Retrievals of sulfur dioxide from the Global Ozone Monitoring Experiment 2 (GOME-2) using an optimal estimation approach: Algorithm and initial validation
Bibliographic record
Abstract
[1] We apply an optimal estimation algorithm originally developed for retrieving ozone profiles from the Global Ozone Monitoring Experiment (GOME) and the Ozone Monitoring Instrument (OMI) to make global observations of sulfur dioxide from the Global Ozone Monitoring Experiment 2 (GOME-2) on the MetOp-A satellite. Our approach combines a full radiative transfer calculation, retrieval algorithm, and trace gas climatologies to implicitly include the effects of albedo, clouds, ozone, and SO2 profiles in the retrieval. Under volcanic conditions, the algorithm may also be used to directly retrieve SO2 plume altitude. Retrieved SO2 columns over heavy anthropogenic pollution typically agree with those calculated using a two-step slant column and air mass factor approach to within 10%. Retrieval uncertainties are quantified for GOME-2 SO2 amounts; these are dominated by uncertainty contributions from noise, surface albedo, profile shape, correlations with other retrieved parameters, atmospheric temperature, choice of wavelength fitting window, and aerosols. When plume altitudes are also simultaneously retrieved, additional significant uncertainties result from uncertainties in the a priori altitude, the model's vertical layer resolution, and instrument calibration. Retrieved plume height information content is examined using the Mount Kasatochi volcanic plume on 9 August 2008. An a priori altitude of 10 km and uncertainty of 2 km produce degrees of freedom for signal of at least 0.9 for columns >30 Dobson units. GOME-2 estimates of surface SO2 are compared with in situ annual means over North America in 2008 from the Clear Air Status and Trends Network (CASTNET; r = 0.85, N = 65) and Air Quality System (AQS) and National Air Pollution Surveillance (NAPS; r = 0.40, N = 438) networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".