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Record W4392586208 · doi:10.5194/egusphere-egu24-5182

Combining TROPOMI with high-resolution satellites to detect, attribute, and monitor large methane emission events.

2024· preprint· en· W4392586208 on OpenAlexaff
Tobias A. de Jong, Joannes D. Maasakkers, Shubham Sharma, Berend J. Schuit, Matthieu Dogniaux, Paul Tol, Itziar Irakulis‐Loitxate, Cynthia A. Randles, Ilse Aben

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsGHGSat (Canada)
Fundersnot available
KeywordsMethaneRemote sensingEnvironmental scienceHigh resolutionMethane emissionsResolution (logic)Computer scienceGeologyChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Anthropogenic methane emissions play an important role in exacerbating climate change, and thus there is a need for accurate and timely monitoring and mitigation of these emissions. With daily global coverage, TROPOMI, onboard Sentinel-5P, maps methane concentrations at 5.5 x 7 km2 resolution and can detect methane super-emitters (>~8 t hr-1) globally [1,2]. Here, we show how we detect, attribute, and quantify methane emissions from super-emitters using TROPOMI in combination with information from high-resolution satellite instruments to support the UNEP IMEO Methane Alert Response System (MARS). To determine optimal targets for high-resolution hyperspectral observations (e.g. PRISMA, EnMAP), we combine longer term TROPOMI data over persistent emitters. When emissions are transient, we combine TROPOMI with data from non-targeted high-resolution band imagers (also known as multispectral sensors) such as Sentinel-2 and Sentinel-3 to trace emissions to facility-level emission sources, in particular oil and gas infrastructure [3]. We illustrate how the combination of satellites with different overpass times and different spatial resolutions gives a comprehensive picture of these emissions. To evaluate the detections, we compare methane enhancements retrieved from band imagers with values from TROPOMI. Even when overpass times do not match, we achieve this by using transient emissions that result in methane plumes with a constant total mass, once detached from the source. Finally, we show how combining information from multiple satellites enables critical evaluation of the winds taken from global reanalysis products that underlie almost all high-resolution emission quantifications based on mass-balance methods. References[1] Maasakkers JD, Varon DJ, Elfarsdóttir A, McKeever J, Jervis D, Mahapatra G, et al. Using satellites to uncover large methane emissions from landfills. Sci Adv 2022;8:eabn9683. https://doi.org/10.1126/sciadv.abn9683.[2] Irakulis-Loitxate I, Guanter L, Maasakkers JD, Zavala-Araiza D, Aben I. Satellites Detect Abatable Super-Emissions in One of the World’s Largest Methane Hotspot Regions. Environ Sci Technol 2022;56:2143–52. https://doi.org/10.1021/acs.est.1c04873.[3] Pandey, Sudhanshu, et al. "Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p." Remote Sensing of Environment 296 (2023): 113716. https://doi.org/10.1016/j.rse.2023.1137

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)0.001
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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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