Simulation of entrainment and transport of dust particles within North America in April 2001 (“Red Dust Episode”)
Bibliographic record
Abstract
A size‐resolved, multicomponent, regional‐scale particulate‐matter (PM) model named AURAMS (A Unified Regional Air‐quality Modelling System) has been used to study the entrainment and transport of dust from the southwestern United States and northwestern Mexico to eastern North America during the so‐called “Red Dust Episode” in April, 2001. Two different wind‐blown‐dust emission schemes, the Marticorena‐Bergametti‐Alfaro (MBA) scheme and the Shao scheme, were incorporated into AURAMS to simulate dust generation, and sensitivity analyses for various dust‐emission‐scheme parameters were performed. Comparison of the model results with satellite observations and surface measurements showed that the model simulation reasonably reproduced the temporal and spatial distribution of wind‐blown dust particles during the episode period in the downwind area of Oklahoma but not in the source region. Both dust‐emission schemes captured the main features of the dust transport. The dust‐emission‐scheme parameter most responsible for inaccurate prediction of wind‐blown‐dust emission in the source region in this study appeared to be soil moisture content. The soil grain size distribution and the soil plastic pressure were also shown to be important parameters that should be accurately estimated for better model performance. For further validation and reliable use of wind‐blown‐dust emission schemes, accurate field and remote sensing measurements of those parameters are imperative. The unusually fast transport of dust during the episode appeared to be due to vigorous vertical mixing and uplift of emitted dust. Appropriate parameterization of additional vertical mixing by sub‐grid‐scale convection may help to better predict the long‐range transport of dust storms.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".