Detection Limits of the GHGSat Constellation for Carbon Dioxide and Methane
Bibliographic record
Abstract
GHGSat launched its first satellite dedicated to carbon dioxide monitoring in October 2023, joining GHGSat’s ten satellite methane-sensing constellation. All the satellites are designed to measure and attribute emissions at the facility level, leveraging their ~30 meter-scale spatial resolution. Understanding the detection threshold of both CO2 and CH4 satellites is crucial, not only as a fundamental performance metric, but also for interpreting observations where no emissions are detected (null observations). This understanding is especially important when combining observations from multiple sites or times. For methane-sensitive satellites, GHGSat has built a large dataset of controlled releases, including both self-organized and third-party single-blind studies. Analysis of this dataset shows a detection threshold of 102 kg/hr, with a 50% probability of detection (PoD) at a wind speed of 3 m/s. One shortcoming is that controlled releases repeatedly measure the same sites at varying source rates, and the number of controlled release sites is limited. These sites might not represent the full spectrum of measurement conditions encountered by the constellation around the world. To address this issue, we adapt the non-linear PoD model developed by Conrad et al.[1] with the goal of providing site- and time-specific detection thresholds. We will also present an update on the performance of GHGSat’s first CO2-sensitive satellite. We will highlight the difference between CH4 and CO2 point source detection such as co-emission of CO2 with aerosols, multiple release points at a given facility and the high elevation of the release points. We will also present a preliminary analysis of detection threshold by comparing detection events and continuous emission monitoring data available from some power plants. [1] Conrad, B. M., Tyner, D. R. & Johnson, M. R. Robust probabilities of detection and quantification uncertainty for aerial methane detection: examples for three airborne technologies. Remote Sens. Environ. 288, 113499 (2023).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".