The characterization of long-range transported North American biomass burning plumes: what can a multi-wavelength Mie-Raman-polarization-fluorescence lidar provide?
Bibliographic record
Abstract
Abstract. This article presents a study of long-range transported biomass burning aerosols (BBA) originated from the North American wildfires in September 2020. The BBA plumes presented in this study were in the troposphere and underwent 1–2 weeks aging before arriving at the observations site. A novel dataset 2α+3β+3δ+φ (α: extinction coefficient, β: backscatter coefficient, δ: particle linear depolarization ratio, PLDR, φ: fluorescence capacity) derived from lidar observations is provided for the characterization of long-range transported BBA. The fluorescence capacity describes the ability of aerosols in producing fluorescence when exposed to UV excitation. In the observations of BBA episode, plumes from different wildfire activities have been characterized. In the BBA plumes, we detected low PLDRs, i.e. lower than 0.03 at all wavelengths, as well as enhanced PLDRs (PLDR355,532,1064 ≈ 0.15–0.18, 0.12–0.14, 0.01–0.02) with a similar spectral dependence that had been observed in the aged BBA plumes in the upper troposphere and lower stratosphere (Canadian smoke in 2017 and Australian smoke in 2019–2020). Obvious variations in Angström exponent (−0.3–1.5), lidar ratios (20–50 sr at 355 nm, 42–90 sr at 532 nm) and fluorescence capacity (1.0 × 10−4– 4.0 × 10−4) are also observed during the BBA episode. These variations are coupled with the variation of altitudes, water vapor content and wildfire events. It reflects that the properties of aged BBA particles are highly varied and depend on complex mechanisms, such as burning process and the aging process. The results also pointed out the inhomogeneity of the aging process in the BBA plumes, which means that particles in the core of the plume aged differently with those at the plume edge due to the impact of water vapor, temperatures, particle concentration and so on. These chemical and physical processes involved in BBA aging and how they could impact the particle properties are not yet well understood. In addition, our observations identified the ice crystals mixing with BBA particles, which indicates that BBA could act as ice nucleating particles (INP) at tropospheric conditions. The lidar fluorescence proves to be an efficient tool in studying the interaction of clouds and BBAs due to its high sensitivity. Recent studies claimed that aged BBA particles are more effective INPs than they were thought. As BBAs are becoming an important atmospheric aerosols with growing global wildfires, our observations could improve our characterization about aged BBA particles and the understanding of their importance in ice cloud formation.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".