Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

Abstract

Methane (CH 4 ) is the primary component of natural gas and has a larger global warming potential than CO 2 . Recent top-down studies based on observations showed CH 4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH 4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH 4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH 4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R 2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R 2 = 0.5). The emission estimates for CH 4 in the posterior, 46.3 ± 9.2 Mg CH 4 /h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH 4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. We estimate that dairies contributed 5.9 ± 1.7 Mg CH 4 /h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH 4 /h in the SoCAB.

Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley

“…The surface emission optimization applied in this study is based on the inverse modeling framework applied in Cui et al . []. Most CH 4 mixing ratio enhancements were measured below 2.0 km altitude asl during the four flights.…”

“…The largest uncertainty in R is that of the transport models. We assume a larger uncertainty in the models in this study than in the Los Angeles basin [ Cui et al ., ] because of the inherent difficulty in modeling the transport within the complex terrain of the Central Valley.…”

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

“…Existing top-down studies at most provide spatially aggregated (basin total) monthly CH 4 emission estimates for the SoCAB (Wong et al, 2016). Gridded bottom-up annual inventories (~10 km) are too coarse to identify individual sources (Jeong et al, 2012;Maasakkers et al, 2016), and downscaled versions of state-wide CH 4 emissions inventories have been found to consistently underestimate SoCAB emissions compared to top-down atmospheric studies (Cui et al, 2015;Jeong et al, 2016;Peischl et al, 2013;Santoni et al, 2014;Wecht et al, 2014;Wennberg et al, 2012;Wong et al, 2016;Wunch et al, 2009Wunch et al, , 2016. On-road field surveys of near-surface CH 4 hot spots provide insight into spatial gradients for selected transects across the urban domain and verification of selected point sources (Hopkins et al, 2016).…”

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

“…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).…”