Detecting and Assessing Trends of CFCs and Substitutes from IASI Measurements
Bibliographic record
Abstract
The Infrared Atmospheric Sounding Instrument (IASI) on the Metop satellite suite has just achieved 10 years of continuous operation. The instrument stability and the consistency between the different instruments on the successive Metop (A, B and C) is remarkable and offer the potential to investigate the trends in the concentration of various species better than with any other previous or current hyperspectral IR sounder. The low noise of IASI radiances is also such that even weakly absorbing species can be identified, on single or at least on averaged spectra. In this work we exploit the first decadal record of IASI measurements to (1) detect and monitor halogenated compounds regulated by the Montreal protocol (CFCs) or used as substitutes (HCFCs, HFCs), as well as fluorinated compounds (CF4, SF6) and potentially short lived chlorine species, for which substantial emissions are suspected (2) give a first assessment of the trend evolution of these species over the 2008-2018 period covered by IASI on Metop-A. This is done by targeting various geographical areas on the globe and looking at remote ocean and continental source regions separately. The trend evolution in the different compounds, either negative or positive, is validated against that from ground-based measurement network. We will conclude by assessing the usefulness of IASI and follow-on mission to contribute to global measurements of ozone depleting substances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".