Enhanced monitoring of atmospheric methane from space over the Permian basin with hierarchical Bayesian inference
Bibliographic Data
| ID | 15544153 |
|---|---|
| Authors | Clayton Roberts (0000-0002-5184-7485, University of Cambridge, corresponding author), Oliver Shorttle (0000-0002-8713-1446, University of Cambridge), Kaisey S Mandel (0000-0001-9846-4417, Turing Institute), Matthew Jones (0000-0002-9272-5687, Shell (Netherlands)), Rutger Ijzermans (Shell (Netherlands)), Bill Hirst (0000-0003-2214-3144), Philip Jonathan (0000-0001-7651-9181, Shell (United Kingdom)) |
| Year | 2022 |
| Volume | 17 |
| Issue | 6 |
| Pages | 064037-064037 |
| Publication date | 2022-05-17 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac7062 |
| OpenAlex | W4225823742 |
| Language | EN |
| Citations received | 1 |
| References cited | 36 |
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
Atmospheric methane · Atmospheric sciences · Carbon dioxide · Climate change · Geography · Greenhouse gas · Meteorology · Methane · Radiative forcing · Remote sensing · Troposphere · Atmospheric and Environmental Gas Dynamics · Atmospheric Ozone and Climate · Chemistry · Environmental Science · Methane Hydrates and Related Phenomena · Geology
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
Rank-Normalization, Folding, and Localization
TROPOMI on the ESA Sentinel-5 Precursor
Assessment of methane emissions from the U.S. oil and gas supply chain
The Kolmogorov-Smirnov Test for Goodness of Fit
The ERA5 global reanalysis
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |