The Impact of GEM and MM5 Modeled Meteorological Conditions on CMAQ Air Quality Modeling Results in Eastern Canada and the Northeastern United States
Bibliographic record
Abstract
Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) is currently the meteorological model most widely used as input into the Community Multiscale Air Quality (CMAQ) modeling system. In this study, meteorological fields produced by the Global Environmental Multiscale (GEM) meteorological model were compared with those from MM5, and the impact of using the two different modeled datasets as inputs to CMAQ was investigated. Two CMAQ model runs, differing only in meteorological inputs and meteorologically influenced emissions, were conducted for a domain covering eastern Canada and the northeastern United States for a 9-day period in July 1999. Comparison of the two modeled meteorological datasets with surface measurements revealed that GEM and MM5 gave comparable results. For a direct comparison of the two meteorological datasets the differences were small for pressure and temperature but larger for wind speed and relative humidity (RH). The variations in meteorological fields affect emissions and air quality results in differing ways and to differing degrees. The most influential meteorological field on emissions was temperature, which had a minor impact on on-road mobile emissions and a larger impact on biogenic emissions. Performance statistics for O3 and for particulate matter less than 10 μm and less than 2.5 μm (PM10, and PM2.5, respectively) show that GEM-based and MM5-based CMAQ results compare similarly to hourly measurement data, with minor statistical differences. A direct comparison of O3, PM10, PM2.5, and speciated PM2.5 showed that the results correlate to varying degrees and that the differences in RH affect total particulate matter (PM) mass and aerosol species concentrations significantly. Relative humidity affects total particle mass and particle diameters, which in turn affect PM2.5 and PM10 concentrations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".