A satellite observation system simulation experiment for carbon monoxide in the lowermost troposphere
Bibliographic record
Abstract
We demonstrate the feasibility of using observing system simulation experiment (OSSE) studies to help define quantitative trace gas measurement requirements for satellite missions and to evaluate the expected performance of proposed observing strategies. The 2007 U.S. National Research Council Decadal Survey calls for a geostationary (GEO) satellite mission for atmospheric composition and air quality applications (Geostationary Coastal and Air Pollution Events Mission (GEO‐CAPE)). The requirement includes a multispectral (near‐infrared and thermal infrared) measurement of carbon monoxide (CO) at high spatiotemporal resolution with information on lowermost troposphere concentration. We present an OSSE to assess the improvement in surface CO characterization that would result from the addition of a GEO‐CAPE CO measurement to current low Earth orbit (LEO) thermal infrared‐only measurements. We construct instrument simulators for these two measurement scenarios and study the case of July 2004 when wildfires in Alaska and Canada led to significant CO pollution over the contiguous United States. Compared to a control experiment, an ensemble‐based data assimilation of simulated satellite observations in a global model leads to improvements in both the surface CO distributions and the time evolution of CO profiles at locations affected by wildfire plumes and by urban emissions. In all cases, an experiment with the GEO‐CAPE CO measurement scenario (overall model skill of 0.84) performed considerably better than the experiment with the current LEO/thermal infrared measurement (skill of 0.58) and the control (skill of 0.07). This demonstrates the advantages of increased sampling from GEO and enhanced measurement sensitivity to the lowermost troposphere with a multispectral retrieval.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".