Measurement of low‐altitude CO over the Indian subcontinent by MOPITT
Bibliographic record
Abstract
We show that the dayside MOPITT retrievals in the lower troposphere can provide useful information on surface sources of atmospheric CO over the Indian subcontinent. We find that MOPITT retrievals at 850 hPa show localized enhancements over the Indian subcontinent, which correlate with similar enhancements seen in the tropospheric NO2 columns from the SCIAMACHY instrument. In particular, high concentrations of CO over the Indo‐Gangetic basin and some prominent cities are captured in the lower‐tropospheric retrievals in spring. MOPITT averaging kernels (normalized to take into account the absorber amounts in the layers) indicate that the retrievals are sensitive to CO in the lower troposphere. In winter, MOPITT retrievals at 850 hPa can detect the strongest source areas over the eastern states of Bihar and West Bengal, thus confirming the so‐called “Bihar pollution pool,” which was detected earlier in the aerosol measurements by the multiangle imaging spectroradiometer (MISR) aboard Terra. The pollution features are consistent with the spatial distribution of CO emissions in India, as reflected in the GEOS‐Chem simulation of CO. Furthermore, these lower‐tropospheric features in the simulation are still present after smoothing the modeled fields using the MOPITT averaging kernels and a priori profile, demonstrating that the retrievals do have sensitivity in the lower troposphere. This work indicates that although MOPITT retrievals are often most sensitive to CO in the middle and upper troposphere, they do provide information on lower‐tropospheric CO in selected continental regions with strong thermal contrast and could be useful for pollution studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".