Urban impacts on the structure and evolution properties of warm-season thunderstorms over Nanjing, China
Bibliographic record
Abstract
Urban impacts on spatial and temporal rainfall variabilities present significant challenges for effective urban flood mitigation and adaptation strategies. However, the physical mechanisms underlying these impacts remain unclear. In this study, we conducted modeling analyses using the Weather Research and Forecast (WRF) model, combined with thunderstorm identification and tracking algorithms, to investigate the influence of urban areas on warm-season thunderstorms in Nanjing, China. Our findings reveal divergent urban impacts on the structure and evolution properties of thunderstorms based on different pre-storm synoptic conditions. When the synoptic conditions are weak, the urban heat island effect dominates, enhancing convective activities over urban areas. This leads to a reduction in the number of storm cells but an expansion in spatial coverage, ultimately resulting in increased rainfall over downtown areas. Conversely, when the synoptic conditions are strong, the urban canopy effect becomes prominent, slowing down storm movement and increasing the frequency of small storm cells over urban regions. These storm cells exhibit distinct "sharp" structures in terms of rainfall distribution and tend to intensify over downwind areas due to moist convergence. As a result, both downtown and downwind regions experience enhanced rainfall. This study improves our understanding of urban rainfall modification and offers valuable insights for storm nowcasting algorithms and the design of urban-specific rainfall events.
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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) | 0.000 | 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".