Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the Tropospheric Emission Spectrometer
Bibliographic record
Abstract
We assimilate ozone and CO retrievals from the Tropospheric Emission Spectrometer (TES) for July and August 2006 into the GEOS‐Chem and AM2‐Chem models. We show that the spatiotemporal sampling of the TES measurements is sufficient to constrain the tropospheric ozone distribution in the models despite their different chemical and transport mechanisms. Assimilation of TES data reduces the mean differences in ozone between the models from almost 8 ppbv to 1.5 ppbv. Differences between the mean model profiles and ozonesonde data over North America are reduced from almost 30% to within 5% for GEOS‐Chem, and from 40% to within 10% for AM2‐Chem, below 200 hPa. The absolute biases are larger in the upper troposphere and lower stratosphere (UT/LS), increasing to 10% and 30% in GEOS‐Chem and AM2‐Chem, respectively, at 200 hPa. The larger bias in the UT/LS reflects the influence of the spatial sampling of TES, the vertical smoothing of the TES retrievals, and the coarse vertical resolution of the models. The largest discrepancy in ozone between the models is associated with the ozone maximum over the southeastern USA. The assimilation reduces the mean bias between the models from 26 to 16 ppbv in this region. In GEOS‐Chem, there is an increase of about 11 ppbv in the upper troposphere, consistent with the increase in ozone obtained by a previous study using GEOS‐Chem with an improved estimate of lightning NOx emissions over the USA. Our results show that assimilation of TES observations into models of tropospheric chemistry and transport provides an improved description of free tropospheric ozone.
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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 | 0.000 |
| 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 | 0.000 |
| 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".