Enhanced monitoring of atmospheric methane from space with hierarchical Bayesian inference
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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 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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