A modeling approach to design a mesoscale sampling campaign in order to estimate surface emissions

Lagrangian particle dispersion (LPD) in a receptor-oriented mode can be used to derive influence functions that provide information on potential contributions from different emission sources to concentrations observed at a given receptor. The flexibility of Lagrangian particle modeling allows one to consider receptors of arbitrary geometry and time characteristics, and therefore to mimic different atmospheric sampling systems.

The goal of our research is to use this modeling approach to design a sampling strategy of the atmospheric boundary layer with the aid of aircraft and other measuring systems in order to estimate surface emissions within the range of several tens of kilometers. It is motivated by our outgoing efforts to determine surface emission of greenhouse gases in selected parts of the world including the Amazon area, New Zealand, and Wisconsin, USA. For this purpose the LPD model has been linked to CSU RAMS (Regional Atmospheric Modeling System). The RAMS simulations performed for the Hauraki area, a site of future field campaign in New Zealand, are used to develop and test the proposed methodology. The influence functions are applied to explore different sampling strategies with the aid of aircraft including possible flight patterns, height and vertical spacing of samples, horizontal extent of samples, and time of day. A Bayesian approach for inverse calculations taking into account uncertainty in both observational data and modeling results is implemented to derive rates of surface emission sources from concentration samples. Different aggregation of emission sources is also tested to avoid the singularity problem in the source-receptor system. The inverse calculations provide us with criteria for evaluating which sampling strategy offers maximum information on the emission sources to be determined.