (9) the desired distance from the source, the parameterization will predict (1) the fraction of the emitted SO2 that is oxidized to H2SO4, (2) the fraction of that H2SO4 that forms new particles instead of condensing onto pre-existing particles, (3) the mean mass per particle of the newly formed particles, and (4) the number of newly formed particles per kilogram SO2 emitted. The parameterization we describe here should allow for more accurate predictions of aerosol size distributions and a greater confidence in the effects of aerosols in climate and health studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".