Retrieval of vertical columns of sulfur dioxide from SCIAMACHY and OMI: Air mass factor algorithm development, validation, and error analysis
Bibliographic record
Abstract
We develop an improved retrieval of sulfur dioxide (SO2) vertical columns from two satellite instruments (SCIAMACHY and OMI) that measure ultraviolet solar backscatter. For each SCIAMACHY and OMI observation, a local air mass factor (AMF) algorithm converts line‐of‐sight “slant” columns to vertical columns using altitude‐dependent scattering weights computed with a radiative transfer model (LIDORT), weighted by relative vertical SO2 profile (shape factor) determined locally with a global atmospheric chemistry model (GEOS‐Chem). The scattering weights account for viewing geometry, surface albedo, cloud scattering, absorption by ozone, and scattering and absorption by aerosols. Absorption of radiation by mineral dust can reduce seasonal mean instrument sensitivity by 50%. Mean SO2 shape factors simulated with GEOS‐Chem and used in the AMF calculation are highly consistent with airborne in situ measurements (INTEX‐A and INTEX‐B); differences would affect the retrieved SO2 columns by 10%. The retrieved vertical columns are validated with coincident airborne in situ measurements (INTEX‐A, INTEX‐B, and a campaign over east China). The annual mean AMF errors are estimated to be 35–70% in polluted regions (e.g., East Asia and the eastern United States) and less than 10% over clear ocean regions. The overall SO2 error assessment is 45–80% for yearly averages over polluted regions. Seasonal mean SO2 columns retrieved from SCIAMACHY and OMI for 2006 are significantly spatially correlated with those from GEOS‐Chem, in particular over the United States (r = 0.85 for SCIAMACHY and 0.82 for OMI). A sensitivity study confirms the sensitivity of SCIAMACHY and OMI to anthropogenic SO2 emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".