Bibliographic record
Abstract
Abstract. GHGSat comprises a constellation of high spatial and spectral resolution satellites, specializing in monitoring methane emissions at 1.65 μm. This study investigates the ability to accurately retrieve both the methane mixing ratio enhancement (ΔXCH4) and aerosol optical depth (XAOD) simultaneously from simulated GHGSat observations that incorporate angle-dependent scattering information. Results indicate that the polarity of ΔXCH4 when neglecting aerosols changes from negative to positive as surface albedo increases, which is consistent with previous studies. Biases in ΔXCH4 are most pronounced when XAOD is not simultaneously retrieved, ranging from −3.0 % to 6.3 % with a 0.1 AOD, a 60° solar zenith angle, and a 0.2 surface albedo for the nadir-only retrieval. Using multiple satellite viewing angles during the GHGSat observation sequence with a scattering angle ranging from 100° to 140°, the study shows that the mean bias and standard deviation of XCH4 are within 0.2 % and 2.7 % relative to the background. The correlation between simultaneously retrieved ΔXCH4 and XAOD shifts from positive to negative as surface albedo increases and aerosol asymmetry factor decreases, signifying a transition of the dominating aerosol effect from aerosol-only scattering to aerosol-surface multiple scattering. The variety of scattering angle ranges has little impact on the performance of the multi-angle viewing method. This study improves the understanding of the aerosol impact on the GHGSat ΔXCH4 retrieval and provides guidance for improving future GHGSat-like point-source imagers.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.197 | 0.167 |
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".