A comparison of atmospheric dispersion model predictions with observations of SO<sub>2</sub> and sulphate aerosol from volcanic eruptions
Bibliographic record
Abstract
The UK Met Office's Numerical Atmospheric‐dispersion Modeling Environment (NAME) is used both operationally and for research investigations. It has previously been used to model volcanic ash at the London Volcanic Ash Advisory Centre (VAAC), including that from the eruptions in Iceland of Eyjafjallajökull in 2010 and Grímsvötn in 2011. In this paper, the ability of NAME to model the release and dispersion of volcanic SO2, the chemical processes leading to the production of sulphate aerosol, and the subsequent dispersion of sulphate aerosol, has been investigated. Sensitivity tests were carried out to investigate the suitability of the NAME chemistry scheme for use in both the troposphere and the stratosphere. The eruptions of Sarychev in 2009, Kasatochi in 2008 and Eyjafjallajökull in 2010 were simulated and results for SO2 column density and sulphate aerosol optical depth (AOD) were compared with satellite retrievals. NAME results compare favorably with available observations in terms of both geographical distribution and magnitude for all three cases. The NAME modeled values of SO2 show a correlation of 0.8 with the corresponding observations for Sarychev. Ninety percent of modeled values of northern hemisphere averaged sulphate AOD are within a factor of 2 of those observed for Kasatochi and 71% are within a factor of 2 of those observed for Sarychev. Although significant uncertainties are present in both the model and observations, this work demonstrates that NAME's current chemistry scheme shows promise as a tool for modeling SO2 and sulphate from volcanoes.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".