Surface data assimilation of chemical compounds over North America and its impact on air quality and Air Quality Health Index (AQHI) forecasts
Bibliographic record
Abstract
The aim of this paper is to analyze the impact of initializing GEM-MACH, Environment and Climate Change Canada’s air quality (AQ) forecast model, with multi-pollutant surface objective analyses (MPSOA). A series of 48-h air quality forecasts were launched for July 2012 (summer case) and January 2014 (winter case) for ozone, NO 2 , and PM 2.5 . In this setup, the GEM-MACH model (version 1.3.8.2) was initialized with surface analysis increments (from MPSOA) which were projected in the vertical by applying an appropriate fractional weighting in order to obtain 3D analyses in the lower troposphere. Here, we have used a methodology based on sensitivity tests to obtain the optimum vertical correlation length (VCL). Overall, results showed that for PM 2.5 , more specifically for sulfate and crustal materials, AQ forecasts initialized with MPSOA showed a very significant improvement compared to forecasts without data assimilation, which extended beyond 48 h in all seasons. Initializing the model with ozone analyses also had a significant impact but on a shorter time scale than that of PM 2.5 . Finally, assimilation of NO 2 was found to have much less impact than longer-lived species. The impact of simultaneous assimilation of the three pollutants (PM 2.5 , ozone, and NO 2 ) was also examined and found very significant in reducing the total error of the Air Quality Health Index (AQHI) over 48 h and beyond. We suggest that the period over which there is a significant improvement due to assimilation could be an adequate measure of the pollutant atmospheric lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".