Abstract View
Developing a Framework for Refining Ammonia Emissions Estimates with Satellite-based Observations with Air Quality Modeling
CONGMENG LYU, Mahmoudreza Momeni, Shannon Capps, Matthew Lombardo, Mark Shephard, Amir Hakami, Daven Henze, Steven Thomas, Peter Rayner, Drexel University
Abstract Number: 414
Working Group: Satellite-Data and Environmental Health Applications
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). The framework, including the adjoint of CMAQ and the CrIS observation operator, are evaluated. Additionally, the py4dvar implementation is tested. Specifically, 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.