Methanol from TES global observations: retrieval algorithm and seasonal and spatial variability
Bibliographic record
Abstract
OH) retrieval algorithm, along with initial global results showing the seasonal and spatial distribution of methanol in the lower troposphere. The full development of the TES methanol retrieval is described, including microwindow selection, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem chemical transport model. Retrieval simulations and a sensitivity analysis using the developed retrieval strategy show that TES: (i) generally provides less than 1.0 piece of information, (ii) is sensitive in the lower troposphere with peak sensitivity typically occurring between ~900-700 hPa (~1-3 km) at a vertical resolution of ~5 km, (iii) has a limit of detectability between 0.5 and 1.0 ppbv Representative Volume Mixing Ratio (RVMR) depending on the atmospheric conditions, corresponding roughly to a profile with a maximum concentration of at least 1 to 2 ppbv, and (iv) in a simulation environment has a mean bias of 0.16 ppbv with a standard deviation of 0.34 ppbv. Applying the newly derived TES retrieval globally and comparing the results with corresponding GEOS-Chem output, we find generally consistent large-scale patterns between the two. However, TES often reveals higher methanol concentrations than simulated in the Northern Hemisphere spring, summer and fall. In the Southern Hemisphere, the TES methanol observations indicate a model overestimate over the bulk of South America from December through July, and a model underestimate during the biomass burning season.
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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 | 0.001 |
| 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) | 0.001 | 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".