Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the Tropospheric Emission Spectrometer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it