Enhanced monitoring of atmospheric methane from space over the Permian basin with hierarchical Bayesian inference

TL;DR

This paper presents a hierarchical Bayesian model that leverages nitrogen dioxide data from satellite observations to significantly improve the spatial coverage of atmospheric methane monitoring over the Permian basin, aiding climate change mitigation efforts.

Contribution

The authors develop a novel statistical approach that predicts methane levels using NO2 data, increasing observational coverage from 16% to 88%, enabling more accurate emission estimates.

Findings

Expanded methane observation coverage from 16% to 88% in 2019.

Predicted methane data supports more precise emission rate estimations.

Method enhances satellite-based monitoring of industrial methane emissions.

Abstract

Methane is a strong greenhouse gas, with a higher radiative forcing per unit mass and shorter atmospheric lifetime than carbon dioxide. The remote sensing of methane in regions of industrial activity is a key step toward the accurate monitoring of emissions that drive climate change. Whilst the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinal-5P satellite is capable of providing daily global measurement of methane columns, data are often compromised by cloud cover. Here, we develop a statistical model which uses nitrogen dioxide concentration data from TROPOMI to efficiently predict values of methane columns, expanding the average daily spatial coverage of observations of the Permian basin from 16% to 88% in the year 2019. The addition of predicted methane abundances at locations where direct observations are not available will support inversion methods for estimating methane emission rates at shorter timescales than is currently possible.