Effects of aerosols on precipitation in north-eastern North America
Bibliographic record
Abstract
Abstract. The changes in precipitation in north-eastern North America caused by chemistry – and particularly anthropogenic aerosols – are investigated using the Weather Research Forecasting with Chemistry (WRF/Chem v3.2) model. The simulations were carried out for a five-month period from April to August 2009. The model results show that non-negligible changes in both convective and cloud-resolved (non-convective) precipitation are caused by chemistry and/or aerosols over most parts of the domain. The changes can be attributed to both radiative and microphysical interactions with the meteorology. A chemistry-induced change of approximately −15% is found in the five-month mean daily convective precipitation over areas with high convective rain; most of this can be traced to radiative effects. Total convective rain is greater than total non-convective rain in the domain, but a chemistry-induced increase of about 30% is evident in the five-month mean daily non-convective precipitation over the heavily urbanized parts of the Atlantic coast. The effects of aerosols on cloud microphysics and precipitation were examined for two particle size ranges, 0.039–0.1 μm and 1–2.5 μm, representing the nucleation and accumulation modes respectively. Strongly positive spatial correlation between cloud droplet number and non-convective rain are found for activated (cloud-borne) aerosols in both size ranges. Non-activated (interstitial) aerosols have a positive correlation with cloud droplet number and non-convective rain when they are small and an inverse correlation for larger sizes.
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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 | 0.000 | 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 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".