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Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a-priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian Particle Dispersion Model (LPDM) FLEXPART, driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main source of errors in LPDMs is the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gases measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens) and we conduct both high- (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate the spatial distribution of emissions. Furthermore, the total emission estimates from the high-resolution inversions (351±44 Mg yr−1 for HFC-134a, 101±21 Mg yr−1 for HFC-125, 50±8 Mg yr−1 for HFC-32, 9.0±1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20–40 % increase) and result in total a-posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10–20 % difference between high-resolution inversion estimates and inventory values compared to 30–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a-priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the above-mentioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a-priori emissions have the largest impact on the a-posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of the national GHG emission inventories and support policymakers.

Halocarbons are emitted by various anthropogenic activities to the atmosphere, where they contribute to global warming and stratospheric ozone-depletion. To determine national halocarbon emissions, the so-called "top-down" approach relies on atmospheric observations, at sites that reflect emissions on a country level, and com-bines these observations with inverse modelling methods. In this study, we present 12 months (September 2019 to September 2020) of continuous atmospheric observations of 28 halocarbons from a measurement campaign at the Beromünster tall tower in Switzerland. The site is sensitive to the most densely populated area of Switzer-land, the Swiss Plateau, thus the measurements were well suited to derive Swiss halocarbon emissions. Emissions were calculated by two different top-down methods, a tracer-ratio method (TRM) with carbon monoxide (CO) as the independent tracer, and a Bayesian inversion (BI), based on atmospheric transport simulations using FLEXPART–COSMO. The results were compared to previously reported top-down emission estimates, based on measurements at the high-Alpine site Jungfraujoch, and to the "bottom-up" Swiss national greenhouse gas (GHG) inventory, as annually reported to the United Nations Framework Convention on Climate Change (UN-FCCC). We observed ongoing outgassing from existing foams and refrigerators for the ozone-depleting, banned chlorofluorocarbons (CFCs) and the regulated hydrochlorofluorocarbons (HCFCs), confirming their large historical use. For the major hydrofluorocarbons (HFCs) HFC-125 (CHF2CF3) and HFC-32 (CH2F2), our calcu-lated emissions of 99 ± 29 Mg yr−1 and 46 ± 13 Mg yr−1 were in good agreement with the national Swiss inventory values, whereas for HFC 134a (CH2FCF3) our result of 300 ± 85 Mg yr−1 was about 30 % lower than the UNFCCC reported value. For the other investigated HFCs, perfluorocarbons (PFCs), SF6 and NF3, emissions were small and in agreement with the inventory. Finally, we report the first country-based emission estimates of a total of 50 Mg yr−1 for three recently phased-in, unregulated hydrofluoroolefins (HFOs), HFO 1234yf (CF3CF=CH2), HFO-1234ze(E) ((E)-CF3CH=CHF) and HCFO-1233zd(E) ((E) CF3CH=CHCl).