Evaluating the wildfire forecasting performance of an air quality model
Bibliographic record
Abstract
While wildfires are a natural phenomenon in Canadian landscapes, the severity of Canada’s ongoing wildfire crisis – caused by climate change, suppression-based legislation, and fortress conservation – have led to increasing annual burned area and more intense wildfires. Increasing wildfire intensities results in greater quantities of smoke and risks to public health. Therefore, continuously refining our ability to accurately report wildfire smoke is crucial. Direct observations of wildfire smoke offer valuable information with regards to air quality; however, conducting surface- or airborne-level measurements of wildfire plumes is often impractical due to logistical, spatiotemporal, and safety constraints. To address this gap, atmospheric scientists and policymakers use atmospheric chemical transport models (CTMs) to provide air quality forecasts. Originally focused on anthropogenic and biogenic emissions, CTMs are being adapted to incorporate wildfire emissions given their growing influence on atmospheric dynamics. In Canada, this work is evident in the ongoing development of FireWork-CFFEPS, a framework that computes wildfire emissions within the existing Global Environmental Multi-scale Modelling Air Quality and Chemistry (GEM-MACH) CTM. To ensure that FireWork-CFFEPS is providing appropriate air quality forecasts with respect to wildfire smoke influences, evaluating key inputs and processes used to drive FireWork-CFFEPS is necessary. Chapter 2 looks at the extent to which primary particle speciation profiles can influence surface concentration predictions made in GEM-MACH. We find that the selection of primary speciation profiles affects modelled concentrations of particle nitrate and sulfate, given that particle nitrate and sulfate emissions are predominantly formed as secondary pollutants. Furthermore, it is not appropriate to evaluate the fire event with the surface-based monitoring network due to air quality influences from oil sands extraction and spatiotemporal limitations. To address the latter, chapter 3 examines how different hotspot approaches in CFFEPS affects pollutant concentrations by evaluating against in-situ, piloted aircraft observations. The new, ‘quasi-hotspot’ setup brings targeted improvements to modelled concentrations of gas-phase species; however, future simulations should also consider the emission factors used in forecasting. Finally, chapter 4 will summarize key findings from chapters 2 and 3 and provide recommendations for (1) future research in wildfire forecasting capabilities of GEM-MACH, and (2), broader implications for wildfire research.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".