Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts

• Published in: Atmospheric chemistry and physics Vol. 21; no. 6; pp. 5117 - 5136
• Main Authors: Barré, Jérôme, Aben, Ilse, Agustí-Panareda, Anna, Balsamo, Gianpaolo, Bousserez, Nicolas, Dueben, Peter, Engelen, Richard, Inness, Antje, Lorente, Alba, McNorton, Joe, Vincent-Henri Peuch, Radnoti, Gabor, Ribas, Roberto
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 01.04.2021; Copernicus Publications
• Subjects: Albedo; Anomalies; Anthropogenic factors; Atmospheric monitoring; Bias; Classification; Climate change; Drill pipe; Drilling; Emission inventories; Emission measurements; Emissions; Emissions control; Greenhouse gases; High resolution; Landfill; Methane; Monitoring; Monitoring instruments; Nitrogen dioxide; Plumes; Regions; Resolution; Retrieval; Satellite observation; Satellites; Submarine pipelines; Middle East; United States > US
• ISSN: 1680-7316; 1680-7324
• DOI: 10.5194/acp-21-5117-2021

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.