Improving the Retrieval of XCO <sub>2</sub> from Total Carbon ColumnNetwork Solar Spectra
Bibliographic record
Abstract
Abstract. High-resolution absorption spectra of the a1∆g←X3Σg− O2 band measured using cavity ring-down spectroscopy were fitted using the Voigt and speed-dependent Voigt line shapes. We found that the speed-dependent Voigt line shape was better able to model the measured absorption coefficients than the Voigt line shape. Total columns of O2 were retrieved from ground-based high-resolution absorption spectra from four Total Carbon Column Observing Network (TCCON) sites using both Voigt and speed-dependent Voigt line shapes to calculate absorption coefficients. A lower O2 concentration was retrieved with the speed-dependent Voigt line shape, with the difference increasing as a function of solar zenith angle. CO2 total columns were also retrieved from the same spectra using a Voigt line shape and speed-dependent Voigt with line mixing. The column-averaged dry-air mole fraction of CO2 (XCO2) was calculated using the CO2 and O2 columns retrieved with both line shapes from measurements made over a one-year period at the four sites and compared. The inclusion of speed dependence reduces the airmass dependence of XCO2. The TCCON empirical airmass correction factor for XCO2 derived from a year of measurements from TCCON sites at Darwin, Lamont, and Park Falls for XCO2 improved from −0.0071±0.0057 to −0.0012±0.0054 when speed dependence was included. XCO2 retrieved with the Voigt and speed-dependent Voigt line shapes was compared to aircraft profiles measured at 13 TCCON sites. The bias between the TCCON measurements and the integrated aircraft profile measurements was reduced from 0.9897±0.0005 to 1.0041±0.0005 for XCO2 retrieved with the Voigt and speed-dependent Voigt line shapes respectively. These results suggest that speed dependence should be included in the forward model when fitting near-infrared CO2 and O2 spectra to improve the accuracy of XCO2 measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".