Observations of near‐surface carbon monoxide from space using MOPITT multispectral retrievals
Bibliographic record
Abstract
Using both thermal infrared (TIR) and near infrared (NIR) channels of MOPITT (Measurements of Pollution in the Troposphere) on EOS‐Terra, we demonstrate the first coincident multispectral retrievals of carbon monoxide (CO) from space. Exploiting both TIR and NIR channels has been possible due to recent progress in characterizing NIR channel radiance errors. This has allowed us to trade off sensitivity to near surface CO for larger random errors in the combined retrieval. By examining retrieval diagnostics such as DFS (degrees of freedom for signal) and averaging kernels for the multispectral retrieval (TIR + NIR) as compared to the TIR‐only retrieval, we find that adding the NIR channel to the retrieval significantly increases sensitivity to CO, especially near the surface, but with high spatial variability due to surface albedo variations. The cases with the largest increases in DFS are over regions with low thermal contrast between the surface and lower atmosphere. In the tropics (23.4°S–23.4°N), the fraction of daytime land cases with at least 0.4 DFS in the surface layer (surface to 800 hPa) is 20% for TIR‐only retrievals compared to 59% for multispectral retrievals. Vertical resolution for the surface layer is also improved, in some cases from around 6 km for TIR‐only to roughly 1 km for TIR + NIR. Since we apply a single a priori CO profile (unlike MOPITT V4) and error covariance in all the retrievals reported here, these increases are due solely to the addition of the NIR channel. Enhanced sensitivity to near surface CO is especially evident in a case study for central/east Asia where source regions for urban areas with high population density are clearly identifiable. Although these retrievals are still a research product and require further validation and scientific evaluation, they demonstrate the increased sensitivity to CO in the lowermost troposphere that can be obtained from multispectral MOPITT data.
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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".