The Bayesian inversion method is commonly used to estimate surface emissions of CO2 and other trace gases in a global scale. The application of this approach to limited domains used in mesoscale or regional scale modeling is more challenging. In addition to estimation of the surface tracer flux it is necessary to evaluate unknown fluxes through model lateral boundaries. The inflow flux of CO2 may be several orders of magnitude larger than the CO2 flux from the surface of regional modeling domain.

The proposed modeling framework is based on a Lagrangian Particle Dispersion Model (LPDM) linked to CSU RAMS (Regional Atmospheric Modeling System). The LPDM is used in a receptor-oriented mode (tracing particles backward in time) to derive influence functions for each concentration sample. The influence function provides information on potential contributions from surface sources and inflow fluxes through the modeling domain boundaries into tracer concentration sampled at the receptor. Then the Bayesian inversion technique is applied in an attempt to estimate unknown surface emissions. This approach was tested for different configurations of surface sources using model generated pseudo-data (perturbated concentrations derived from the model). Different sampling strategies can be evaluated and compared with the aid of the error in estimated surface fluxes and/or the reduction of uncertainty in flux estimations.

The current research using the proposed framework has been focused on evaluating area averaged surface fluxes and inflow fluxes for different sizes of mesoscale domains for a tracer with constant in time emission and CO2 like tracer with a strong diurnal cycle. The explored sampling strategies included aircraft sampling and concentration time series from tall (400m) towers. Further testing and developing work will use pseudo-data from regional RAMS/LPDM simulations as well as real CO2 concentration data collected during the COBRA project in August 2000 in Wisconsin and the LBA project in August 2001 at Santarem in Amazonia.