Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe – Part 2: Lidar study of depolarization and lidar ratios at 355, 532, and 1064 nm and of microphysical properties
Bibliographic record
Abstract
Abstract. Extremely high particle extinction coefficients, an order of magnitude higher than after the Mt. Pinatubo eruption in 1991, were measured in the stratosphere over Leipzig, Germany, on 22 August 2017. In a series of two articles, we present our observations of this record-breaking smoke event. In part 1 (Ansmann et al., 2018), we provide an overview of the smoke situation. The particle extinction coefficients reached 500 Mm−1 at 532 nm in the lower stratosphere around 15 km height and the smoke-related aerosol optical thickness (AOT) was close to 1.0 around noon. In part 2, we present the optical and microphysical properties of the fire smoke observed in a tropospheric layer from 5–6.5 km height and in a stratospheric layer from 15–16 km height. Three Raman lidars were run at Leipzig after sunset on 22 August. As a highlight, triple-wavelength polarization/Raman lidar measurements of the particle depolarization ratio and extinction-to-backscatter ratio (lidar ratio) at all three important lidar wavelengths of 355, 532, and 1064 nm could be performed. Very different particle depolarization ratios were found in the troposphere and in the stratosphere. The obviously compact and spherical tropospheric smoke particles caused almost no depolarization of backscattered laser radiation at all three wavelength (
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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) | 0.000 | 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".