Systematic detection of local CH 4 anomalies by combining satellite measurements with high-resolution forecasts

Abstract. In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration\nanomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric\nbudget and by biases in the satellite retrieval data. The method uses high-resolution (7 km  ×  7 km) retrievals of\ntotal column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are\ncombined with high-resolution CH4 forecasts ( ∼  9  km ) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide\ndepartures (observations minus forecasts) at close to the satellite\u0027s native resolution at appropriate time. Investigating these departures is an effective\nway to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the\nsynoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered\ndepartures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or\noverreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to\nlocal satellite retrieval biases linked to albedo and scattering issues.