Evaluation of a regional air quality forecast model for tropospheric NO <sub>2</sub> columns using the OMI/AURA satellite tropospheric NO <sub>2</sub> product
Bibliographic record
Abstract
Abstract. Results from a regional air quality forecast model, AIRPACT-3, are compared to OMI tropospheric NO2 integrated column densities for an 18 month period over the Pacific Northwest. AIRPACT column densities were well correlated with cloud-free monthly averages of tropospheric NO2 (R=0.75) to NASA retrievals for months without wildfires, but were poorly correlated with significant model overpredictions (R=0.21) for months with wildfires when OMI and AIRPACT were compared over the entire domain. AIRPACT forecasted higher NO2 in some US urban areas, and lower NO2 in many Canadian urban areas, when compared to OMI. There are significant changes in results after spatially averaging model results to the daily OMI swath. Also, it is shown that applying the averaging kernel to model results in cloudy conditions has a large effect, but applying the averaging kernel in cloud free conditions has little effect. The KNMI and NASA retrievals of tropospheric NO2 from OMI (collection 3) are compared. The NASA product is shown to be significantly different than the KNMI tropospheric NO2 product, i.e. July 2007 (R=0.60) and January 2008 (R=0.69).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".