Modeling the Effects of Explicit Urban Canopy Representation on the Development of Thunderstorms above a Tropical Mega City
Bibliographic record
Abstract
The effects of an explicit three dimensional (3D) urban canopy representation on the development of convective thunderstorms were analyzed with the tropical town energy budget (tTEB) scheme integrated into the advanced regional prediction system (ARPS). The study provides a detailed description of the procedure to couple the system ARPS-tTEB and analyzed the simulation results of the 12 January 2015 sea-breeze event that developed a severe thunderstorm above the metropolitan area of São Paulo (MASP), Brazil. The simulation used realistic boundary and initial conditions from the Global Forecast System (GFS) and sea surface temperature (SST) from the Tropical Rainfall Measurement Mission (TRMM). The system ARPS-tTEB runs of up to 3 km horizontal resolution were carried out with high resolution topography features and land-use types currently available for Southeastern Brazil. The simulated spatial distribution of precipitation was verified against the Climate Prediction Center Morphing Technique (CMORPH), the Global Precipitation Measurement (GPM) and the São Paulo weather radar (SPWR) precipitation estimates by indexes scores. Time series of grid precipitation estimates (ARPS-tTEB and SPWR) and point measurements (rain gauges) were evaluated with a Bayesian statistical method. Results indicate that the urban area of the MASP modulates the precipitation spatial distribution over it. Furthermore, phase and amplitude precipitation accuracy increased with the 3D urban canyon and the urban energy budget scheme in relationship to control runs without urban environment effects.
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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 | 0.001 |
| 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 | 0.001 | 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".