TROPOMI SO2 column retrievals: validation, inter-comparison with other satellite data sets and algorithm evolution
Bibliographic record
Abstract
Since nearly two years, the operational SO2 product from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5 Precursor (S5P) platform has provided important information on volcanic and anthropogenic SO2 emissions, with an unprecedented level of details. In this presentation, we critically discuss the advantages and disadvantages of the current operational algorithm in light of the validation results obtained so far, and present how the retrieval scheme could evolve in the future. In the first part, we briefly present the main algorithm features and an overview of the SO2 product validation. One challenge in this respect is the current lack of ground-based SO2 measurements for anthropogenic source regions. We therefore rely largely on comparisons with other satellite datasets (e.g., OMI and OMPS). The main lesson learnt is that satellite SO2 retrievals generally agree very well for large SO2 columns (mostly volcanic) while persisting differences exist for low columns when different algorithms are compared. This motivates the second part of the presentation which aims at extensively comparing the results from existing S5P SO2 operational and scientific algorithms, notably DOAS and PCA retrievals (or other alternative approaches). Here, all configuration settings and auxiliary data (e.g. absorption cross-sections) are aligned in order to better understand the differences through sensitivity tests. This effort is not only important to improve the TROPOMI SO2 results but it is also particularly relevant in the context of the forthcoming Sentinel-4 mission that will mainly probe weak anthropogenic SO2 sources. The last part of the presentation gives a general overview of new features planned for the next versions of the operational SO2 algorithm.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".