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Methane is a strong greenhouse gas, with a higher radiative forcing per unitmass and shorter atmospheric lifetime than carbon dioxide. The remote sensingof methane in regions of industrial activity is a key step toward the accuratemonitoring of emissions that drive climate change. Whilst the TROPOsphericMonitoring Instrument (TROPOMI) on board the Sentinal-5P satellite is capableof providing daily global measurement of methane columns, data are oftencompromised by cloud cover. Here, we develop a statistical model which usesnitrogen dioxide concentration data from TROPOMI to efficiently predict valuesof methane columns, expanding the average daily spatial coverage ofobservations of the Permian basin from 16% to 88% in the year 2019. Theaddition of predicted methane abundances at locations where direct observationsare not available will support inversion methods for estimating methaneemission rates at shorter timescales than is currently possible.

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