Model evaluation of methods for estimating surface emissions and chemical lifetimes from satellite data

Column densities from satellite retrievals can provide valuable information for estimating emissions and chemical lifetimes objectively across the globe. To better understand the uncertainties associated with these estimates, we test four methods using simulated column densities from a point source: a box model approach, a 2D Gaussian fit, an Inverse Radius fit and an Exponentially-Modified Gaussian fit. The model results were simulated using the WRF and CAMx models for the year 2005, for a single point source outside Atlanta in Georgia, USA with specified emissions and three chemical scenarios: no chemical reactions, 12 h chemical lifetime and 1 h chemical lifetime. No other sources were included in the simulations. We find that the box model provides reliable estimates irrespective of plume speed and plume direction, if the plume speed and the chemical lifetime are known accurately. The 2D Gaussian fit was found to be sensitive to plume speed and direction, and requires omnidirectional dispersion in order to have a decent fit. However, the 2D Gaussian fit is only an approximate fit to the data, and the discrepancies mean that the results are dependent on the geographical domain used for the optimization. An Inverse Radius fit is introduced to correct this issue, which is found to provide improved emissions and lifetime estimates. The Exponentially-Modified Gaussian fit also gave improved estimates. It is however dependent on accurate plume rotation such that reported chemical lifetimes with this method could be significantly underestimated. [Display omitted] •Exponentially-Modified Gaussian fit yield accurate emissions by wind speed groups.•EMG lifetime estimates need careful plume rotation and are biased low.•2D Gaussian fit can be sensitive to winds and domain choice.•2D Gaussian lifetime estimates are a measure of dispersion not chemistry.•A new Inverse Radius fit yields improved results compared with the 2D Gaussian fit.