Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE‐P) observations to estimate Asian sources of carbon monoxide
Bibliographic record
Abstract
We use an inverse model analysis to compare the top‐down constraints on Asian sources of carbon monoxide (CO) in spring 2001 from (1) daily MOPITT satellite observations of CO columns over Asia and the neighboring oceans and (2) aircraft observations of CO concentrations in Asian outflow from the TRACE‐P aircraft mission over the northwest Pacific. The inversion uses the maximum a posteriori method (MAP) and the GEOS‐CHEM chemical transport model (CTM) as the forward model. Detailed error characterization is presented, including spatial correlation of the model transport error. Nighttime MOPITT observations appear to be biased and are excluded from the inverse analysis. We find that MOPITT and TRACE‐P observations are independently consistent in the constraints that they provide on Asian CO sources, with the exception of southeast Asia for which the MOPITT observations support a more modest decrease in emissions than suggested by the aircraft observations. Our analysis indicates that the observations do not allow us to differentiate source types (i.e., anthropogenic versus biomass burning) within a region. MOPITT provides ten pieces of information to constrain the geographical distribution of CO sources, while TRACE‐P provides only four. The greater information from MOPITT reflects its ability to observe all outflow and source regions. We conducted a number of sensitivity studies for the inverse model analysis using the MOPITT data. Temporal averaging of the MOPITT data (weekly and beyond) degrades the ability to constrain regional sources. Merging source regions beyond what is appropriate after careful selection of the state vector leads to significant aggregation errors. Calculations for an ensemble of realistic assumptions lead to a range of inverse model solutions that has greater uncertainty than the a posteriori errors for the MAP solution. Our best estimate of total Asian CO sources is 361 Tg yr−1, over half of which is attributed to east Asia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".