Representation of Precipitation Phases and a New Parameterization for Below-Cloud Scavenging in Regional Air Quality modelling
Bibliographic record
Abstract
Below-cloud scavenging is the process of aerosol removal from the atmosphere between cloud-base and the ground by precipitation (e.g. rain or snow), and affects aerosol number/mass concentrations, lifetime and distributions. An accurate representation of precipitation phases is important in treating below-cloud scavenging as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. To study cloud processes and precipitation chemistry, we examined representation of below-cloud aerosol scavenging of in the current GEM-MACH model, including a revised approach in precipitation phase partitioning and implementing a new aerosol below-cloud scavenging scheme (from Wang et al., 2014) and comparing with the GEM-MACH’s existing scavenging scheme, based on Slinn (1984). Overall, the multi-phase partitioning and Wang et al. (2014) scavenging scheme improve GEM-MACH performance as compared with observations. Including multi-phase approach leads to a decrease on SO42- scavenging and impacts the below-cloud scavenging of SO2 into the aqueous phase. The impact of the new scheme on wet deposition of NO3- and NH4+ varies, with both increases and decreases in wet scavenging, and is more important at specific cloud locations. The two aerosol scavenging rates differ during liquid precipitation in the 0.1-1 µm size range mostly at high precipitation intensity. The two aerosol scavenging schemes diverge for aerosols smaller than 1 µm for solid precipitation at lower intensity (R=0.01 mm/h), while at higher precipitation intensities (R=10 mm/h), the two schemes show larger differences for aerosols larger than 1 µm. The changes on the speciated particles (sulphate, nitrate and ammonium) are consistent with the changes in the wet scavenging, leading to higher modelled concentrations of particulate sulphate in the atmosphere.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".