Analysis of a June 2023 Smoke Transport over the Northeast US Caused by Wildfires in Canada- leveraging the HRRR-Smoke model predictions and remote sensing observations.
Bibliographic record
Abstract
The intensification of wildland fires across portions of Canada in early June 2023 led to significant smoke transport into the eastern United States, resulting in poor air quality and reduced visibility. Significant degradation of air quality was evident from PM2.5 surface measurements along the smoke plume as it moved southwards, which was confirmed by satellite data and the operational HRRR-Smoke model. The availability of Doppler lidar data and the publicity surrounding the poor conditions in Washington DC led to a focused effort addressing the timing and enhancement of smoke in the nation’s capital. The predictive skill of the operational HRRR-Smoke model was evaluated against wind profile and PM2.5 surface measurements. In general, the HRRR-Smoke forecast accurately predicted the meteorological conditions, particularly the diurnal evolution of regional winds and boundary layer depth. Despite capturing the diurnal evolution of winds, errors were still seen in the wind shear structure and magnitude (biases ranged between ± 2 m s-1and ± 4 m s-1). In terms of air quality, good agreement was found between modeled and observed PM2.5 concentrations at four sites in Washington DC. Confidence in modeled winds and PM2.5 allowed HRRR-Smoke to be used to determine the development of a lee trough as a regional transport mechanism for circulating smoke into Washington DC and leading to bad air quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".