High-resolution atmospheric transport simulations were used to investigate the potential for detecting carbon dioxide (CO₂) plumes of the city of Berlin and neighboring power stations with the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, which is a proposed constellation of CO₂ satellites with imaging capabilities. The potential for detecting plumes was studied for satellite images of CO₂ alone or in combination with images of nitrogen dioxide (NO₂) and carbon monoxide (CO) to investigate the added value of measurements of other gases coemitted with CO₂ that have better signal-to-noise ratios. The additional NO₂ and CO images were either generated for instruments on the same CO2M satellites (2 km× 2 km resolution) or for the Sentinel-5 instrument (7.5 km× 7.5 km) assumed to fly 2 h earlier than CO2M. Realistic CO₂, CO and ${\mathrm{NO}}_X(=\mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}})$ fields were simulated at 1 km× 1 km horizontal resolution with the Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) for the year 2015, and they were used as input for an orbit simulator to generate synthetic observations of columns of CO₂, CO and NO₂ for constellations of up to six satellites. A simple plume detection algorithm was applied to detect coherent structures in the images of CO₂, NO₂ or CO against instrument noise and variability in background levels. Although six satellites with an assumed swath of 250 km were sufficient to overpass Berlin on a daily basis, only about 50 out of 365 plumes per year could be observed in conditions suitable for emission estimation due to frequent cloud cover. With the CO₂ instrument only 6 and 16 of these 50 plumes could be detected assuming a high-noise (σ_VEG50=1.0 ppm) and low-noise (σ_VEG50=0.5 ppm) scenario, respectively, because the CO₂ signals were often too weak. A CO instrument with specifications similar to the Sentinel-5 mission performed worse than the CO₂ instrument, while the number of detectable plumes could be significantly increased to about 35 plumes with an NO₂ instrument. CO₂ and NO₂ plumes were found to overlap to a large extent, although NO_X had a limited lifetime (assumed to be 4 h) and although CO₂ and NO_X were emitted with different NO_X:CO₂ emission ratios by different source types with different temporal and vertical emission profiles. Using NO₂ observations from the Sentinel-5 platform instead resulted in a significant spatial mismatch between NO₂ and CO₂ plumes due to the 2 h time difference between Sentinel-5 and CO2M. The plumes of the coal-fired power plant Jänschwalde were easier to detect with the CO₂ instrument (about 40–45 plumes per year), but, again, an NO₂ instrument could detect significantly more plumes (about 70). Auxiliary measurements of NO₂ were thus found to greatly enhance the capability of detecting the location of CO₂ plumes, which will be invaluable for the quantification of CO₂ emissions from large point sources.

Inverse modeling of anthropogenic and biospheric CO₂ fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50 % of CO₂ in Europe is emitted by large point sources such as power plants and industrial facilities. In this study, we conduct high-resolution atmospheric simulations of CO₂ with the mesoscale Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and Czech Republic. By including separate tracers for anthropogenic CO₂ emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO₂ only at the surface overestimates near-surface CO₂ concentrations in the afternoon on average by 14 % in summer and 43 % in winter over the selected model domain. Differences in column-averaged dry air mole XCO₂ fractions are smaller, between 5 % in winter and 8 % in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggest that the traditional approach of emitting CO₂ only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport.

One important goal of the Copernicus CO₂ monitoring (CO2M) mission is to quantify CO₂ emissions of large point sources. We analyzed the feasibility of such quantifications using synthetic CO₂ and NO₂ observations for a constellation of CO2M satellites. Observations were generated from kilometer-scale COSMO-GHG simulations over parts of the Czech Republic, Germany and Poland. CO₂ and NO_X emissions of the 15 largest power plants (3.7–40.3 Mt CO₂ yr⁻¹) were quantified using a data-driven method that combines a plume detection algorithm with a mass-balance approach. CO₂ and NO_X emissions could be estimated from single overpasses with 39–150% and 33–116% uncertainty (10–90th percentile), respectively. NO₂ observations were essential for estimating CO₂ emissions as they helped detecting and constraining the shape of the plumes. The uncertainties are dominated by uncertainties in the CO2M observations (2–72%) and limitations of the mass-balance approach to quantify emissions of complex plumes (25–95%). Annual CO₂ emissions could be estimated with 23–119% and 18–65% uncertainties with two and three satellites, respectively. The uncertainty in the temporal variability of emissions contributes about half to the total uncertainty. The estimated uncertainty was extrapolated to determine uncertainties for point sources globally, suggesting that two satellites would be able to quantify the emissions of up to 300 point sources with <30% uncertainty, while adding a third satellite would double the number to about 600 point sources. Annual NO_X emissions can be determined with better accuracy of 16–73% and 13–52% with two and three satellites, respectively. Estimating CO₂ emissions from NO_X emissions using a CO₂:NO_X emission ratio may thus seem appealing, but this approach is significantly limited by the high uncertainty in the emission ratios as determined from the same CO2M observations. The mass-balance approach studied here will be particularly useful for estimating emissions in countries where power plant emissions are not routinely monitored and reported. Further reducing the uncertainties will require the development of advanced atmospheric inversion systems for emission plumes and an improved constraint on the temporal variability of emissions using additional sources of information such as other satellite observations or energy demand statistics.

We investigate the potential of the Copernicus Anthropogenic Carbon Dioxide (CO₂) Monitoring (CO2M) mission, a proposed constellation of CO₂ imaging satellites, to estimate the CO₂ emissions of a city on the example of Berlin, the capital of Germany. On average, Berlin emits about 20 Mt CO₂ yr⁻¹ during satellite overpass (11:30 LT). The study uses synthetic satellite observations of a constellation of up to six satellites generated from 1 year of high-resolution atmospheric transport simulations. The emissions were estimated by (1) an analytical atmospheric inversion applied to the plume of Berlin simulated by the same model that was used to generate the synthetic observations and (2) a mass-balance approach that estimates the CO₂ flux through multiple cross sections of the city plume detected by a plume detection algorithm. The plume was either detected from CO₂ observations alone or from additional nitrogen dioxide (NO₂) observations on the same platform. The two approaches were set up to span the range between (i) the optimistic assumption of a perfect transport model that provides an accurate prediction of plume location and CO₂ background and (ii) the pessimistic assumption that plume location and background can only be determined reliably from the satellite observations. Often unfavorable meteorological conditions allowed us to successfully apply the analytical inversion to only 11 out of 61 overpasses per satellite per year on average. From a single overpass, the instantaneous emissions of Berlin could be estimated with an average precision of 3.0 to 4.2 Mt yr⁻¹ (15 %–21 % of emissions during overpass) depending on the assumed instrument noise ranging from 0.5 to 1.0 ppm. Applying the mass-balance approach required the detection of a sufficiently large plume, which on average was only possible on three overpasses per satellite per year when using CO₂ observations for plume detection. This number doubled to six estimates when the plumes were detected from NO₂ observations due to the better signal-to-noise ratio and lower sensitivity to clouds of the measurements. Compared to the analytical inversion, the mass-balance approach had a lower precision ranging from 8.1 to 10.7 Mt yr⁻¹ (40 % to 53 %), because it is affected by additional uncertainties introduced by the estimation of the location of the plume, the CO₂ background field, and the wind speed within the plume. These uncertainties also resulted in systematic biases, especially without the NO₂ observations. An additional source of bias was non-separable fluxes from outside of Berlin. Annual emissions were estimated by fitting a low-order periodic spline to the individual estimates to account for the seasonal variability of the emissions, but we did not account for the diurnal cycle of emissions, which is an additional source of uncertainty that is difficult to characterize. The analytical inversion was able to estimate annual emissions with an accuracy of < 1.1 Mt yr⁻¹ (< 6 %) even with only one satellite, but this assumes perfect knowledge of plume location and CO₂ background. The accuracy was much smaller when applying the mass-balance approach, which determines plume location and background directly from the satellite observations. At least two satellites were necessary for the mass-balance approach to have a sufficiently large number of estimates distributed over the year to robustly fit a spline, but even then the accuracy was low (> 8 Mt yr⁻¹ (>40 %)) when using the CO₂ observations alone. When using the NO₂ observations to detect the plume, the accuracy could be greatly improved to 22 % and 13 % with two and three satellites, respectively. Using the complementary information provided by the CO₂ and NO₂ observations on the CO2M mission, it should be possible to quantify annual emissions of a city like Berlin with an accuracy of about 10 % to 20 %, even in the pessimistic case that plume location and CO₂ background have to be determined from the observations alone. This requires, however, that the temporal coverage of the constellation is sufficiently high to resolve the temporal variability of emissions.

Emission inventories serve as crucial input for atmospheric chemistry transport models. To make them usable for a model simulation, they have to be pre-processed and, traditionally, provided as input files at discrete model time steps. In this paper, we present an “online” approach, which produces a minimal number of input data read-in at the beginning of a simulation and which handles essential processing steps online during the simulation. For this purpose, a stand-alone Python package “emiproc” was developed, which projects the inventory data to the model grid and generates temporal and vertical scaling profiles for individual emission categories. The package is also able to produce “offline” emission files if desired. Furthermore, we outline the concept of the online emission module (written in Fortran 90) and demonstrate its implementation in two different atmospheric transport models: COSMO-GHG and COSMO-ART. Simulation results from both modeling systems show the equivalence of the online and offline procedure. While the model run time is very similar for both approaches, input size and pre-processing time are greatly reduced when online emissions are utilized.

Urban areas are recognised as a significant source of greenhouse gas emissions (GHG), such as carbon dioxide (CO2) and methane (CH4). The total amount of urban GHG emissions, especially for CH4, however, is not well quantified. Here we report on airborne in situ measurements using a Picarro G1301-m analyser aboard the DLR Cessna Grand Caravan to study GHG emissions downwind of the German capital Berlin. In total, five aircraft-based mass balance experiments were conducted in July 2018 within the Urban Climate Under Change [UC]2 project. The detection and isolation of the Berlin plume was often challenging because of comparatively small GHG signals above variable atmospheric background concentrations. However, on July 20th enhancements of up to 4 ppm CO2 and 21 ppb CH4 were observed over a horizontal extent of roughly 45 to 65 km downwind of Berlin. These enhanced mixing ratios are clearly distinguishable from the background and can partly be assigned to city emissions. The estimated CO2 emission flux of 1.39 ± 0.76 t s–1 is in agreement with current inventories, while the CH4 emission flux of 5.20 ± 1.70 kg s–1 is almost two times larger than the highest reported value in the inventories. We localized the source area with HYSPLIT trajectory calculations and the global/regional nested chemistry climate model MECO(n) (down to ~1 km), and investigated the contribution from sewage-treatment plants and waste deposition to CH4, which are treated differently by the emission inventories. Our work highlights the importance of strong CH4 sources in the vicinity of Berlin and shows, that there is limited understanding of CH4 emissions from urban regions, even for major cities in highly developed countries like Germany. Furthermore, we show that a detailed knowledge of GHG inflow mixing ratios is necessary to suitably estimate emission rates for Berlin.