IASI carbon monoxide validation over the Arctic during POLARCAT spring and summer campaigns
Bibliographic record
Abstract
Abstract. In this paper, we provide a detailed comparison between carbon monoxide (CO) data measured by the Infrared Atmospheric Sounding Interferometer (IASI)/MetOp and aircraft observations over the Arctic. The CO measurements were obtained during North American (NASA ARCTAS and NOAA ARCPAC) and European campaigns (POLARCAT-France, POLARCAT-GRACE and YAK-AEROSIB) as part of the International Polar Year (IPY) POLARCAT activity in spring and summer 2008. During the campaigns different air masses were sampled including clean air, polluted plumes originating from anthropogenic sources in Europe, Asia and North America, and forest fire plumes originating from Siberia and Canada. The paper illustrates that CO-rich plumes following different transport pathways were well captured by the IASI instrument, in particular due to the high spatial coverage of IASI. The comparison between IASI CO total columns, 0–5 km partial columns and profiles with collocated aircraft data was achieved by taking into account the different sensitivity and geometry of the sounding instruments. A detailed analysis is provided and the agreement is discussed in terms of information content and surface properties at the location of the observations. For profiles, the data were found to be in good agreement in spring with differences lower than 17%, whereas in summer the difference can reach 20% for IASI profiles below 8 km for polluted cases. For total columns the correlation coefficients ranged from 0.15 to 0.74 (from 0.47 to 0.77 for partial columns) in spring and from 0.26 to 0.84 (from 0.66 to 0.88 for partial columns) in summer. A better agreement is seen over the sea in spring (0.73 for total column and 0.78 for partial column) and over the land in summer (0.69 for total columns and 0.81 for partial columns). The IASI vertical sensitivity was better over land than over sea, and better over land than over sea ice and snow allowing a higher potential to detect CO vertical distribution during summer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".