Evaluating CO2 and CH4 absorption models with open-path dual-comb spectroscopy at the mauna loa observatory
Bibliographic record
Abstract
Remote-sensing measurements of atmospheric CO 2 require accurate absorption models to infer CO 2 concentration. To test the accuracy of these absorption models, we collected dual-comb spectra (in the near-infrared from 1570 nm - 1690 nm) across a 1 km long open path at NOAA’s Mauna Loa Atmospheric Baseline Observatory, which is the site of the longest record of atmospheric CO 2 measurements. These spectra cover the 30012, 30013, and 30014 CO 2 bands as well as the 2ν 3 CH 4 band. We fit these dual-comb spectra with several different CO 2 and CH 4 absorption models and find that the HITRAN2020 speed-dependent Voigt model without H 2 O broadening provides the smallest CO 2 concentration bias (0.1 %) from the Mauna Loa record. Fits using different line parameters agreed to better than 0.5 %. However, there are larger biases when fitting individual branches: CO 2 fits vary by ∼0.5 % between the P and R branches within a single band, with biases up to 1 % observed. Full-band CH 4 fits using HITRAN2008 result in -1.1 % concentration bias relative to the Mauna Loa record, with larger biases observed for newer HITRAN versions. Work of the US Government not subject to copyright.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".