Uncertainty in limb aerosol measurements following the Raikoke eruption
Bibliographic record
Abstract
The eruption of Raikoke on June 22nd, 2019 was one of the largest in recent decades, spewing approximately 1.5 Tg of sulfur up to 17 km altitude. This eruption has been widely studied using a combination of climate models and measurement systems, including ground based lidars, in situ particle counters, and a variety of satellite platforms. The early plume has been well categorized by high-resolution measurements from CALIPSO, MODIS, VIIRS, IASI and other nadir viewing instruments, but as the plume ages investigation often shifts to limb sounding instruments that provide greater sensitivity to lower aerosol levels. These instruments have proven critical in understanding the long-term radiative and climatic impacts of stratospheric aerosol burdens after these explosive events, but the complexity of the measurements, sampling, and retrievals has made error characterization in high-loading conditions difficult. This work explores systematic biases in limb measurements after the Raikoke eruption due to a variety of factors often implicit in the retrievals and analysis. Near-coincident CALIPSO, SAGE III and OMPS-LP measurements are used to investigate saturation of limb-sounding measurement in the early plume. The recent OMPS-LP v2 stratospheric aerosol product is compared with the University of Saskatchewan product to investigate benefits and drawbacks of the tomographic approach. SAGE III measurements are used as a validation when available although coverage limitations preclude comparisons in the thickest parts of the plume. This work highlights the subtleties in comparing limb observations, with implications for model comparisons after large events such as volcanic eruptions and forest fires. Not only in the early plume, where sampling can be sparse, but also in the weeks and months following the eruption.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".