Bibliographic record
Abstract
Abstract. Volatile organic compounds (VOCs) play an important role in modulating the atmosphere’s oxidizing capacity and affect tropospheric ozone, carbon monoxide, formaldehyde, and organic aerosol formation. Space-based observations can provide powerful global information to advance our knowledge of these processes and their changes over time. We present here the development of new retrievals for four key VOCs (methanol, ethene, ethyne, and HCN) based on thermal infrared radiance observations from the satellite-borne Cross-track Infrared Sounder (CrIS). We update the Retrieval of Organics from CrIS Radiances (ROCR) algorithm developed previously for isoprene to explicitly account for the spectral signal dependence on the VOC vertical profile shape, and apply this updated retrieval (ROCRv2) to derive column abundances for the targeted species across the full Suomi-NPP CrIS record (2012–2023). The CrIS data are well-correlated with ground-based Network for the Detection of Atmospheric Composition Change (NDACC) retrievals for methanol (r=0.77–0.84); HCN and ethyne exhibit lower correlations (r=0.36–0.44 and 0.56–0.65, respectively) with an apparent 40 % CrIS/NDACC disparity for ethyne. The results reveal robust global distributions of the target VOCs from known biogenic, biomass burning, and industrial source regions, and demonstrate the impact of anomalous events such as the 2015–2016 El Niño. They also highlight the importance of accurate vertical profile constraints when evaluating and interpretating thermal infrared data records. Initial comparisons of the CrIS observations to predicted VOC distributions from the GEOS-Chem chemical transport model point to large uncertainties in our current understanding of the atmospheric ethene budget as well as to underestimated HCN, ethyne, and methanol sources.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.297 | 0.202 |
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".