Identification of Smoke and Sulfuric Acid Aerosol in SAGE III/ISS Extinction Spectra Following the 2019 Raikoke Eruption
Bibliographic record
Abstract
Abstract. The 2019 eruption of Raikoke was the largest volcanic eruption since 2011 and it was coincident with 2 major wildfires in the northern hemisphere. The impact of these events was manifest in the SAGE III/ISS extinction coefficient measurements. As the volcanic aerosol layers moved southward, a secondary peak emerged at an altitude higher than that which is expected for sulfuric acid aerosol. It was hypothesized that this secondary plume may contain a non-negligible amount of smoke contribution. We developed a technique to classify the composition of enhanced aerosol layers as either smoke or sulfuric acid aerosol. This method takes advantage of the different spectral properties of smoke and sulfuric acid aerosol, which is manifest in distinctly different spectral slopes in the SAGE III/ISS data. Herein we demonstrate the utility of this method using 4 case-study events (2018 Ambae eruption, 2019 Ulawun eruption, 2017 Canadian pyroCb, and 2020 Australian pyroCb) and provide corroborative data from the CALIOP instrument before applying it to the Raikoke plumes. We determined that, in the time period following the Raikoke eruption, smoke and sulfuric acid aerosol were present throughout the atmosphere and the 2 aerosol types were preferentially partitioned to higher (smoke) and lower (sulfuric acid) altitudes. Herein, we present an evaluation of the performance of this classification scheme within the context of the aforementioned case-study events followed by a brief discussion of this method's applicability to other events as well as its limitations.
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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 | 0.000 | 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".