Impact of stray light on greenhouse gas concentration retrievals and emission estimates as observed with the passive airborne remote sensing imager MAMAP2D-Light
Bibliographic record
Abstract
Abstract. MAMAP2D-Light is an airborne passive remote sensing push-broom spectrometer developed at the Institute for Environmental Physics at the University of Bremen to measure atmospheric methane (CH4) and carbon dioxide (CO2) column anomalies to quantify point-source emissions in the 1.6 μm-band. In its initial version, as flown in 2022, a significant stray light level of ~4 % of the measured signal has been observed, causing apparent error patterns in the retrieved CO2 and CH4 column anomalies. In this paper, we report the successful application of a stray light correction developed for the instrument. Measurement data collected during an airborne campaign in 2022 in Canada offer the unique opportunity to investigate the end-to-end impact of stray light and its correction on the retrieved CO2 and CH4 column anomalies, as well as the retrieved emission rates. Stray light caused apparent error patterns in the retrieved column anomaly maps. In nearly all cases, applying the CH4/CO2 proxy method reduced the stray-light-related column errors below the column noise, leading to comparable final emission rate estimates for proxy-only and stray-light-corrected data. In this paper, we additionally investigate the special scene contrast conditions under which the correction by applying the proxy method is no longer sufficient. Following the initial campaign in 2022, the stray light was reduced by ~ 75 % by the implementation of a hardware modification from 2023 onward.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".