Transmission Spectroscopy with the ACE-FTS Infrared Spectral Atlas of Earth: A Model Validation and Sensitivity Study
Bibliographic record
Abstract
ACE-FTS, the Atmospheric Chemistry Experiment - Fourier Transform Spectrometer onboard the Canadian Earth observation satellite "SciSat" is recording solar occultation spectra for about fifteen years. Five infrared atmospheric atlases for arctic summer and winter, midlatitude summer and winter, and the tropics and 31 limb rays (6 - 128 km) have been created by co-adding hundreds of cloud-free infrared spectra (2.2 - 13.3 mue) (Hughes et al., JQSRT 2014). These spectra provide a unique opportunity for model validation and to study the impact of individual molecules, spectral resolution, molecular spectroscopy data (HITRAN, GEISA, continua, etc.), and auxiliary data. Here we use GARLIC - Generic Atmospheric Radiative Transfer Line-by-Line Infrared Code (Schreier et al., JQSRT 2014) and compare observed and modeled "effective height spectra" obtained by integrating (summming) the entire limb sequence. This kind of spectra are typically used for remote sensing of (exo-)planetary atmospheres by transit spectroscopy, where only disk-averaged observations are possible. The Earth effective height spectrum varies between a few kilometers (in atmospheric window regions) and about 50 km in the CO2 v2 and v3 bands with small variations due to season and latitude. The largest impact on the transit spectra is due to water, carbon dioxide, ozone, methane, nitrous oxide, nitrogen, nitric acid, oxygen, and some chlorofluorocarbons (CFC11 and CFC12). The effect of further molecules considered in the modeling is either marginal or absent. The impact of spectroscopic input data on the model spectra is small. The best matching model with 17 molecules absorbing has a mean residuum of 0.4 km and a maximum difference of 2 km to the measured effective height.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".