Air Quality Forecast Verification Using Satellite Data
Bibliographic record
Abstract
Abstract NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with the errors on the lower end during the 18–22 July 2004 time period when a regional-scale sulfate event occurred. Spatially, the errors are small over the regions where sulfate plumes were present. The correlation coefficient also showed similar features (spatially and temporally) with a peak value of ∼0.6 during the 18–22 July 2004 time period. The dominance of long-range transport of smoke into the United States during the summer of 2004, neglected in the model predictions, skewed the model forecast performance. Enhanced accuracy and reduced normalized mean errors during the time period when a sulfate event prevailed show that the forecast system has skill in predicting PM2.5 associated with urban/industrial pollution events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".