PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data

doi:10.5194/gmd-13-5813-2020

	PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data
	Wang, Yilong 1,2; Broquet, Gregoire 1; Breon, Francois-Marie 1; Lespinas, Franck 1,3; Buchwitz, Michael 4; Reuter, Maximilian 4; Meijer, Yasjka 5; Loescher, Armin 5; Janssens-Maenhout, Greet 6; Zheng, Bo 1
刊名	GEOSCIENTIFIC MODEL DEVELOPMENT
	2020-11-26
卷号	13 期号:11 页码:5813-5831
ISSN号	1991-959X
DOI	10.5194/gmd-13-5813-2020
通讯作者	Wang, Yilong(wangyil@igsnrr.ac.cn)
英文摘要	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 11:3000:00) 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.
资助项目	ESA[4000120184/17/NL/FF/mg] ; French National Research Agency (ANR)'s program Chaires Industrielles 2017 through the TRACE Industrial Chair (UVSQ/CEA/CNRS/Thales Alenia Space/TOTAL/SUEZ)[ANR-17-CHIN-0004] ; National Key Research and Development Program of China[2017YFA0605303]
WOS关键词	GREENHOUSE-GAS EMISSIONS ; ANTHROPOGENIC CO2 ; SPACE ; CH4 ; ENHANCEMENTS ; INVENTORY ; GEOCARB ; MISSION ; SCALE
WOS研究方向	Geology
语种	英语
出版者	COPERNICUS GESELLSCHAFT MBH
WOS记录号	WOS:000595555300001
资助机构	ESA ; French National Research Agency (ANR)'s program Chaires Industrielles 2017 through the TRACE Industrial Chair (UVSQ/CEA/CNRS/Thales Alenia Space/TOTAL/SUEZ) ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156439]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Yilong
作者单位	1.Univ Paris Saclay, Lab Sci Climat & Environm, CEA CNRS UVSQ, F-91191 Gif Sur Yvette, France 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 3.Canadian Ctr Meteorol & Environm Predict, 2121 Transcanada Highway, Dorval, PQ H9P 1J3, Canada 4.Univ Bremen, Inst Environm Phys IUP, FB1,Otto Hahn Allee 1, D-28334 Bremen, Germany 5.European Space Agcy ESA, Noordwijk, Netherlands 6.European Commiss, Joint Res Ctr, Directorate Sustainable Resources, Via E Fermi 2749,TP 123, I-21027 Ispra, Italy
推荐引用方式 GB/T 7714	Wang, Yilong,Broquet, Gregoire,Breon, Francois-Marie,et al. PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data[J]. GEOSCIENTIFIC MODEL DEVELOPMENT,2020,13(11):5813-5831.
APA	Wang, Yilong.,Broquet, Gregoire.,Breon, Francois-Marie.,Lespinas, Franck.,Buchwitz, Michael.,...&Ciais, Philippe.(2020).PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data.GEOSCIENTIFIC MODEL DEVELOPMENT,13(11),5813-5831.
MLA	Wang, Yilong,et al."PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data".GEOSCIENTIFIC MODEL DEVELOPMENT 13.11(2020):5813-5831.