Aerosol Composition and Extinction of the 2022 Hunga Plume Using CALIOP
Bibliographic record
Abstract
Abstract. We use the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument to determine the microphysical properties of the stratospheric aerosol plume after the Hunga eruption in 2022, the largest so far after Pinatubo in 1991. In the early stages, low depolarization (<2 %) is found everywhere except in patches of high depolarization (up to 35 %) detected within the plumes of sulfur compounds up to 3 days after the eruption. As standard CALIOP L2 products are not operational in the case of the Hunga aerosol plume, we implement an iterative method of successive approximations to retrieve extinction profiles, by estimating the aerosol optical depth (AOD) and then the Lidar Ratio (LR). The AOD of the plume at 532 nm is between 0.5 and 1.25 on the first four days, then decreases rapidly and stabilizes at 0.047 ± 0.011 for March 2022. LR, initially above 70 sr, is estimated at 48 ± 6 sr between late January and late March 2022. Results are compared and validated with the solar occultation instrument SAGE III (Stratospheric Aerosol and Gas Experiment) on board the International Space Station (ISS) and Mie calculations. A comparison with limb-viewing instruments highlights significant quantitative disagreements in extinction and AOD estimates, which we attribute, in part, to the unusual size distribution of the aerosols within the Hunga plume.
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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 | 0.001 | 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".