Preprints
https://doi.org/10.5194/egusphere-2026-4693
https://doi.org/10.5194/egusphere-2026-4693
15 Sep 2026
 | 15 Sep 2026
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Quantifying total methane emissions in the Permian Basin using MFIM: the MethaneSAT FLEXPART Inverse Model

Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy

Abstract. MethaneSAT was a spaceborne imaging spectrometer that observed column-averaged dry-air mole fractions of methane (XCH4) in the atmosphere from March 2024 to June 2025. The data have fine spatial resolution (110 m x 400 m at nadir), high precision (~3 ppb at 2 km x 2 km aggregation) and wide swath (220 km at nadir). The goal of MethaneSAT is to provide comprehensive emissions characterization for oil and gas production regions, spanning the gap between area flux mappers and point source imagers. In this paper we introduce the MethaneSAT FLEXPART Inverse Model (MFIM), an inverse modeling framework for quantifying total emissions and their spatial distribution from MethaneSAT observations. We use a Lagrangian particle dispersion model, the FLEXible PARTicle dispersion model (FLEXPART v.11), in time reversed mode to determine the influence of surface emissions on atmospheric concentrations across a MethaneSAT scene (aggregated to 0.02° x 0.02° resolution). We then obtain the optimum emissions field using a Hamiltonian Monte Carlo (HMC) sampler to solve the inverse problem.  We apply the inversion system to 9 scenes from the Permian Basin, yielding an annual emission rate of 3.49 ±1.43 Tg/yr (398 ±163 tons/hr). Emission estimates from MFIM resolve high emitting regions within the basin without the use of a spatially informed prior. MFIM leverages the strengths of the MethaneSAT observing system (104 independent observations) to produce spatially explicit, high-resolution (0.05° x 0.05°) emission estimates for application to monitoring and mitigation of methane emissions. Our results compare well to the MethaneSAT Level 4 product from the operational CORE (Column Observations to Regional Emissions) inversion algorithm, as well as other top-down studies in the region, lending confidence that regional methane flux estimates from MethaneSAT are robust against differences in modeled transport.

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Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy

Status: open (until 21 Oct 2026)

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Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
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Latest update: 15 Sep 2026
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Short summary
MethaneSAT was a designed to measure the concentration of methane in the atmosphere over oil and gas producing regions. Our modeling framework uses observations from MethaneSAT to estimate emissions from the Permian Basin. We show that the model accurately captures spatial patterns in emissions and compares well to other models that rely on different atmospheric transport. This data set is valuable for emissions monitoring and mitigation at high spatial resolution and with high precision.
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