Stratospheric Australian fire smoke layers over Punta Arenas, Chile, measured by ground-based lidar and AEOLUS
Bibliographic record
Abstract
Exceptionally strong wildfire activity in Australia in summer 2019-2020 triggered the evolution of pyrocumulonimbus clouds, releasing enormous amounts of fire smoke into the upper troposphere and lower stratosphere region of the usually very clean southern hemisphere. Measurements at the lidar site of Punta Arenas (53°S), Chile, show that the first stratospheric smoke layers arrived over Punta Arenas at 6 Jan 2020. First results show striking similarities to a record-breaking event of stratospheric smoke layers from wildfires in Canada in 2017 (Baars et al., ACP 2019). At Punta Arenas, lidar ratios reach values of 45-50 sr at 355 nm, and 60-65 sr at 532 nm wavelength. Particle linear depolarization ratios reach values of 19% at 355 nm, and 15% at 532 nm wavelength. Aeolus is able to detect these intense layers of stratospheric smoke in the southern hemisphere as well. In this contribution, we will discuss our findings of extensive and intensive smoke optical properties over Punta Arenas to the related Aeolus aerosol spin off products of nearby overpasses. Especially the particle linear depolarization ratio at 355 nm are of relevance as AEOLUS is only able to measure the co-polarized 355 nm signal.
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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 | 0.000 | 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) | 0.001 | 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".