AS3.9

In this study, we analyse the capability of the Copernicus CO₂ monitoring (CO2M) satellite mission to quantify the CO₂ emissions of individual power plants, which is one of the prime goals of the mission. The study relies on synthetic CO₂ and NO₂ satellite observations over parts of the Czech Republic, Germany and Poland and quantifies the CO₂ and NO_x emissions of the 15 largest power plants in that region using a data-driven mass-balance approach.

The synthetic observations were generated for six CO2M satellites based on high-resolution simulations of the atmospheric transport model COSMO-GHG. To identify the emission plumes, we developed a plume detection algorithm that identifies the location, orientation and extent of multiple plumes from CO2M's NO₂ observations. Afterwards, a mass-balance approach was applied to individual plumes to estimate CO₂ and NO_x emissions by fitting Gaussian curves to the across-plume signals. Annual emissions were obtained by interpolating the temporally sparse individual estimates applying a low-order spline fit.

Individual CO₂ emissions were estimated with an accuracy <65% for a source strength >10 Mt CO₂ yr^-1, while NO_x emissions >10 kt NO₂ yr^-1 were estimated with <56% accuracy. NO₂ observations were essential for detecting the plume and constraining the shape of the Gaussian curve. With three CO2M satellites, annual CO₂ emissions were estimated with an uncertainty <30% for source strengths larger than 10 Mt yr^-1, which includes an estimate of the uncertainty in the temporal variability of emissions. Annual NO_x emissions were estimated with an uncertainty <21%. Since NO_x emissions can be determined with better accuracy, estimating CO₂ emissions directly from the NO_x emissions by applying a representative CO₂:NO_x emission ratio  seems appealing but this approach was found to suffer significantly from the high uncertainty in the  CO₂:NO_x emission ratios determined from the same CO2M observations.

Our study shows that CO2M should be able to quantify the emissions of the 400 largest point sources globally with emissions larger than 10 Mt yr^-1 that account for about 20 % of global anthropogenic CO₂ emissions. However, the mass-balance approach used here has relatively high uncertainties that are dominated by the uncertainties in the estimated CO₂ background and the wind speed in the plume, and uncertainties associated with the sparse temporal sampling of the varying emissions. Estimates could be significantly improved if these parameters can be better constrained, e.g., with atmospheric transport simulations and independent observations.

How to cite: Kuhlmann, G., Henne, S., Meijer, Y., Emmenegger, L., and Brunner, D.: Quantifying CO2 emissions of power plants with the CO2M mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3260, https://doi.org/10.5194/egusphere-egu21-3260, 2021.

We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4º×5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with large production or use of fossil methane, including the U.S., Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

How to cite: Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3810, https://doi.org/10.5194/egusphere-egu21-3810, 2021.

Monitoring of anthropogenic carbon dioxide (CO2) emission sources with air- and space-borne remote sensing instruments relies on high-spatial resolution measurements. Such observations can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra.

In preparation for the CO2IMAGE mission (Δλ ~ 1.3 nm) – a compact satellite proposal currently in phase A at the German Aerospace Center (DLR) – we present here a dedicated study of CO2 monitoring capabilities with the airborne AVIRIS-NG sensor (Δλ ~ 5 nm). We conduct CO2 retrievals of several clear-sky AVIRIS-NG point source observations with the RemoTeC algorithm, based on the short-wave infrared absorption bands of CO2. Favorable state vector and spectral retrieval window configurations are identified that reduce correlations between the carbon dioxide and water vapor column concentrations and surface reflection properties. We also discuss the use of a posteriori correction methods to minimize biases in the retrieved CO2 fields and, finally, we carry out source rate estimates for these case studies.

How to cite: Wilzewski, J. S., Strandgren, J., Baumgartner, A., Haschberger, P., Köhler, C., Krutz, D., Paproth, C., Chapman, J. W., Thompson, D. R., Thorpe, A. K., Mayer, B., Roiger, A., and Butz, A.: Towards the CO2Image mission: performance studies using AVIRIS-NG, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7165, https://doi.org/10.5194/egusphere-egu21-7165, 2021.

How to cite: Hemmer, B., Proß, C., Sander, S. P., Pongetti, T. J., Zeng, Z.-C., Hase, F., Kostinek, J., Kleinschek, R., and Butz, A.: Toward CO2 and CH4 measurements by ground-based observations of surface-scattered sunlight, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7569, https://doi.org/10.5194/egusphere-egu21-7569, 2021.

Global coverage of carbon dioxide (CO₂) satellite observations is necessary for accurate seasonal carbon flux estimates. Sufficient seasonal coverage is particularly important for quantifying the carbon cycle at high Northern latitudes which are sensitive to the rapidly changing climate. However, high latitudes pose significant challenges to reliable space-based observations of greenhouse gases. One reason for the shortage of good quality CO₂ observations in the high latitudes is the low reflectivity of snow-covered surfaces in the CO₂ absorption bands, in addition to large solar zenith angles and frequent cloud coverage over the Arctic and boreal regions. Snow surfaces are highly forward-scattering and therefore the traditional nadir-viewing geometries over land might not be optimal. In addition, the contributions from the 1.6 um and 2.0 um CO₂ absorption bands need to be evaluated over snow. In this work, we present a realistic, non-Lambertian surface reflection model of snow based on snow reflectance measurements and examine results of atmospheric radiative transfer simulations in various satellite observation geometries and the contributions from different absorption bands. This research lays important ground work for a dedicated feasibility study of CO₂ retrievals over snow, which would ultimately help increase the quantity and reliability of satellite observations at high latitudes from late winter to spring – an important period for the carbon cycle in the rapidly changing Arctic climate.

How to cite: Mikkonen, A., Lindqvist, H., Peltoniemi, J., and Tamminen, J.: Satellite-based remote sensing of carbon dioxide over snow-covered surfaces, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8207, https://doi.org/10.5194/egusphere-egu21-8207, 2021.

The natural ecosystems of tropical Africa represent a significant store of carbon, and play an important but uncertain role in the atmospheric budgets of carbon dioxide and methane. Recent studies using satellite data have concluded that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for a third of global methane growth (Lunt et al 2019), and that the tropical Africa region dominates net carbon emission across the tropics (Palmer et al 2019). The conclusions of such studies are based on the accuracy of various satellite datasets and atmospheric transport models, over a geographical region where there are few independent observations available to check the robustness and validity of these datasets.

Here we present the first ground-based observations of greenhouse gas (GHG) column concentrations over tropical East Africa, obtained using the University of Leicester EM27/SUN spectrometer during its deployment at the National Fisheries Resources Research Institute (NaFIRRI) in Jinja, Uganda. During the deployment we were able to operate the instrument remotely, using an automated weatherproof enclosure designed by the Technical University of Munich (Heinle and Chen 2018, Dietrich et al 2020). The instrument ran near-continuously for a three month period in early 2020, observing total atmospheric column concentrations of carbon dioxide and methane, along with other gases of interest including water vapour and carbon monoxide. We describe the data obtained during this period, processed using tools developed under the COCCON project (COllaborative Carbon Column Observing Network, Frey et al 2019), and demonstrate the value of performing GHG column measurements over tropical East Africa. We then evaluate the performance of CO₂ observations from OCO-2 and CH₄ from Sentinel 5P TROPOMI - datasets previously used in the studies of Palmer et al 2019 and Lunt et al 2019 respectively - and interpret the comparison with the ground-based observations in the light of data from the GEOS-Chem atmospheric chemistry transport model and the CAMS (Copernicus Atmospheric Monitoring Service) reanalyses.

REFERENCES: Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721–14740, https://doi.org/10.5194/acp-19-14721-2019, 2019.

Palmer, P.I., Feng, L., Baker, D., Chevallier, F., Boesch, H., and Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat Commun 10, 3344, https://doi.org/10.1038/s41467-019-11097-w, 2019.

Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173–2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.

Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discussions, 2020, 1–24, https://doi.org/10.5194/amt-2020-300, 2020.

Frey, M. et al.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stabilityand ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513–1530, https://doi.org/10.5194/amt-12-1513-2019, 2019

How to cite: Humpage, N., Boesch, H., Okello, W., Dietrich, F., Chen, J., Lunt, M., Feng, L., Palmer, P., and Hase, F.: Greenhouse gas column observations from a portable spectrometer in Uganda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10156, https://doi.org/10.5194/egusphere-egu21-10156, 2021.

The GOSAT TANSO-FTS sensor has been collecting high spectral resolution measurements of reflected solar radiation in the Oxygen A-band (0.76 microns) and two shortwave-infrared carbon dioxide (CO₂) absorption bands (1.6 and 2.0 microns) since April, 2009. The measured radiances allow for estimates of the total column carbon dioxide (XCO₂) via retrieval inversion. An eleven year long record of XCO₂ retrieved via NASA’s Atmospheric Carbon Observations from Space (ACOS) build 9 software suite is analyzed and discussed. The v9 XCO₂ data has been publicly available on the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) since the spring of 2020.

The ACOS GOSAT v9 XCO₂  is evaluated against CO₂ flux inversion models, observations from the Total Carbon Column Observation Network (TCCON), as well as against collocated measurements from NASA’s OCO-2 satellite. The results indicate a product that agrees with OCO-2 and models within approximately 0.25 ppm with less than 1 ppm standard deviation (σ). Agreement with TCCON is within approximately 0.1 ppm with approximately 1 ppm σ for daily overpass mean aggregated data. The ACOS GOSAT v9 XCO₂ product will allow CO₂ flux inversion modelers and terrestrial ecologists to address questions about long term (decadal) carbon cycle dynamics related to net and gross carbon fluxes.

How to cite: Taylor, T., O'Dell, C., Eldering, A., Crisp, D., Gunson, M., Fisher, B., Nelson, R., Payne, V., Lindqvist, H., Rigel, K., Griffith, D., Osterman, G., Wunch, D., and Kuze, A.: An 11 year record of GOSAT XCO2 measurements from NASA's ACOS version 9 retrieval algorithms: comparisons to models, TCCON, and OCO-2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10193, https://doi.org/10.5194/egusphere-egu21-10193, 2021.

While initial plans for measuring carbon dioxide from space hoped for 1-2 ppm levels of accuracy (bias) and precision in the CO₂ column mean dry air mole fraction (XCO₂), in the past few years it has become clear that accuracies better than 0.5 ppm are required for most current science applications.  These include measuring continental (1000+ km) and regional scale (100s of km) surface fluxes of CO₂ at monthly-average timescales.  Considering the 400+ ppm background, this translates to an accuracy of roughly 0.1%, an incredibly challenging target to hit. 

Improvements in both instrument calibration and retrieval algorithms have led to significant improvements in satellite XCO₂ accuracies over the past decade.  The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, including post-retrieval filtering and bias correction, has demonstrated unprecedented accuracy with our latest algorithm version as applied to the Orbiting Carbon Observatory-2 (OCO-2) satellite sensor.   This presentation will discuss the performance of the v10 XCO₂ product by comparisons to TCCON and models, and showcase its performance with some recent examples, from the potential to infer large-scale fluxes to its performance on individual power plants.  The v10 product yields better agreement with TCCON over land and ocean, plus reduced biases over tropical oceans and desert areas as compared to a median of multiple global carbon inversion models, allowing better accuracy and faith in inferred regional-scale fluxes.  More specifically, OCO-2 has single sounding precision of ~0.8 ppm over land and ~0.5 ppm over water, and RMS biases of 0.5-0.7 ppm over both land and water.  Given the six-year and growing length of the OCO-2 data record, this also enables new studies on carbon interannual variability, while at the same time allowing identification of more subtle and temporally-dependent errors.  Finally, we will discuss the prospects of future improvements in the next planned version (v11), and the long-term prospects of greenhouse gas retrievals in the coming years. 

How to cite: ODell, C., Eldering, A., Gunson, M., Crisp, D., Fisher, B., Kiel, M., Kuai, L., Laughner, J., Merrelli, A., Nelson, R., Osterman, G., Payne, V., Rosenberg, R., Taylor, T., Wennberg, P., Kulawik, S., Lindqvist, H., Miller, S., and Nassar, R.: Improvements in XCO2 accuracy from OCO-2 with the latest ACOS v10 product, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10484, https://doi.org/10.5194/egusphere-egu21-10484, 2021.

Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO₂ concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO₂ mole fractions (XCO₂). XCO₂ time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Using the satellite observations as reference, the CMIP6 models have a lower bias in the the multi-model mean than CMIP5, but the spread remains large. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing seasonal cycle amplitude (SCA) with increasing XCO₂ in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (mean XCO₂, growth rate, SCA and trend in SCA). This study shows that the availability of column-integral CO₂ from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

How to cite: Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., and Eyring, V.: Spatially resolved evaluation of Earth system models with satellite column-averaged CO2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11848, https://doi.org/10.5194/egusphere-egu21-11848, 2021.

The Mexico City Metropolitan Area (MCMA), located in proximity to an active volcano, is the largest urban center in North America and there is great interest to better characterize carbon emissions of this and other major urban centers in the country. NASA’s Orbiting Carbon Observatory (OCO-3) was installed in the International Space (ISS) in 2019. The inclusion of a Pointing Mirror Assembly (PMA) in this third iteration allows for a new mode of data collection that samples an area of ~80 x 80 km in approximately 2 minutes. This mode is used to collect map-like data, called Snapshot Area Maps (SAMs), over areas of interest (e.g. volcanos or urban areas). The OCO-3 module has collected SAMs over the MCMA (and the Popocatépetl volcano) throughout 2020, and also of the metropolitan areas of Guadalajara and Monterrey throughout the second half of 2020.

Using data from the public release of OCO-3 Level 2 (L2) “Lite EarlyR” product, available at the Goddard Earth Sciences Data and Information Services Center (GES DISC), we have built maps of the spatial distribution of xCO₂ for these regions. Data is filtered according to the reported quality flag in the data product, compared with ground-based FTIR measurements of column xCO₂ over the MCMA region and averaged with an oversampling method. Surface pressure data with the averaged xCO₂ is used to calculate the concentrations within the mixed layer (xCO₂^ML) in order to compensate for the effects of the complex terrain.  This product is also used  for comparison with CO spatial distributions obtained from TROPOMI data products and a simple xCO^ML/xCO₂^ML ratio is obtained and mapped for the three urban centers. This work showcases the utility of SAMs in cooperation with ground-based measurements to produce detailed descriptions of the spatial distribution of CO₂ for a wide variety of applications, as well as the importance of frequent soundings over important emission sources around the world.

How to cite: Campos-Pineda, M., Taquet, N., Stremme, W., Bezanilla, A., Lauvaux, T., Ramonet, M., and Grutter, M.: CO2 spatial distribution over Mexican urban centers from satellite observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12572, https://doi.org/10.5194/egusphere-egu21-12572, 2021.

An increasingly-large number of cities have designed ambitious climate mitigation plans to contribute to national GHG emission reduction objectives, typically starting with city-scale accountability of their direct and indirect fossil fuel emissions (Self Reported Inventories). Several concepts of spaceborne instruments providing high resolution 2D view of CO₂ total column concentrations (XCO2) have been developed to monitor the CO₂ anthropogenic emissions. Those images target mainly the CO₂ atmospheric plumes from cities and large power plants, expecting that their study may quantify the emissions of those sources. However, there is still a need to develop and asses estimation methods which could process a large number of images in a robust way for such quantifications.

In this study, we evaluate the ability to quantify CO₂ urban emissions from XCO2 2D images by conducting sensitivity experiments with synthetic images over the Paris area during the winter 2019/2020. Synthetic data were simulated using state-of-the-art mesoscale model simulations at 1km resolution coupled to a high-resolution inventory, all validated against in situ CO₂ tower measurements. We compared multiple direct flux calculation methods as described in various studies including Source Pixel, Integrated Mass Enhancement and Cross-sectional methods [Varon et al.,2018], further examined with various configurations, in addition to several formulations of Gaussian plume inversion techniques. These methods are computationally affordable compared to mesoscale inversions based on Eulerian or Lagrangian models, hence able to process rapidly a large amount of data over various cities in the future.

We quantified the uncertainties and accuracy for these methods using different combinations of assumptions to i) identify the plume from the city, ii) to determine the corresponding background concentrations from natural and anthropogenic sources outside the city, and iii) to estimate the effective wind speed and direction of the plume. From this large ensemble of approaches and configurations, we identified the most robust methods and parametrizations with their corresponding precisions under various meteorological conditions and specifications of the XCO2 images (esp. spatial resolution and measurement errors).

Starting with ideal cases without measurement noise and with perfectly known transport, we further increase the complexity of the experiments towards more realistic conditions in order to quantify the impact of the various sources of uncertainties (i.e. measurement errors, uncertainties in background conditions, uncertain plume detection, transport uncertainties). We show that most methods have to be adapted to handle the spatial extent of the targeted sources and that their performance are good in near steady state conditions. The source pixel method seems to be the less suited for extended source estimation. However, the final uncertainty is mainly driven by the pre-processing steps (background, plume limits and effective wind estimations).

How to cite: Danjou, A., Broquet, G., Lian, J., Bréon, F.-M., Eldering, A., Utard, H., and Lauvaux, T.: Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12639, https://doi.org/10.5194/egusphere-egu21-12639, 2021.

Remote sensing measurements of carbon dioxide and methane at the Sodankylä facility in northern Finland cover a 12-year time period. The measurements have been taken by a Fourier Transform Spectrometer (FTS), operating in the near-infrared spectral region.  The Sodankylä site is participating in the Total Carbon Column Observing Network (TCCON). Here we present long-term measurements of column-averaged, dry-air mole fractions of carbon dioxide and methane and comparisons with satellite borne measurements. The relevant satellite missions include the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Copernicus Sentinel-5 Precursor satellite, the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse Gases Observing Satellite (GOSAT). We have performed AirCore observations in the vicinity of the TCCON instrument at Sodankylä during all seasons. AirCore measurements are directly related to the World Meteorological Organization in situ trace gas measurement scales. The AirCore data are used in this study to provide comparisons with remote sensing retrievals.

How to cite: Kivi, R., Hatakka, J., Heikkinen, P., Laurila, T., Lindqvist, H., and Chen, H.: Remote Sensing Measurements of Carbon Dioxide and Methane over Northern Finland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13073, https://doi.org/10.5194/egusphere-egu21-13073, 2021.

A large fraction of global anthropogenic greenhouse gas emissions originates from localized point sources. International climate treaties foresee their independent monitoring. Given the high number of point sources and their global spatial distribution, local monitoring is challenging, whereas a global satellite-based observing system is advantageous. In this perspective, a promising measurement approach is active remote sensing by airborne lidar, such as provided by the integrated-path differential-absorption lidar CHARM-F. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO₂ and CH₄ below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken at the largest European point sources of anthropogenic CO₂ and CH₄ emissions, i.e. coal-fired power plants and ventilation shafts of coal mines. The measurement flights aimed to transect isolated exhaust plumes, in order to derive the corresponding emission rates from the resulting enhancement in concentration, along the plume crossing. For the first time, multiple measurements of power plant emissions were made using airborne lidar. On average, we find that our measurements are consistent with reported numbers, but observe high discrepancies between successive plume crossings of up to 50 %. As an explanation for these high discrepancies, we assess the influence of inhomogeneity in the exhaust plume, caused by atmospheric turbulence. This assessment is based on the Weather Research and Forecasting Model (WRF). We find a pronounced diurnal cycle of plume inhomogeneity associated with local turbulence, predominately driven by midday solar irradiance. Our results reveal that periods of high turbulence, specifically during midday and afternoon, should be avoided whenever possible. Since lidar is intrinsically independent of sun light, measurements can be performed under conditions of weak turbulence, such as at night or in the early morning.

How to cite: Wolff, S., Ehret, G., Kiemle, C., Amediek, A., Quatrevalet, M., Wirth, M., and Fix, A.: Quantification of CO2 Emission Rates from Large Coal-Fired Power Plants Using Airborne Lidar during CoMet, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14303, https://doi.org/10.5194/egusphere-egu21-14303, 2021.