Effects of Fire Plume Height on the Geophysical Estimation of Surface Fine Particulate Matter from Satellite Aerosol Optical Depth during North American Wildfires
Bibliographic record
Abstract
Wildfires can inject smoke at high altitudes into the atmosphere. The resulting free tropospheric aerosols may affect inference of ground-level fine particulate matter (PM 2.5 ) from satellite aerosol optical depth (AOD), yet the effects of accounting for plume height in this inference are poorly understood. Here, we include in the GEOS-Chem chemical transport model a fire plume height parametrization (GFAS, Global Fire Assimilation System) to examine its effect on PM 2.5 inferred from satellite AOD during wildfires over the United States and Canada. Comparison with six years satellite observations of plume height reveals a low bias of a factor 1.7 in the GFAS plume height over evergreen needleleaf forests. We scale the GFAS plume height over evergreen needleleaf forests in GEOS-Chem to better represent the satellite observations, focusing on 2018 and 2020 when large wildfires yield prominent signals. Replacing the default ground-level wildfire emissions in GEOS-Chem with the scaled GFAS vertically distributed emissions reduces the bias between measured PM 2.5 and PM 2.5 inferred from satellite AOD, and significantly improves the consistency of simulated AOD with sun photometer measurements. Overall, this study signifies the importance of vertically distributing wildfire emissions for the inference of PM 2.5 from satellite AOD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".