Microphysical analysis on the impacts of reduced aerosol concentrations on tropical cyclone precipitation in South China Area
Bibliographic record
Abstract
Abstract Previous studies have discovered that an increase in aerosol concentration could affect the development of precipitation and tropical cyclones. However, with increasing efforts to mitigate climate change, the amount of aerosols in the atmosphere is expected to decrease in the future. This study aims at understanding how such a reduction in aerosol number concentration could affect the precipitation pattern of tropical cyclones through case studies of Typhoon Haikui and Koinu. The Thompson aerosol-aware microphysics scheme in the Weather Research and Forecst(WRF) model is used, and the water-friendly aerosol number concentration is reduced by a factor of 100. For both tropical cyclones, a reduction in aerosol results in consistent expansion of precipitation area due to the enhanced warm rain process. However, the mean precipitation intensity of Koinu decreases while that of Haikui increases, depending on whether warm-rain or ice-phase dominates and whether upper-level convection is altered.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".