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Record W4392578130 · doi:10.5194/egusphere-egu24-11333

Quantifying anthropogenic methane emissions and their uncertainties using very high spatial and spectral resolution satellite and airborne data

2024· preprint· en· W4392578130 on OpenAlexaff
Quentin Taupin, Dirk Schüttemeyer, Marianne Girard, Marvin Knapp, A. Butz, Justyna Swolkień, R. A. Field, Heidi Huntrieser, E. O. Forster, Gerrit Kuhlmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsGHGSat (Canada)
Fundersnot available
KeywordsEnvironmental scienceSatelliteRemote sensingMethaneMethane emissionsGreenhouse gasMeteorologyImage resolutionAtmospheric sciencesClimatologyGeographyComputer scienceGeologyOceanographyPhysics

Abstract

fetched live from OpenAlex

Methane is one of the most powerful greenhouse gases that has contributed to about a third of the 2010-2019 global warming relative to the pre-industrial times in 1850-1900. The Upper Silesian Coal Basin in southern Poland is one of the strongest anthropogenic methane (CH4) emitters in Europe, with emissions ranging from 228 to 339 ktCH4yr-1. In that region, ventilation shafts and drainage stations used in coal mines are the main sources of CH4 emissions, of which the mass flows and their sources of uncertainties can be assessed using an adapted version of the Integrated Mass Enhancement (IME) method.This challenge can be tackled using observations from Fabry-Perot imaging Short Wave InfraRed (SWIR) spectrometers onboard of the GHGSat aircraft and GHGSat satellite constellation. GHGSat acquisitions were made in June and July 2022 during a campaign including other measurements and which was partially funded in the framework of UNEP’s International Methane Emissions Observatory. The GHGSat level-2 data provide full-swath CH4 concentration estimations and filtered CH4 plumes with spatial resolutions < 1.1 m on a swath width < 0.75 km for the aircraft, and < 28 m on a swath width < 12 km for the satellites, both featuring a spectral resolution of 0.1 nm. Furthermore, another version of the methane plumes was generated through a Z-test filter.These observations were complemented with local wind profile and plume profile observations to estimate the effective wind speed that accounts for the effects of turbulent diffusion in the plume dissipation. This was achieved using two instruments from the University of Heidelberg: a wind lidar measuring the wind profile up to 200 m height at a sampling rate of ~8 seconds and a hyperspectral SWIR camera featuring a 1 min scanning time, a spatial resolution of 0.8 m and a spectral resolution of 7 nm. Since local wind profile measurements are rarely accessible, this study attempted to find a relationship between the effective wind speed for the methane plumes of that region as a function of the wind speed at 10 m height from the ERA5-Land reanalysis (spatial resolution of 9 km and temporal resolution of 1 h).Finally, a comparison is performed between the methane mass flow estimations derived from GHGSat satellites and aircraft observations with coinciding mass flow estimation from the CH4 safety sensors located inside four of the same ventilation shafts (data collected by AGH University of Kraków) and the hyperspectral camera in June and July 2022. Moreover, another comparison is done with data acquired from a helicopter towed probe (HELiPOD) operated by the DLR and the Technical University of Braunschweig over one of the same shafts in June 2022. While bottom-up inventories may have delays of a few years before being available and require a certain level of trust, satellites can solve these issues through a faster top-down approach but still with relatively high uncertainties and multiple sources. The findings presented in this study can help to quantify the level of contribution from the different sources of uncertainties with high resolution data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.280
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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