Quantifying Emissions of CO and NO<sub>x</sub> Using Observations From MOPITT, OMI, TES, and OSIRIS
Bibliographic record
Abstract
Abstract We use the GEOS‐Chem four‐dimensional variational data assimilation system to estimate emissions of carbon monoxide (CO) and nitrogen oxides (NOx) in November 2009 and July 2010. We assimilated CO retrievals from The Measurements of Pollution In The Troposphere (MOPITT), ozone (O3) retrievals from the Tropospheric Emission Spectrometer (TES) and the Optical Spectrograph and InfraRed Imager System (OSIRIS), and NO2 columns from the Ozone Monitoring Instrument (OMI). By assimilating data for multiple chemical species with the four‐dimensional variational scheme we obtain a consistent chemical state over the assimilation period. The modeled O3 bias was reduced to less than 3.5 ppbv everywhere, relative to independent aircraft O3 data, except in the high‐latitude upper troposphere and lower stratosphere. We found that in November, the CO and NOx emission estimates obtained with the multiple species assimilation were generally comparable to those inferred from assimilating only MOPITT or OMI data, respectively, whereas in July the differences were larger. For the main anthropogenic source regions, the inferred North American and European emissions responded most strongly to the multispecies information in the assimilation. In July, North American and European CO emission estimates differed by 31% and 65% relative to the MOPITT‐only estimates, respectively. We also found large differences for the North American and European NOx emission estimates, which differed by 27% and 16% relative to the OMI‐only estimates, respectively. Our results highlight the potential benefit of exploiting the additional constraints offered by multispecies chemical data assimilation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".