|Title||Atmospheric inverse estimates of methane emissions from Central California|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Chuanfeng Zhao, Arlyn E Andrews, Laura Bianco, Janusz Eluszkiewicz, Adam Hirsch, Clinton MacDonald, Thomas Nehrkorn, Marc L Fischer|
|Journal||Journal of Geophysical Research - Atmospheres|
|Keywords||atmospheric transport, inverse modeling, methane|
 Methane mixing ratios measured at a tall tower are compared to model predictions to estimate surface emissions of CH4 in Central California for October–December 2007 using an inverse technique. Predicted CH4 mixing ratios are calculated based on spatially resolved a priori CH4 emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH4 emissions. Three inverse model estimates of CH4 emissions are reported. First, linear regressions of modeled and measured CH4 mixing ratios obtain slopes of 0.73 ± 0.11 and 1.09 ± 0.14 using California-specific and Edgar 3.2 emission maps, respectively, suggesting that actual CH4 emissions were about 37 ± 21% higher than California-specific inventory estimates. Second, a Bayesian "source" analysis suggests that livestock emissions are 63 ± 22% higher than the a priori estimates. Third, a Bayesian "region" analysis is carried out for CH4 emissions from 13 subregions, which shows that inventory CH4 emissions from the Central Valley are underestimated and uncertainties in CH4 emissions are reduced for subregions near the tower site, yielding best estimates of flux from those regions consistent with "source" analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH4 emissions for multiple regions.