Abstract View
Refining Ammonia Emissions Estimates with Satellite-based Observations Using a Novel Framework and an Air Quality Model
CONGMENG LYU, Shannon Capps, Mark W. Shephard, Daven Henze, Matthew Lombardo, Shunliu Zhao, Amir Hakami, Steven Thomas, Jeremy Silver, Peter Rayner, Drexel University
Abstract Number: 363
Working Group: Remote and Regional Atmospheric Aerosol
Abstract
The Community Multiscale Air Quality (CMAQ) model calculates the impact of emissions on atmospheric composition, including inorganic aerosols, while considering the transport and reactions of chemical constituents. Adjusting emissions by comparing modeled concentrations with observations is justified when the science processes are well understood as is the case for inorganic species such as ammonia (NH3). The Finite Difference Mass Balance (FDMB) method and four-dimensional variational (4D-Var) data assimilation leverage differences in simulated and actual observations to revise estimates of emissions with spatial specificity. In this study, we evaluate the capability 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, the iterative FDMB and a Python-based four-dimensional variational framework (py4dvar) are integrated with CMAQ and its adjoint to constrain NH3 emissions with observations from the satellite-based Cross-track Infrared Sounder (CrIS). Observing System Simulation Experiments (OSSEs) are conducted with the CrIS observation operator to evaluate the extent to which emissions are expected to be recovered with the hybrid assimilation framework. The OSSE conducted with the 2007 modeling platform and 2016 CrIS data on a regional domain in Georgia results in promising recovery of the true emissions. The framework is then ported to a 2017 modeling platform for assimilation of 2017 CrIS NH3 observations to mitigate the mismatch between modeling platform and satellite observation years. Three suitable periods are selected from April through October 2017 for assimilation. Independent surface measurements are used to evaluate posterior modeled concentrations.