Towards regional CH4 inversions with ICON-ART assimilating satellite TROPOMI data over Europe

Inversion modeling is a top-down technique to infer greenhouse gas (GHG) emissions using atmospheric observations. In particular, the use of satellite retrievals have been attractive due to their advantage in dense spatial coverage compared to typically sparse surface networks.
The goal of this study is to assimilate satellite data at high spatial resolution to independently locate and quantify GHG sources and sinks, which can be used as a reference for carbon budget studies and policy makers. The findings also contribute as groundwork for the development of the Integrated GHG Monitoring System for Germany (ITMS).
For this purpose, we coupled the numerical weather prediction and atmospheric transport model ICON-ART with an Ensemble Kalman Filter (EnKF) based inversion system, using the CarbonTracker Data Assimilation Shell (CTDAS). We use column data (XCH4) measured by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, targeting anthropogenic CH₄ fluxes over Europe. Prior anthropogenic emissions are taken from the EDGARv4.3.2 inventory, while natural fluxes were derived from peatlands, mineral soils, lakes, oceans, biofuels, biomass burning, termites, and geology. 
We first present a synthetic study of our system by performing an ensemble simulation forward in time with randomly perturbed emission fields. CH₄ fluxes were retrieved at 0.25° x 0.25° resolution, and prior emissions are scaled to optimally fit the measured values. With this idealized experiment, we demonstrate that the system is capable of capturing the prescribed spatial pattern applied onto the emission field, using pseudo-observations of satellite retrievals with realistic coverage. In addition, we report on the results of assimilating real observations into the system, including emission estimates and their associated uncertainties. 
This study demonstrates the potential of incorporating satellite retrievals into inverse modeling, enabling us to extend its application to other GHG species. It also serve as a preparation work for the planned Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) satellite mission.