PMIF v1.0: an inversion system to estimate the potential of satellite observations to monitor fossil fuel CO 2 emissions over the globe

. This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO 2 (XCO 2 ) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during one year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring 20 (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 XCO 2 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 8:30-11:30 local time) before satellite overpasses and for the mean emissions during other hours of 25 the day (over the aggregation between 0:00-8:30 and 11:30-0:00) for each clump and for the 366 days 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 or few large cities 30 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 per year can potentially have their emissions between 8:30 and 11:30 constrained with a posterior uncertainty smaller than 20% for more than 10 times within one year (ignoring the potential to cross or extrapolate information between 8:30-11:30 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 8:30-11:30 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, 40 stressing the need of systematic assessment of the sources of uncertainty in the spatiotemporally-resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty to constrain global and national fossil fuel CO 2 emissions with satellite XCO 2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements.

(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 XCO 2 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 8:30-11:30 local time) before satellite overpasses and for the mean emissions during other hours of 25 the day (over the aggregation between 0:00-8:30 and 11:30-0:00) for each clump and for the 366 days 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 or few large cities 30 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 per year can potentially have their emissions between 8:30 and 11:30 constrained with a posterior uncertainty smaller than 20% for more than 10 times within one year (ignoring the potential to cross or extrapolate information between 8:30-11:30 time windows on 35 with a wide swath (typically on the order of 200km -300 km), a high resolution (< 2-3 km horizontal resolution) and a high single sounding precision (< 2 ppm) are required for satellite XCO 2 measurements for the monitoring of fossil fuel CO 2 emissions from large point sources and cities. Several satellite XCO 2 imagery concepts have been proposed: i) the OCO-3 NASA (National Aeronautics and Space Administration) mission which has been installed on the International Space Station 70 (ISS) in May 2019; ii) the CarbonSat mission which was a candidate for ESA's Earth Explorer 8 opportunity (ESA, 2015), but was not selected; iii) the "city-mode" of the MicroCarb mission of the Centre National d'Etudes Spatiales (CNES) which should be launched in 2021 (Bertaux et al., 2019); iv) the GeoCARB geostationary mission which was selected as the Earth Venture Mission-2 by NASA; and v) the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission consisting of a constellation of CO 2 imagers that is currently studied by the European Space Agency (ESA) on behalf of the European 75 Commission in the context of the European Union Copernicus programme. This CO2M satellite constellation is a crucial element that will contribute to the operational anthropogenic CO2 monitoring & verification support capacity currently under development by the European Commission with the support from ESA, European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Centre for Medium-Range Weather Forecasts (ECMWF) (Ciais et al., 2015;Pinty et al., 2017Pinty et al., , 2019. 80 The main approach currently investigated for the estimate of CO 2 emissions from satellite XCO 2 images consists in identifying the XCO 2 plumes downwind the main CO 2 emission sources. The size of the plumes and the magnitude of XCO 2 enhancements in these plumes are tightly linked to the emissions. Wang et al. (2019) developed an algorithm to extract, from gridded emission maps, a conservative set of area (cities) and point sources (power plants) with intense emissions around the globe which can generate coherent XCO 2 plumes that may be observed from space, given the precision of current satellite 85 observations. This set was conservative because it is inferred for idealized meteorological condition without wind. These emitting sources were called "emission clumps". Wang et al. (2019) identified 11,314 individual clumps which contribute 72% of the global fossil fuel CO 2 emissions from the ODIAC (Open-source Data Inventory for Anthropogenic CO 2 version 2017, Oda et al., 2018) 1 km resolution inventory. Broquet et al. (2018) showed that the part of the XCO 2 plumes exploited by the atmospheric inversion in satellite images 90 correspond to few hours of the clump emissions before the satellite overpass. The XCO 2 signature of the earlier clump emissions is too diluted to be filtered from the measurement errors and the signature of other CO 2 sources and sinks. Further, emissions from a given clump vary in time during the day, for instance due to the variations of traffic in cities (Yang et al., 2019), from day to day and between seasons, with more emissions associated to heating in winter over cold regions (Bré on et al., 2017). Therefore, the estimate of annual budgets of the clump emissions based on satellite observation during daytime 95 (generally for a fixed local time since most of the missions use heliosynchronous orbits) and for low cloud coverage is a challenge, and cannot rely on the direct information from the satellite imagery. It relies on the extrapolation of information from the time windows for which the emissions are well constrained. Such an extrapolation is based on the correlation of the uncertainty in emissions in time, and more precisely, in the atmospheric inversion framework, on the temporal auto-correlations of the uncertainty in the inventories used as a prior knowledge by the Bayesian framework of the inversion (see Sect. 2.6). 100 Previous studies on the potential of the satellite XCO 2 imagery to constrain the emissions from clumps were limited to single or few large targets, such as power plants in Bovensmann et al. (2010), Berlin in Pillai et al. (2016) and in Kuhlmann et al. (2019), and Paris in Broquet et al. (2018). However, much of the global CO 2 emissions occur in smaller cities and plants.
The potential and design of satellite missions dedicated to the monitoring of the CO 2 emissions like CO2M needs to be assessed for a much more representative range of sources over the whole globe. The inversion framework used by Pillai et al. (2016) 105 andBroquet et al. (2018) were based on a full 3-D Eulerian atmospheric transport models at high spatial resolution (on the order of 2 km). Such inversions are much too expensive in terms of computation cost, to be applied in a systematic way to the full set of clumps across the globe. Therefore, in this study, we develop a Plume Monitoring Inversion Framework (PMIF) and conduct a set of Observing System Simulation Experiments (OSSEs) to assess, for the first time, the performance of a satellite instrument to monitor the 110 emissions of all the clumps across the globe and over a whole year. The imager studied has the foreseen characteristics of the individual satellites of the forthcoming CO2M mission. It would be a high-resolution spectrometer, with 2 km × 2 km resolution pixels and a swath of 300 km, and it would be placed on a sun-synchronous orbit ensuring global coverage in 4 days. The PMIF inversion system relies on the list of clumps extracted by Wang et al. (2019) from the ODIAC inventory, on the Gaussian plume model to simulate the XCO 2 plumes generated by the emissions from these clumps, on an analytical inverse modeling 115 framework, and on a combination of overlapping assimilation windows to solve for the inversion problem over the globe and a full year. It also addresses the question of temporal extrapolation that is needed to generate estimates of annual emissions from the information of a limited number of time windows for which emissions are well constrained by the direct satellite images, by accounting for the temporal auto-correlation of the prior uncertainties. The performance is assessed in terms of the uncertainties in the emissions (Sect. 2.1) at different scales. The PMIF uses a Gaussian plume model at the local scale to ensure 120 that the computation cost is affordable. Such a model can often hardly fit with actual plumes over the distances considered in this study (due to variations in the wind field, topography, vertical mixing etc. over such distances) but is shown, when driven with suitable parameters, to provide a satisfactory simulation of the plume extent and amplitudes, which appear to be the main drivers of the targeted computations of uncertainties in the emissions in our OSSE framework (as shown in section 3.1). In PMIF, we also ignore the impact of some sources of uncertainties on the inversion of emissions, including systematic errors 125 on the XCO 2 retrievals, the impact of uncertainties in diffuse anthropogenic emissions outside clumps, in non-fossil CO 2 fluxes (within and outside clumps), and in the spatial and temporal variations of emissions within the clump and the short time windows that the inversion aims to solve. These impacts are discussed in detail afterwards.
This PMIF system provides indication on the satellite system capabilities for the full range of cities and power plants varying in topography, emission budget and spread, proximity to other major sources, and for a large range of meteorological 130 conditions. It complements other systems that focus on specific regions with more complex (but area-limited) models and consideration of diffuse sources and natural fluxes, allowing for extrapolating and up-scaling results of those more complex systems to get a more systematic understanding of their implications for the monitoring of CO 2 emissions from all detectible clumps over the globe.
The PMIF system and the OSSEs analyzed in this first study are described in Section 2. The results obtained with the 135 PMIF for the city of Paris is compared with that of Broquet et al. (2018) in Sect. 3.1. The uncertainty in the retrieved emissions of individual clumps with one imaging satellite for 3 h time windows, for daily emissions and for annual emissions are assessed in Sect. 3.2-3.4. Sect. 4 discusses the drivers of the spatial variations of the uncertainty in the retrieved emissions, the limitations of PMIF, and the implications for a future operational observing system.

Plume Monitoring Inversion Framework
The theoretical framework of the inversion system developed in this study is the same as the traditional atmospheric inversions. The inversion derives a statistical estimate for a set of control variables x in a model x→y=Mx that simulates the satellite XCO 2 measurements y o . The model M linking x and y is a combination of flux and atmospheric transport models (detailed in Sect. 2.4), and is called observation operator hereafter. As explained below, we do not have a constant term added 145 to Mx in the observation operator of the PMIF that would gather the atmospheric CO 2 signature of the fluxes not controlled by the inversion (like non-fossil fluxes and the background XCO 2 field) since the uncertainty in such fluxes is ignored. The inversion follows a Bayesian statistical framework, updating the statistical prior estimate of x based on the statistical information from the assimilation of XCO 2 measurements y into the observation operator. The distributions of the prior estimate and of the misfits between the actual observations y o and simulated ones due to errors in the observations and in the 150 observation operator (called the "observation errors") are assumed to be unbiased and to have the Gaussian forms N(x b , B) and N(0, R), where B and R are the prior and observation error covariance matrices. The statistical distribution of the posterior estimate of x, given the observation operator, x b and y o , also follows a Gaussian distribution N(x a , A), with x a being the mean and A being the error covariance matrix characterizing the posterior uncertainty. The problem is solved by deriving: (1) 155 Where T and -1 denote the transpose and inverse of a given matrix.
Equation (1) shows that A only depends on prior and observation error covariance matrices, on the matrix part of the observation operator (hereafter, we simplify the notation by calling M the observation operator), and implicitly on the structure of the observation vector (i.e., on the time, location and representation of the observations in M), while Eq.
(2) shows that x a 160 also depends on the actual value of x b and y o . PMIF is an analytical inversion system that solves for Eq. (1) or for an approximation of this equation (when accounting for temporal correlations in B) by building the different matrices involved in this equation.
We characterize B, R and A by the corresponding standard deviations (σ) of uncertainty in individual or aggregations of control parameters and by the temporal auto-correlations of the uncertainties (Sect. 2.6). In the following, the "uncertainty 165 reduction" for a given control variable or for an aggregation of control variables (like emission budgets over larger timescales than that of the control vector) refers to the relative difference between its prior and posterior uncertainty: 1 -σ a /σ b .
We use a Gaussian plume model (Sect. 2.4) to simulate the atmospheric transport at a spatial resolution consistent with that of the XCO 2 measurements from the planned CO 2 imager and with the highly heterogeneous distribution of emissions.
Compared with complex 3-D atmospheric transport models, Gaussian plume models have a very low computational cost, 170 making the global assessment of posterior uncertainty and uncertainty reduction at the scale of emissions clumps from the assimilation of high resolution data feasible. However, since a Gaussian plume model provides a highly simplified approximation of the atmospheric transport from emission clumps, we need to verify that its use in the PMIF yields estimates of the uncertainties in the inverted emissions that are consistent with those that would be based on more complex models. Therefore, we first compare the results for Paris from PMIF against those acquired based on a 3-D Eulerian atmospheric 175 transport model by Broquet et al. (2018), the latter also accounting for uncertainties in diffuse CO 2 fluxes. On the one hand, the signals from these diffuse and natural CO 2 fluxes cannot be modelled effectively by a Gaussian plume model. On the other hand, the diffuse and natural CO 2 fluxes in Paris was shown to have only a weak impact on the inversion of fossil fuel CO 2 emissions (Staufer et al., 2016). For this comparison, we use the same simulation of the XCO 2 sampling by CarbonSat (Sect. Broquet et al. (2018). The corresponding inversion with the PMIF is called PMIF-Paris 180 hereafter. Then we apply the system to all the emission clumps over the globe and over 1 year using a different control vector and a simulation of the XCO 2 sampling by a single CO2M satellite (Sect. 2.2). The inversions for all emission clumps over the globe are called PMIF-Globe. In PMIF-Globe, we first investigate the potential of satellite observations in constraining emissions from individual days (ExpNoCor in Sect. 2.6). Then we assess the ability of satellite observations to constrain emissions at annual scale by accounting for the temporal auto-correlation of the prior uncertainties (other experiments in Sect. 185 2.6). Table 1 and 2 summarize the different options for the configuration of the system and of the OSSEs. One distinction between PMIF-Paris and PMIF-Globe is that PMIF-Paris relates XCO 2 signals with the mean emissions 6 hours before overpasses, while it is assumed that in PMIF-Globe that the XCO 2 signals only provide effective constraints on 3 h mean emissions before individual overpasses. The 6-hour period corresponds to the period of emissions from Paris whose signature in the XCO 2 field can still be detected by the satellite despite the atmospheric diffusion . While Broquet 190 et al. (2018) indicated that the period of "detectable" emissions from a large megacity like Paris could last up to 6-hours, most of the clumps across the globe have smaller emission rates than Paris, or are located in more complex environment close to other major emission areas where XCO 2 signals can be attributed to multiple sources, making the detection of the XCO 2 signature of emissions few hours before the satellite overpass even more difficult. For the PMIF-Globe experiments, we thus conservatively assume that the XCO 2 signals can only provide effective constraints on 3 h mean emissions before individual 195 overpasses in general. The potential correlations between the 6-hour mean emissions of different days are ignored for the diagnostics Table 2 The different options for the configuration of PMIF-Globe inversions

Observation space
In this study, we consider the samplings from two different virtual CO 2 imagers.
The first sampling used in PMIF-Paris (Table 1 and Sect. 2.7.1) is the simulation of the sampling for CarbonSat by 205 Buchwitz et al. (2013) exactly as in Broquet et al. (2018). XCO 2 is sampled by a 240 km swath instrument with 2 km spatial resolution. Given the presence of cloud and aerosol and their impacts on the precision of XCO 2 retrievals, only "good" XCO 2 observations, for which the sum of the retrieved aerosol optical depth (AOD) at NIR wavelength and atmosphere cirrus optical depth (COD) is less than 0.3, are used in the inversions. The preferable condition, AOD(NIR)+COD<0.3, for a good XCO 2 observation is referred to as "clear sky" hereafter. The CarbonSat sampling was simulated over the whole globe and for a full 210 year by Buchwitz et al. (2013), but it is used here for the inversion of the emission of Paris only. Thus, only the passes with at least one good XCO 2 measurement in the 100km radius circle centered on Paris are used, as in Broquet et al. (2018).
The second sampling is global and is used for all the other experiments of PMIF-Globe (Table 2 and Sect. 2.7.2). It corresponds to that of a single CO2M satellite with a 300 km swath and 2 km spatial resolution. CO2M is similar to CarbonSat for sampling, but has a larger swath, and a better precision (Sect. 2.5). The simulation is based on the method and model 215 described by Buchwitz et al. (2013), but uses different values for the parameters in the model.

Control vector
In the PMIF-Paris inversion, the satellite observations are sampled at 11:00 local time, in line with the experiments from Broquet et al. (2018). The inversion solves for the mean emissions for the 6 hours before 11:00 local time. Broquet et al. (2018) solved for the hourly emissions during this 6-hour period but PMIF can only solve for the mean emissions during the 6-hour 220 period due to the fact that the Gaussian plume model cannot be used to compute the signatures in the XCO 2 field of individual hourly emissions during that period. The control parameter in PMIF-Paris for each overpass (Sect. 2.7.1) is thus a scaling factor λ for the mean emission between 05:00 and 11:00. The prior and posterior scaling factors are used to rescale the 1 h and ~1 km resolution emission fields from an emission map and its temporal profile which are parts of the observation operator (Sect. 2.4). 225 In the PMIF-Globe inversion, the satellite observations are sampled at a local time of approximately 11:30 over all the clumps. The inversion solves for a scaling factor for 3-hour mean emissions between 8:30 and 11:30 and a scaling factor for the emissions during of the rest of the day (0:00-8:30 plus 11:30-24:00) for each day over one year and for all the clumps over the globe: x=[λ clump1 day1,morning , λ clump1 day1,rest , λ clump1 day2,morning , λ clump1 day2,rest , …, λ clump1 day366,morning , λ clump1 day366,rest , λ clump2 day1,morning , 230 While Broquet et al. (2018) indicated that the period of "detectable" emissions from a large megacity like Paris could last up to 6-hours, most of the clumps across the globe have smaller emission rates than Paris, or are located in more complex environment close to other major emission areas where XCO 2 signals can be attributed to multiple sources, making the detection of the XCO 2 signature of emissions few hours before the satellite overpass more difficult. For the experiments other 235 than PMIF-Paris, we thus conservatively assume that the XCO 2 signals can only provide effective constraints on 3 h mean emissions before individual overpasses in general, and we use the 8:30-11:30 time window for all emission clumps over the globe. The control vector is defined using this time window for all the days of the year, and not only for the days with satellite local overpasses, to facilitate the definition of the prior uncertainties and the combination of results at the annual scale. .
In both types of experiments, we do not include the diffuse emissions outside the selected clumps and the natural fluxes 240 (more generally, any parameter of the "background concentrations", Kuhlmann et al., 2019) in the control vector. The set-up of the R matrix also ignores uncertainties in the background concentrations (Sect. 2.5). This is another divergence with the inversion configuration of Broquet et al. (2018) who accounted for such uncertainties.

Observation operator
The observation operator in PMIF (which is used in Eq. 1) is composed of two sub-operators. The first operator (M inventory ) 245 describes the spatial distribution (within the clumps) and temporal variations of the emissions whose budgets are controlled by the inversion during 8:30-11:30 and during the remaining 21 hours for each clump: x → E = M inventory x. The spatial distribution of the emissions are based on estimates from ODIAC (Oda et al., 2018) for the year 2016. ODIAC provides the monthly mean emissions for 12 months through a year at a 0.0083º×0.0083º (approximately 1 km×1 km) spatial resolution.
The weekly and diurnal (at hourly resolution) profiles from the Temporal Improvements for Modeling Emissions by Scaling 250 (TIMES) product (Nassar et al., 2013) are applied to the monthly emission maps of ODIAC to generate the hourly emission fields. The second operator (M plume ) simulates the plumes of XCO 2 enhancement above the background at and downwind the emission clumps at 11:30: E → y = M plume E. We assume that the plume of XCO 2 enhancement related to a given emitting pixel within a clump of the ODIAC map has a Gaussian shape and the plume from a clump is a sum of multiple Gaussian plumes from all the ODIAC pixels within that clump. For a given emitting pixel, the Gaussian plume model writes: 255 Where y is the XCO 2 enhancement (in ppm) downwind of the emitting pixel. The i-direction is parallel to the wind direction and the j-direction is perpendicular to the wind direction. y depends on the mean emission rate during 8:30-11:30 at local time (E, in g/s), the wind speed (u, in m/s), the cross-wind distance (j) and the parameter σ j (see below). The wind direction and speed is taken from the Cross-Calibrated Multi-Platform (CCMP) gridded surface wind fields for the year 2008 (Atlas et al., 260 2011). The CCMP product uses a Variational Analysis Method (VAM) to combine the data from Version-7 RSS radiometer wind speeds, QuikSCAT and ASCAT scatterometer wind vectors, moored buoy wind data, and ERA-Interim model wind fields.
The σ j is a function of downwind distance i and atmospheric stability parameter: σ j =βj/(1+γj) -1/2 , where α is a coefficient that converts the computed XCO 2 enhancement in the unit of ppm, and β and γ are coefficients depending on the atmospheric Pasquill stability category which is a function of the wind speed and solar radiation (Turner, 1970). The values for β and γ can 265 be found in Bowers et al. (1980). The original Gaussian plume model generates a stationary plume of an infinite length and width downwind the emissions. Because we assume that the XCO 2 plumes sampled from a satellite overpass is only related to the emissions 3 h before, the Gaussian plume corresponding to each emitting pixel is cut off at the downwind distance equaling the wind speed multiplied by 3 h. The width of the plume is also cut off beyond 3 times of σ j in the cross-wind direction. The observation operator is null for emission of the remaining 21 hours (0:00-8:30 plus 11:30-24:00). 270 The size of the full theoretical control vector corresponds to 11,314 emission clumps times two time windows for each day times 366 days. The size of this full theoretical observation vector over the year is thus more than 30,000,000. Building matrices and applying Eq. 1 with such spaces is, in practice, not computationally affordable. Therefore, we divide the globe into 5,400 spatial inversion windows (from 180 W to 180 E and from 90 N to 60 S), each inversion window covering an area of 10 ×10º and being extended on the four boundaries with margins of 500 km to ensure that the plumes from the clumps near 275 the boundary of inversion windows are fully simulated and accounted for in the corresponding inversions. Mplume is defined as a block matrix, each block representing a single spatial inversion window and a single day. When an emission clump and its plume are comprised within more than one inversion window on a single day, only the results obtained in the window that covers the full plume is used in Mplume.

Observation error 280
We evaluate the projection of the measurement noise of the satellite observation, and ignore uncertainties in the observation operator. The measurement noise is derived from the simulations of random measurement errors from Buchwitz et al. (2013) and the impact of the systematic measurement errors is ignored. The random measurement errors are simulated as a function of geographic location (e.g., solar zenith angle, SZA), surface (e.g. albedo) and atmosphere characteristics (e.g. aerosol optical depth, AOD). The random measurement error is 1.4 ppm for vegetation albedo and SZA 50 in the CS sampling, 285 and it is 0.7 ppm in the CO2M sampling, thus two-fold smaller for the latter. The random measurement errors are uncorrelated from one XCO 2 data to the other, and the R matrix is thus built as a diagonal matrix as generally done in atmospheric inversion.

Specification of the prior uncertainties and of their temporal auto-correlations
Two configurations for the prior uncertainty are used in the OSSEs (Sect. 2.7). In the PMIF-Paris inversion, the prior uncertainty is 22.4% for the 6-hour mean emissions, the choice of this value being consistent with the configuration used by 290 Broquet et al. (2018).
In the PMIF-Globe inversions, the prior uncertainty is downscaled from its estimate for the annual budget of emissions of each clump. A prior uncertainty in annual emission of 30% is assumed for all clumps. This value is chosen to be of the same order of magnitude as the typical difference between emission inventories for a single point source and city. For example, Gurney et al. (2016) found that one-fifth of the power plants had monthly emission differences larger than 13% between the 295 estimates by two different US agencies. Gurney et al. (2019) compared the emission maps from ODIAC and Hestia for four US cities and found the whole-city differences are between -1.5% and +20.8%. Gately and Hutyra (2017) compared the inventories reported by local authorities and bottom-up fossil fuel CO 2 emission maps for 11 US cities and found the differences range from 33% to 78%. Then, the downscaling of the uncertainty in annual emissions into uncertainties at the sub-daily scale of the control variables (i.e. 3 h mean emission over 8:30-11:30 and 21 h mean emission during the rest of the day; Sect. 2.3) 300 follows a decomposition of the total uncertainty into components with different temporal auto-correlations.
The hourly emissions in inventories are usually derived from the periodic typical temporal profiles to annual emissions (Andres et al., 2011;Nassar et al., 2013). There are large variations in actual emissions from hour to hour and from day to day, resulting in large differences between the emission estimates derived based on typical temporal profiles and actual emissions.
These differences are sources of uncertainties in the emission inventories which are used in the inversion as prior information. 305 However, there is no consensus regarding the uncertainty in emission inventories and their error structures . We compare the typical temporal profiles of transport emissions and energy sector from the TIMES product respectively with the TOMTOM traffic index (https://www.tomtom.com/en_gb/, that provides indications on the level of variability in the traffic even though not on that of the CO 2 emission themselves), and with the actual hourly CO 2 emissions from electricity production in France (https://www.services-rte.com/en/home.html). Although these comparisons are only made for two sectors, 310 the results already show that it is challenging to describe the temporal auto-correlations of the uncertainty in emissions with simple exponentially decaying functions ( Fig. S1 and S2) like what is usually done in traditional atmospheric inversions (Chevallier et al., 2010;Kountouris et al., 2015). We thus make several assumptions regarding the decomposition of the prior uncertainty into components with different modes of auto-correlation.
In some scenarios, we consider an "annual component" that is fully correlated in time over 1 year. We also consider 315 "uncorrelated" components whose temporal auto-correlations are null and "sub-annual" components whose temporal autocorrelations follow the exponential decaying model with a correlation length smaller than 1 year. Specifically, we assume that the correlation between two instants of the sub-annual component at the hourly scale is described by: r=exp(-Δh/τ 1 )×exp(-Δd/τ 2 ) Where Δh is the time lag (in hours) between the two times of the day that are considered and Δd is the time lag (in days) 320 between the two dates that are considered. The parameters τ 1 and τ 2 follow the fit of the misfits between the TIMES profiles and the TOMTOM and electricity production indices to the exponential functions respectively at the hourly scale and at the daily scale ( Fig. S1 and S2). The temporal auto-correlations between the emissions during the aggregated time windows ( annual components for all sectors follow the same formulation Eq. (5), but with different τ 1 and τ 2 . For the emissions in the industry sector, τ 1 =2400h and τ 2 =180d; for the emissions in the transport sector, τ 1 =12h and τ 2 =7d; for the emissions from energy sector: τ 1 =24h and τ 2 =7d; and for the emissions from other sectors: τ 1 =24h and τ 2 =14d. For each clump, the share of emissions from each sector are estimated according to EDGARv4.3.2 (https://edgar.jrc.ec.europa.eu/). This leads to an uncertainty in 3 h emissions ranges between 40% and 198%, and in 21 h emissions ranges between 40% and 154%. The prior uncertainty in the 3-h mean emissions between 8:30 and 11:30 is close to or larger than 100% in scenarios SCS and MCS, and it even reaches an abnormally huge value of 1623% in NoCor. Andres et al. (2016) estimated the uncertainty in 355 the widely used emission map CDIAC (Carbon Dioxide Information Analysis Center). They found that the average uncertainty in monthly emissions for one 1º×1º grid cell is 120% and further suspected that the uncertainties in hourly and daily emissions at urban scale could be even larger (from a few percent to 1000%). But these large values challenges the assumption that the uncertainty in anthropogenic emissions is normally distributed . In this study, we follow the traditional assumption used in atmospheric inversions that the prior uncertainty follows a Gaussian distribution, allowing the prior 360 uncertainty to exceed 100% in some scenarios. This assumption ensures that the system is analytically solvable using Eq. (1) and (2). In addition, we focus our analysis on 8:30-11:30 time windows or days for which the posterior uncertainties of underlying emissions are smaller than 20% (Sect. 2.7.2), a value that is significantly smaller than the prior uncertainty in any scenario. In these cases, Eq. (1) ensures that the posterior uncertainty is almost driven the projection of the observation error on the control space and is not sensitive to the level of prior uncertainty. 365

Practical implementation of the OSSEs
Two sets of OSSEs are conducted under different configurations adapted to different purposes, as described below. Table   1 and 2 summarize the different configurations of the OSSEs.

Comparison of results between PMIF and a previous study on a single city: Paris
In the first OSSE PMIF-Paris, the configuration of the control vector, observation sampling and errors, and prior 370 uncertainties are made such that they resemble those in the MC-2 experiments from Broquet et al. (2018): 1) the inversion controls the 6-h mean emissions from Paris before the satellite overpasses on single days; 2) the observation sampling and errors are obtained from CarbonSat mission simulation (Buchwitz et al., 2013); 3). We ignore temporal auto-correlation of the uncertainty in 6-h mean emissions between different days. We select the same 69 satellite CarbonSat overpasses over Paris

Applying the PMIF over all emission clumps across the globe
In this second set of OSSEs, PMIF-Globe, we conduct inversions for all the clumps over one year. However, the large 380 sizes of the control vector, of the observation vector and of the associated covariance matrices prevent the derivation of a full A for all the clumps and all the time windows using Eq. (1). In PMIF, we thus propose and apply a two-step computation that approximates Eq. (1). This computation assumes that the system has a limited capability to improve the separation between plumes from distinct clumps on a given day by crossing the information obtained from different days. In that sense, the inversion considers the uncertainty reduction obtained for individual days when considering all the clumps together (first step, 385 see below) before focusing on individual clumps to account for temporal correlations in the prior uncertainty (the second step, see below). In other words, we assume that when crossing information between different time windows for a given clump, the impact of filtering information from different spatial overlaps of plumes on different days is relatively smaller than that of temporal auto-correlation in the prior uncertainty. It is proven that this method provides a good approximation of A at daily to annual scales for individual clumps (Supplementary text S1). 390 In a first step, Eq. (1) is applied to each 10 ×10 spatial inversion windows on each day separately (corresponding to an 8:30-11:30 time window for clumps within the spatial inversion windows), by using the corresponding blocks in B: Where i is the ith spatial inversion window and j is the jth day during one year. Here, B spt,i,j is a diagonal matrix that only contains the variances of prior uncertainties in emissions during 8:30-11:30 for the clumps within the inversion window. M spt,i,j 395 accounts for the spatial overlap of plumes generated from nearby clumps. Then we derive a "instant" M T R -1 M (denoted as i,j,k T i,j,k −1 i,j,k ) for a given clump k at each 8:30-11:30 time window: Where a spt,i,j (k) is a scalar from A spt,i,j representing the variance of posterior uncertainty of emission from clump k in ith spatial inversion window and in 8:30-11:30 time window on day j obtained by Eq. (6), and b spt,i,j (k) is the scalar from B spt,i,j 400 representing the variance of prior uncertainty for the same control variable.
In the second step, the inversion is conducted for each clump k separately, considering the correlation in time in B, using i,j,k T i,j,k −1 i,j,k derived from the first step: Where n=366×2, representing the time windows for 8:30-11:30 and for the rest 21 hours on the 366 days of one year 405 (2008). B tmp,k is the covariance matrix accounting for the temporal auto-correlation in the prior uncertainty for a single clump: In PMIF-Globe, we first conduct the inversion in which the prior uncertainty has no temporal auto-correlation (Exp-NoCor). This is made by applying step 1 to all the 10 ×10 spatial inversion windows and all the days separately. This case is used to label the "well constrained" 8:30-11:30 time windows for a given clump when the associated plume is sufficiently well 410 sampled by the XCO 2 observation to yield a posterior uncertainty in the 3 h mean emission that is smaller than 20%. We then conduct inversions with different assumptions about the decomposition of the prior uncertainty, accounting for the impact of

Comparison between results from PMIF and a more complex but local system over an isolated megacity: Paris 420
The comparison of the results from the PMIF-Paris experiment to that of Broquet et al. (2018) is used to demonstrate that the PMIF produce meaningful statistics for other clumps despite its relative simplicity at the local scale (its complexity being linked to its global and annual coverage). Figure 1 shows the theoretical uncertainty reduction for the 6 h mean emissions obtained in PMIF-Paris inversions with the 1st, 5th, 10th, 15th, 19th and 25th best observation sampling from CarbonSat over 31 inversion days (Sect. 2.7.1), each day being characterized by the average wind speed over Paris. We 425 compare these results with the Fig. 6 from Broquet et al. (2018). Like Broquet et al. (2018), Fig. 1 illustrates the strong correlation between the uncertainty reduction and the average wind speed, indicating that lower wind speed results on a larger signal close to the city that is easier to assimilate than elongated plumes under large wind speeds. For the best observation sampling, the uncertainty reduction remains smaller than 40% when the wind speed is larger than 13 m s -1 , and this value is generally twice as low as the values obtained when the wind speed is smaller than 5 m s -1 . 430 Some differences are seen in Fig. S3, between the results obtained by PMIF and by Broquet et al. (2018). For example, the PMIF-Paris inversion slightly overestimates the uncertainty reduction under high wind speed (> 15 m s -1 ) using the best observation sampling compared to Broquet et al. (2018). These differences reflect the impact of using the Gaussian plume model instead of a 3-D atmospheric transport model, and more importantly, the impact of accounting for more sources of uncertainties (in diffuse emissions and natural fluxes) in Broquet et al. (2018). Despite these differences, the general 435 coherence in the ranges of uncertainty reductions (Fig. S3) under different wind speeds between the PMIF-Paris experiment and Broquet et al. (2018) is a strong indication that the PMIF generates the correct order of magnitude for the uncertainty reduction for a single clump. In addition, Nassar et al. (2017) used the Gaussian plume model to process actual XCO 2 plumes generated from several power plants, which were sampled by OCO-2, adding the indication that Gaussian plume model can simulate the typical spread and amplitude of actual XCO 2 plumes and thus supporting the application of PMIF to a large 440 range of clumps. Figure 1 shows that the uncertainty reduction on 6-hourly emissions from Paris before the satellite overpass can be up to 74% under calm wind condition (wind speed < 1 m s -1 ) with the best observation sampling (in clear sky and with the satellite swath nearly centered on Paris), while it is systematically smaller than 45% for the 25th best observation sampling, over a full year of CS simulation. In addition, the uncertainty reductions have a large variation for narrow range of wind 445 speeds, illustrating the strong impacts of the satellite track position with respect to the target and plume, together with the fraction of "clear sky" that modulates the sampling. In particular, the number of observations sampling the plume on the days when the wind direction is perpendicular to the satellite overpass tends to be less than the days when the wind direction is parallel to the satellite overpass. This is illustrated in Fig. 1 by the uncertainty reductions on the days when the wind speeds are 1.73 m s -1 , 7.6 m s -1 and 8.1 m s -1 that are lower than on the days with similar wind speeds. 450  3 h mean emissions is smaller than 20% (this number is called N20) in Exp-NoCor. Clumps with small emission budgets tend to have lower N20 values than those with large budgets, due to the fact that the atmospheric plume generated by small emission clumps is difficult to distinguish from the measurement noise. Typically, N20 is smaller than 5 days for clumps emitting less than 2 MtC per year (like the city of Aswan, Egypt). Conversely, N20 is larger than 10 days for clumps emitting more than 2

Potential of space observations for monitoring fossil fuel CO2 emissions from individual clumps over 3 h time windows
MtC per year (like the cities of Manchester, UK, Boston, USA, and Chongqing, China). Note that clumps with emissions larger 465 than 2 MtC, although representing less than 25% of the total number of clumps, contribute more than 83% of the total clump emissions. At regional scale (Figs. S4, S5), South America, North America, and Africa tend to have larger N20 values for same bin of clump annual emission than the other regions, while Middle East and Asia have the lowest ones. In addition, there are large variations and spatial heterogeneity in the N20 values within each emission bins (Fig. S5), which will be further discussed in Sect. 4. 470 We also show the numbers of 8:30-11:30 time windows per clump being labeled as "well-constrained" when the posterior uncertainty of 3 h mean emission is smaller than other thresholds, e.g. 10% and 30% (Fig. 2b). In general, using a posterior uncertainty larger than 20% as a threshold, we could expect more "well-constrained" cases. But for a given threshold, we still find the number of well-constrained cases increases with the emission budgets. Figure 3 shows the posterior uncertainty in the clump emissions for the "well constrained" 8:30-11:30 time windows 475 (identified in Exp-NoCor) from different OSSEs. It confirms that in all OSSEs, the posterior uncertainties for clumps with larger emissions are smaller than those with lower emissions. Within a given bin of clump annual emission, the posterior uncertainties from the various OSSEs are very similar, even though they are obtained with different hypothesis regarding the temporal auto-correlation in the prior uncertainty. The interpretation is that, for the inversion of the 3 h emissions before a given satellite overpass, most of the constraint is imposed by the direct satellite observations during this overpass. These 480 observations are independent on different days, and the constraints on different days are not strongly crossed even when errors in the prior estimate are highly correlated in time. However, although small, the impact of temporal auto-correlations in the prior uncertainties can be seen. For example, the posterior uncertainties in ASS (SCS) are systematically smaller than those in AMS (MCS), which confirms that the capability of the inversion system to use the information from observations from previous/subsequent days to reduce the posterior uncertainties increases with the temporal auto-correlations. In SectCS, the 485 posterior uncertainties are smaller than those in MCS and SCS in most regions (Fig. S5), due to the fact that the uncertainty in industrial emissions has a long temporal auto-correlation (τ 2 =180d).

Potential of space observations for monitoring daily fossil fuel CO2 emissions
In previous sections, we analyzed the uncertainty reduction and the posterior uncertainty for the 3 h emissions that 510 generate the atmospheric plume observed from space at 11:30. We now analyze the potential to monitor the daily emission, relying on the extrapolation of constraints on emissions between 8:30-11:30 using temporal auto-correlation of the prior uncertainties in the step 2 of the inversion (Sect. 2.7.2). Fig. 4 shows the distribution of the number of days when the posterior uncertainties in daily emissions are smaller than 20% (D20) for the same bins of emission clumps as in the previous section.
Similar to the distribution of N20, clumps with small emission budgets tend to have lower D20 values than those with large 515 budgets, due to having smaller signal-to-noise ratios for clumps with smaller emissions. The D20 values also strongly depend on the temporal auto-correlation in the prior uncertainty. When no correlation (Exp-NoCor) or short correlation (MCS) are assumed, D20 remains zero even for the largest clumps, since most of the daily emission are disconnected from the 3-hour emissions that are constrained by the satellite observation and keep on bearing the large prior uncertainties associated with the Exp-NoCor and MCS scenarios. When significant temporal auto-correlations (e.g. in the case of AMS, ASS and SCS) are 520 assumed, the results get better and the posterior uncertainties for the daily emissions become less than 20% for more than 100 days for clumps emitting more than 5 MtC per year. At regional scale (Fig. S6), the distribution of D20 values shows a similar pattern as N20: North America, South America and Africa have larger D20 values than Middle East and Asia for same bin of clump annual emission. But the distribution D20 values in SectCS have large regional variations, reflecting the regional differences in the share of emissions from different sectors.

Potential of space observations for monitoring annual fossil fuel CO2 emissions
We now analyze the results for the annual emissions, allowed again by the derivation of the posterior uncertainty 540 covariance matrix A for individual clumps in step 2 of the inversion, and thus the aggregation of the posterior uncertainties in time. Figure 5 shows the posterior uncertainties in annual emissions from the OSSEs. When we assume that there is no temporal auto-correlations in the prior uncertainties, the uncertainties obtained from the inversions remain very close to the prior uncertainties (30%) for all emission bins since the information from the few well-constrained 8:

Discussion and conclusions
PMIF provides information on the potential of space-borne imagery to constrain fossil fuel CO 2 emissions from emission 565 clumps over the globe at the few-hour scale to the annual scale. It uses a simple Gaussian plume model to relate the emissions and the XCO 2 plumes. This is a strong simplification of the physics which impacts the range of uncertainties that can be accounted for in the inversion problem, but a preliminary evaluation against a more complex set-up (that of Broquet et al., 2018) indicates that it provides the correct order of magnitude for the uncertainties in the inverted emissions for an individual city: Paris. 570 In this study, we focused on the projection of uncertainties in satellite observations on the uncertainty of inverted emissions. Some sources of uncertainties that could have some impacts on the inversions when dealing with real data are ignored. Firstly, the plumes generated by the Gaussian plume model are straight along the wind direction at the source pixel.
As a result, we allow the plumes from nearby clumps to potentially cross each other, but these plumes will systematically diverge on long distances. The Gaussian plume model cannot reproduce plumes overlapping along the atmospheric circulation 575 like Eulerian transport models. In this sense, the overlapping effect of plumes can be underestimated in PMIF. In a realistic situation of atmospheric transport, if plumes from multiple clumps overlap very often, the inversion performance for individual clumps will be degraded since it will have the difficulties to accurately attribute the XCO 2 signals to individual clumps.
Furthermore, we assume that the Gaussian plume model can perfectly link the emissions and XCO 2 and ignore the transport model error. If forced with erroneous wind fields, the simulation of XCO 2 plumes can have wrong shape and location, and thus 580 generate large uncertainties in the inversions. In the inversion with actual XCO 2 observations from OCO-2, Nassar et al. (2017) allowed the wind direction to change from the wind re-analysis used to force the Gaussian plume model, if it improved the fit between simulated plumes and the observed signals. Reuter et al. (2019) and Kuhlmann et al. (2019) showed that the colocated NO 2 satellite observations could help to detect and constrain the location and shape of XCO 2 plumes. The transport model error may be partly reduced by incorporating additional information from other tracers when fitting the model to real 585 data, but it is unknown to which extent these additional constraints is useful to improve the inversion of fossil fuel CO 2 emissions. With the current design of PMIF, the impact of transport error is hard to evaluate. Secondly, we ignore systematic measurement errors from the XCO 2 imagery. Broquet et al. (2018) showed that systematic error could hamper the ability of the inversion system to reduce the errors in the emissions estimates. Thirdly, we neglect the impact of uncertainties in diffuse fossil fuel CO 2 emissions (outside clumps) and non-fossil CO 2 fluxes (within and outside clumps), the latter including net 590 ecosystem exchange (NEE) from the terrestrial biosphere, the CO 2 emitted by the burning of biofuel, the respiration from human and animals (Ciais et al., 2020) and the net CO 2 fluxes between the atmosphere and ocean. For example, the signals from terrestrial NEE can be strong during the growing season, and the signals from ocean CO 2 fluxes may have a critical impact on the overall XCO 2 patterns in the proximity of coastlines. In principle, the signals of diffuse fossil fuel CO 2 emissions and non-fossil CO 2 fluxes outside the clumps can be potentially filtered by removing the local background XCO 2 field to 595 extract plumes generated only by emissions from clumps (Kuhlmann et al., 2019;Reuter et al., 2019;Ye et al., 2020;Zheng et al., 2020). The non-fossil CO 2 fluxes within clumps vary from clump to clump, and could contribute a non-negligible fraction of the total CO 2 fluxes in many clumps (Bréon et al., 2015;Ciais et al., 2020;Wu et al., 2018a). The satellite observations alone cannot effectively differentiate the fossil fuel CO 2 emissions and the non-fossil CO 2 fluxes within clumps. In the clumps with non-negligible non-fossil CO 2 fluxes, the inversion of fossil fuel CO 2 emissions could be influenced (Ye et al., 2020;Yin 600 et al., 2019). Fourthly, the PMIF system controls the scaling factors for the mean emissions of daily 3-h and 21-h windows and for each clump, ignoring uncertainties in the spatial distribution and temporal profile of the emissions (described by the operator M inventory ) within the clumps and over the time windows. Such uncertainties are called aggregation errors (Wang et al., 2017;Wu et al., 2011). However, Broquet et al. (2018) compared the results of inversions using the realistic spatial distribution of emissions and using a homogenous one over two discs with different radius for M inventory , and found that having imperfect 605 spatial distribution of emissions to model M inventory (thus the aggregation error) only has a small impact on the uncertainties and errors in the inverted emissions. Future developments in PMIF should attempt at quantifying the impacts of such sources of uncertainties, while keeping its power of constraining the emissions from a large range of sources with global coverage.
Although it ignores the sources of uncertainties listed above, the current PMIF can still be used to investigate the impacts of some key parameters of inversion problem and to allow, for the first time, to make a first-order extrapolation of the results 610 from single-city studies to all significant emission clumps over the globe and under a full range of meteorological conditions during a year.
The key result summarized in Figure 2 is that using a single CO2M satellite, only the clumps with annual budget higher than 2 MtC per year (e.g. Manchester, UK, Boston, USA and Chongqing, China) can potentially be well constrained with N20 being larger than 10 within a year. However, there are large variations in the N20 values for clumps with such levels of emission. 615 Figures 6a and 6b show the maps of the number of observations within each 2º×2º grid cell during one year in the USA and China, which is an indicator for the frequency of clear-sky days: the larger the number of observations, the higher frequency of clear-sky days. It is clearly seen in Fig. 6c and 6d that the clumps in Southern China have low N20 values when they are located in areas with a low frequency of clear-sky days. For clumps that have emissions between 2 and 5 MtC per year, N20 values are below 10 days in a cloudy/hazy region like Southeastern China, and are close to 30 days in a clear-sky region like 620 the Western Coast of the USA. These results illustrate the dependence of the potential of satellite observations to constrain emissions on the frequency of clear-sky conditions. The relative uncertainty in the inversion of the emissions from a clump is primarily driven by the budget of these emissions, and by the wind speed (as illustrated by Fig. 1). The frequency of clear-sky days modulates the number of direct observation of the plume from a clump and thus the number of days for which the inversion can decrease the uncertainty when ignoring temporal auto-correlations in the prior uncertainty in Exp-NoCor. The 625 frequency of clear-sky day, together with the emission rate and wind speed, are the main drivers of the posterior uncertainty in daily to annual emissions when accounting for temporal auto-correlations in the prior uncertainty.  We showed that one CO2M imager can provide a direct constraint for the estimate of emissions from clumps with emissions larger than 2 MtC per year, but over limited periods only. N20 is smaller than 25 for most clumps, indicating that even for emissions during 8:30-11:30, one cannot expect more than 25 days when the CO2M observations sample the plumes from clumps with sufficient number of observations (Fig. 2) during one year. The use of a constellation of CO2M satellites in 635 the current plan could potentially improve the frequency of good samplings. Imaging from geostationary orbit (GEO) imagers like NASA's GeoCarb mission (O'Brien et al., 2016;Polonsky et al., 2014) could offer sampling during different periods within a day to constrain the diurnal profile of emissions. Highly elliptical orbit (HEO) imagers could also provide observations at northern high latitudes with a similar high frequency as GEO (Nassar et al., 2014). However, even though multiple spaceborne platforms can sample the plumes more frequently, the satellites using passive sensors like that planed for CO2M can 640 never sample the plumes on cloudy/hazy conditions. We also investigated the possibility of extrapolating the information obtained from the time windows for which the emissions are constrained by satellite observations to estimate emissions on other hours, days and through a year. Such an extrapolation relies on the model of the emission inventories used as a prior of PMIF, that is, in the framework of PMIF, the temporal auto-correlation of the uncertainty of prior emissions. The analysis of posterior uncertainties in the 3 h mean 645 emissions, in daily emissions and in annual emissions all show that the configuration of this temporal auto-correlation has a large impact on the inversion results. For example, posterior uncertainties in annual emissions range from less than 10% with strong auto-correlation (ASS) to 25% with medium auto-correlation (MCS) for clumps with emissions higher than 2 MtC per year. The orders of magnitude in the posterior uncertainty will be critical to the objective assessment of annual emissions.
However, since state-of-the-art emission products rarely report their uncertainties and temporal auto-correlations (Andres et 650 al., 2016;Gurney et al., 2019), it is difficult to exclude any configuration of OSSEs in this study. The strong impact of the prior uncertainty on the inversion results thus highlights the priority of future researches to systematically assess the uncertainty, especially the temporal error co-variances, in the emission products.
Even if emissions can be effectively constrained by CO2M for clumps whose emissions are larger than 2 MtC per year, the sum of annual emission budgets from these large clumps account only for 54% of the total CO 2 clump emissions and for 655 36% of the total global fossil fuel CO 2 emissions (accounting for diffuse emissions outside the clumps), according to the clump definition of Wang et al. (2019) and the ODIAC emission map. For a specific country, clumps with emissions larger than 2 MtC per year typically represent less than 50% of the total national emissions (accounting for diffuse emissions outside the clumps). It thus shows the difficulty to use a single CO2M imager as the only source of information to constrain national emissions. This limitation of a single CO2M imager calls for innovations to integrate other types of observations in inversion 660 systems to improve the ability to estimate emissions at both city scale (Lauvaux et al., 2016;Sargent et al., 2018;Staufer et al., 2016) and larger spatial scales (Palmer et al., 2018;Wang et al., 2018).

Code availability
The source code for PMIFv1.0 is included in the Supplement. To run PMIF, some input files are needed. The ODIAC inventory is available at http://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2018.html. The clump dataset is available at 665 https://doi.org/10.6084/m9.figshare.7217726.v1. The list of clump information (e.g. index, latitude and longitude of the center), which is also needed as an input, is included in the Supplement. The wind fields from CCMP are available at http://www.remss.com/measurements/ccmp/. EDGAR v4.3.2 emission maps are needed to run the SectCS inversion, and are available at https://edgar.jrc.ec.europa.eu/overview.php?v=432_GHG.