Towards a 16-year surface reanalysis of air quality over North America using EnOI
Bibliographic record
Abstract
The technique of EnOI has been used to produce operational surface air quality analysis at Environment and Climate Change Canada since 2022 (a simpler assimilation technique was used from 2003 to 2012). We have adopted an ensemble modeling of the background error covariance, based on air quality forecasts of GEM-MACH over the previous two months. The error correlations obtained from these ensembles are anisotropic and non-homogeneous, capturing the effects of topography, large water surfaces, valleys, major highways, and local sources, but are not as such flow-dependent. Error variances are also modeled using empirical relationship obtained from innovation covariance and concentrations. Other parameters of the analyis system are estimated minimizing the analysis error variance as perceived using cross-validation. This analysis scheme has distinct computational advantage as it does not require additional forecasting costs, and yet provide an accurate and optimized analysis using anisotropic non-homogeneous correlations. In this talk we will present the methodology and some result of operational implementation. In reanaysis mode it is even more accurate than the operational implementation. Because it is computationally fast, it allows easily for reanalyses over decades. Early results of a 16 year reanalysis (2001-2017) will be also be presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".