Ghost Plumes: Artificial splitting of greenhouse gas emission plumes in passive remote sensing observations in special viewing geometries
Bibliographic record
Abstract
Spectrally high-resolution passive remote sensing imaging spectrometers are becoming increasingly important for reliable quantification of anthropogenic greenhouse gas (GHG) emissions from a wide variety of carbon dioxide (CO2) and methane (CH4) sources. These nadir-looking air- or spaceborne instruments collect backscattered solar radiation from the Earth's surface, from which 2D atmospheric concentration maps of CO2 and CH4 are retrieved. Using, for example, mass balance approaches, emission rates can be derived from the observed GHG concentration gradients or plumes.Depending on the atmospheric conditions, these plumes can be Gaussian-like shaped or severely distorted by the prevailing turbulence during a single overpass. In the case of a calm atmosphere and special viewing geometries, combined with an elevated emission height, such as CO2 emissions from a coal-fired power plant chimney, the observed plume appears widened, or even two plumes are detected from the same point source in the imaging data. This secondary plume is shifted in the opposite direction to the position of the sun and the effect is most pronounced close to the emission source and the higher the point of release and the solar zenith angle (sza) are. The effect is less noticeable as the gases are better mixed both horizontally and vertically down to the surface when advecting further downwind of the source.In this work, we will analyse passive remote sensing observations from the MAMAP2D-Light imaging spectrometer collected over a coal-fired power plant near Edmonton, Canada, during the CoMet 2.0 Arctic mission in 2022. The power plant was investigated for distinct double plume structures on two different days with near-perfect conditions (moderately high sza and sun perpendicular to the prevailing wind direction). We will compare these with simultaneously acquired observations from an active lidar remote sensing instrument (CHARM-F) flown aboard the same aircraft. As the CHARM-F instrument uses its own light source in the nadir viewing geometry, no plume splitting is expected. We will also show that the plume broadening or splitting in the passive remote sensing instrument does not lead to a double counting of molecules and thus not to an increased emission rate of the power plant estimated from the observations. Furthermore, we compare the MAMAP2D-Light concentration gradients with Gaussian plume model simulations using the conditions encountered during the flight, which also show a similar plume widening or double-plume structure as observed in the real measurements. This effect can, for example, also be used in an inverse manner to retrieve the plume height of emissions. Conclusions on the conditions this plume splitting is observed by imaging spectrometer will be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".