Constraints on methane emissions in North America from future geostationary remote-sensing measurements

Abstract

Abstract. The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific objectives. In this study, an Observation System Simulation Experiment (OSSE) is performed to quantify the constraints on methane (CH 4 ) emissions in North America obtained from shortwave infrared (SWIR), thermal infrared (TIR), and multi-spectral (SWIR+TIR) measurements in geostationary orbit and from future SWIR low-Earth orbit (LEO) measurements. An efficient stochastic algorithm is used to compute the information content of the inverted emissions at high spatial resolution (0.5 • × 0.7 • ) in a variational framework using the GEOSChem chemistry-transport model and its adjoint. Our results show that at sub-weekly timescales, SWIR measurements in GEO orbit can constrain about twice as many independent flux patterns than in LEO orbit, with a degree of freedom for signal (DOF) for the inversion of 266 and 115, respectively. Comparisons between TIR GEO and SWIR LEO configurations reveal that poor boundary layer sensitivities for the TIR measurements cannot be compensated for by the high spatiotemporal sampling of a GEO orbit. The benefit of a multi-spectral instrument compared to current SWIR products in a GEO context is shown for sub-weekly timescale constraints, with an increase in the DOF of about 50 % for a 3-day inversion. Our results further suggest that both the SWIR and multi-spectral measurements on GEO orbits could almost fully resolve CH 4 fluxes at a spatial resolution of at least 100 km × 100 km over source hotspots (emissions > 4 × 10 5 kg day −1 ). The sensitivity of the optimized emission scaling factors to typical errors in boundary and initial conditions can reach 30 and 50 % for the SWIR GEO or SWIR LEO configurations, respectively, while it is smaller than 5 % in the case of a multi-spectral GEO system. Overall, our results demonstrate that multi-spectral measurements from a geostationary satellite platform would address the need for higher spatiotemporal constraints on CH 4 emissions while greatly mitigating the impact of inherent uncertainties in source inversion methods on the inferred fluxes.

Satellite observations of atmospheric methane and their value for quantifying methane emissions

“…4. Bousserez et al (2016) explored the potential of geostationary observations to constrain methane emissions on the continental scale of North America over weekly and monthly timescales. Again they used a CTM with 1/2 • × 2/3 • spatial resolution as the forward model and averaged the 4 × 4 km 2 geostationary observation pixels over that coarser grid with corresponding error reduction.…”

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…Other future satellites have a wide swath (CarbonSat, proposed) or are geostationary (GeoCARB and GEO-CAPE; selected and proposed, respectively). They would generate higher density observations across the US relative to OCO-2 and GOSAT (Fishman et al, 2012;Polonsky et al, 2014;Bovensmann et al, 2015;Buchwitz et al, 2013;Bousserez et al, 2016;Pillai et al, 2016). Lidar-based missions (e.g., MERLIN and ASCENDS; selected and proposed, respectively) measure in the absence of sunlight and through thin or scattered clouds (Kiemle et al, 2011;ASCENDS Ad Hoc Science Definition Team, 2015).…”

Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs

“…Over the last decade, there has been increased use of data assimilation techniques to constrain model forecasts and reanalyses of atmospheric constituents (e.g., Arellano Jr. et al, 2007;Edwards et al, 2009;Claeyman et al, 2011;Lahoz et al, 2012;Pagowski and Grell, 2012;Bowman, 2013;Gaubert et al, 2014;Hache et al, 2014;Saide et al, 2014;Zoogman et al, 2014;Barré et al, 2015;Bousserez et al, 2016;Mizzi et al, 2016). Assimilation of chemicals can be extended to optimize model inputs such as emissions, thereby providing insight into how to improve the processes that govern the model performance (e.g., Elbern et al, 2007;Barbu et al, 2009;Chatterjee et al, 2012;Miyazaki et al, 2012b;Koohkan et al, 2013;Yumimoto, 2013;Cui et al, 2015;Guerrette and Henze, 2015;Turner et al, 2015).…”