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 (CH4) emissions in North America obtained from Short Wave Infrared (SWIR), Thermal Infrared (TIR) and multi-spectral measurements in geostationary orbit compared to existing SWIR low earth (LEO) measurements. A stochastic algorithm is used to compute the information content of a variational inversion at high spatial resolution (0.5° × 0.7°) using the GEOS-Chem chemical transport model and its adjoint. Both the SWIR LEO and TIR GEO configurations generally provide poor constraints on CH4 emissions (error reduction <30 %), with the exception of a few hotspots (e.g., Los Angeles, Toronto urban areas and Appalachian Mountains) where the error reduction is greater than 50 %. On weekly time scales and for a GEO orbit, the degree of freedom for signal (DOFs) of the inversion from multi-spectral observations (500) is a factor of two higher than that obtained from a SWIR instrument (255) due to the increase in measurement sensitivity to boundary layer concentrations in the multi-spectral case. On a monthly time scale and for a GEO orbit, a SWIR instrument would reduce error in emission estimates by more than 70 % for hotspots of CH4 sources (emissions > 4 × 105 kg day−1 grid−1) at model grid scale, while a TIR instrument would provide a relative error reduction of 25–60 % over those areas. While performing similarly for monthly inversions, a multi-spectral instrument would allow for more than 70 % error reduction for these emissions for 7 or 3 day inversions. Sensitivity of the inversions to error in boundary conditions are found to be negligible. Moreover, estimates of the model resolution matrix over significant emitting regions (CH4 emissions > 2 × 105 kg day−1 grid−1) show that for all instrument configurations in GEO orbit the inversion is able to independently constrain CH4 sources at spatial scales smaller than 200 km. These results highlight the importance of using observations sensitive to boundary layer concentrations (i.e., SWIR) to achieve significant improvements in constraining CH4 sources compared to current LEO capabilities.

How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35)

“…In the U cases, we scale the true-state emissions uniformly by 0.5×. This is informative when the prior emissions have strong spatial fidelity with the truth and is a common OSSE approach (e.g., Bousserez et al, 2016;Sheng et al, 2018a;Turner et al, 2018). However, when the spatial allocation of emissions is uncertain, as is frequently the case for methane, such analyses are likely to yield overly optimistic results.…”

“…We compute these information content metrics because they are commonly used for evaluating satellite instrument capabilities (Bousserez et al, 2016;Sheng et al, 2018a). However, posterior error reduction estimates can only match the emission improvements if the prior emissions are unbiased, which is not usually the case.…”

“…For example, Wecht et al (2014b) and Sheng et al (2018a) found that TROPOMI observations can provide comparable methane emission constraints as dedicated aircraft measurements spanning the same time intervals and regions. Other analyses have concluded that 1 week of TROPOMI methane observations is sufficient to resolve time-invariant fluxes at 30 km (Turner et al, 2018) and to achieve 100 % error reduction over emission hotspots (Bousserez et al, 2016), while a single satellite overpass is able to monitor the 20 highest-emitting locations in the GEPA inventory (Jacob et al, 2016). However, the above work has focused primarily on resolving emission magnitudes without explicitly considering the impacts of spatial errors.…”

What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH<sub>4</sub> emissions as a case study

“…Atmospheric inverse estimates of CH 4 emissions are expected to improve with tropospheric CH 4 measurements from the upcoming ESA TROPOMI mission (Butz et al, 2012). Furthermore, geostationary missions (such as GEOCAPE) will potentially provide the measurements needed to substantially improve CH 4 emission estimates (Wecht et al, 2014;Bousserez et al, 2015). Ultimately, the precision and sampling configuration of atmospheric CH 4 observations both determine the observing system (OS) capability of retrieving surface CH 4 fluxes.…”

“…We note that wetland emissions are the largest and most uncertain source of CH 4 within the Amazon river basin (Wilson et al, 2016;Melton et al, 2013). We henceforth assume that the non-wetland CH 4 contribution (namely fires and anthropogenic CH 4 sources) can be relatively well characterized using ancillary datasets and global inventories (Bloom et al, 2015;Turner et al, 2015 and references therein).…”