Evaluating a Framework for Refining Ammonia Emissions Estimates with Satellite-based Observations with Regional Air Quality Modeling

Abstract:

The Community Multiscale Air Quality (CMAQ) model calculates the impact of emission on atmospheric composition, including inorganic aerosols, while considering the transport and reactions of chemical constituents. Adjusting emissions by comparing modeled concentrations with observations is possible when the science processes are well understood as is the case for inorganic species such as ammonia (NH₃). Four-dimensional variational data assimilation leverages differences in simulated and actual observations to revise estimates of emissions with spatial specificity. In this study, we evaluate the capacity of a CMAQ-based data assimilation system to improve NH₃ emissions, which are relatively uncertain given the diversity of emissions processes in the agricultural sector. To do so, a Python-based four-dimensional variational framework (py4dvar) is integrated with CMAQ and its adjoint to constrain NH₃ emissions with observations from the satellite-based Cross-track Infrared Sounder (CrIS). Pseudo-observation tests are conducted with the CrIS observation operator to evaluate the extent to which emissions are expected to be recovered with the assimilation. Then, the framework is ported to a 2017 modeling platform for assimilation of CrIS NH₃ observations. Based on the spatial and temporal coverage of the CrIS observations, three suitable periods are selected from April through October 2017 for assimilation.