Identification of smoke and sulfuric acid aerosol in SAGE III/ISS extinction spectra
Bibliographic record
Abstract
Abstract. We developed a technique to classify the composition of enhanced aerosol layers as either smoke or sulfuric acid aerosol using extinction spectra from the Stratospheric Aerosol and Gas Experiment III instrument aboard the International Space Station (SAGE III/ISS). 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 and present an evaluation of its performance using four case-study events of two moderate volcanic eruptions (2018 Ambae eruption and 2019 Ulawun eruption, both of which released <0.5 Tg of SO2) and two large wildfire events (2017 Canadian pyroCb and 2020 Australian pyroCb). We provide corroborative data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument to support these classifications. This method correctly classified smoke and sulfuric acid plumes in the case-study events >81 % and >99.5 % of the time, respectively. The application of this method to a large volcanic event (i.e., the 2019 Raikoke eruption; ≥1.5 Tg SO2) serves as an example of why this method is limited to small and moderate volcanic events as it incorrectly classified Raikoke's larger sulfuric acid particles as smoke. We evaluated the possibility of smoke being present in the stratosphere before and after the Raikoke eruption. While smoke was present during this time period it was insufficient to account for the magnitude of smoke classifications we observed. Therefore, while this method worked well for large-scale wildfire events and eruptions that inject less SO2, the size of the aerosol created by the Raikoke eruption was outside the applicable range of this method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".