Advanced insights into biomass burning aerosols during the 2023 Canadian wildfires from dual-site Raman and fluorescence lidar observations
Bibliographic record
Abstract
Abstract. This study presents lidar observations of long-range transported biomass burning aerosol (BBA) plumes from the exceptional 2023 Canadian wildfire season, recorded between May and September at the ATOLL observatory (France) and the GPI site (Russia). ATOLL operates a multi-wavelength Raman lidar with 3 polarization channels (355, 532 and 1064 nm) and a single fluorescence channel at 466 nm. GPI uses a fluorescence lidar with 5 broadband fluorescence channels excited by 355 nm. The dual-site dataset combines multi-wavelength elastic scattering and depolarization measurements with fluorescence observations, enabling a comprehensive characterization of BBA properties in the free troposphere (FT) and upper troposphere–lower stratosphere (UTLS). UTLS layers exhibit higher particle depolarization ratios, slightly lower lidar ratios, lower extinction- and backscatter-related Angström exponents, and a redshift in fluorescence spectral peaks. Cross-site comparisons show consistent fluorescence magnitudes and spectral shapes, highlighting the potential of coordinated multi-lidar fluorescence observations. Correlation analysis indicates that depolarization ratio, extinction-related Angström exponent, and fluorescence color ratio are moderately (r2 ≈ 0.61–0.68) correlated with layer altitude, however, this correlation is not sufficient to confirm a solid altitude dependence. It is likely that altitude is an intermediate variable linked to other controlling factors such as injection height of the plume, in-layer temperature and the plume origin. In addition, we observed BBAs showing no clear hygroscopic growth at RH of 90 %–100 % and statistically low RH values in the detected nearly 100 layers, suggesting aged BBAs, which were typically considered as hygroscopic, may have limited water uptake capability.
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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".