Impacts of Urban Canopy on Two Convective Storms With Contrasting Synoptic Conditions Over Nanjing, China
Bibliographic record
Abstract
Abstract Diverse urban‐induced rainfall anomalies highlight the need for improved understanding on extreme rainfall in cities. In this study, we examine urban modification of rainfall over Nanjing, China. Our results are based on climatological analyses of hourly rainfall observations and high‐resolution Weather Research and Forecasting model simulations coupled with different urban physics schemes. A gridded dataset of urban canopy parameters (UCPs) was developed to better characterize the geometrical features of downtown Nanjing. Two convective storms were investigated to shed light on the impacts of urban canopy on spatial and temporal rainfall variabilities for storms with contrasting synoptic conditions. We show that the model simulations coupled with the multi‐layer urban physics scheme and the gridded UCPs can noticeably reduce biases in surface thermal and dynamic fields, but its performance in rainfall patterns varies with storm events. There is a strong convergence zone over the urban‐rural interface induced by building complexes, leading to intensified convection for the storm with strong synoptic conditions but bifurcated moisture fluxes for the storm with weak synoptic conditions. Lagrangian analysis of storm elements further illustrates the role of urban canopy in deflecting storm cells approaching the city for the storm under weak synoptic conditions. The magnitudes of positive rainfall anomalies induced by urban canopy range from 60% to 100% of the storm‐total rainfall. Our study highlights the necessity of improved characterization of urban canopy and the perturbed atmospheric boundary layer processes in examining urban convective rainfall.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".