AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Developing a Four-dimensional Variational Assimilation Framework for Refining U.S. Ammonia Emissions with Size-Resolved Aerosol
SHANNON CAPPS, Mahmoudreza Momeni, Matthew Lombardo, Amir Hakami, Daven Henze, Steven Thomas, Jeremy Silver, Peter Rayner, CMAQ Adjoint Development Team, Drexel University
Abstract Number: 683 Working Group: Source Apportionment
Abstract Chemical transport models such as the Community Multiscale Air Quality (CMAQ) model provide a platform for evaluating the influence of emissions on atmospheric composition. They are designed to represent scientific consensus on the chemical and physical transformations of emitted species into pollutant concentrations. For processes represented with sufficient certainty, such as transport, aerosol dynamics, and inorganic aerosol thermodynamics, the difference in modeled and observed concentrations is useful for evaluating the emissions inventory. Specifically, these differences in concentration can be leveraged in a four-dimensional variational assimilation framework to provide revised estimates of emissions with spatial specificity.
This work introduces the CMAQ adjoint integrated in a Python-based four-dimensional variational framework. Given the difficulty of estimating emissions of ammonia from the agricultural sector and the complexity of relating emissions to concentrations of this condensable species, this work focuses on the refinement of ammonia emissions through satellite observations of ammonia from the Cross-track Infrared Sounder (CrIS). The assimilation of CrIS pseudo-observations demonstrates the capability of the framework to improve estimates of perturbed emissions.