Measurement Report: Lidar measurements of stratospheric aerosol following the 2019 Raikoke and Ulawun volcanic eruptions
Bibliographic record
Abstract
At 18:00 UTC on 21 June 2019 the Raikoke volcano in the Kuril islands began a large-magnitude explosive eruption, sending a plume of ash and sulfur dioxide into the stratosphere. A Raman lidar system at Capel Dewi Atmospheric Observatory, UK, was deployed to measure the vertical extent and optical depth of the volcanic aerosol cloud following the eruption. The elastic channel at 355 nm allowed measurements up to 25 km, but the Raman channel was only sensitive to the troposphere. Therefore, retrievals of backscatter ratio profiles from the raw backscatter measurements required aerosol-free profiles derived from nearby radiosondes and allowance for aerosol extinction using a lidar ratio of 40–50 sr. Small amounts of aerosol were measured prior to the arrival of the volcanic cloud (27 June–5 July 2019), from pyroconvection over Canada. Model simulations by de Leeuw et al. (2020) and Kloss et al. (2020) show that volcanic ash may have reached Europe from 1 July onwards and was certainly present over the UK after 10 July. The lidar detected a thin layer at an altitude of 14 km late on 3 July, with the first detection of the main aerosol cloud on 13 July. In this initial period the aerosol was confined below 16 km, but eventually the cloud extended to 20.5 km. A sustained period of clearly enhanced stratospheric aerosol optical depths began in early August, with a maximum value (at 355 nm) around 0.05 in mid-August and remaining above 0.02 until early November. Thereafter, optical depths decayed to around 0.01 by the end of 2019 and remained around that level until May 2020. The altitude of peak backscatter varied considerably (between 14 and 18 km) but was generally around 15 km. However, on one notable occasion on 25 August 2019, a layer around 300 m thick with peak lidar backscatter ratio around 1.5 was observed as high as 21 km.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".