Statistical Comparison of Measured and Predicted Signals

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 statistical weights to the inventory and the model-measurement difference. The result is a posterior estimate of the scaling factor for each of the different sources or sub-regions of California (Zhao et al., 2009)

Bayesian Inverse Scaling

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