Application of the NARCM model to high‐resolution aerosol simulations: Case study of Mexico City basin during the Investigación sobre Materia Particulada y Deterioro Atmosférico‐Aerosol and Visibility Research measurements campaign
Bibliographic record
Abstract
The Northern Aerosol Regional Climate Model (NARCM) is used to study the visibility and three‐dimensional (3‐D) evolution of aerosol distributions within the Mexico City basin. NARCM simulates transport, diffusion, deposition, and size distributions of sulphur aerosol particles in the region. The model assumes only simple sulphur oxidation, not taking explicitly into account the urban air chemistry. Rather, it focuses on detailed aerosol microphysics and 3‐D optical properties. The simulation performance is compared with upper air and ground‐based observations for the following specific days of intensive measurement: 2, 4, and 14 March 1997. Time series at Mexico City airport shows lower values of visibility in the morning due to a shallow mixed layer and higher values in the afternoon following the evolution of the mixed layer depth. Modeled visibility shows large dependence on cardinal direction and size distribution of particles. It is found that better resolution of particle size leads to better representation of coagulation processes and to realistic size distributions which show a characteristic accumulation mode around 0.3 μm. As a result, visibility simulations are closer to those observed at the airport location. Comparing visibility is a stringent test for the model because it requires an accurate representation of 3‐D meteorological fields together with a realistic aerosol simulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".