Meteorological modeling of winter and summer periods for the Swiss-Canadian research project on aerosol modeling (SCRAM)
Bibliographic record
Abstract
The focus of the Swiss-Canadian project SCRAM is the simulation of formation and transport of atmospheric aerosols using two chemical transport models (CTM): the <u>C</u>omprehensive <u>A</u>ir Quality <u>M</u>odel with E<u>x</u>tensions (CAMx) and the Community Multiscale Air Quality (CMAQ) modeling system, which includes the <u>M</u>odel of <u>A</u>erosol <u>D</u>ynamics, <u>R</u>eaction, <u>I</u>onization, and <u>D</u>issolution (MADRID). Both CTMs are driven by the meteorological model MM5. Two regions will be investigated, Switzerland and Southern Ontario (Canada).<br /><br />For Switzerland, a winter period during January and February 2006 is investigated. This period is characterized by elevated aerosol concentrations due to anti-cyclonic conditions associated with temperature inversions and low wind patterns. Three nested domains were defined for the MM5 simulations. The coarse domain includes Central and Southern Europe, the fine domains are focused on Switzerland. MM5 was initialized by assimilated data of the <u>aL</u>pine <u>Mo</u>del (aLMo), the forecast model of MeteoSwiss. Snow information was taken from aLMo data and from snow cover maps based on NOAA AVHRR satellite data. The aLMo data was also used to perform a 4-dimensional data assimilation (FDDA). Preliminary results show that the wind fields show an acceptable agreement with measurements at moderate to high wind speed, whereas the agreement at low wind speed is worse.<br /><br />The three Canadian domains cover Eastern North America, the Great Lakes and Southern Ontario, respectively. MM5 forecasts were performed for a period between July and September 2001. An example of the wind field is given.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".