Bibliographic record
Abstract
Abstract. The potential impacts of dust aerosols and atmospheric convective available potential energy (CAPE) on the vertical development of precipitating clouds in southeastern China (20–30∘ N, 110–125∘ E) in June, July, and August from 2000 to 2013 were studied using multisource observations. In southeastern China, heavy-dust conditions are coupled with strong northerly winds that transport air masses containing high concentrations of mineral dust particles, with cold temperatures, and with strong wind shear. This leads to weaker CAPE on dusty days compared with that on pristine days. Based on satellite observations, precipitating drops under dusty conditions grow faster in the middle atmospheric layers (with a temperature of between −5 and +2 ∘C) but slower in the upper and lower layers compared with their pristine counterparts. For a given precipitation top height (PTH), the precipitation rate under dusty conditions is lower in the upper layer but higher in the middle and lower layers. Moreover, the associated latent heating rate released by precipitation in the middle layer is higher. The precipitation top temperature (PTT) shows a fairly good linear relationship with the near-surface rain rate (NSRR): the linear regression slope between the PTT and NSRR is stable under dusty and pristine conditions. However, the PTT0 (the PTT related to rain onset) at the onset of precipitation is highly affected by both the CAPE and aerosol conditions. On pristine days, a stronger CAPE facilitates the vertical development of precipitation and leads to a decrease in PTT0, at a rate of −0.65 ∘C per 100 J kg−1 of CAPE for deep convective precipitation (with a variation of 15 %) and at a rate of −0.41 ∘C per 100 J kg−1 of CAPE for stratiform precipitation (with variation of 12 %). After removing the impacts of CAPE on PTT, dust aerosols led to an increase in PTT0, at a rate of +4.19 ∘C per unit aerosol optical depth (AOD) for deep convective precipitation and at a rate of +0.35 ∘C per unit AOD for stratiform precipitation. This study showed clear evidence that meteorological conditions and aerosol conditions combine to impact the vertical development of precipitation clouds. A quantitative estimation of the sensitivity of PTT to CAPE and dust was also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.185 | 0.159 |
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".