Impacts of isolated hills on daytime shallow convective clouds in southeastern China
Bibliographic record
Abstract
Shallow-convective clouds (SCCs) play important roles in the Earth system. Previous studies have mostly focused on SCCs over the oceans or over the plains. This study, however, focused on SCCs over the topography. Five isolated hills were selected from southeastern China. The hills were characterized by their maximum heights ( H ), half widths ( W ), and their product ( H W ). Tens of 30-m-resolution satellite images of SCCs were collected for each hill. It was found that the size of SCCs increases with H W , and also increases with H . When SCCs are separated into three classes, which correspond to different meteorological conditions, the relation between the size of SCCs and H W (or H ) remains valid. A series of semi-idealized large-eddy simulations (LESs) were conducted using idealized hill shapes and mean meteorological conditions of each hill. The LES results reveal that increasing H W increases the strength of upslope winds, whose convergence produces wider updrafts over the ridge tops. Consequently, the SCCs are enlarged. The LES results also suggest that the topographic impacts derived from the observations are underestimated, because the selection of satellite images forces the meteorological conditions over hills with smaller H W to be more conducive to cloud formation than those over hills with larger H W . The results imply that the topographic impacts on SCCs may be parameterized using H W or H in large-scale models. 浅积云在地气系统中发挥着重要作用.前人对浅积云的研究主要集中在海洋或平原上, 而本研究关注地形上的浅积云.本研究选取中国东南部的五座孤立山体, 并以其最大高度( H ),半宽( W )及其乘积( H W )为特征.研究发现, 浅积云随 H W 或 H 增加而增大.本研究还使用理想化的山体形状和每座山体的平均气象条件进行一系列大涡模拟.结果表明, 增加 H W 增加上坡风的强度, 在山顶产生更宽的上升气流, 浅积云因此变得更大.模拟结果还表明观测会低估地形影响, 因为卫星图像的选择使 H W 较小的山体上的气象条件比 H W 较大的山体上的气象条件更有利于云的形成.这些结果表明可以用 H W 或 H 来参数化大尺度模式中地形对浅积云的影响
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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 | 0.001 | 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".