Potential of error-evolving tracer forecasts for operational assimilation of PM2.5 during wildfire smoke episodes
Bibliographic record
Abstract
2023 was record-breaking for wildfires in Canada with unprecedented impacts on local ecosystems as well as large scale smoke hazards. These exceptional fire impacts rose the public demand for accurate forecasts of smoke plumes as well as analysis of air quality impacts. However, fire smoke plumes are extreme air quality events with exceptionally high concentrations and related uncertainties fall outside statistical ranges. These particular conditions induce specific challenges for data assimilation algorithms, because error estimates need to capture the high uncertainties and spatial gradients. At the same time, operational forecast systems require high computational efficiency to deliver fast, yet accurate forecasts to the public.This study explores the potential of a novel assimilation approach, called parametric Kalman filter (PKF), for operational air quality forecasting during extreme air quality events. By explicitly evolving the main error parameters, the PKF has been proven to provide accurate uncertainty estimates at very low computational costs. In this work, a dynamical propagation of error standard deviations is implemented in the Canadian atmospheric-chemical forecast model GEM-MACH. This extended forecast model is applied to a case study of Quebec wildfires in early July 2023. First results indicate that the forecast error distributions during this events can be sufficiently approximated by a passive error-tracer. It is demonstrated that vertical diffusion is a critical component for dynamical error forecasting of extreme air quality events. The error standard-deviation forecasts are used in the current objective analysis (OA) for surface air quality at ECCC (Environment and Climate Change Canada) and compared to operational OA results.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".