Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE‐Asia: 2. Model simulation and validation
Bibliographic record
Abstract
A size‐segregated soil dust emission and transport model, Northern Aerosol Regional Climate Model (NARCM), was used to simulate the production and transport of Asian soil dust during the Aerosol Characterization Experiment‐Asia (ACE‐Asia) period from March to May 2001. The model is driven by the NCEP reanalyzed meteorology and has all the atmospheric aerosol physical processes of soil dust: production, transport, growth, coagulation, and dry and wet deposition. A Chinese soil texture map that infers the soil grain‐size distribution with 12 categories was generated to drive the size‐distributed soil dust emission scheme [ Alfaro et al. , 1997 ; Marticorena and Bergametti , 1995 ]. The size distribution of vertical dust flux was derived from the observed surface dust‐size distribution in the desert regions. Parameters applicable to the Asian deserts for the dust emission scheme are assessed. Model simulations were compared with ground‐based measurements in East Asia and North America and with satellite measurements for the same period of time. The model captured most of the dust mobilization episodes during this period in China and reasonably simulated the concentrations in source regions and downwind areas from East China to western North America. About 252.8 Mt of soil dust below d < 40 μm was estimated to be emitted in the East Asian deserts between 1 March and 31 May 2001 with ∼60% attributed to four major dust storms. The vertical dust loadings above 700 hPa correlate reasonably well with Total Ozone Mapping Spectrometer aerosol index (TOMS AI) observations. The sensitivity analysis of model performance to soil size distribution, water moisture, and meteorology was carried out with the observational data to establish the most appropriate parameters and conditions for the Chinese soil dust production and transport.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".