Evaluation of operational radiances for the Measurements of Pollution in the Troposphere (MOPITT) instrument CO thermal band channels
Bibliographic record
Abstract
The ability of operational radiative transfer models to accurately predict remote sensing instrument observations (e.g., calibrated radiances) over a wide variety of geophysical situations is critical to the performance of trace gas retrieval algorithms. As part of the validation of the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument, we present a technique for comparing operational calibrated thermal band (4.7 μm) Earth‐view MOPITT radiances with corresponding values calculated using the MOPITT operational radiative transfer model. In situ carbon monoxide (CO) profiles sampled from aircraft in coordination with MOPITT overpasses serve as the foundation for MOPITT validation. Characteristics of radiance errors due to in situ sampling characteristics, CO temporal and spatial variability, and surface emissivity are discussed. Results indicate that radiance biases for most of the MOPITT thermal channel radiances are typically on the order of 1%. Observed radiance biases are largest and most variable for the pressure modulation cell difference‐signal radiances, probably because of the lack of in situ data in the upper troposphere and lower stratosphere.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".