Meter-scale retrieval of industrial methane emission using GHGSat’s satellite constellation
Bibliographic record
Abstract
To reduce green house gases emissions, it is crucial to be able to give actionable feedback to industrial facility operators on their emissions. For this purpose GHGSat is building a constellation of satellites capable of monitoring and quantifying emissions from individual sites. In 2016, GHGSat launched a demonstration satellite called GHGSat-D. It is the first and only satellite able to retrieve the methane column with a spatial resolution of less than 50 meters. We will present examples of detection and quantification of methane leaks in Central Asia using GHGSat-D. The retrieved methane column density shows plumes originating from known source locations and aligned with the local wind direction. The largest and most persistent of those sources was estimated to have an emission rate of 10-42 tons.h-1, a magnitude comparable to the Aliso Canyon and Ohio blowouts. The complementarity of GHGSat's observations with other satellites observations will be highlighted using a comparison of GHGSat-D and Sentinel-5P in the same Central Asia region. We will provide an update of GHGSat's constellation, with news from GHGSat-C1 (launch March 2020) and GHGSat-C2 (launch summer 2020). Lessons learned and improvements to the new satellites will be discussed. The anticipated vertical column density precision of GHGSat-C1 and C2 are 2% and 1% of background methane concentration respectively, compared to 13% for GHGSat-D. We will also introduce the data calibration and validation plans for the new satellites.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".