Remote sensing and inverse transport modeling of the Kasatochi eruption sulfur dioxide cloud
Bibliographic record
Abstract
An analytical inversion method is used to estimate the vertical profile of sulfur dioxide (SO2) emissions from the major 2008 eruption of Kasatochi Volcano, located on the Aleutian Arc, Alaska. The method uses satellite‐observed total SO2 columns from the Global Ozone Monitoring Experiment‐2 (GOME‐2), Ozone Monitoring Instrument (OMI), and Atmospheric InfraRed Sounder (AIRS) during the first 2 days after the eruption, and an atmospheric transport model, FLEXPART, to calculate the vertical emission profile. The inversion yields an emission profile with two large emission maxima near 7 km above sea level (asl) and around 12 km asl, with smaller emissions up to 20 km. The total mass of SO2 injected into the atmosphere by the eruption is estimated to 1.7 Tg, with ∼1 Tg reaching the stratosphere (above 10 km asl). The estimated vertical emission profile is robust against changes of the assumed eruption time, meteorological input data, and satellite data used. Using the vertical emission profile, a simulation of the transport extending for 1 month after the eruption is performed. The simulated cloud agrees very well with SO2 columns observed by GOME‐2, OMI, and AIRS until 6 days after the eruption, and the altitudes agree with both Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation measurements and ground‐based lidar observations to within 1 km. The method is computationally very fast. It is therefore suitable for implementation within an operational environment, such as the Volcanic Ash Advisory Centers, to predict the threat posed by volcanic emissions for air traffic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".