Enhanced monitoring of atmospheric methane from space with hierarchical Bayesian inference

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 accurately 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.

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