Around the world in 17 days - hemispheric-scale transport of forest fire smoke from Russia in May 2003
Bibliographic record
Abstract
Abstract. In May 2003, severe forest fires in southeast Russia resulted in smoke plumes extending widely across the Northern Hemisphere. This study combines satellite data from a variety of platforms (Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Earth Probe Total Ozone Mapping Spectrometer (TOMS) and Global Ozone Monitoring Experiment (GOME)) and vertical aerosol profiles derived with Raman lidar measurements with results from a Lagrangian particle dispersion model to understand the transport processes that led to the large haze plumes observed over North America and Europe. The satellite images provided a unique opportunity for validating model simulations of tropospheric transport on a truly hemispheric scale. Transport of the smoke occurred in two directions: Smoke travelling northwestwards towards Scandinavia was lifted over the Urals and arrived over the Norwegian Sea. Smoke travelling eastwards to the Okhotsk Sea was also lifted, it then crossed the Bering Sea to Alaska from where it proceeded to Canada and was later even observed over Scandinavia and Eastern Europe on its way back to Russia. Not many events of this kind, if any, have been observed, documented and simulated with a transport model comprehensively. The total transport time was about 17 days. We compared transport model simulations using meteorological analysis data from both the European Centre for Medium-Range Weather Forecast (ECMWF) and the National Center for Environmental Prediction (NCEP) in order to find out how well this event could be simulated using these two datasets. Although differences between the two simulations are found on small scales, both agree remarkably well with each other and with the observations on large scales. On the basis of the available observations, it cannot be decided which simulation was more realistic.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".