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  • 1
    Online Resource
    Online Resource
    Exploring Greenhouse Gases Water and Climate Changes: Scientific Opportunities for the Climate and Atmospheric Composition Exploring Satellites Mission
    National Space Science Center, Chinese Academy of Sciences ; 2020
    In:  Chinese Journal of Space Science Vol. 40, No. 2 ( 2020), p. 151-
    Details
    In: Chinese Journal of Space Science, National Space Science Center, Chinese Academy of Sciences, Vol. 40, No. 2 ( 2020), p. 151-
    Type of Medium: Online Resource
    ISSN: 0254-6124 , 0254-6124
    URL: Article
    DOI: 10.11728/cjss2020.02.151
    Language: Unknown
    Publisher: National Space Science Center, Chinese Academy of Sciences
    Publication Date: 2020
    SSG: 16,12
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  • 2
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    A global map of emission clumps for future monitoring of fossil fuel CO〈sub〉2〈/sub〉 emissions from space
    Copernicus GmbH ; 2019
    In:  Earth System Science Data Vol. 11, No. 2 ( 2019-05-17), p. 687-703
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 11, No. 2 ( 2019-05-17), p. 687-703
    Abstract: Abstract. A large fraction of fossil fuel CO2 emissions emanate from “hotspots”, such as cities (where direct CO2 emissions related to fossil fuel combustion in transport, residential, commercial sectors, etc., excluding emissions from electricity-producing power plants, occur), isolated power plants, and manufacturing facilities, which cover a small fraction of the land surface. The coverage of all high-emitting cities and point sources across the globe by bottom-up inventories is far from complete, and for most of those covered, the uncertainties in CO2 emission estimates in bottom-up inventories are too large to allow continuous and rigorous assessment of emission changes (Gurney et al., 2019). Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what a hotspot is needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers and a XCO2 accuracy and precision of 〈1 ppm for individual measurements of vertically integrated columns of dry-air mole fractions of CO2 (XCO2). This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of diffuse emitting areas and intense point sources is sought. In this study, we characterize area and point fossil fuel CO2 emitting sources which generate coherent XCO2 plumes that may be observed from space. We characterize these emitting sources around the globe and they are referred to as “emission clumps” hereafter. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 individual clumps, with 5088 area clumps, and 6226 point-source clumps (power plants) are identified. These clumps contribute 72 % of the global fossil fuel CO2 emissions according to the ODIAC inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2. The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    URL: Article
    DOI: 10.5194/essd-11-687-2019
    DOI: 10.5194/essd-11-687-2019-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2475469-9
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  • 3
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    Detectability of CO〈sub〉2〈/sub〉 emission plumes of cities and power plants with the Copernicus Anthropogenic CO〈sub〉2〈/sub〉 Monitoring (CO2M) mission
    Copernicus GmbH ; 2019
    In:  Atmospheric Measurement Techniques Vol. 12, No. 12 ( 2019-12-19), p. 6695-6719
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 12, No. 12 ( 2019-12-19), p. 6695-6719
    Abstract: Abstract. 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 NOX(=NO+NO2) 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 (σVEG50=1.0 ppm) and low-noise (σVEG50=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 NOX had a limited lifetime (assumed to be 4 h) and although CO2 and NOX were emitted with different NOX: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.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    URL: Article
    DOI: 10.5194/amt-12-6695-2019
    DOI: 10.5194/amt-12-6695-2019-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2505596-3
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  • 4
    Online Resource
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    PMIF v1.0: assessing the potential of satellite observations to constrain CO〈sub〉2〈/sub〉 emissions from large cities and point sources over the globe using synthetic data
    Copernicus GmbH ; 2020
    In:  Geoscientific Model Development Vol. 13, No. 11 ( 2020-11-26), p. 5813-5831
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 13, No. 11 ( 2020-11-26), p. 5813-5831
    Abstract: Abstract. This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the “posterior uncertainty”) of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr−1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    URL: Article
    DOI: 10.5194/gmd-13-5813-2020
    DOI: 10.5194/gmd-13-5813-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2456725-5
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  • 5
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    DataSheet1.PDF
    Frontiers Media SA ; 2022
    In:  Frontiers in Remote Sensing Vol. 3 ( 2022-5-11)
    Details
    In: Frontiers in Remote Sensing, Frontiers Media SA, Vol. 3 ( 2022-5-11)
    Abstract: The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO 2 Monitoring Mission, CO2M mission) for observing atmospheric CO 2 content with the aim to estimate fossil fuel CO 2 emissions. We explore the impact of potential CO2M observations of column-averaged CO 2 (XCO 2 ), nitrogen dioxide (NO 2 ), and aerosols in a 200 × 200 km 2 domain around Berlin. For the quantification of anticipated XCO 2 random and systematic errors we developed and applied new error parameterisation formulae based on artificial neural networks. For the interpretation of these data, we further established a CCFFDAS modelling chain from parameters of emission models to XCO 2 and NO 2 observations to simulate the 24 h periods preceeding simulated CO2M overpasses over the study area. For one overpass in winter and one in summer, we present a number of assessments of observation impact in terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to 200 km. This means the assessments include temporal and spatial scales typically not covered by inventories. The assessments differentiate the fossil fuel CO 2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector.” We find that combined measurements of XCO 2 and aerosols provide a powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel emissions from the largest three power plants in the domain was reduced by 60%–90% after assimilating the observations. Likewise, these measurements achieve an uncertainty reduction for the other sector that increases when aggregated to larger spatial scales. When aggregated over Berlin the uncertainty reduction for the other sector varies between 28% and 48%. Our assessments show a considerable contribution of aerosol observations onboard CO2M to the constraint of the XCO 2 measurements on emissions from all power plants and for the other sector on all spatial scales. NO 2 measurements onboard CO2M provide a powerful additional constraint on the emissions from power plants and from the other sector. We further apply a Jacobian representation of the CCFFDAS modelling chain to decompose a simulated CO 2 column in terms of spatial emission impact. This analysis reveals the complex structure of the footprint of an observed CO 2 column, which indicates the limits of simple mass balances approaches for interpretation of such observations.
    Type of Medium: Online Resource
    ISSN: 2673-6187
    URL: Article
    URL: Article
    DOI: 10.3389/frsen.2022.887456
    DOI: 10.3389/frsen.2022.887456.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 3091289-1
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  • 6
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    Online Resource
    The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources
    Springer Science and Business Media LLC ; 2020
    In:  Carbon Balance and Management Vol. 15, No. 1 ( 2020-12)
    Details
    In: Carbon Balance and Management, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2020-12)
    Abstract: Satellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO 2 concentration plumes generated by emission hotspots. CO 2 emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO 2 emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO 2 emissions. This study assesses the uncertainties in the inversion estimates of emissions (“posterior uncertainties”). Results The posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO 2 plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO 2 retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance. Conclusions For all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30–11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps.
    Type of Medium: Online Resource
    ISSN: 1750-0680
    URL: Article
    DOI: 10.1186/s13021-020-00153-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2243512-8
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  • 7
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    Assimilation of atmospheric CO 2 observations from space can support national CO 2 emission inventories
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 1 ( 2022-01-01), p. 014015-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 1 ( 2022-01-01), p. 014015-
    Abstract: The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO 2 ) emissions. It’s core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO 2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO 2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO 2 concentration (XCO 2 ), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO 2 and other observations to constrain fossil fuel CO 2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO 2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/ac3cea
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2255379-4
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  • 8
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    Accounting for the vertical distribution of emissions in atmospheric CO〈sub〉2〈/sub〉 simulations
    Copernicus GmbH ; 2019
    In:  Atmospheric Chemistry and Physics Vol. 19, No. 7 ( 2019-04-05), p. 4541-4559
    Details
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 19, No. 7 ( 2019-04-05), p. 4541-4559
    Abstract: Abstract. 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.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    URL: Article
    DOI: 10.5194/acp-19-4541-2019
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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