Regional CO 2 and CH 4 inversion system using WRF-Chem (v4.4)/DART (v9.8.0) and continuous high-precision observations over the Korean Peninsula

Abstract. We develop a high-resolution dual-species greenhouse gas (GHG) top-down inversion framework by integrating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem v4.4) and the Data Assimilation Research Testbed (DART v9.8.0). This framework jointly performs the assimilation of near-surface CO2 and CH4 concentrations alongside standard meteorological data across the Korean Peninsula. To improve the simulation of GHG turbulent dispersion in the atmospheric boundary layer over complex terrain, we incorporate surface heterogeneity parameterizations (roughness sublayer and canopy height) into the model physics in the inversion system. The system assimilates continuous in situ observations from three World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) stations and produces dynamically consistent updates of CO2 and CH4 emissions. Prior flux estimates include anthropogenic emissions (EDGAR v8.0), biogenic exchanges (the region-optimized VPRM), biomass burning (FINN v2.5 data), and oceanic CO2 exchanges (SeaFlux data). In a 2020 case study, the top-down estimates improve the agreement with ground observations, reducing root-mean-square errors by 30–60 % and correcting bias error of 1–10 ppm and 30–60 ppb for surface CO2 and CH4 concentrations at the high-precision surface observatory respectively. Independent aircraft profiles suggest consistency between the boundary and prior CH4 emissions. The posterior anthropogenic emissions show decreases over the Seoul Metropolitan Area and western coastal sources for CO2 and increases over agricultural areas for CH4, indicating potential areas that need to refine the global emission inventories. The posterior annual national total emissions for CO2 and CH4 fall within the ranges reported in the Republic of Korea’s Biennial Transparency Report of Korea). This case study demonstrates the utility of an observation-constrained top-down framework in supporting the Measurement-Monitoring-Reporting-Verification (MMRV) framework for national and sub-national assessments of GHG emissions and provide a scalable path toward multi-platform (satellite, aircraft, shipborne) integration.