Comparison of Chemical Lateral Boundary Conditions for Air Quality Predictions over the Contiguous United States during Intrusion Events
Bibliographic record
Abstract
Abstract. The existing National Air Quality Forecast Capability (NAQFC) operated at NOAA provides operational forecast guidance for ozone and particle matter with aerodynamic diameter less than 2.5 μm (PM2.5) over the contiguous 48 U.S. states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. Currently NAQFC is using chemical lateral boundary conditions (CLBCs) from a monthly climatology, which cannot capture pollutant intrusion events originated outside of the model domain. In this study, we developed a model framework to introduce the time-varying chemical simulation from the Goddard Earth Observing System Model, version 5 (GEOS) as the CLBCs to drive NAQFC. The method of mapping GEOS chemical species to CMAQ CB05-Aero6 species was also developed. We then evaluated NAQFC's performance using the new CLBCs from GEOS. The utilization of the GEOS dynamic CLBCs showed an overall best score when comparing the NAQFC simulation with the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015: the PM2.5 correlation coefficient R was improved from 0.18 to 0.37 and the mean bias was narrowed from −6.74 μg/m3 to −2.96 μg/m3 over CONUS. The CLBCs' influences depended on not only the distance from the inflow boundary, but also species and their regional characteristics. For the PM2.5 prediction, the CLBC's effect on the correlations was mainly near the inflow boundary, and its impact on the background could reach farther inside the domain. The CLBCs also altered background ozone through the inflows of ozone itself and its precursors. It was further found that aerosol optical thickness (AOT) from VIIRS retrieval correlated well to the column CO and elemental carbon from GEOS, based on which the new CLBCs for wildfire intrusion event was derived. The AOT derived CLBCs successfully captured the wildfire intrusion events in our case study for summer 2018. It can be a useful alternative in case the CLBCs of GEOS are not available.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".