Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network

Regional methane emissions monitoring is rapidly expanding with increased coverage of surface, airborne, and satellite instruments. We pilot a multitiered observing system in the Los Angeles Basin. We combine surface methane measurements from the Los Angeles Megacities Carbon Project, mountaintop retrievals from the CLARS‐FTS instrument, and space‐based XCH4 retrievals from the TROPOMI instrument into a single monitoring framework. We simulate these observations using a high‐resolution tracer transport model. Using inverse methods, we compare the sensitivity of each observing system component to various emissions sources. Combining multiple observing system into one framework allows for increased spatial and temporal sensitivity to methane emissions. We find a close correspondence between these inverse flux trends and independent airborne AVIRIS‐NG methane plume trends over a large landfill in the Los Angeles Basin. These results show that multitiered observing systems can reveal insights about sub‐basin scale methane emissions, which can be used to drive decision support. Plain Language Summary Methane is a powerful greenhouse gas. In order to effectively reduce its atmospheric concentrations, we need advanced methane observing strategies to pinpoint large emissions on small spatial scales. In this study, we combine surface, mountaintop, and satellite observations of methane over Los Angeles (called a multitiered observing system) and use these data to infer information about urban methane emissions. We assess how much information each component of the observing system provides to this analytics system. We validate our findings with independent airborne methane fluxes derived from the AVIRIS‐NG airborne instrument over a large landfill. Both systems detected large emission reductions that resulted from improved management practices. A multitiered observing and analytics system can potentially provide sub‐basin scale decision support for methane mitigation.