Atmospheric GHG Measurements

Statistical Inverse Model

The a priori emission inventories are modified by scaling the emissions to best match the measured and predicted GHG signals in a Bayesian model which gives separate statistical weights to the model-measurement difference and the a priori emissions. The result is a posterior estimate of the scaling factor for each of the different sources or sub-regions of California (Jeong et al., 2012).

Sponsors

State of California Energy Commission U.S. Department of Energy Lawrence Berkeley National Lab U.S. National Oceanic and Atmospheric Administration California Air Resources Board