Comparison of VAAC atmospheric dispersion models using the 1 November 2004 Grimsvötn eruption
Bibliographic record
Abstract
Abstract The robustness of the Numerical Atmospheric‐dispersion Modelling Environment (NAME) for forecasting the dispersion of volcanic ash clouds is investigated by comparing the output from different Volcanic Ash Advisory Centre (VAAC) models initialised using the parameters for the 2004 Grimsvötn, Iceland, volcanic eruption. London, Darwin, Washington, Montreal and Toulouse VAAC dispersion models are all run operationally as if responding to the eruption. Comparison of the model set‐ups reveals differing approaches between the VAACs for model averaging times, ash release rates, and thresholds for defining the ash cloud, amongst others. The importance of these factors is considered in detail. Despite using different weather conditions and having different structures, the models all demonstrate strong similarities for forecasting regional ash cloud transport. The dispersal of volcanic ash is simulated over Scandinavia and as far as Eastern Europe in all cases. Greater variations are seen between the forecast ash concentrations for different aircraft flight levels. The model forecasts are highly dependent on the amount of eruption information available at the time. Copyright © 2007 Royal Meteorological Society
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".