The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

Abstract

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO 2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO 2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO 2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO 2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO 2 emissions for urban areas like Paris with CO 2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO 2 and in the inversion systems that exploit it.

Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations

“…Because all of these changes will influence the maximum likelihood estimation results, we assess the emission estimation sensitivity to the above variation in H, y, and R. For the sensitivity to transport model non-linearity, we calculate the K (and thus H) for a set of four emission vectors e: for cases with one power plant (table 1), e is set to [1], [25] ] 100 100 T . We note that calculating K at different emission vectors e in our maximum likelihood estimation is similar to choosing different emission priors for a Bayesian inversion [12,13]. For a Bayesian inversion that seeks to minimize the cost func-…”

“…Hx y R Hx y , 3 T 1 where the state vector = [ ] b x e T consists of two components: the power plant emission rates vector e (section 2.3), and the background XCO 2 b. The background XCO 2 is similar to that used in Pillai et al [12] and Broquet et al [13]. y is the observation vector whose elements are XCO 2 values from OCO-2 footprints.…”

“…The second category solves for short term emission rates through assimilating remotely sensed column average CO 2 (XCO 2 ) measurements. This category includes the Observation System Simulation Experiments (OSSE) studies by Pillai et al [12] and Broquet et al [13] using the simulated CarbonSat XCO 2 data [14], by Nassar et al [1] using OCO-2 [15] XCO 2 , and by Krings et al [11] using airborne XCO 2 measurements. These studies utilize the spatial coverage of XCO 2 to distinguish the CO 2 plume caused by localized anthropogenic sources from the background.…”

“…These studies utilize the spatial coverage of XCO 2 to distinguish the CO 2 plume caused by localized anthropogenic sources from the background. This is done either by identifying the background XCO 2 before extracting the plume enhancement [1,11], or through simultaneous optimization of anthropogenic emissions and the background XCO 2 [12,13].…”

“…The objective of this manuscript is to estimate the power plant emission rates through OCO-2 XCO 2 data and high resolution WRF-Chem simulations [17,18]. Our method is similar to that of Pillai et al [12] and Broquet et al [13], but differs in a key aspect: instead of Bayesian inversion, we use a maximum likelihood estimation, which obtains power plant CO 2 emissions from OCO-2 XCO 2 data alone without prior emission information.…”